Research
The research interests of the employees of the Artificial Intelligence include the broadly understood methods of computational intelligence and their application.
Papers (836)
2025 (11)
Xia Y., Xiao K., Cao J., Precup R.-E., Arya Y., Lam H.-K., Rutkowski L., Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance. (3)
Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance
, Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance, IEEE Transactions on Cybernetics, 55, 55, 867-879, 2025, Cites: 3
Cheng J., Liu N., Rutkowski L., Cao J., Yan H., Hua L., Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities. (2)
Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities
, Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities, IEEE Transactions on Circuits and Systems I: Regular Papers, 72, 72, 1452-1461, 2025, Cites: 2
Liu J., Shen H., Wang J., Cao J., Rutkowski L., H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method. (1)
H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method
, H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method, IEEE Transactions on Automation Science and Engineering, 22, 22, 6388-6397, 2025, Cites: 1
Liu J., Shen H., Wang J., Cao J., Rutkowski L., H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method. (1)
H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method
, H<inf>∞</inf> Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method, IEEE Transactions on Automation Science and Engineering, 22, 22, 6388-6397, 2025, Cites: 1
Xia Y., He J., Lam H.-K., Rutkowski L., Precup R.-E., Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism. (0)
Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism
, Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism, Information Sciences, 711, 711, 2025, Cites: 0
Kaczmarek K., Sevastjanov P., Dymova L., Kulawik A., Rutkowski L., A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty. (0)
A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty
, A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty, Applied Soft Computing, 169, 169, 2025, Cites: 0
Sevastjanov P., Kaczmarek K., Dymova L., Rutkowski L., Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization. (0)
Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization
, Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization, Fuzzy Sets and Systems, 511, 511, 2025, Cites: 0
Podolszanska J., Leveraging Deep Q-Network Agents with Dynamic Routing Mechanisms in Convolutional Neural Networks for Enhanced and Reliable Classification of Alzheimer’s Disease from MRI Scans. (0)
Leveraging Deep Q-Network Agents with Dynamic Routing Mechanisms in Convolutional Neural Networks for Enhanced and Reliable Classification of Alzheimer’s Disease from MRI Scans
, Leveraging Deep Q-Network Agents with Dynamic Routing Mechanisms in Convolutional Neural Networks for Enhanced and Reliable Classification of Alzheimer’s Disease from MRI Scans, International Conference on Agents and Artificial Intelligence, 3, 3, 1172-1179, 2025, Cites: 0
Li H., Yao Y., Xiao M., Wang Z., Rutkowski L., Turing pattern dynamics in a fractional-diffusion oregonator model under PD control. (0)
Turing pattern dynamics in a fractional-diffusion oregonator model under PD control
, Turing pattern dynamics in a fractional-diffusion oregonator model under PD control, Nonlinear Analysis: Modelling and Control, 30, 30, 291-311, 2025, Cites: 0
Bernacki J., Forecasting the concentration of the components of the particulate matter in Poland using neural networks. (0)
Forecasting the concentration of the components of the particulate matter in Poland using neural networks
, Forecasting the concentration of the components of the particulate matter in Poland using neural networks, Environmental Science and Pollution Research, 2025, Cites: 0
Bernacki J., Forecasting the air pollution concentration with neural networks. (0)
Forecasting the air pollution concentration with neural networks
, Forecasting the air pollution concentration with neural networks, Urban Climate, 59, 59, 2025, Cites: 02024 (46)
Dong S., Tang J., Abbas K., Hou R., Kamruzzaman J., Rutkowski L., Buyya R., Task offloading strategies for mobile edge computing: A survey. (14)
Task offloading strategies for mobile edge computing: A survey
, Task offloading strategies for mobile edge computing: A survey, Computer Networks, 254, 254, 2024, Cites: 14
Lin L., Cao J., Lam J., Rutkowski L., Dimirovski G.M., Zhu S., A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains. (13)
A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains
, A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains, IEEE Transactions on Automatic Control, 69, 69, 5743-5758, 2024, Cites: 13
Lin L., Cao J., Lam J., Zhu S., Azuma S.-I., Rutkowski L., Leader-Follower Consensus Over Finite Fields. (11)
Leader-Follower Consensus Over Finite Fields
, Leader-Follower Consensus Over Finite Fields, IEEE Transactions on Automatic Control, 69, 69, 4718-4725, 2024, Cites: 11
Xin Y., Cheng Z., Cao J., Rutkowski L., Wang Y., Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks. (10)
Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks
, Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 35, 35, 1394-1400, 2024, Cites: 10
Huang Z., Lv W., Liu C., Xu Y., Rutkowski L., Huang T., Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks. (9)
Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks
, Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks, IEEE Transactions on Industrial Informatics, 20, 20, 4218-4226, 2024, Cites: 9
Lv X., Cao J., Rutkowski L., Duan P., Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies. (8)
Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies
, Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies, IEEE Transactions on Automatic Control, 69, 69, 771-782, 2024, Cites: 8
Lin L., Cao J., Lu J., Rutkowski L., Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy. (7)
Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy
, Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy, IEEE/CAA Journal of Automatica Sinica, 11, 11, 806-808, 2024, Cites: 7
Qi S., Wei W., Wang J., Sun S., Rutkowski L., Huang T., Kacprzyk J., Qi Y., Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage. (6)
Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage
, Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage, IEEE Transactions on Mobile Computing, 23, 23, 2566-2582, 2024, Cites: 6
Slowik A., Cpalka K., Xue Y., Hapka A., An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm. (5)
An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm
, An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm, Applied Energy, 364, 364, 2024, Cites: 5
Zhou Y., Lv W., Tao J., Xu Y., Huang T., Rutkowski L., Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel. (4)
Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel
, Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel, Neural Networks, 169, 169, 485-495, 2024, Cites: 4
Tao M., Guo L., Cao J., Rutkowski L., A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems. (4)
A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems
, A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems, IEEE Transactions on Circuits and Systems II: Express Briefs, 71, 71, 1316-1320, 2024, Cites: 4
Sevastjanov P., Kaczmarek K., Rutkowski L., A multi-model approach to the development of algorithmic trading systems for the Forex market. (4)
A multi-model approach to the development of algorithmic trading systems for the Forex market
, A multi-model approach to the development of algorithmic trading systems for the Forex market, Expert Systems with Applications, 236, 236, 2024, Cites: 4
Cheng H., Xiao M., Yu W., Rutkowski L., Cao J., How to regulate pattern formations for malware propagation in cyber-physical systems. (3)
How to regulate pattern formations for malware propagation in cyber-physical systems
, How to regulate pattern formations for malware propagation in cyber-physical systems, Chaos, 34, 34, 2024, Cites: 3
Szmidt E., Kacprzyk J., Bujnowski P., Starczewski J.T., Siwocha A., RANKING OF ALTERNATIVES DESCRIBED BY ATANASSOV'S INTUITIONISTIC FUZZY SETS – RECONCILING SOME MISUNDERSTANDINGS. (3)
RANKING OF ALTERNATIVES DESCRIBED BY ATANASSOV'S INTUITIONISTIC FUZZY SETS – RECONCILING SOME MISUNDERSTANDINGS
, RANKING OF ALTERNATIVES DESCRIBED BY ATANASSOV'S INTUITIONISTIC FUZZY SETS – RECONCILING SOME MISUNDERSTANDINGS, Journal of Artificial Intelligence and Soft Computing Research, 14, 14, 237-250, 2024, Cites: 3
Chen G., Xu G., He F., Hong Y., Rutkowski L., Tao D., Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games. (2)
Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games
, Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 46, 10797-10813, 2024, Cites: 2
Duda P., Wojtulewicz M., Rutkowski L., Accelerating deep neural network learning using data stream methodology. (2)
Accelerating deep neural network learning using data stream methodology
, Accelerating deep neural network learning using data stream methodology, Information Sciences, 669, 669, 2024, Cites: 2
Lapa K., Increasing the explainability and trustiness of Wang–Mendel fuzzy system for classification problems. (2)
Increasing the explainability and trustiness of Wang–Mendel fuzzy system for classification problems
, Increasing the explainability and trustiness of Wang–Mendel fuzzy system for classification problems, Applied Soft Computing, 167, 167, 2024, Cites: 2
Wu J., Cheng J., Yan H., Rutkowski L., Cao J., Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol. (2)
Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol
, Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol, IEEE Transactions on Fuzzy Systems, 32, 32, 7109-7117, 2024, Cites: 2
Kong F., Cao J., Rutkowski L., Zhang Y., Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control. (2)
Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control
, Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control, IEEE Transactions on Fuzzy Systems, 32, 32, 3059-3070, 2024, Cites: 2
Gabryel M., Kocic E., Kocic M., Patora-Wysocka Z., Xiao M., Pawlak M., ACCELERATING USER PROFILING IN E-COMMERCE USING CONDITIONAL GAN NETWORKS FOR SYNTHETIC DATA GENERATION. (2)
ACCELERATING USER PROFILING IN E-COMMERCE USING CONDITIONAL GAN NETWORKS FOR SYNTHETIC DATA GENERATION
, ACCELERATING USER PROFILING IN E-COMMERCE USING CONDITIONAL GAN NETWORKS FOR SYNTHETIC DATA GENERATION, Journal of Artificial Intelligence and Soft Computing Research, 14, 14, 309-319, 2024, Cites: 2
Starzec G., Starzec M., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Ant colony optimization using two-dimensional pheromone for single-objective transport problems. (2)
Ant colony optimization using two-dimensional pheromone for single-objective transport problems
, Ant colony optimization using two-dimensional pheromone for single-objective transport problems, Journal of Computational Science, 79, 79, 2024, Cites: 2
Lu Y., Yao Y., Huang X., Xiao M., Jiang G., Rutkowski L., Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion. (1)
Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion
, Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion, International Journal of Bifurcation and Chaos, 34, 34, 2024, Cites: 1
Huang X.-X., Xiao M., Rutkowski L., Bao H.-B., Huang X., Cao J.-D., Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks. (1)
Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks
, Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks, Chinese Physics B, 33, 33, 2024, Cites: 1
Cheng H., Xiao M., Lu Y., Bao H., Rutkowski L., Cao J., Complex pattern evolution of a two-dimensional space diffusion model of malware spread. (1)
Complex pattern evolution of a two-dimensional space diffusion model of malware spread
, Complex pattern evolution of a two-dimensional space diffusion model of malware spread, Physica Scripta, 99, 99, 2024, Cites: 1
Ju Y., Xiao M., Huang C., Rutkowski L., Cao J., Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus. (1)
Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus
, Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus, International Journal of Systems Science, 55, 55, 2187-2210, 2024, Cites: 1
Rutkowska D., Duda P., Cao J., Jaworski M., Kisiel-Dorohinicki M., Tao D., Rutkowski L., Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring. (1)
Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring
, Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring, Applied Soft Computing, 161, 161, 2024, Cites: 1
Cheng J., Liu N., Rutkowski L., Cao J., Yan H., Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction-Diffusion Neural Networks. (0)
Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction-Diffusion Neural Networks
, Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction-Diffusion Neural Networks, IEEE Transactions on Fuzzy Systems, 2024, Cites: 0
Bernacki J., Scherer R., Compact Representation of Digital Camera’s Fingerprint with Convolutional Autoencoder. (0)
Compact Representation of Digital Camera’s Fingerprint with Convolutional Autoencoder
, Compact Representation of Digital Camera’s Fingerprint with Convolutional Autoencoder, Proceedings of the International Conference on Security and Cryptography, 792-797, 2024, Cites: 0
Urbanczyk A., Kucaba K., Wojtulewicz M., Kisiel-Dorohinicki M., Rutkowski L., Duda P., Kacprzyk J., Yao X., Chong S.Y., Byrski A., (μ +λ) Evolution Strategy with Socio-Cognitive Mutation. (0)
(μ +λ) Evolution Strategy with Socio-Cognitive Mutation
, (μ +λ) Evolution Strategy with Socio-Cognitive Mutation, Journal of Automation, Mobile Robotics and Intelligent Systems, 18, 18, 1-11, 2024, Cites: 0
Du X., Xiao M., Luan Y., Ding J., Rutkowski L., Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks. (0)
Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks
, Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks, Journal of Computational and Nonlinear Dynamics, 19, 19, 2024, Cites: 0
Li H., Xiao M., Wang Z., Xu F., Wang Z., Zheng W., Rutkowski L., A new chemical networked system: spatial-temporal evolution and control. (0)
A new chemical networked system: spatial-temporal evolution and control
, A new chemical networked system: spatial-temporal evolution and control, Physica Scripta, 99, 99, 2024, Cites: 0
Ademola A.T., Wen S., Feng Y., Zhang W., Rutkowski L., Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations. (0)
Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations
, Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations, Proyecciones, 43, 43, 985-1009, 2024, Cites: 0
Vizniuk A., Diachenko G., Laktionov I., Siwocha A., Xiao M., Smolag J., A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities. (0)
A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities
, A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities, Journal of Artificial Intelligence and Soft Computing Research, 15, 15, 115-146, 2024, Cites: 0
Podolszanska J., Development and Optimization of Deep Learning Systems for MRI Analysis in Alzheimer’s Disease Monitoring. (0)
Development and Optimization of Deep Learning Systems for MRI Analysis in Alzheimer’s Disease Monitoring
, Development and Optimization of Deep Learning Systems for MRI Analysis in Alzheimer’s Disease Monitoring, Journal of Telecommunications and Information Technology, 56-61, 2024, Cites: 0
Bilski J., Kowalczyk B., Dymova L., Xiao M., Accelerating Neural Network Training with FSGQR: A Scalable and High-Performance Alternative to Adam. (0)
Accelerating Neural Network Training with FSGQR: A Scalable and High-Performance Alternative to Adam
, Accelerating Neural Network Training with FSGQR: A Scalable and High-Performance Alternative to Adam, Journal of Artificial Intelligence and Soft Computing Research, 15, 15, 95-113, 2024, Cites: 0
De Magistris G., Grycuk R., Mandelli L., Scherer R., New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations. (0)
New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations
, New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations, CEUR Workshop Proceedings, 3870, 3870, 67-72, 2024, Cites: 0
Osowski M., Krasnodebska A., Drozda P., Scherer R., Professionally Diverse: AI-Generated Faces for Targeted Advertising. (0)
Professionally Diverse: AI-Generated Faces for Targeted Advertising
, Professionally Diverse: AI-Generated Faces for Targeted Advertising, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14795 LNAI, 14795 LNAI, 171-183, 2024, Cites: 0
Grycuk R., De Magistris G., Napoli C., Scherer R., Toward Real-Time Solar Content-Based Image Retrieval. (0)
Toward Real-Time Solar Content-Based Image Retrieval
, Toward Real-Time Solar Content-Based Image Retrieval, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14832 LNCS, 14832 LNCS, 107-120, 2024, Cites: 0
Lucas T.J., Passos L.A., Rodrigues D., Jodas D., Papa J.P., Da Costa K.A.P., Scherer R., Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems. (0)
Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems
, Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems, International Conference on Systems, Signals, and Image Processing, 2024, Cites: 0
Vovna O., Kaydash H., Rutkowski L., Sakhno I., Laktionov I., Kabanets M., Zozulya S., Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines. (0)
Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines
, Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines, Journal of Control Science and Engineering, 2024, 2024, 2024, Cites: 0
Cierniak R., A New MLEM Reconstruction Algorithm for Ultra-low Dose PET. (0)
A New MLEM Reconstruction Algorithm for Ultra-low Dose PET
, A New MLEM Reconstruction Algorithm for Ultra-low Dose PET, Communications in Computer and Information Science, 2166 CCIS, 2166 CCIS, 406-418, 2024, Cites: 0
Dziwinski P., A new Hybrid Parameter Independent Particle Swarm Optimization Algorithm. (0)
A new Hybrid Parameter Independent Particle Swarm Optimization Algorithm
, A new Hybrid Parameter Independent Particle Swarm Optimization Algorithm, GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 739-742, 2024, Cites: 0
Zalasinski M., Cader A., Patora-Wysocka Z., Xiao M., EVALUATING NEURAL NETWORK MODELS FOR PREDICTING DYNAMIC SIGNATURE SIGNALS. (0)
EVALUATING NEURAL NETWORK MODELS FOR PREDICTING DYNAMIC SIGNATURE SIGNALS
, EVALUATING NEURAL NETWORK MODELS FOR PREDICTING DYNAMIC SIGNATURE SIGNALS, Journal of Artificial Intelligence and Soft Computing Research, 14, 14, 361-372, 2024, Cites: 0
He J., Xiao M., He H., Wang Z., Xing Zheng W., Rutkowski L., Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks. (0)
Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks
, Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks, IEEE Transactions on Circuits and Systems I: Regular Papers, 71, 71, 4131-4144, 2024, Cites: 0
Korytkowski M., Advanced Techniques of Artificial Intelligence in IT Security Systems. (0)
Advanced Techniques of Artificial Intelligence in IT Security Systems
, Advanced Techniques of Artificial Intelligence in IT Security Systems, Studies in Big Data, 146, 146, 1-91, 2024, Cites: 0
Wojtulewicz M., Duda P., Nowicki R., Rutkowski L., On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism. (0)
On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism
, On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism, Proceedings of Machine Learning Research, 263, 263, 17-24, 2024, Cites: 02023 (74)
Wang J., Wu J., Shen H., Cao J., Rutkowski L., Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method. (78)
Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method
, Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method, IEEE Transactions on Cybernetics, 53, 53, 7380-7391, 2023, Cites: 78
Bilski J., Smolag J., Kowalczyk B., Grzanek K., Izonin I., Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks. (29)
Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks
, Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 45-61, 2023, Cites: 29
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study. (24)
Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study
, Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study, Sensors, 23, 23, 2023, Cites: 24
Alfarano A., De Magistris G., Mongelli L., Russo S., Starczewski J., Napoli C., A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection. (22)
A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection
, A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, 3-16, 2023, Cites: 22
Lin A., Cheng J., Rutkowski L., Wen S., Luo M., Cao J., Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol. (21)
Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol
, Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol, IEEE Transactions on Neural Networks and Learning Systems, 34, 34, 9004-9015, 2023, Cites: 21
Zhu S., Cao J., Lin L., Rutkowski L., Lu J., Lu G., Observability and Detectability of Stochastic Labeled Graphs. (20)
Observability and Detectability of Stochastic Labeled Graphs
, Observability and Detectability of Stochastic Labeled Graphs, IEEE Transactions on Automatic Control, 68, 68, 7299-7311, 2023, Cites: 20
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control. (18)
Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control
, Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control, IEEE Transactions on Automatic Control, 68, 68, 1215-1222, 2023, Cites: 18
Shen H., Zhang Y., Wang J., Cao J., Rutkowski L., Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems. (18)
Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems
, Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems, IEEE Transactions on Automatic Control, 68, 68, 6255-6261, 2023, Cites: 18
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos. (17)
YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos
, YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos, Sensors (Basel, Switzerland), 23, 23, 2023, Cites: 17
Wang J., Wu J., Shen H., Cao J., Rutkowski L., A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism. (15)
A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism
, A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 53, 4934-4943, 2023, Cites: 15
Niksa-Rynkiewicz T., Stomma P., Witkowska A., Rutkowska D., Slowik A., Cpalka K., Jaworek-Korjakowska J., Kolendo P., AN INTELLIGENT APPROACH TO SHORT-TERM WIND POWER PREDICTION USING DEEP NEURAL NETWORKS. (10)
AN INTELLIGENT APPROACH TO SHORT-TERM WIND POWER PREDICTION USING DEEP NEURAL NETWORKS
, AN INTELLIGENT APPROACH TO SHORT-TERM WIND POWER PREDICTION USING DEEP NEURAL NETWORKS, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 197-210, 2023, Cites: 10
Lucas T.J., De Figueiredo I.S., Tojeiro C.A.C., De Almeida A.M.G., Scherer R., Brega J.R.F., Papa J.P., Da Costa K.A.P., A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks. (8)
A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks
, A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks, IEEE Access, 11, 11, 122638-122676, 2023, Cites: 8
Laktionov I., Rutkowski L., Vovna O., Byrski A., Kabanets M., A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques. (8)
A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques
, A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques, Engineering Applications of Artificial Intelligence, 126, 126, 2023, Cites: 8
Ghaffari R., Helfroush M.S., Khosravi A., Kazemi K., Danyali H., Rutkowski L., Toward domain adaptation with open-set target data: Review of theory and computer vision applications. (8)
Toward domain adaptation with open-set target data: Review of theory and computer vision applications
, Toward domain adaptation with open-set target data: Review of theory and computer vision applications, Information Fusion, 100, 100, 2023, Cites: 8
Izonin I., Tkachenko R., Gurbych O., Kovac M., Rutkowski L., Holoven R., A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis. (7)
A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis
, A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis, Mathematical Biosciences and Engineering, 20, 20, 13398-13414, 2023, Cites: 7
Rutkowska D., Duda P., Cao J., Rutkowski L., Byrski A., Jaworski M., Tao D., The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks. (7)
The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks
, The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks, Information Sciences, 631, 631, 346-368, 2023, Cites: 7
Wang Y., Yan J., Huang W., Rutkowski L., Cao J., Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track. (7)
Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track
, Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track, Science China Information Sciences, 66, 66, 2023, Cites: 7
Guan H., Liu Y., Kou K.I., Cao J., Rutkowski L., Collaborative neurodynamic optimization for solving nonlinear equations. (6)
Collaborative neurodynamic optimization for solving nonlinear equations
, Collaborative neurodynamic optimization for solving nonlinear equations, Neural Networks, 165, 165, 483-490, 2023, Cites: 6
Wang J., Chen Z., Shen H., Cao J., Rutkowski L., Fuzzy H<inf>∞</inf> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation. (6)
Fuzzy H<inf>∞</inf> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation
, Fuzzy H<inf>∞</inf> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation, IEEE Transactions on Fuzzy Systems, 31, 31, 4374-4384, 2023, Cites: 6
Wei W., Li X., Zhang B., Li L., Damasevicius R., Scherer R., LSTM-SN: complex text classifying with LSTM fusion social network. (6)
LSTM-SN: complex text classifying with LSTM fusion social network
, LSTM-SN: complex text classifying with LSTM fusion social network, Journal of Supercomputing, 79, 79, 9558-9583, 2023, Cites: 6
Chen G., Zou W., Jing W., Wei W., Scherer R., Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data. (6)
Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data
, Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data, IEEE Transactions on Industrial Informatics, 19, 19, 594-604, 2023, Cites: 6
Woldan P., Duda P., Cader A., Laktionov I., A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps. (5)
A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps
, A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 63-72, 2023, Cites: 5
Krokosz T., Rykowski J., Zajecka M., Brzoza-Woch R., Rutkowski L., Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding. (3)
Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding
, Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding, Sensors, 23, 23, 2023, Cites: 3
Sevastjanov P., Kaczmarek K., Rutkowski L., A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization. (2)
A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization
, A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization, Applied Soft Computing, 147, 147, 2023, Cites: 2
Zhu W., Cao J., Shi X., Rutkowski L., Leader-following consensus of finite-field networks with time-delays. (2)
Leader-following consensus of finite-field networks with time-delays
, Leader-following consensus of finite-field networks with time-delays, Information Sciences, 647, 647, 2023, Cites: 2
Bernacki J., Scherer R., IMAGINE Dataset: Digital Camera Identification Image BenchmarkinDataset. (2)
IMAGINE Dataset: Digital Camera Identification Image BenchmarkinDataset
, IMAGINE Dataset: Digital Camera Identification Image BenchmarkinDataset, Proceedings of the International Conference on Security and Cryptography, 1, 1, 799-804, 2023, Cites: 2
Pluta P., A New Approach to Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot Using a Rebinning Method. (2)
A New Approach to Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot Using a Rebinning Method
, A New Approach to Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot Using a Rebinning Method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 286-299, 2023, Cites: 2
Pluta P., Cierniak R., A New Rebinning Reconstruction Method for the Low Dose CT Scanners with Flying Focal Spot. (1)
A New Rebinning Reconstruction Method for the Low Dose CT Scanners with Flying Focal Spot
, A New Rebinning Reconstruction Method for the Low Dose CT Scanners with Flying Focal Spot, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, 269-278, 2023, Cites: 1
Przybyl A., FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives. (1)
FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives
, FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives, Electronics (Switzerland), 12, 12, 2023, Cites: 1
Walczak J., Najgebauer P., Wojciechowski A., Scherer R., Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented]. (1)
Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented]
, Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented], Applied Soft Computing, 132, 132, 2023, Cites: 1
Gabryel M., Lada D., Kocic M., Autoencoder Neural Network for Detecting Non-human Web Traffic. (1)
Autoencoder Neural Network for Detecting Non-human Web Traffic
, Autoencoder Neural Network for Detecting Non-human Web Traffic, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 232-242, 2023, Cites: 1
Zalasinski M., Duda P., Lota S., Cpalka K., Dynamic Signature Verification Using Selected Regions. (1)
Dynamic Signature Verification Using Selected Regions
, Dynamic Signature Verification Using Selected Regions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 388-397, 2023, Cites: 1
Dziwinski P., Bartczuk L., A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm with Self-Adaptation Mechanism. (1)
A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm with Self-Adaptation Mechanism
, A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm with Self-Adaptation Mechanism, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 363-374, 2023, Cites: 1
Wei W., Liu W., Zhang B., Scherer R., Damasevicius R., Discovery of New Words in Tax-related Fields Based on Word Vector Representation. (1)
Discovery of New Words in Tax-related Fields Based on Word Vector Representation
, Discovery of New Words in Tax-related Fields Based on Word Vector Representation, Journal of Internet Technology, 24, 24, 923-930, 2023, Cites: 1
Cheng J., Wu F., Liu L., Zhang Q., Rutkowski L., Tao D., InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement. (1)
InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement
, InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement, IEEE Transactions on Multimedia, 25, 25, 8279-8293, 2023, Cites: 1
Zhang N., Wang J., Rutkowski L., Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization. (1)
Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization
, Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization, Neural Computing and Applications, 35, 35, 9947-9949, 2023, Cites: 1
Wu X., Zhu X., Baralis E., Lu R., Kumar V., Rutkowski L., Tang J., On Computing Paradigms - Where Will Large Language Models Be Going. (1)
On Computing Paradigms - Where Will Large Language Models Be Going
, On Computing Paradigms - Where Will Large Language Models Be Going, Proceedings - IEEE International Conference on Data Mining, ICDM, 1577-1582, 2023, Cites: 1
Starzec G., Starzec M., Bandyopadhyay S., Maulik U., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Two-Dimensional Pheromone in Ant Colony Optimization. (1)
Two-Dimensional Pheromone in Ant Colony Optimization
, Two-Dimensional Pheromone in Ant Colony Optimization, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14162 LNAI, 14162 LNAI, 459-471, 2023, Cites: 1
Wei W., Chen K.-C., Rayes A., Scherer R., Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems. (1)
Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems
, Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems, IEEE Transactions on Intelligent Transportation Systems, 24, 24, 8393-8398, 2023, Cites: 1
Lapa K., Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization. (1)
Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization
, Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 399-414, 2023, Cites: 1
Korytkowski M., Nowak J., Scherer R., Wei W., Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks. (1)
Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks
, Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 277-285, 2023, Cites: 1
Jing W., Kuang Z., Scherer R., Wozniak M., Editorial: Big data and artificial intelligence technologies for smart forestry. (1)
Editorial: Big data and artificial intelligence technologies for smart forestry
, Editorial: Big data and artificial intelligence technologies for smart forestry, Frontiers in Plant Science, 14, 14, 2023, Cites: 1
Dedek M., Scherer R., Transformer-Based Original Content Recovery from Obfuscated PowerShell Scripts. (0)
Transformer-Based Original Content Recovery from Obfuscated PowerShell Scripts
, Transformer-Based Original Content Recovery from Obfuscated PowerShell Scripts, Communications in Computer and Information Science, 1794 CCIS, 1794 CCIS, 284-295, 2023, Cites: 0
Podolszanska J., VALIDATION OF A THREE-DIMENSIONAL HEAD PHANTOM FOR IMAGING DATA. (0)
VALIDATION OF A THREE-DIMENSIONAL HEAD PHANTOM FOR IMAGING DATA
, VALIDATION OF A THREE-DIMENSIONAL HEAD PHANTOM FOR IMAGING DATA, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska, 13, 13, 29-32, 2023, Cites: 0
Bilski J., Kowalczyk B., A Novel Approach to the GQR Algorithm for Neural Networks Training. (0)
A Novel Approach to the GQR Algorithm for Neural Networks Training
, A Novel Approach to the GQR Algorithm for Neural Networks Training, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 3-11, 2023, Cites: 0
Feng Z.C., Xu W.Y., Cao J.D., Yang S.F., Rutkowski L., Distributed online bandit tracking for Nash equilibrium under partial-decision information setting. (0)
Distributed online bandit tracking for Nash equilibrium under partial-decision information setting
, Distributed online bandit tracking for Nash equilibrium under partial-decision information setting, Science China Technological Sciences, 66, 66, 3129-3138, 2023, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, v-vi, 2023, Cites: 0
Sosnowski J., Pluta P., Najgebauer P., Hand Gesture Recognition for Medical Purposes Using CNN. (0)
Hand Gesture Recognition for Medical Purposes Using CNN
, Hand Gesture Recognition for Medical Purposes Using CNN, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 80-88, 2023, Cites: 0
Galkowski T., Krzyzak A., Fast Estimation of Multidimensional Regression Functions by the Parzen Kernel-Based Method. (0)
Fast Estimation of Multidimensional Regression Functions by the Parzen Kernel-Based Method
, Fast Estimation of Multidimensional Regression Functions by the Parzen Kernel-Based Method, Communications in Computer and Information Science, 1791 CCIS, 1791 CCIS, 251-262, 2023, Cites: 0
Najgebauer P., Scherer R., Grycuk R., Walczak J., Wojciechowski A., Lada-Tondyra E., Fast Visual Imperfection Detection when Real Negative Examples are Unavailable. (0)
Fast Visual Imperfection Detection when Real Negative Examples are Unavailable
, Fast Visual Imperfection Detection when Real Negative Examples are Unavailable, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, 58-68, 2023, Cites: 0
De Magistris G., Comminiello D., Napoli C., Starczewski J.T., Visual Odometry with Depth-Wise Separable Convolution and Quaternion Neural Networks. (0)
Visual Odometry with Depth-Wise Separable Convolution and Quaternion Neural Networks
, Visual Odometry with Depth-Wise Separable Convolution and Quaternion Neural Networks, CEUR Workshop Proceedings, 3417, 3417, 70-80, 2023, Cites: 0
Slowik A., Cpalka K., Hassanien A.E., Evolutionary Algorithms and Their Applications in Intelligent Systems. (0)
Evolutionary Algorithms and Their Applications in Intelligent Systems
, Evolutionary Algorithms and Their Applications in Intelligent Systems, Lecture Notes on Data Engineering and Communications Technologies, 184, 184, 143-153, 2023, Cites: 0
Osowski M., Lorenc K., Drozda P., Scherer R., Szalapak K., Komar-Komarowski K., Szymanski J., Sobecki A., Previous Opinions is All You Need—Legal Information Retrieval System. (0)
Previous Opinions is All You Need—Legal Information Retrieval System
, Previous Opinions is All You Need—Legal Information Retrieval System, Communications in Computer and Information Science, 1864 CCIS, 1864 CCIS, 57-67, 2023, Cites: 0
Grycuk R., Korytkowski M., Scherer R., Fuzzy-Based Solar Magnetogram Image Retrieval. (0)
Fuzzy-Based Solar Magnetogram Image Retrieval
, Fuzzy-Based Solar Magnetogram Image Retrieval, IEEE International Conference on Fuzzy Systems, 2023, Cites: 0
Korytkowski M., Nowak J., Scherer R., Detecting Sensitive Data with GANs and Fully Convolutional Networks. (0)
Detecting Sensitive Data with GANs and Fully Convolutional Networks
, Detecting Sensitive Data with GANs and Fully Convolutional Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13995 LNAI, 13995 LNAI, 273-283, 2023, Cites: 0
Bilski J., Kowalczyk B., Smolag J., On Speeding up the Levenberg-Marquardt Learning Algorithm. (0)
On Speeding up the Levenberg-Marquardt Learning Algorithm
, On Speeding up the Levenberg-Marquardt Learning Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 12-22, 2023, Cites: 0
Mastalerczyk M., Szczepanik T., Zalasinski M., A New Method of Verification of Dynamic Signatures Changing over Time with Decomposition and Selection of Characteristic Descriptors. (0)
A New Method of Verification of Dynamic Signatures Changing over Time with Decomposition and Selection of Characteristic Descriptors
, A New Method of Verification of Dynamic Signatures Changing over Time with Decomposition and Selection of Characteristic Descriptors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, 251-257, 2023, Cites: 0
Lapa K., Rutkowska D., Byrski A., Napoli C., A NEW APPROACH TO DETECTING AND PREVENTING POPULATIONS STAGNATION THROUGH DYNAMIC CHANGES IN MULTI-POPULATION-BASED ALGORITHMS. (0)
A NEW APPROACH TO DETECTING AND PREVENTING POPULATIONS STAGNATION THROUGH DYNAMIC CHANGES IN MULTI-POPULATION-BASED ALGORITHMS
, A NEW APPROACH TO DETECTING AND PREVENTING POPULATIONS STAGNATION THROUGH DYNAMIC CHANGES IN MULTI-POPULATION-BASED ALGORITHMS, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 289-306, 2023, Cites: 0
Krzyzak A., Galkowski T., Partyka M., Convergence of RBF Networks Regression Function Estimates and Classifiers. (0)
Convergence of RBF Networks Regression Function Estimates and Classifiers
, Convergence of RBF Networks Regression Function Estimates and Classifiers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13588 LNAI, 13588 LNAI, 363-376, 2023, Cites: 0
Bernacki J., Scherer R., Remarks on Speeding up the Digital Camera Identification using Convolutional Neural Networks. (0)
Remarks on Speeding up the Digital Camera Identification using Convolutional Neural Networks
, Remarks on Speeding up the Digital Camera Identification using Convolutional Neural Networks, Vietnam Journal of Computer Science, 10, 10, 537-555, 2023, Cites: 0
Grycuk R., Scherer R., Sun Magnetograms Retrieval from Vast Collections Through Small Hash Codes. (0)
Sun Magnetograms Retrieval from Vast Collections Through Small Hash Codes
, Sun Magnetograms Retrieval from Vast Collections Through Small Hash Codes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14076 LNCS, 14076 LNCS, 259-273, 2023, Cites: 0
Dudzik S., Kowalczyk B., Forecasting of solar energy production using the NEXO platform and VRM Portal. (0)
Forecasting of solar energy production using the NEXO platform and VRM Portal
, Forecasting of solar energy production using the NEXO platform and VRM Portal, Przeglad Elektrotechniczny, 2023, 2023, 224-227, 2023, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, v-vi, 2023, Cites: 0
Grycuk R., Najgebauer P., Scherer R., Edge Detection-Based Full-Disc Solar Image Hashing. (0)
Edge Detection-Based Full-Disc Solar Image Hashing
, Edge Detection-Based Full-Disc Solar Image Hashing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 243-251, 2023, Cites: 0
Pluta P., Cierniak R., A New Statistical Approach to Image Reconstruction with Rebinning for the X-Ray CT Scanners with Flying Focal Spot Tube. (0)
A New Statistical Approach to Image Reconstruction with Rebinning for the X-Ray CT Scanners with Flying Focal Spot Tube
, A New Statistical Approach to Image Reconstruction with Rebinning for the X-Ray CT Scanners with Flying Focal Spot Tube, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14074 LNCS, 14074 LNCS, 670-677, 2023, Cites: 0
Duda P., Wojtulewicz M., Rutkowski L., The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach. (0)
The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach
, The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 46-55, 2023, Cites: 0
Bilski J., Kowalczyk B., Smolag J., A New Computational Approach to the Levenberg-Marquardt Learning Algorithm. (0)
A New Computational Approach to the Levenberg-Marquardt Learning Algorithm
, A New Computational Approach to the Levenberg-Marquardt Learning Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13588 LNAI, 13588 LNAI, 16-26, 2023, Cites: 0
Kocic E., Gabryel M., Kocic M., Profiling of Webshop Users in Terms of Price Sensitivity. (0)
Profiling of Webshop Users in Terms of Price Sensitivity
, Profiling of Webshop Users in Terms of Price Sensitivity, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 522-529, 2023, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, v-vi, 2023, Cites: 0
Kucharski D., Cpalka K., Multi-population Algorithm Using Surrogate Models and Different Training Plans. (0)
Multi-population Algorithm Using Surrogate Models and Different Training Plans
, Multi-population Algorithm Using Surrogate Models and Different Training Plans, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 385-398, 2023, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13588 LNAI, 13588 LNAI, v-vi, 2023, Cites: 0
Korytkowski M., Nowak J., Scherer R., Zbieg A., Zak B., Relikowska G., Mader P., Employee Turnover Prediction From Email Communication Analysis. (0)
Employee Turnover Prediction From Email Communication Analysis
, Employee Turnover Prediction From Email Communication Analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 252-263, 2023, Cites: 0
Bartczuk L., A New Linguistic Fuzzy PRISM Algorithm. (0)
A New Linguistic Fuzzy PRISM Algorithm
, A New Linguistic Fuzzy PRISM Algorithm, IEEE International Conference on Fuzzy Systems, 2023, Cites: 0
Urbanczyk A., Kipinski P., Nabywaniec M., Rutkowski L., Chong S.Y., Yao X., Boryczko K., Byrski A., Socio-cognitive caste-based optimization. (0)
Socio-cognitive caste-based optimization
, Socio-cognitive caste-based optimization, Journal of Computational Science, 72, 72, 2023, Cites: 02022 (50)
Tan X., Xiang C., Cao J., Xu W., Wen G., Rutkowski L., Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption. (70)
Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption
, Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption, IEEE Transactions on Cybernetics, 52, 52, 8246-8257, 2022, Cites: 70
Yu T., Cao J., Rutkowski L., Luo Y.-P., Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control. (54)
Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control
, Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 3938-3947, 2022, Cites: 54
Song Y., Cao J., Rutkowski L., A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy. (53)
A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy
, A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy, IEEE Transactions on Network Science and Engineering, 9, 9, 1154-1162, 2022, Cites: 53
De Magistris G., Russo S., Roma P., Starczewski J.T., Napoli C., An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19. (52)
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
, An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19, Information (Switzerland), 13, 13, 2022, Cites: 52
Luo Y., Zhu W., Cao J., Rutkowski L., Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems. (40)
Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems
, Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems, IEEE Transactions on Network Science and Engineering, 9, 9, 1527-1539, 2022, Cites: 40
Kordos M., Blachnik M., Scherer R., Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems. (27)
Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems
, Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems, Information Sciences, 587, 587, 23-40, 2022, Cites: 27
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays. (26)
Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays
, Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays, IEEE Transactions on Circuits and Systems I: Regular Papers, 69, 69, 2095-2107, 2022, Cites: 26
Staszewski P., Jaworski M., Cao J., Rutkowski L., A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers. (23)
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers
, A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 7913-7920, 2022, Cites: 23
Slowik A., Cpalka K., Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications. (21)
Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications
, Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications, IEEE Transactions on Industrial Informatics, 18, 18, 546-558, 2022, Cites: 21
Shen H., Wang X., Wang J., Cao J., Rutkowski L., Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method. (21)
Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method
, Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method, IEEE Transactions on Cybernetics, 52, 52, 12712-12721, 2022, Cites: 21
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications. (19)
Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications
, Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications, Sensors, 22, 22, 2022, Cites: 19
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control. (17)
Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control
, Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control, IEEE Transactions on Cybernetics, 52, 52, 10290-10301, 2022, Cites: 17
Feng Y., Zhang W., Xiong J., Li H., Rutkowski L., Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes. (16)
Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes
, Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes, IEEE Transactions on Cybernetics, 52, 52, 748-757, 2022, Cites: 16
Zhou Y., Jing W., Wang J., Chen G., Scherer R., Damasevicius R., MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution. (16)
MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution
, MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution, IET Image Processing, 16, 16, 659-668, 2022, Cites: 16
Feng L., Liu L., Cao J., Rutkowski L., Lu G., General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching. (15)
General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching
, General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching, IEEE Transactions on Cybernetics, 52, 52, 5441-5453, 2022, Cites: 15
Hammad M., Meshoul S., Dziwinski P., Plawiak P., Elgendy I.A., Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification. (14)
Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification
, Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification, Sensors, 22, 22, 2022, Cites: 14
Zhu W., Cao J., Shi X., Rutkowski L., Synchronization of Finite-Field Networks With Time Delays. (12)
Synchronization of Finite-Field Networks With Time Delays
, Synchronization of Finite-Field Networks With Time Delays, IEEE Transactions on Network Science and Engineering, 9, 9, 347-355, 2022, Cites: 12
Bilski J., Kowalczyk B., Kisiel-Dorohinicki M., Siwocha A., Zurada J., Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm. (12)
Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm
, Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 181-195, 2022, Cites: 12
Hu J., Cao J., Rutkowski L., Xue C., Yu J., Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators. (11)
Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators
, Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators, Electric Power Systems Research, 208, 208, 2022, Cites: 11
De Magistris G., Romano M., Starczewski J., Napoli C., A Novel DWT-based Encoder for Human Pose Estimation. (10)
A Novel DWT-based Encoder for Human Pose Estimation
, A Novel DWT-based Encoder for Human Pose Estimation, CEUR Workshop Proceedings, 3360, 3360, 33-40, 2022, Cites: 10
Gabryel M., Lada D., Filutowicz Z., Patora-Wysocka Z., Kisiel-Dorohinicki M., Chen G.Y., Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder. (10)
Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder
, Detecting Anomalies in Advertising Web Traffic with the Use of the Variational Autoencoder, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 255-256, 2022, Cites: 10
Hou Y., Zheng X., Han C., Wei W., Scherer R., Polap D., Deep Learning Methods in Short-Term Traffic Prediction: A Survey. (9)
Deep Learning Methods in Short-Term Traffic Prediction: A Survey
, Deep Learning Methods in Short-Term Traffic Prediction: A Survey, Information Technology and Control, 51, 51, 139-157, 2022, Cites: 9
Slowik A., Cpalka K., Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications. (9)
Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications
, Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications, IEEE Transactions on Industrial Informatics, 18, 18, 542-545, 2022, Cites: 9
Sun Z., Zhao G., Scherer R., Wei W., Wozniak M., Overview of Capsule Neural Networks. (9)
Overview of Capsule Neural Networks
, Overview of Capsule Neural Networks, Journal of Internet Technology, 23, 23, 33-44, 2022, Cites: 9
Li Z., Tang Y., Fan Y., Huang T., Rutkowski L., Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses. (9)
Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses
, Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses, IEEE Transactions on Network Science and Engineering, 9, 9, 2224-2236, 2022, Cites: 9
Grycuk R., Galkowski T., Scherer R., Rutkowski L., A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder. (7)
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
, A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2022-July, 2022-July, 2022, Cites: 7
Cpalka K., Slowik A., Lapa K., A population-based algorithm with the selection of evaluation precision and size of the population. (6)
A population-based algorithm with the selection of evaluation precision and size of the population
, A population-based algorithm with the selection of evaluation precision and size of the population, Applied Soft Computing, 115, 115, 2022, Cites: 6
Starczewski J.T., Przybyszewski K., Byrski A., Szmidt E., Napoli C., A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems. (6)
A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems
, A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 197-206, 2022, Cites: 6
Zalasinski M., Laskowski L., Niksa-Rynkiewicz T., Cpalka K., Byrski A., Przybyszewski K., Trippner P., Dong S., Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach. (6)
Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach
, Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 267-279, 2022, Cites: 6
Bernacki J., Digital camera identification by fingerprint’s compact representation. (5)
Digital camera identification by fingerprint’s compact representation
, Digital camera identification by fingerprint’s compact representation, Multimedia Tools and Applications, 81, 81, 21641-21674, 2022, Cites: 5
Grycuk R., Scherer R., Marchlewska A., Napoli C., Semantic Hashing for Fast Solar Magnetogram Retrieval. (5)
Semantic Hashing for Fast Solar Magnetogram Retrieval
, Semantic Hashing for Fast Solar Magnetogram Retrieval, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 299-306, 2022, Cites: 5
Lapa K., Cpalka K., Kisiel-Dorohinicki M., Paszkowski J., Debski M., Le V.-H., Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation. (4)
Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation
, Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 239-253, 2022, Cites: 4
Chen G.Y., Krzyzak A., Duda P., Cader A., Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain. (4)
Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain
, Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 169-180, 2022, Cites: 4
Bernacki J., Costa K.A.P., Scherer R., Individual Source Camera Identification with Convolutional Neural Networks. (3)
Individual Source Camera Identification with Convolutional Neural Networks
, Individual Source Camera Identification with Convolutional Neural Networks, Communications in Computer and Information Science, 1716 CCIS, 1716 CCIS, 45-55, 2022, Cites: 3
Lucas T.J., Da Costa K.A.P., Scherer R., Papa J.P., An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance. (2)
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance
, An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2022-October, 2022-October, 1173-1179, 2022, Cites: 2
Grycuk R., Korytkowski M., Scherer R., Drozda P., Wei W., Kordos M., Fast Solar Image Retrieval and Classification by Fuzzy Rules. (2)
Fast Solar Image Retrieval and Classification by Fuzzy Rules
, Fast Solar Image Retrieval and Classification by Fuzzy Rules, IEEE International Conference on Fuzzy Systems, 2022-July, 2022-July, 2022, Cites: 2
Ponzi V., Iacobelli E., Napoli C., Starczewski J., A Real-time Hand Gesture Recognition System for Human-Computer and Human-Robot Interaction. (1)
A Real-time Hand Gesture Recognition System for Human-Computer and Human-Robot Interaction
, A Real-time Hand Gesture Recognition System for Human-Computer and Human-Robot Interaction, CEUR Workshop Proceedings, 3398, 3398, 52-58, 2022, Cites: 1
Bernacki J., Scherer R., Digital forensics: a fast algorithm for a digital sensor identification. (1)
Digital forensics: a fast algorithm for a digital sensor identification
, Digital forensics: a fast algorithm for a digital sensor identification, Journal of Information and Telecommunication, 6, 6, 399-419, 2022, Cites: 1
Ma R., Angryk R., Scherer R., Special issue on deep learning for time series data. (1)
Special issue on deep learning for time series data
, Special issue on deep learning for time series data, Neural Computing and Applications, 34, 34, 13147-13148, 2022, Cites: 1
Godzik M., Dajda J., Kisiel-Dorohinicki M., Byrski A., Rutkowski L., Orzechowski P., Wagenaar J., Moore J.H., Applying autonomous hybrid agent-based computing to difficult optimization problems. (1)
Applying autonomous hybrid agent-based computing to difficult optimization problems
, Applying autonomous hybrid agent-based computing to difficult optimization problems, Journal of Computational Science, 64, 64, 2022, Cites: 1
Galkowski T., Krzyzak A., Dziwinski P., Fast Estimation of Multidimensional Regression Functions. (1)
Fast Estimation of Multidimensional Regression Functions
, Fast Estimation of Multidimensional Regression Functions, 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022, 211-216, 2022, Cites: 1
Cierniak R., Bilski J., Pluta P., Implementations of statistical reconstruction algorithm for CT scanners with flying focal spot. (0)
Implementations of statistical reconstruction algorithm for CT scanners with flying focal spot
, Implementations of statistical reconstruction algorithm for CT scanners with flying focal spot, Proceedings of SPIE - The International Society for Optical Engineering, 12304, 12304, 2022, Cites: 0
Zhang N., Wang J., Rutkowski L., Editorial: Special Issue on Reliable Machine Learning and Optimization. (0)
Editorial: Special Issue on Reliable Machine Learning and Optimization
, Editorial: Special Issue on Reliable Machine Learning and Optimization, International Journal on Artificial Intelligence Tools, 31, 31, 2022, Cites: 0
Hazra S., Pisipati M., Puhan A., Nandy A., Scherer R., Two Novel Methods for Multiple Kinect v2 Sensor Calibration. (0)
Two Novel Methods for Multiple Kinect v2 Sensor Calibration
, Two Novel Methods for Multiple Kinect v2 Sensor Calibration, Communications in Computer and Information Science, 1568 CCIS, 1568 CCIS, 403-414, 2022, Cites: 0
Kordos M., Kulka R., Steblik T., Scherer R., Local Search in Selected Crossover Operators. (0)
Local Search in Selected Crossover Operators
, Local Search in Selected Crossover Operators, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13352 LNCS, 13352 LNCS, 369-382, 2022, Cites: 0
Utimura L., Costa K., Scherer R., Real-time application of OPF-based classifier in Snort IDS. (0)
Real-time application of OPF-based classifier in Snort IDS
, Real-time application of OPF-based classifier in Snort IDS, Optimum-Path Forest: Theory, Algorithms, and Applications, 55-93, 2022, Cites: 0
Cierniak R., Continuous-to-Continuous Data Model vs. Discrete-to-Discrete Data Model for the Statistical Iterative Reconstruction Method. (0)
Continuous-to-Continuous Data Model vs. Discrete-to-Discrete Data Model for the Statistical Iterative Reconstruction Method
, Continuous-to-Continuous Data Model vs. Discrete-to-Discrete Data Model for the Statistical Iterative Reconstruction Method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13351 LNCS, 13351 LNCS, 493-506, 2022, Cites: 0
Junior P.R.G.H., Scherer R., Januario L.B., Rodrigues D., Papa J.P., Costa K.A.P., From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks. (0)
From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks
, From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks, International Conference on Systems, Signals, and Image Processing, 2022-June, 2022-June, 2022, Cites: 0
Song Y., Wang L., Xiao L., Wei W., Scherer R., Qin G., Wang J., Hypergraph-partitioning-based online joint scheduling of tasks and data. (0)
Hypergraph-partitioning-based online joint scheduling of tasks and data
, Hypergraph-partitioning-based online joint scheduling of tasks and data, Journal of Supercomputing, 78, 78, 16088-16117, 2022, Cites: 0
Cierniak R., Pluta P., An Original Continuous-to-Continuous Forward Model as a Universal Method for the Formulation of Reconstruction Methods for Medical Imaging Techniques. (0)
An Original Continuous-to-Continuous Forward Model as a Universal Method for the Formulation of Reconstruction Methods for Medical Imaging Techniques
, An Original Continuous-to-Continuous Forward Model as a Universal Method for the Formulation of Reconstruction Methods for Medical Imaging Techniques, Communications in Computer and Information Science, 1653 CCIS, 1653 CCIS, 396-405, 2022, Cites: 02021 (59)
Wang J., Yang C., Shen H., Cao J., Rutkowski L., Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters. (122)
Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters
, Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 7579-7586, 2021, Cites: 122
Yang X., Wan X., Zunshui C., Cao J., Liu Y., Rutkowski L., Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault. (105)
Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault
, Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault, IEEE Transactions on Neural Networks and Learning Systems, 32, 32, 4191-4201, 2021, Cites: 105
Xia Z., Liu Y., Lu J., Cao J., Rutkowski L., Penalty method for constrained distributed quaternion-variable optimization. (53)
Penalty method for constrained distributed quaternion-variable optimization
, Penalty method for constrained distributed quaternion-variable optimization, IEEE Transactions on Cybernetics, 51, 51, 5631-5636, 2021, Cites: 53
Shi H., Wang L., Scherer R., Wozniak M., Zhang P., Wei W., Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network. (53)
Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network
, Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network, IEEE Access, 9, 9, 66965-66981, 2021, Cites: 53
Tan X., Cao J., Rutkowski L., Lu G., Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution. (47)
Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution
, Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution, IEEE Transactions on Cybernetics, 51, 51, 624-634, 2021, Cites: 47
Bian J., Wang L., Scherer R., Wozniak M., Zhang P., Wei W., Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism. (35)
Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism
, Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism, IEEE Access, 9, 9, 47252-47265, 2021, Cites: 35
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks. (22)
A novel method for speed training acceleration of recurrent neural networks
, A novel method for speed training acceleration of recurrent neural networks, Information Sciences, 553, 553, 266-279, 2021, Cites: 22
Sun Z., Geng H., Lu Z., Scherer R., Wozniak M., Review of road segmentation for sar images. (19)
Review of road segmentation for sar images
, Review of road segmentation for sar images, Remote Sensing, 13, 13, 1-15, 2021, Cites: 19
Starczewski A., Scherer M.M., Ksiek W., Debski M., Wang L., A Novel Grid-Based Clustering Algorithm. (18)
A Novel Grid-Based Clustering Algorithm
, A Novel Grid-Based Clustering Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 319-330, 2021, Cites: 18
Lv X., Cao J., Rutkowski L., Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control. (18)
Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control
, Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control, Neural Networks, 143, 143, 515-524, 2021, Cites: 18
Xu S., Cao J., Liu Q., Rutkowski L., Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method. (17)
Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method
, Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 3617-3628, 2021, Cites: 17
Li M., Jiang Z., Liu Y., Chen S., Wozniak M., Scherer R., Damasevicius R., Wei W., Li Z., Li Z., Sitsen: Passive sitting posture sensing based on wireless devices. (16)
Sitsen: Passive sitting posture sensing based on wireless devices
, Sitsen: Passive sitting posture sensing based on wireless devices, International Journal of Distributed Sensor Networks, 17, 17, 2021, Cites: 16
Niksa-Rynkiewicz T., Szewczuk-Krypa N., Witkowska A., Cpalka K., Zalasinski M., Cader A., Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network. (16)
Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network
, Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 143-155, 2021, Cites: 16
Bilski J., Kowalczyk B., Marjanski A., Gandor M., Zurada J., A Novel Fast Feedforward Neural Networks Training Algorithm. (15)
A Novel Fast Feedforward Neural Networks Training Algorithm
, A Novel Fast Feedforward Neural Networks Training Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 287-306, 2021, Cites: 15
Kulikajevas A., Maskeliunas R., Damasevicius R., Scherer R., Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction. (12)
Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction
, Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction, Sensors, 21, 21, 2021, Cites: 12
Dziwinski P., Przybyl A., Trippner P., Paszkowski J., Hayashi Y., Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm. (12)
Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm
, Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 243-266, 2021, Cites: 12
Gabryel M., Scherer M.M., Sulkowski L., Damasevicius R., Decision Making Support System for Managing Advertisers by Ad Fraud Detection. (10)
Decision Making Support System for Managing Advertisers by Ad Fraud Detection
, Decision Making Support System for Managing Advertisers by Ad Fraud Detection, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 331-339, 2021, Cites: 10
Przybyl A., Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems. (9)
Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems
, Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems, Electronics (Switzerland), 10, 10, 2021, Cites: 9
Cierniak R., Pluta P., Waligora M., Szymanski Z., Grzanek K., Palka F., Piuri V., A New Statistical Reconstruction Method for the Computed Tomography Using an X-Ray Tube with Flying Focal Spot. (9)
A New Statistical Reconstruction Method for the Computed Tomography Using an X-Ray Tube with Flying Focal Spot
, A New Statistical Reconstruction Method for the Computed Tomography Using an X-Ray Tube with Flying Focal Spot, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 271-286, 2021, Cites: 9
Wei W., Gao F., Scherer R., Damasevicius R., Polap D., Design and implementation of autonomous path planning for intelligent vehicle. (8)
Design and implementation of autonomous path planning for intelligent vehicle
, Design and implementation of autonomous path planning for intelligent vehicle, Journal of Internet Technology, 22, 22, 957-965, 2021, Cites: 8
Bernacki J., Robustness of digital camera identification with convolutional neural networks. (8)
Robustness of digital camera identification with convolutional neural networks
, Robustness of digital camera identification with convolutional neural networks, Multimedia Tools and Applications, 80, 80, 29657-29673, 2021, Cites: 8
Wang T., Zhang B., Wei W., Damasevicius R., Scherer R., Traffic flow prediction based on BP neural network. (7)
Traffic flow prediction based on BP neural network
, Traffic flow prediction based on BP neural network, 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021, 15-19, 2021, Cites: 7
Pawlak M., Panesar G.S., Korytkowski M., A Novel Method for Invariant Image Reconstruction. (7)
A Novel Method for Invariant Image Reconstruction
, A Novel Method for Invariant Image Reconstruction, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 69-80, 2021, Cites: 7
Wei W., Sun Z.-G., Zhang Z.-H., Scherer R., Damasevicius R., Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection. (6)
Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection
, Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection, Journal of Internet Technology, 22, 22, 413-421, 2021, Cites: 6
Bernacki J., On robustness of camera identification algorithms. (6)
On robustness of camera identification algorithms
, On robustness of camera identification algorithms, Multimedia Tools and Applications, 80, 80, 921-942, 2021, Cites: 6
Le V.-H., Scherer R., Human segmentation and tracking survey on masks for mads dataset. (6)
Human segmentation and tracking survey on masks for mads dataset
, Human segmentation and tracking survey on masks for mads dataset, Sensors, 21, 21, 2021, Cites: 6
He J., Liu Y., Lu J., Cao J., Rutkowski L., Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays. (5)
Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays
, Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 6842-6851, 2021, Cites: 5
Nowicki R.K., Seliga R., elasko D., Hayashi Y., Performance Analysis of Rough Set-Based Hybrid Classification Systems in the Case of Missing Values. (5)
Performance Analysis of Rough Set-Based Hybrid Classification Systems in the Case of Missing Values
, Performance Analysis of Rough Set-Based Hybrid Classification Systems in the Case of Missing Values, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 307-318, 2021, Cites: 5
Xiong H., Tang Y.Y., Murtagh F., Rutkowski L., Berkovsky S., A diversified shared latent variable model for efficient image characteristics extraction and modelling. (5)
A diversified shared latent variable model for efficient image characteristics extraction and modelling
, A diversified shared latent variable model for efficient image characteristics extraction and modelling, Neurocomputing, 421, 421, 244-259, 2021, Cites: 5
Sun Z., Zhao G., Wozniak M., Scherer R., Damasevicius R., Bankline detection of GF-3 SAR images based on shearlet. (5)
Bankline detection of GF-3 SAR images based on shearlet
, Bankline detection of GF-3 SAR images based on shearlet, PeerJ Computer Science, 7, 7, 2021, Cites: 5
Tao T., Yang J., Wei W., Wozniak M., Scherer R., Damasevicius R., Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements. (4)
Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements
, Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements, KSII Transactions on Internet and Information Systems, 15, 15, 3554-3570, 2021, Cites: 4
Galkowski T., Krzyzak A., Patora-Wysocka Z., Filutowicz Z., Wang L., A new approach to detection of changes in multidimensional patterns. (4)
A new approach to detection of changes in multidimensional patterns
, A new approach to detection of changes in multidimensional patterns, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 217-227, 2021, Cites: 4
Wei W., Hui M., Zhang B., Scherer R., Damasevicius R., Research on Decision Tree Based on Rough Set. (4)
Research on Decision Tree Based on Rough Set
, Research on Decision Tree Based on Rough Set, Journal of Internet Technology, 22, 22, 1385-1394, 2021, Cites: 4
Fedorchuk A., Walcarius A., Laskowska M., Vila N., Kowalczyk P., Cpalka K., Laskowski L., Synthesis of vertically aligned porous silica thin films functionalized by silver ions. (4)
Synthesis of vertically aligned porous silica thin films functionalized by silver ions
, Synthesis of vertically aligned porous silica thin films functionalized by silver ions, International Journal of Molecular Sciences, 22, 22, 2021, Cites: 4
Wrobel M., Starczewski J.T., Fijalkowska J., Siwocha A., Napoli C., Handwritten word recognition using fuzzy matching degrees. (4)
Handwritten word recognition using fuzzy matching degrees
, Handwritten word recognition using fuzzy matching degrees, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 229-242, 2021, Cites: 4
Wei W., Wang L., Li X., Zhang B., Scherer R., Design and implementation of public opinion monitoring system based on cloud platform. (3)
Design and implementation of public opinion monitoring system based on cloud platform
, Design and implementation of public opinion monitoring system based on cloud platform, Journal of Internet Technology, 22, 22, 569-581, 2021, Cites: 3
Grycuk R., Scherer R., Grid-Based Concise Hash for Solar Images. (3)
Grid-Based Concise Hash for Solar Images
, Grid-Based Concise Hash for Solar Images, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12744 LNCS, 12744 LNCS, 242-254, 2021, Cites: 3
Bilski J., Smolag J., Najgebauer P., Modification of Learning Feedforward Neural Networks with the BP Method. (3)
Modification of Learning Feedforward Neural Networks with the BP Method
, Modification of Learning Feedforward Neural Networks with the BP Method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 54-65, 2021, Cites: 3
Jakub W., Patryk N., Rafal S., Adam W., CVA-GNN: Convolutional Vicinity Aggregation Graph Neural Network for Point Cloud Classification. (3)
CVA-GNN: Convolutional Vicinity Aggregation Graph Neural Network for Point Cloud Classification
, CVA-GNN: Convolutional Vicinity Aggregation Graph Neural Network for Point Cloud Classification, Proceedings of the International Joint Conference on Neural Networks, 2021-July, 2021-July, 2021, Cites: 3
Jaworski M., Rutkowski L., Staszewski P., Najgebauer P., Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines. (2)
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines
, Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 338-346, 2021, Cites: 2
Wei W., Liang H., Zhang B., Damasevicius R., Scherer R., Design and Implementation of Regional Food Distribution Platform Based on Big Data. (2)
Design and Implementation of Regional Food Distribution Platform Based on Big Data
, Design and Implementation of Regional Food Distribution Platform Based on Big Data, 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021, 496-501, 2021, Cites: 2
Ding X., Wei W., Zhang B., Scherer R., Damasevicius R., Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging. (2)
Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging
, Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging, 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021, 614-627, 2021, Cites: 2
Walczak J., Wojciechowski A., Najgebauer P., Scherer R., Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification. (2)
Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification
, Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12744 LNCS, 12744 LNCS, 229-241, 2021, Cites: 2
Starczewski A., A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm. (2)
A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm
, A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 358-368, 2021, Cites: 2
Gabryel M., Kocic M., Application of a Neural Network to Generate the Hash Code for a Device Fingerprint. (1)
Application of a Neural Network to Generate the Hash Code for a Device Fingerprint
, Application of a Neural Network to Generate the Hash Code for a Device Fingerprint, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, 456-463, 2021, Cites: 1
Lapa K., Cpalka K., Slowik A., Population Management Approaches in the OPn Algorithm. (1)
Population Management Approaches in the OPn Algorithm
, Population Management Approaches in the OPn Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 402-414, 2021, Cites: 1
Kuzma D., Kowalczyk P., Cpalka K., Laskowski L., A low-dimensional layout of magnetic units as nano-systems of combinatorial logic: Numerical simulations. (1)
A low-dimensional layout of magnetic units as nano-systems of combinatorial logic: Numerical simulations
, A low-dimensional layout of magnetic units as nano-systems of combinatorial logic: Numerical simulations, Materials, 14, 14, 2021, Cites: 1
Bilski J., Kowalczyk B., A New Variant of the GQR Algorithm for Feedforward Neural Networks Training. (1)
A New Variant of the GQR Algorithm for Feedforward Neural Networks Training
, A New Variant of the GQR Algorithm for Feedforward Neural Networks Training, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 41-53, 2021, Cites: 1
Grycuk R., Scherer R., Solar Image Hashing by Intermediate Descriptor and Autoencoder. (1)
Solar Image Hashing by Intermediate Descriptor and Autoencoder
, Solar Image Hashing by Intermediate Descriptor and Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2021-July, 2021-July, 2021, Cites: 1
Wei W., Ke Q., Gao F., Scherer R., Fan S., Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs. (0)
Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs
, Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs, Journal of Internet Technology, 22, 22, 735-741, 2021, Cites: 0
Gabryel M., Kocic M., Fingerprint Device Parameter Stability Analysis. (0)
Fingerprint Device Parameter Stability Analysis
, Fingerprint Device Parameter Stability Analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, 464-472, 2021, Cites: 0
Zalasinski M., Niksa-Rynkiewicz T., Cpalka K., Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms. (0)
Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms
, Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, 511-518, 2021, Cites: 0
Cierniak R., Pluta P., A New Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot. (0)
A New Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot
, A New Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, 431-441, 2021, Cites: 0
Bernacki J., Scherer R., Fast Imaging Sensor Identification. (0)
Fast Imaging Sensor Identification
, Fast Imaging Sensor Identification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12876 LNAI, 12876 LNAI, 572-584, 2021, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, v-vi, 2021, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, v-vi, 2021, Cites: 0
Bartczuk L., A New Interval Type-2 Fuzzy PRISM Algorithm. (0)
A New Interval Type-2 Fuzzy PRISM Algorithm
, A New Interval Type-2 Fuzzy PRISM Algorithm, IEEE International Conference on Fuzzy Systems, 2021-July, 2021-July, 2021, Cites: 0
Galkowski T., Krzyzak A., Abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates. (0)
Abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates
, Abrupt Change Detection by the Nonparametric Approach Based on Orthogonal Series Estimates, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 318-327, 2021, Cites: 0
Duda P., Rutkowski L., Woldan P., Najgebauer P., The Streaming Approach to Training Restricted Boltzmann Machines. (0)
The Streaming Approach to Training Restricted Boltzmann Machines
, The Streaming Approach to Training Restricted Boltzmann Machines, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 308-317, 2021, Cites: 02020 (107)
Khan M.A., Ashraf I., Alhaisoni M., Damasevicius R., Scherer R., Rehman A., Bukhari S.A.C., Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. (342)
Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists
, Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists, Diagnostics, 10, 10, 2020, Cites: 342
Wei W., Ke Q., Nowak J., Korytkowski M., Scherer R., Wozniak M., Accurate and fast URL phishing detector: A convolutional neural network approach. (158)
Accurate and fast URL phishing detector: A convolutional neural network approach
, Accurate and fast URL phishing detector: A convolutional neural network approach, Computer Networks, 178, 178, 2020, Cites: 158
Yang X., Liu Y., Cao J., Rutkowski L., Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching. (122)
Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching
, Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 5483-5496, 2020, Cites: 122
Nasir I.M., Khan M.A., Yasmin M., Shah J.H., Gabryel M., Scherer R., Damasevicius R., Pearson correlation-based feature selection for document classification using balanced training. (104)
Pearson correlation-based feature selection for document classification using balanced training
, Pearson correlation-based feature selection for document classification using balanced training, Sensors (Switzerland), 20, 20, 1-18, 2020, Cites: 104
Liu Y., Zheng Y., Lu J., Cao J., Rutkowski L., Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach. (103)
Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach
, Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1022-1035, 2020, Cites: 103
Dziwinski P., Bartczuk L., A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic. (91)
A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic
, A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic, IEEE Transactions on Fuzzy Systems, 28, 28, 1140-1154, 2020, Cites: 91
Bilski J., Kowalczyk B., Marchlewska A., Zurada J.M., Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks. (80)
Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks
, Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 299-316, 2020, Cites: 80
Tan X., Cao J., Rutkowski L., Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay. (79)
Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay
, Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay, IEEE Transactions on Network Science and Engineering, 7, 7, 1111-1120, 2020, Cites: 79
Wang Z., Cao J., Cai Z., Rutkowski L., Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay. (76)
Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay
, Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay, IEEE Transactions on Cybernetics, 50, 50, 2758-2769, 2020, Cites: 76
Starczewski A., Goetzen P., Er M.J., A New Method for Automatic Determining of the DBSCAN Parameters. (59)
A New Method for Automatic Determining of the DBSCAN Parameters
, A New Method for Automatic Determining of the DBSCAN Parameters, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 209-221, 2020, Cites: 59
Lin L., Cao J., Rutkowski L., Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks. (47)
Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks
, Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1060-1065, 2020, Cites: 47
Dong L., Fang D., Wang X., Wei W., Damasevicius R., Scherer R., Wozniak M., Prediction of streamflow based on dynamic sliding window lstm. (38)
Prediction of streamflow based on dynamic sliding window lstm
, Prediction of streamflow based on dynamic sliding window lstm, Water (Switzerland), 12, 12, 1-11, 2020, Cites: 38
Chen B., Cao J., Luo Y., Rutkowski L., Asymptotic Output Tracking of Probabilistic Boolean Control Networks. (37)
Asymptotic Output Tracking of Probabilistic Boolean Control Networks
, Asymptotic Output Tracking of Probabilistic Boolean Control Networks, IEEE Transactions on Circuits and Systems I: Regular Papers, 67, 67, 2780-2790, 2020, Cites: 37
Lin L., Cao J., Zhu S., Rutkowski L., Lu G., Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model. (36)
Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model
, Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model, IEEE Transactions on Control of Network Systems, 7, 7, 1859-1869, 2020, Cites: 36
Jouhari H., Lei D., Al-qaness M.A.A., Abd Elaziz M., Damasevicius R., Korytkowski M., Ewees A.A., Modified Harris Hawks optimizer for solving machine scheduling problems. (35)
Modified Harris Hawks optimizer for solving machine scheduling problems
, Modified Harris Hawks optimizer for solving machine scheduling problems, Symmetry, 12, 12, 2020, Cites: 35
Bernacki J., A survey on digital camera identification methods. (32)
A survey on digital camera identification methods
, A survey on digital camera identification methods, Forensic Science International: Digital Investigation, 34, 34, 2020, Cites: 32
Duda P., Rutkowski L., Jaworski M., Rutkowska D., On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification. (32)
On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification
, On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification, IEEE Transactions on Cybernetics, 50, 50, 1683-1696, 2020, Cites: 32
Korytkowski M., Senkerik R., Scherer M.M., Angryk R.A., Kordos M., Siwocha A., Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic. (28)
Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic
, Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 57-69, 2020, Cites: 28
Chen B., Cao J., Lu G., Rutkowski L., Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks. (27)
Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks
, Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks, IEEE Transactions on Circuits and Systems II: Express Briefs, 67, 67, 2537-2541, 2020, Cites: 27
Duda P., Jaworski M., Cader A., Wang L., On Training Deep Neural Networks Using a Streaming Approach. (26)
On Training Deep Neural Networks Using a Streaming Approach
, On Training Deep Neural Networks Using a Streaming Approach, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 15-26, 2020, Cites: 26
Lv Y., Liu Y., Jing W., Wozniak M., Damasevicius R., Scherer R., Wei W., Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis. (24)
Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis
, Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis, Applied Sciences (Switzerland), 10, 10, 2020, Cites: 24
Gabryel M., Grzanek K., Hayashi Y., Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic. (24)
Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic
, Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 243-253, 2020, Cites: 24
Starczewski J.T., Goetzen P., Napoli C., Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems. (23)
Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems
, Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 271-285, 2020, Cites: 23
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays. (22)
Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays
, Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays, Neural Networks, 132, 132, 447-460, 2020, Cites: 22
Laskowska M., Kityk I., Pastukh O., Dulski M., Zubko M., Jedryka J., Cpalka K., Zielinski P.M., Laskowski, Nanocomposite for photonics — Nickel pyrophosphate nanocrystals synthesised in silica nanoreactors. (19)
Nanocomposite for photonics — Nickel pyrophosphate nanocrystals synthesised in silica nanoreactors
, Nanocomposite for photonics — Nickel pyrophosphate nanocrystals synthesised in silica nanoreactors, Microporous and Mesoporous Materials, 306, 306, 2020, Cites: 19
Dziwinski P., Bartczuk L., Paszkowski J., A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm. (18)
A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm
, A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 95-111, 2020, Cites: 18
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Data Stream Mining. (18)
Basic Concepts of Data Stream Mining
, Basic Concepts of Data Stream Mining, Studies in Big Data, 56, 56, 13-33, 2020, Cites: 18
Nowicki R.K., Grzanek K., Hayashi Y., Rough Support Vector Machine for Classification with Interval and Incomplete Data. (16)
Rough Support Vector Machine for Classification with Interval and Incomplete Data
, Rough Support Vector Machine for Classification with Interval and Incomplete Data, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 47-56, 2020, Cites: 16
Bernacki J., Automatic exposure algorithms for digital photography. (16)
Automatic exposure algorithms for digital photography
, Automatic exposure algorithms for digital photography, Multimedia Tools and Applications, 79, 79, 12751-12776, 2020, Cites: 16
Slowik A., Cpalka K., Lapa K., Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems. (15)
Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems
, Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 28, 28, 1125-1139, 2020, Cites: 15
Galkowski T., Krzyaak A., Filutowicz Z., A New Approach to Detection of Changes in Multidimensional Patterns. (14)
A New Approach to Detection of Changes in Multidimensional Patterns
, A New Approach to Detection of Changes in Multidimensional Patterns, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 125-136, 2020, Cites: 14
Jaworski M., Rutkowski L., Angelov P., Concept Drift Detection Using Autoencoders in Data Streams Processing. (14)
Concept Drift Detection Using Autoencoders in Data Streams Processing
, Concept Drift Detection Using Autoencoders in Data Streams Processing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 124-133, 2020, Cites: 14
Cierniak R., Pluta P., KaAmierczak A., A Practical Statistical Approach to the Reconstruction Problem Using a Single Slice Rebinning Method. (13)
A Practical Statistical Approach to the Reconstruction Problem Using a Single Slice Rebinning Method
, A Practical Statistical Approach to the Reconstruction Problem Using a Single Slice Rebinning Method, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 137-149, 2020, Cites: 13
Lapa K., Cpalka K., Laskowski L., Cader A., Zeng Z., Evolutionary Algorithm with a Configurable Search Mechanism. (12)
Evolutionary Algorithm with a Configurable Search Mechanism
, Evolutionary Algorithm with a Configurable Search Mechanism, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 151-171, 2020, Cites: 12
Laskowska M., Pastukh O., Fedorchuk A., Schabikowski M., Kowalczyk P., Zalasinski M., Laskowski L., Nanostructured silica with anchoring units: The 2D solid solvent for molecules and metal ions. (12)
Nanostructured silica with anchoring units: The 2D solid solvent for molecules and metal ions
, Nanostructured silica with anchoring units: The 2D solid solvent for molecules and metal ions, International Journal of Molecular Sciences, 21, 21, 1-38, 2020, Cites: 12
Zalasinski M., Lapa K., Cpalka K., Przybyszewski K., Yen G.G., On-Line Signature Partitioning Using a Population Based Algorithm. (11)
On-Line Signature Partitioning Using a Population Based Algorithm
, On-Line Signature Partitioning Using a Population Based Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 5-13, 2020, Cites: 11
Ke Q., Zeng-Guo S., Liu Y., Wei W., Wozniak M., Scherer R., High-resolution SAR image despeckling based on nonlocal means filter and modified AA model. (11)
High-resolution SAR image despeckling based on nonlocal means filter and modified AA model
, High-resolution SAR image despeckling based on nonlocal means filter and modified AA model, Security and Communication Networks, 2020, 2020, 2020, Cites: 11
Laskowska M., Pastukh O., Konieczny P., Dulski M., Zalsinski M., Laskowski L., Magnetic behaviour of Mn<inf>12</inf>-stearate single-molecule magnets immobilized on the surface of 300 nm spherical silica nanoparticles. (11)
Magnetic behaviour of Mn<inf>12</inf>-stearate single-molecule magnets immobilized on the surface of 300 nm spherical silica nanoparticles
, Magnetic behaviour of Mn<inf>12</inf>-stearate single-molecule magnets immobilized on the surface of 300 nm spherical silica nanoparticles, Materials, 13, 13, 2020, Cites: 11
Grycuk R., Wojciechowski A., Wei W., Siwocha A., Detecting Visual Objects by Edge Crawling. (10)
Detecting Visual Objects by Edge Crawling
, Detecting Visual Objects by Edge Crawling, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 223-237, 2020, Cites: 10
Grycuk R., Najgebauer P., Kordos M., Scherer M.M., Marchlewska A., Fast Image Index for Database Management Engines. (10)
Fast Image Index for Database Management Engines
, Fast Image Index for Database Management Engines, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 113-123, 2020, Cites: 10
Polap D., Wozniak M., Korytkowski M., Scherer R., Encoder-Decoder Based CNN Structure for Microscopic Image Identification. (8)
Encoder-Decoder Based CNN Structure for Microscopic Image Identification
, Encoder-Decoder Based CNN Structure for Microscopic Image Identification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12532 LNCS, 12532 LNCS, 301-312, 2020, Cites: 8
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining. (8)
Decision Trees in Data Stream Mining
, Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 2020, Cites: 8
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images. (7)
Fully Convolutional Network for Removing DCT Artefacts from Images
, Fully Convolutional Network for Removing DCT Artefacts from Images, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 7
Duda P., Przybyszewski K., Wang L., A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment. (7)
A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment
, A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 287-298, 2020, Cites: 7
Zalasinski M., Cpalka K., Laskowski L., Wunsch D.C., Przybyszewski K., An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors. (7)
An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors
, An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 173-187, 2020, Cites: 7
Korytkowski M., Scherer R., Szajerman D., Polap D., Wozniak M., Efficient visual classification by fuzzy rules. (6)
Efficient visual classification by fuzzy rules
, Efficient visual classification by fuzzy rules, IEEE International Conference on Fuzzy Systems, 2020-July, 2020-July, 2020, Cites: 6
Wei W., Wang Z., Fu C., Damasevicius R., Scherer R., Wozniak M., Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model. (6)
Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model
, Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model, Journal of Physics: Conference Series, 1682, 1682, 2020, Cites: 6
Das A., Saha I., Scherer R., Ghomr: Multi-receptive lightweight residual modules for hyperspectral classification. (5)
Ghomr: Multi-receptive lightweight residual modules for hyperspectral classification
, Ghomr: Multi-receptive lightweight residual modules for hyperspectral classification, Sensors (Switzerland), 20, 20, 1-19, 2020, Cites: 5
Laskowski L., Majtyka-Pilat A., Cpalka K., Zubko M., Laskowska M., Synthesis in silica nanoreactor: Copper pyrophosphate quantum dots and silver oxide nanocrystallites inside silica mezochannels. (5)
Synthesis in silica nanoreactor: Copper pyrophosphate quantum dots and silver oxide nanocrystallites inside silica mezochannels
, Synthesis in silica nanoreactor: Copper pyrophosphate quantum dots and silver oxide nanocrystallites inside silica mezochannels, Materials, 13, 13, 2020, Cites: 5
Zalasinski M., Lapa K., Laskowska M., Intelligent Approach to the Prediction of Changes in Biometric Attributes. (5)
Intelligent Approach to the Prediction of Changes in Biometric Attributes
, Intelligent Approach to the Prediction of Changes in Biometric Attributes, IEEE Transactions on Fuzzy Systems, 28, 28, 1073-1083, 2020, Cites: 5
Bilski J., Smolag J., Fast Conjugate Gradient Algorithm for Feedforward Neural Networks. (5)
Fast Conjugate Gradient Algorithm for Feedforward Neural Networks
, Fast Conjugate Gradient Algorithm for Feedforward Neural Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 27-38, 2020, Cites: 5
Nowak J., Holotyak T., Korytkowski M., Scherer R., Voloshynovskiy S., Fingerprinting of url logs: Continuous user authentication from behavioural patterns. (4)
Fingerprinting of url logs: Continuous user authentication from behavioural patterns
, Fingerprinting of url logs: Continuous user authentication from behavioural patterns, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12140 LNCS, 12140 LNCS, 184-195, 2020, Cites: 4
Bernacki J., Digital camera identification based on analysis of optical defects. (4)
Digital camera identification based on analysis of optical defects
, Digital camera identification based on analysis of optical defects, Multimedia Tools and Applications, 79, 79, 2945-2963, 2020, Cites: 4
Zalasinski M., Cpalka K., Lapa K., An interpretable fuzzy system in the on-line signature scalable verification. (4)
An interpretable fuzzy system in the on-line signature scalable verification
, An interpretable fuzzy system in the on-line signature scalable verification, IEEE International Conference on Fuzzy Systems, 2020-July, 2020-July, 2020, Cites: 4
Hazra S., Roy P., Nandy A., Scherer R., A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications. (4)
A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications
, A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 4
Nowak J., Korytkowski M., Scherer R., Discovering Sequential Patterns by Neural Networks. (3)
Discovering Sequential Patterns by Neural Networks
, Discovering Sequential Patterns by Neural Networks, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 3
Talun A., Drozda P., Bukowski L., Scherer R., FastText and XGBoost Content-Based Classification for Employment Web Scraping. (3)
FastText and XGBoost Content-Based Classification for Employment Web Scraping
, FastText and XGBoost Content-Based Classification for Employment Web Scraping, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 435-444, 2020, Cites: 3
Rutkowski L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification. (3)
Probabilistic Neural Networks for the Streaming Data Classification
, Probabilistic Neural Networks for the Streaming Data Classification, Studies in Big Data, 56, 56, 245-277, 2020, Cites: 3
Woldan P., Duda P., Hayashi Y., Visual Hybrid Recommendation Systems Based on the Content-Based Filtering. (3)
Visual Hybrid Recommendation Systems Based on the Content-Based Filtering
, Visual Hybrid Recommendation Systems Based on the Content-Based Filtering, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 455-465, 2020, Cites: 3
Galkowski T., Krzyzak A., A New Approach to Detection of Abrupt Changes in Black-and-White Images. (3)
A New Approach to Detection of Abrupt Changes in Black-and-White Images
, A New Approach to Detection of Abrupt Changes in Black-and-White Images, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 3-18, 2020, Cites: 3
Starczewski A., Cader A., Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm. (3)
Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm
, Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 555-565, 2020, Cites: 3
Cao J.-D., Liu Y., Lu J.-Q., Rutkowski L., Complex systems and networks with their applications. (2)
Complex systems and networks with their applications
, Complex systems and networks with their applications, Frontiers of Information Technology and Electronic Engineering, 21, 21, 195-198, 2020, Cites: 2
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure. (2)
Misclassification Error Impurity Measure
, Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 2
Galkowski T., Krzyzak A., Edge Curve Estimation by the Nonparametric Parzen Kernel Method. (2)
Edge Curve Estimation by the Nonparametric Parzen Kernel Method
, Edge Curve Estimation by the Nonparametric Parzen Kernel Method, Communications in Computer and Information Science, 1332, 1332, 377-385, 2020, Cites: 2
Rutkowski T., Nielek R., Rutkowska D., Rutkowski L., A Novel Explainable Recommender for Investment Managers. (2)
A Novel Explainable Recommender for Investment Managers
, A Novel Explainable Recommender for Investment Managers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 412-422, 2020, Cites: 2
Cierniak R., Pluta P., Statistical iterative reconstruction algorithm based on a continuous-to-continuous model formulated for spiral cone-beam ct. (2)
Statistical iterative reconstruction algorithm based on a continuous-to-continuous model formulated for spiral cone-beam ct
, Statistical iterative reconstruction algorithm based on a continuous-to-continuous model formulated for spiral cone-beam ct, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12139 LNCS, 12139 LNCS, 613-620, 2020, Cites: 2
Kozlowski K., Korytkowski M., Szajerman D., Visual analysis of computer game output video stream for gameplay metrics. (2)
Visual analysis of computer game output video stream for gameplay metrics
, Visual analysis of computer game output video stream for gameplay metrics, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12141 LNCS, 12141 LNCS, 538-552, 2020, Cites: 2
Wrobel M., Starczewski J.T., Napoli C., Grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree. (2)
Grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree
, Grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 103-113, 2020, Cites: 2
Bartczuk L., Dziwinski P., Goetzen P., Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm. (1)
Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm
, Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 315-325, 2020, Cites: 1
Slowik A., Cpalka K., Jin Y., Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems. (1)
Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems
, Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 28, 28, 1019-1022, 2020, Cites: 1
Wei W., Wang B., Zhang B., Scherer R., Damasevicius R., Online job search and recruitment platform for college students based on SSH. (1)
Online job search and recruitment platform for college students based on SSH
, Online job search and recruitment platform for college students based on SSH, Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020, 355-358, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., General Non-parametric Learning Procedure for Tracking Concept Drift. (1)
General Non-parametric Learning Procedure for Tracking Concept Drift
, General Non-parametric Learning Procedure for Tracking Concept Drift, Studies in Big Data, 56, 56, 155-172, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Introduction and Overview of the Main Results of the Book. (1)
Introduction and Overview of the Main Results of the Book
, Introduction and Overview of the Main Results of the Book, Studies in Big Data, 56, 56, 1-10, 2020, Cites: 1
Wei W., Li X., Zhang B., Liu X., Scherer R., Damasevicius R., Educational management system. (1)
Educational management system
, Educational management system, Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020, 53-56, 2020, Cites: 1
Zalasinski M., Cpalka K., Niksa-Rynkiewicz T., The Dynamic Signature Verification Using population-Based Vertical Partitioning. (1)
The Dynamic Signature Verification Using population-Based Vertical Partitioning
, The Dynamic Signature Verification Using population-Based Vertical Partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12532 LNCS, 12532 LNCS, 569-579, 2020, Cites: 1
Gabryel M., Przybyszewski K., Methods of Searching for Similar Device Fingerprints Using Changes in Unstable Parameters. (1)
Methods of Searching for Similar Device Fingerprints Using Changes in Unstable Parameters
, Methods of Searching for Similar Device Fingerprints Using Changes in Unstable Parameters, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 325-335, 2020, Cites: 1
Kazikova A., Lapa K., Pluhacek M., Senkerik R., Cascade PID Controller Optimization Using Bison Algorithm. (1)
Cascade PID Controller Optimization Using Bison Algorithm
, Cascade PID Controller Optimization Using Bison Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 406-416, 2020, Cites: 1
Walczak J., Andrzejczak G., Scherer R., Wojciechowski A., Normal grouping density separation (ngds): A novel object-driven indoor point cloud partition method. (1)
Normal grouping density separation (ngds): A novel object-driven indoor point cloud partition method
, Normal grouping density separation (ngds): A novel object-driven indoor point cloud partition method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12142 LNCS, 12142 LNCS, 100-114, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks. (1)
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
, Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks, Studies in Big Data, 56, 56, 173-244, 2020, Cites: 1
Scherer R., Feature detection. (1)
Feature detection
, Feature detection, Studies in Computational Intelligence, 821, 821, 7-32, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria. (1)
Hybrid Splitting Criteria
, Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
Staszewski P., Jaworski M., Rutkowski L., Tao D., Explainable Cluster-Based Rules Generation for Image Retrieval and Classification. (1)
Explainable Cluster-Based Rules Generation for Image Retrieval and Classification
, Explainable Cluster-Based Rules Generation for Image Retrieval and Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 85-94, 2020, Cites: 1
Scherer R., Concluding remarks and perspectives in computer vision. (1)
Concluding remarks and perspectives in computer vision
, Concluding remarks and perspectives in computer vision, Studies in Computational Intelligence, 821, 821, 137, 2020, Cites: 1
Scherer R., Image retrieval and classification in relational databases. (1)
Image retrieval and classification in relational databases
, Image retrieval and classification in relational databases, Studies in Computational Intelligence, 821, 821, 107-136, 2020, Cites: 1
Grycuk R., Scherer R., Novel Fast Binary Hash for Content-based Solar Image Retrieval. (1)
Novel Fast Binary Hash for Content-based Solar Image Retrieval
, Novel Fast Binary Hash for Content-based Solar Image Retrieval, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 1
Wei W., Gao F., Zhang B., Scherer R., Hui M., Damasevicius R., Design and implementation of forward modeling algorithm for anisotropic seismic waves. (0)
Design and implementation of forward modeling algorithm for anisotropic seismic waves
, Design and implementation of forward modeling algorithm for anisotropic seismic waves, Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020, 341-350, 2020, Cites: 0
Scherer R., Image indexing techniques. (0)
Image indexing techniques
, Image indexing techniques, Studies in Computational Intelligence, 821, 821, 33-82, 2020, Cites: 0
Scherer R., Preface. (0)
Preface
, Preface, Studies in Computational Intelligence, 821, 821, v, 2020, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, v-vi, 2020, Cites: 0
Bilski J., Kowalczyk B., Zurada J.M., A New Algorithm with a Line Search for Feedforward Neural Networks Training. (0)
A New Algorithm with a Line Search for Feedforward Neural Networks Training
, A New Algorithm with a Line Search for Feedforward Neural Networks Training, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 15-26, 2020, Cites: 0
Kowalczyk B., Szelag P., SmartX – IoT MANAGEMENT PLATFORM. (0)
SmartX – IoT MANAGEMENT PLATFORM
, SmartX – IoT MANAGEMENT PLATFORM, Rynek Energii, 2020, 2020, 54-60, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., The General Procedure of Ensembles Construction in Data Stream Scenarios. (0)
The General Procedure of Ensembles Construction in Data Stream Scenarios
, The General Procedure of Ensembles Construction in Data Stream Scenarios, Studies in Big Data, 56, 56, 281-286, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Classification. (0)
Classification
, Classification, Studies in Big Data, 56, 56, 287-308, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Splitting Criteria with the Bias Term. (0)
Splitting Criteria with the Bias Term
, Splitting Criteria with the Bias Term, Studies in Big Data, 56, 56, 83-89, 2020, Cites: 0
Nowak J., Milkowska K., Scherer M., Talun A., Korytkowski M., Job Offer Analysis Using Convolutional and Recurrent Convolutional Networks. (0)
Job Offer Analysis Using Convolutional and Recurrent Convolutional Networks
, Job Offer Analysis Using Convolutional and Recurrent Convolutional Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 380-387, 2020, Cites: 0
Scherer R., Introduction. (0)
Introduction
, Introduction, Studies in Computational Intelligence, 821, 821, 1-5, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Final Remarks and Challenging Problems. (0)
Final Remarks and Challenging Problems
, Final Remarks and Challenging Problems, Studies in Big Data, 56, 56, 323-327, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Probabilistic Neural Networks. (0)
Basic Concepts of Probabilistic Neural Networks
, Basic Concepts of Probabilistic Neural Networks, Studies in Big Data, 56, 56, 117-154, 2020, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, v-vi, 2020, Cites: 0
Lapa K., Cpalka K., Niksa-Rynkiewicz T., Wang L., A Population-Based Method with Selection of a Search Operator. (0)
A Population-Based Method with Selection of a Search Operator
, A Population-Based Method with Selection of a Search Operator, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 429-444, 2020, Cites: 0
Duda P., Wang L., On a Streaming Approach for Training Denoising Auto-encoders. (0)
On a Streaming Approach for Training Denoising Auto-encoders
, On a Streaming Approach for Training Denoising Auto-encoders, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 315-324, 2020, Cites: 0
Zalasinski M., Cpalka K., Niksa-Rynkiewicz T., Hayashi Y., Signature Partitioning Using Selected Population-Based Algorithms. (0)
Signature Partitioning Using Selected Population-Based Algorithms
, Signature Partitioning Using Selected Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 480-488, 2020, Cites: 0
Wei W., Hui M., Zhang B., Scherer R., Gao F., Damasevicius R., Research on variable scale algorithm. (0)
Research on variable scale algorithm
, Research on variable scale algorithm, Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020, 316-322, 2020, Cites: 0
Scherer R., Novel methods for image description. (0)
Novel methods for image description
, Novel methods for image description, Studies in Computational Intelligence, 821, 821, 83-105, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Regression. (0)
Regression
, Regression, Studies in Big Data, 56, 56, 309-322, 2020, Cites: 0
Grycuk R., Costa K., Scherer R., Active Region-Based Full-Disc Solar Image Hashing. (0)
Active Region-Based Full-Disc Solar Image Hashing
, Active Region-Based Full-Disc Solar Image Hashing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 19-30, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Splitting Criteria Based on the McDiarmid’s Theorem. (0)
Splitting Criteria Based on the McDiarmid’s Theorem
, Splitting Criteria Based on the McDiarmid’s Theorem, Studies in Big Data, 56, 56, 51-62, 2020, Cites: 02019 (47)
Dirvanauskas D., Maskeliunas R., Raudonis V., Damasevicius R., Scherer R., HEMIGEN: Human embryo image generator based on generative adversarial networks. (44)
HEMIGEN: Human embryo image generator based on generative adversarial networks
, HEMIGEN: Human embryo image generator based on generative adversarial networks, Sensors (Switzerland), 19, 19, 2019, Cites: 44
Rutkowski T., Lapa K., Nielek R., On Explainable Fuzzy Recommenders and their Performance Evaluation. (26)
On Explainable Fuzzy Recommenders and their Performance Evaluation
, On Explainable Fuzzy Recommenders and their Performance Evaluation, International Journal of Applied Mathematics and Computer Science, 29, 29, 595-610, 2019, Cites: 26
Starczewski A., Cader A., Determining the eps parameter of the DBSCAN algorithm. (17)
Determining the eps parameter of the DBSCAN algorithm
, Determining the eps parameter of the DBSCAN algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 420-430, 2019, Cites: 17
Rutkowski T., Lapa K., Jaworski M., Nielek R., Rutkowska D., On explainable flexible fuzzy recommender and its performance evaluation using the akaike information criterion. (15)
On explainable flexible fuzzy recommender and its performance evaluation using the akaike information criterion
, On explainable flexible fuzzy recommender and its performance evaluation using the akaike information criterion, Communications in Computer and Information Science, 1142 CCIS, 1142 CCIS, 717-724, 2019, Cites: 15
Najgebauer P., Scherer R., Inertia-based Fast Vectorization of Line Drawings. (14)
Inertia-based Fast Vectorization of Line Drawings
, Inertia-based Fast Vectorization of Line Drawings, Computer Graphics Forum, 38, 38, 203-213, 2019, Cites: 14
Lapa K., Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics. (11)
Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics
, Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics, Information Sciences, 489, 489, 193-204, 2019, Cites: 11
Rutkowski T., Lapa K., Nowicki R., Nielek R., Grzanek K., On Explainable Recommender Systems Based on Fuzzy Rule Generation Techniques. (8)
On Explainable Recommender Systems Based on Fuzzy Rule Generation Techniques
, On Explainable Recommender Systems Based on Fuzzy Rule Generation Techniques, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 358-372, 2019, Cites: 8
Dziwinski P., Bartczuk L., Goetzen P., A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm. (6)
A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm
, A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 432-444, 2019, Cites: 6
Gabryel M., Przybyszewski K., The dynamically modified BoW algorithm used in assessing clicks in online ads. (6)
The dynamically modified BoW algorithm used in assessing clicks in online ads
, The dynamically modified BoW algorithm used in assessing clicks in online ads, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 350-360, 2019, Cites: 6
Najgebauer P., Grycuk R., Rutkowski L., Scherer R., Siwocha A., Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection. (6)
Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection
, Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 164-171, 2019, Cites: 6
Lapa K., Cpalka K., Zalasinski M., Algorithm Based on Population with a Flexible Search Mechanism. (5)
Algorithm Based on Population with a Flexible Search Mechanism
, Algorithm Based on Population with a Flexible Search Mechanism, IEEE Access, 7, 7, 132253-132270, 2019, Cites: 5
Jaworski M., Rutkowski L., Duda P., Cader A., Resource-aware data stream mining using the restricted boltzmann machine. (4)
Resource-aware data stream mining using the restricted boltzmann machine
, Resource-aware data stream mining using the restricted boltzmann machine, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 384-396, 2019, Cites: 4
Nowicki R.K., Rough Set Theory Fundamentals. (4)
Rough Set Theory Fundamentals
, Rough Set Theory Fundamentals, Studies in Computational Intelligence, 802, 802, 7-16, 2019, Cites: 4
Lagiewka M., Korytkowski M., Scherer R., Distributed image retrieval with colour and keypoint features. (4)
Distributed image retrieval with colour and keypoint features
, Distributed image retrieval with colour and keypoint features, Journal of Information and Telecommunication, 3, 3, 430-445, 2019, Cites: 4
Grycuk R., Scherer R., Software framework for fast image retrieval. (3)
Software framework for fast image retrieval
, Software framework for fast image retrieval, 2019 24th International Conference on Methods and Models in Automation and Robotics, MMAR 2019, 588-593, 2019, Cites: 3
Starczewski J.T., Nowicki R.K., Nieszporek K., Fuzzy-rough fuzzification in general FL classifiers. (3)
Fuzzy-rough fuzzification in general FL classifiers
, Fuzzy-rough fuzzification in general FL classifiers, IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence, 335-342, 2019, Cites: 3
Nowicki R.K., Rough Fuzzy Classification Systems. (3)
Rough Fuzzy Classification Systems
, Rough Fuzzy Classification Systems, Studies in Computational Intelligence, 802, 802, 17-70, 2019, Cites: 3
Feng L., Cao J., Hu J., Wu Z., Rutkowski L., Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode. (3)
Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode
, Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode, Neural Processing Letters, 50, 50, 2797-2819, 2019, Cites: 3
Lapa K., Cpalka K., Paszkowski J., Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection. (3)
Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection
, Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 456-468, 2019, Cites: 3
Nowicki R.K., Rough Nearest Neighbour Classifier. (2)
Rough Nearest Neighbour Classifier
, Rough Nearest Neighbour Classifier, Studies in Computational Intelligence, 802, 802, 133-159, 2019, Cites: 2
Opielka P., Starczewski J.T., Wrobel M., Nieszporek K., Marchlewska A., Application of Spiking Neural Networks to Fashion Classification. (2)
Application of Spiking Neural Networks to Fashion Classification
, Application of Spiking Neural Networks to Fashion Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 172-180, 2019, Cites: 2
Wrobel M., Starczewski J.T., Nieszporek K., Opielka P., Kazmierczak A., A greedy algorithm for extraction of handwritten strokes. (2)
A greedy algorithm for extraction of handwritten strokes
, A greedy algorithm for extraction of handwritten strokes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 464-473, 2019, Cites: 2
Zalasinski M., Lapa K., Cpalka K., Marchlewska A., The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms. (2)
The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms
, The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 540-549, 2019, Cites: 2
Bilski J., Kowalczyk B., Cader A., Modifications of the Givens Training Algorithm for Artificial Neural Networks. (2)
Modifications of the Givens Training Algorithm for Artificial Neural Networks
, Modifications of the Givens Training Algorithm for Artificial Neural Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 14-28, 2019, Cites: 2
Petraitis T., Maskeliunas R., Damasevicius R., Polap D., Wozniak M., Gabryel M., Environment scene classification based on images using bag-of-words. (2)
Environment scene classification based on images using bag-of-words
, Environment scene classification based on images using bag-of-words, Studies in Computational Intelligence, 829, 829, 281-303, 2019, Cites: 2
Jaworski M., Duda P., Rutkowska D., Rutkowski L., On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine. (2)
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine
, On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine, Communications in Computer and Information Science, 1143 CCIS, 1143 CCIS, 347-354, 2019, Cites: 2
Cierniak R., Dobosz P., Grzybowski A., EM-ML algorithm based on continuous-to-continuous model for PET. (1)
EM-ML algorithm based on continuous-to-continuous model for PET
, EM-ML algorithm based on continuous-to-continuous model for PET, Proceedings of SPIE - The International Society for Optical Engineering, 11072, 11072, 2019, Cites: 1
Korytkowski M., Nowak J., Nowicki R., Milkowska K., Scherer M., Goetzen P., Sequential Data Mining of Network Traffic in URL Logs. (1)
Sequential Data Mining of Network Traffic in URL Logs
, Sequential Data Mining of Network Traffic in URL Logs, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 125-130, 2019, Cites: 1
Grycuk R., Najgebauer P., Nowicki R., Scherer R., Multilayer architecture for content-based image retrieval systems. (1)
Multilayer architecture for content-based image retrieval systems
, Multilayer architecture for content-based image retrieval systems, Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019, 119-126, 2019, Cites: 1
Rutkowska D., Rutkowski L., On the hermite series-based generalized regression neural networks for stream data mining. (1)
On the hermite series-based generalized regression neural networks for stream data mining
, On the hermite series-based generalized regression neural networks for stream data mining, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11955 LNCS, 11955 LNCS, 437-448, 2019, Cites: 1
Nowicki R.K., Starczewski J.T., Grycuk R., Extended possibilistic fuzzification for classification. (1)
Extended possibilistic fuzzification for classification
, Extended possibilistic fuzzification for classification, IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence, 343-350, 2019, Cites: 1
Nowak J., Korytkowski M., Scherer R., Convolutional Recurrent Neural Networks for Computer Network Analysis. (1)
Convolutional Recurrent Neural Networks for Computer Network Analysis
, Convolutional Recurrent Neural Networks for Computer Network Analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11730 LNCS, 11730 LNCS, 747-757, 2019, Cites: 1
Nowicki R.K., Ensembles of Rough Set–Based Classifiers. (0)
Ensembles of Rough Set–Based Classifiers
, Ensembles of Rough Set–Based Classifiers, Studies in Computational Intelligence, 802, 802, 161-184, 2019, Cites: 0
Nowicki R.K., Introduction. (0)
Introduction
, Introduction, Studies in Computational Intelligence, 802, 802, 1-6, 2019, Cites: 0
Cierniak R., Pluta P., Iterative Statistical Reconstruction Algorithm Based on C-C Data Model with the Direct Use of Projections Performed in Spiral Cone-Beam CT Scanners. (0)
Iterative Statistical Reconstruction Algorithm Based on C-C Data Model with the Direct Use of Projections Performed in Spiral Cone-Beam CT Scanners
, Iterative Statistical Reconstruction Algorithm Based on C-C Data Model with the Direct Use of Projections Performed in Spiral Cone-Beam CT Scanners, Advances in Intelligent Systems and Computing, 1011, 1011, 56-66, 2019, Cites: 0
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)). (0)
Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028))
, Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)), Information Sciences, 477, 477, 545, 2019, Cites: 0
Wrobel M., SEARCHING THE BEGINNING OF THE CLIMB TO GET THE MOST RELIABLE DIFFICULTY INDEX. (0)
SEARCHING THE BEGINNING OF THE CLIMB TO GET THE MOST RELIABLE DIFFICULTY INDEX
, SEARCHING THE BEGINNING OF THE CLIMB TO GET THE MOST RELIABLE DIFFICULTY INDEX, Journal of Applied Mathematics and Computational Mechanics, 18, 18, 97-103, 2019, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, v-vi, 2019, Cites: 0
Starczewski J.T., Nowicki R.K., Nieszporek K., Fuzzy–rough Fuzzification in General FL Classifiers. (0)
Fuzzy–rough Fuzzification in General FL Classifiers
, Fuzzy–rough Fuzzification in General FL Classifiers, International Joint Conference on Computational Intelligence, 1, 1, 335-342, 2019, Cites: 0
Galkowski T., Przybyszewski K., A new concept of nonparametric kernel approach for edge detection. (0)
A new concept of nonparametric kernel approach for edge detection
, A new concept of nonparametric kernel approach for edge detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 361-370, 2019, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, v-vi, 2019, Cites: 0
Cierniak R., Bilski J., Pluta P., Filutowicz Z., Realizations of the statistical reconstruction method based on the continuous-to-continuous data model. (0)
Realizations of the statistical reconstruction method based on the continuous-to-continuous data model
, Realizations of the statistical reconstruction method based on the continuous-to-continuous data model, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 149-156, 2019, Cites: 0
Nowicki R.K., Rough Neural Network Classifier. (0)
Rough Neural Network Classifier
, Rough Neural Network Classifier, Studies in Computational Intelligence, 802, 802, 95-132, 2019, Cites: 0
Woldan P., Staszewski P., Rutkowski L., Grzanek K., On Proper Designing of Deep Structures for Image Classification. (0)
On Proper Designing of Deep Structures for Image Classification
, On Proper Designing of Deep Structures for Image Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 223-235, 2019, Cites: 0
Nowicki R.K., Fuzzy Rough Classification Systems. (0)
Fuzzy Rough Classification Systems
, Fuzzy Rough Classification Systems, Studies in Computational Intelligence, 802, 802, 71-93, 2019, Cites: 0
Nowicki R.K., Final Remarks. (0)
Final Remarks
, Final Remarks, Studies in Computational Intelligence, 802, 802, 185-188, 2019, Cites: 0
Nowicki R.K., Starczewski J.T., Grycuk R., Extended Possibilistic Fuzzification for Classification. (0)
Extended Possibilistic Fuzzification for Classification
, Extended Possibilistic Fuzzification for Classification, International Joint Conference on Computational Intelligence, 1, 1, 343-350, 2019, Cites: 02018 (43)
Jaworski M., Duda P., Rutkowski L., New Splitting Criteria for Decision Trees in Stationary Data Streams. (90)
New Splitting Criteria for Decision Trees in Stationary Data Streams
, New Splitting Criteria for Decision Trees in Stationary Data Streams, IEEE Transactions on Neural Networks and Learning Systems, 29, 29, 2516-2529, 2018, Cites: 90
Rutkowski T., Romanowski J., Woldan P., Staszewski P., Nielek R., Rutkowski L., A content-based recommendation system using neuro-fuzzy approach. (50)
A content-based recommendation system using neuro-fuzzy approach
, A content-based recommendation system using neuro-fuzzy approach, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 50
Duda P., Jaworski M., Rutkowski L., Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. (31)
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
, Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks, International Journal of Neural Systems, 28, 28, 2018, Cites: 31
Lapa K., Cpalka K., Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction. (28)
Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction
, Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction, IEEE Transactions on Industrial Informatics, 14, 14, 1078-1088, 2018, Cites: 28
Duda P., Jaworski M., Rutkowski L., Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks. (24)
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences, 460-461, 460-461, 497-518, 2018, Cites: 24
Zalasinski M., Lapa K., Cpalka K., Prediction of values of the dynamic signature features. (19)
Prediction of values of the dynamic signature features
, Prediction of values of the dynamic signature features, Expert Systems with Applications, 104, 104, 86-96, 2018, Cites: 19
Lapa K., Cpalka K., Przybyl A., Genetic programming algorithm for designing of control systems. (17)
Genetic programming algorithm for designing of control systems
, Genetic programming algorithm for designing of control systems, Information Technology and Control, 47, 47, 668-683, 2018, Cites: 17
Gabryel M., Damasevicius R., Przybyszewski K., Application of the bag-of-words algorithm in classification the quality of sales leads. (16)
Application of the bag-of-words algorithm in classification the quality of sales leads
, Application of the bag-of-words algorithm in classification the quality of sales leads, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 615-622, 2018, Cites: 16
Lapa K., Cpalka K., Rutkowski L., New aspects of interpretability of fuzzy systems for nonlinear modeling. (16)
New aspects of interpretability of fuzzy systems for nonlinear modeling
, New aspects of interpretability of fuzzy systems for nonlinear modeling, Studies in Computational Intelligence, 738, 738, 225-264, 2018, Cites: 16
Gabryel M., Data Analysis Algorithm for Click Fraud Recognition. (15)
Data Analysis Algorithm for Click Fraud Recognition
, Data Analysis Algorithm for Click Fraud Recognition, Communications in Computer and Information Science, 920, 920, 437-446, 2018, Cites: 15
Jaworski M., Duda P., Rutkowski L., Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine. (14)
Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine
, Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine, Proceedings of the International Joint Conference on Neural Networks, 2018-July, 2018-July, 2018, Cites: 14
Kordos M., Lapa K., Multi-objective evolutionary instance selection for regression tasks. (13)
Multi-objective evolutionary instance selection for regression tasks
, Multi-objective evolutionary instance selection for regression tasks, Entropy, 20, 20, 2018, Cites: 13
Gabryel M., The bag-of-words method with different types of image features and dictionary analysis. (13)
The bag-of-words method with different types of image features and dictionary analysis
, The bag-of-words method with different types of image features and dictionary analysis, Journal of Universal Computer Science, 24, 24, 357-371, 2018, Cites: 13
Zalasinski M., Cpalka K., A new method for signature verification based on selection of the most important partitions of the dynamic signature. (12)
A new method for signature verification based on selection of the most important partitions of the dynamic signature
, A new method for signature verification based on selection of the most important partitions of the dynamic signature, Neurocomputing, 289, 289, 13-22, 2018, Cites: 12
Nowak J., Korytkowski M., Nowicki R., Scherer R., Siwocha A., Random forests for profiling computer network users. (11)
Random forests for profiling computer network users
, Random forests for profiling computer network users, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 734-739, 2018, Cites: 11
Bilski J., Kowalczyk B., Grzanek K., The parallel modification to the levenberg-marquardt algorithm. (11)
The parallel modification to the levenberg-marquardt algorithm
, The parallel modification to the levenberg-marquardt algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 15-24, 2018, Cites: 11
Lapa K., Cpalka K., Przybyl A., Grzanek K., Negative space-based population initialization algorithm (NSPIA). (8)
Negative space-based population initialization algorithm (NSPIA)
, Negative space-based population initialization algorithm (NSPIA), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 449-461, 2018, Cites: 8
Przybyl A., Hard real-time communication solution for mechatronic systems. (6)
Hard real-time communication solution for mechatronic systems
, Hard real-time communication solution for mechatronic systems, Robotics and Computer-Integrated Manufacturing, 49, 49, 309-316, 2018, Cites: 6
Zalasinski M., Cpalka K., Rutkowski L., Fuzzy-genetic approach to identity verification using a handwritten signature. (6)
Fuzzy-genetic approach to identity verification using a handwritten signature
, Fuzzy-genetic approach to identity verification using a handwritten signature, Studies in Computational Intelligence, 738, 738, 375-394, 2018, Cites: 6
Wrobel M., Nieszporek K., Starczewski J.T., Cader A., A fuzzy measure for recognition of handwritten letter strokes. (6)
A fuzzy measure for recognition of handwritten letter strokes
, A fuzzy measure for recognition of handwritten letter strokes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 761-770, 2018, Cites: 6
Duda P., Jaworski M., Rutkowski L., Online grnn-based ensembles for regression on evolving data streams. (6)
Online grnn-based ensembles for regression on evolving data streams
, Online grnn-based ensembles for regression on evolving data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10878 LNCS, 10878 LNCS, 221-228, 2018, Cites: 6
Dziwinski P., Bartczuk L., Przybyszewski K., A population based algorithm and fuzzy decision trees for nonlinear modeling. (4)
A population based algorithm and fuzzy decision trees for nonlinear modeling
, A population based algorithm and fuzzy decision trees for nonlinear modeling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 516-531, 2018, Cites: 4
Starczewski J.T., Nieszporek K., Wrobel M., Grzanek K., A fuzzy SOM for understanding incomplete 3D faces. (4)
A fuzzy SOM for understanding incomplete 3D faces
, A fuzzy SOM for understanding incomplete 3D faces, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 73-80, 2018, Cites: 4
Zalasinski M., Cpalka K., A method for genetic selection of the dynamic signature global features’ subset. (3)
A method for genetic selection of the dynamic signature global features’ subset
, A method for genetic selection of the dynamic signature global features’ subset, Advances in Intelligent Systems and Computing, 655, 655, 73-82, 2018, Cites: 3
Kordos M., Wydrzynski M., Lapa K., Obtaining pareto front in instance selection with ensembles and populations. (3)
Obtaining pareto front in instance selection with ensembles and populations
, Obtaining pareto front in instance selection with ensembles and populations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 438-448, 2018, Cites: 3
Nowak J., Korytkowski M., Scherer R., Classification of computer network users with convolutional neural networks. (3)
Classification of computer network users with convolutional neural networks
, Classification of computer network users with convolutional neural networks, Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, 501-504, 2018, Cites: 3
Nowicki R.K., Korytkowski M., Scherer R., Rough neural network ensemble for interval data classification. (3)
Rough neural network ensemble for interval data classification
, Rough neural network ensemble for interval data classification, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 3
Duda P., On ensemble components selection in data streams scenario with gradual concept-drift. (3)
On ensemble components selection in data streams scenario with gradual concept-drift
, On ensemble components selection in data streams scenario with gradual concept-drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 311-320, 2018, Cites: 3
Najgebauer P., Grycuk R., Scherer R., Fast Two-Level Image Indexing Based on Local Interest Points. (2)
Fast Two-Level Image Indexing Based on Local Interest Points
, Fast Two-Level Image Indexing Based on Local Interest Points, 2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018, 613-617, 2018, Cites: 2
Lapa K., Cpalka K., PID-fuzzy controllers with dynamic structure and evolutionary method for their construction. (1)
PID-fuzzy controllers with dynamic structure and evolutionary method for their construction
, PID-fuzzy controllers with dynamic structure and evolutionary method for their construction, Advances in Intelligent Systems and Computing, 655, 655, 138-148, 2018, Cites: 1
Starczewski A., Przybyszewski K., Improvement of the simplified silhouette validity index. (1)
Improvement of the simplified silhouette validity index
, Improvement of the simplified silhouette validity index, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 433-444, 2018, Cites: 1
Grycuk R., Najgebauer P., Scherer R., Siwocha A., Architecture of database index for content-based image retrieval systems. (1)
Architecture of database index for content-based image retrieval systems
, Architecture of database index for content-based image retrieval systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 36-47, 2018, Cites: 1
Jaworski M., Najgebauer P., Goetzen P., Estimation of probability density function, differential entropy and other relative quantities for data streams with concept drift. (1)
Estimation of probability density function, differential entropy and other relative quantities for data streams with concept drift
, Estimation of probability density function, differential entropy and other relative quantities for data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 376-386, 2018, Cites: 1
Lapa K., Cpalka K., Evolutionary approach for automatic design of PID controllers. (1)
Evolutionary approach for automatic design of PID controllers
, Evolutionary approach for automatic design of PID controllers, Studies in Computational Intelligence, 738, 738, 353-373, 2018, Cites: 1
Cao J., Rutkowski L., On the global convergence of the parzen-based generalized regression neural networks applied to streaming data. (1)
On the global convergence of the parzen-based generalized regression neural networks applied to streaming data
, On the global convergence of the parzen-based generalized regression neural networks applied to streaming data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 25-34, 2018, Cites: 1
Zalasinski M., Cpalka K., Grzanek K., Stability of features describing the dynamic signature biometric attribute. (0)
Stability of features describing the dynamic signature biometric attribute
, Stability of features describing the dynamic signature biometric attribute, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 250-261, 2018, Cites: 0
Galkowski T., Cader A., Outliers detection in regressions by nonparametric parzen kernel estimation. (0)
Outliers detection in regressions by nonparametric parzen kernel estimation
, Outliers detection in regressions by nonparametric parzen kernel estimation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 354-363, 2018, Cites: 0
Lapa K., Population-based algorithm with selectable evolutionary operators for nonlinear modeling. (0)
Population-based algorithm with selectable evolutionary operators for nonlinear modeling
, Population-based algorithm with selectable evolutionary operators for nonlinear modeling, Advances in Intelligent Systems and Computing, 655, 655, 15-26, 2018, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, V-VI, 2018, Cites: 0
Bartczuk L., Dziwinski P., Cader A., Symbolic regression with the AMSTA+GP in a non-linear modelling of dynamic objects. (0)
Symbolic regression with the AMSTA+GP in a non-linear modelling of dynamic objects
, Symbolic regression with the AMSTA+GP in a non-linear modelling of dynamic objects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 504-515, 2018, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, V-VI, 2018, Cites: 0
Olas T., Mleczko W.K., Wozniak M., Nowicki R.K., Gepner P., Performance evaluation of DBN learning on intel multi- and manycore architectures. (0)
Performance evaluation of DBN learning on intel multi- and manycore architectures
, Performance evaluation of DBN learning on intel multi- and manycore architectures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10777 LNCS, 10777 LNCS, 565-575, 2018, Cites: 0
Cierniak R., Dobosz P., Pluta P., Filutowicz P., Analytical realization of the EM algorithm for emission positron tomography. (0)
Analytical realization of the EM algorithm for emission positron tomography
, Analytical realization of the EM algorithm for emission positron tomography, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 127-136, 2018, Cites: 02017 (53)
Nowak J., Taspinar A., Scherer R., LSTM recurrent neural networks for short text and sentiment classification. (134)
LSTM recurrent neural networks for short text and sentiment classification
, LSTM recurrent neural networks for short text and sentiment classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 553-562, 2017, Cites: 134
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?. (66)
How to adjust an ensemble size in stream data mining?
, How to adjust an ensemble size in stream data mining?, Information Sciences, 381, 381, 46-54, 2017, Cites: 66
Starczewski A., A new validity index for crisp clusters. (50)
A new validity index for crisp clusters
, A new validity index for crisp clusters, Pattern Analysis and Applications, 20, 20, 687-700, 2017, Cites: 50
Gabryel M., Damasevicius R., The image classification with different types of image features. (33)
The image classification with different types of image features
, The image classification with different types of image features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 497-506, 2017, Cites: 33
Nowicki R.K., Starczewski J.T., A new method for classification of imprecise data using fuzzy rough fuzzification. (25)
A new method for classification of imprecise data using fuzzy rough fuzzification
, A new method for classification of imprecise data using fuzzy rough fuzzification, Information Sciences, 414, 414, 33-52, 2017, Cites: 25
Jaworski M., Duda P., Rutkowski L., On applying the Restricted Boltzmann Machine to active concept drift detection. (22)
On applying the Restricted Boltzmann Machine to active concept drift detection
, On applying the Restricted Boltzmann Machine to active concept drift detection, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-8, 2017, Cites: 22
Korytkowski M., Novel visual information indexing in relational databases. (17)
Novel visual information indexing in relational databases
, Novel visual information indexing in relational databases, Integrated Computer-Aided Engineering, 24, 24, 119-128, 2017, Cites: 17
Duda P., Jaworski M., Rutkowski L., On ensemble components selection in data streams scenario with reoccurring concept-drift. (16)
On ensemble components selection in data streams scenario with reoccurring concept-drift
, On ensemble components selection in data streams scenario with reoccurring concept-drift, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-7, 2017, Cites: 16
Wrobel M., Starczewski J.T., Napoli C., Handwriting recognition with extraction of letter fragments. (14)
Handwriting recognition with extraction of letter fragments
, Handwriting recognition with extraction of letter fragments, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 183-192, 2017, Cites: 14
Bilski J., Wilamowski B.M., Parallel levenberg-marquardt algorithm without error backpropagation. (12)
Parallel levenberg-marquardt algorithm without error backpropagation
, Parallel levenberg-marquardt algorithm without error backpropagation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 25-39, 2017, Cites: 12
Kapuscinski T., Nowicki R.K., Napoli C., Comparison of effectiveness of multi-objective genetic algorithms in optimization of invertible S-boxes. (12)
Comparison of effectiveness of multi-objective genetic algorithms in optimization of invertible S-boxes
, Comparison of effectiveness of multi-objective genetic algorithms in optimization of invertible S-boxes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 466-476, 2017, Cites: 12
Jaworski M., Duda P., Rutkowski L., Najgebauer P., Pawlak M., Heuristic regression function estimation methods for data streams with concept drift. (10)
Heuristic regression function estimation methods for data streams with concept drift
, Heuristic regression function estimation methods for data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 726-737, 2017, Cites: 10
Gabryel M., Capizzi G., The bag-of-words method with dictionary analysis by evolutionary algorithm. (8)
The bag-of-words method with dictionary analysis by evolutionary algorithm
, The bag-of-words method with dictionary analysis by evolutionary algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 43-51, 2017, Cites: 8
Cpalka K., Interpretability of fuzzy systems designed in the process of evolutionary learning. (7)
Interpretability of fuzzy systems designed in the process of evolutionary learning
, Interpretability of fuzzy systems designed in the process of evolutionary learning, Studies in Computational Intelligence, 684, 684, 91-130, 2017, Cites: 7
Zebik M., Korytkowski M., Angryk R., Scherer R., Convolutional neural networks for time series classification. (7)
Convolutional neural networks for time series classification
, Convolutional neural networks for time series classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 635-642, 2017, Cites: 7
Bartczuk L., Dziwinski P., Red'Ko V.G., The concept on nonlinear modelling of dynamic objects based on state transition algorithm and genetic programming. (6)
The concept on nonlinear modelling of dynamic objects based on state transition algorithm and genetic programming
, The concept on nonlinear modelling of dynamic objects based on state transition algorithm and genetic programming, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 209-220, 2017, Cites: 6
Damasevicius R., Maskeliunas R., Wozniak M., Polap D., Sidekerskiene T., Gabryel M., Detection of saliency map as image feature outliers using random projections based method. (5)
Detection of saliency map as image feature outliers using random projections based method
, Detection of saliency map as image feature outliers using random projections based method, ICENCO 2017 - 13th International Computer Engineering Conference: Boundless Smart Societies, 2018-January, 2018-January, 85-90, 2017, Cites: 5
Lapa K., Cpalka K., Przybyl A., Saito T., Fuzzy PID controllers with FIR filtering and a method for their construction. (5)
Fuzzy PID controllers with FIR filtering and a method for their construction
, Fuzzy PID controllers with FIR filtering and a method for their construction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 292-307, 2017, Cites: 5
Petraitis T., Maskeliunas R., Damasevicius R., Polap D., Wozniak M., Gabryel M., Environment recognition based on images using bag-of-words. (5)
Environment recognition based on images using bag-of-words
, Environment recognition based on images using bag-of-words, IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence, 166-176, 2017, Cites: 5
Zalasinski M., Cpalka K., Hayashi Y., A method for genetic selection of the most characteristic descriptors of the dynamic signature. (5)
A method for genetic selection of the most characteristic descriptors of the dynamic signature
, A method for genetic selection of the most characteristic descriptors of the dynamic signature, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 747-760, 2017, Cites: 5
Bilski J., Kowalczyk B., Zurada J.M., Parallel implementation of the givens rotations in the neural network learning algorithm. (5)
Parallel implementation of the givens rotations in the neural network learning algorithm
, Parallel implementation of the givens rotations in the neural network learning algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 14-24, 2017, Cites: 5
Dziwinski P., Bartczuk L., Tingwen H., A method for non-linear modelling based on the capabilities of PSO and GA algorithms. (5)
A method for non-linear modelling based on the capabilities of PSO and GA algorithms
, A method for non-linear modelling based on the capabilities of PSO and GA algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 221-232, 2017, Cites: 5
Gabryel M., The bag-of-words methods with pareto-fronts for similar image retrieval. (4)
The bag-of-words methods with pareto-fronts for similar image retrieval
, The bag-of-words methods with pareto-fronts for similar image retrieval, Communications in Computer and Information Science, 756, 756, 374-384, 2017, Cites: 4
Bernacki J., Klonowski M., Syga P., Some remarks about tracing digital cameras-faster method and usable countermeasure. (4)
Some remarks about tracing digital cameras-faster method and usable countermeasure
, Some remarks about tracing digital cameras-faster method and usable countermeasure, ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, 4, 4, 343-350, 2017, Cites: 4
Zalasinski M., Cpalka K., Er M.J., Stability evaluation of the dynamic signature partitions over time. (4)
Stability evaluation of the dynamic signature partitions over time
, Stability evaluation of the dynamic signature partitions over time, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 733-746, 2017, Cites: 4
Lapa K., Cpalka K., Hayashi Y., Hybrid initialization in the process of evolutionary learning. (4)
Hybrid initialization in the process of evolutionary learning
, Hybrid initialization in the process of evolutionary learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 380-393, 2017, Cites: 4
Lapa K., Cpalka K., Wang L., A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators. (4)
A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators
, A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 263-278, 2017, Cites: 4
Zalasinski M., Lapa K., Cpalka K., Saito T., A method for changes prediction of the dynamic signature global features over time. (3)
A method for changes prediction of the dynamic signature global features over time
, A method for changes prediction of the dynamic signature global features over time, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 761-772, 2017, Cites: 3
Lapa K., Elastic FOPID+FIR controller design using hybrid population-based algorithm. (3)
Elastic FOPID+FIR controller design using hybrid population-based algorithm
, Elastic FOPID+FIR controller design using hybrid population-based algorithm, Advances in Intelligent Systems and Computing, 522, 522, 15-26, 2017, Cites: 3
Najgebauer P., Rutkowski L., Scherer R., Novel method for joining missing line fragments for medical image analysis. (3)
Novel method for joining missing line fragments for medical image analysis
, Novel method for joining missing line fragments for medical image analysis, 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017, 861-866, 2017, Cites: 3
Starczewski A., Krzyzak A., Improvement of the validity index for determination of an appropriate data partitioning. (3)
Improvement of the validity index for determination of an appropriate data partitioning
, Improvement of the validity index for determination of an appropriate data partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 159-170, 2017, Cites: 3
Lagiewka M., Korytkowski M., Scherer R., Distributed image retrieval with color and keypoint features. (3)
Distributed image retrieval with color and keypoint features
, Distributed image retrieval with color and keypoint features, Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, 45-50, 2017, Cites: 3
Najgebauer P., Rutkowski L., Scherer R., Interest point localization based on edge detection according to gestalt laws. (2)
Interest point localization based on edge detection according to gestalt laws
, Interest point localization based on edge detection according to gestalt laws, 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, 2017-January, 2017-January, 349-353, 2017, Cites: 2
Cpalka K., Introduction to fuzzy system interpretability. (2)
Introduction to fuzzy system interpretability
, Introduction to fuzzy system interpretability, Studies in Computational Intelligence, 684, 684, 27-36, 2017, Cites: 2
Laskowska M., Laskowski L., Jelonkiewicz J., Piech H., Galkowski T., Boullanger A., Porous silica templated nanomaterials for artificial intelligence and IT technologies. (2)
Porous silica templated nanomaterials for artificial intelligence and IT technologies
, Porous silica templated nanomaterials for artificial intelligence and IT technologies, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 509-517, 2017, Cites: 2
Cpalka K., Case study: Interpretability of fuzzy systems applied to nonlinear modelling and control. (2)
Case study: Interpretability of fuzzy systems applied to nonlinear modelling and control
, Case study: Interpretability of fuzzy systems applied to nonlinear modelling and control, Studies in Computational Intelligence, 684, 684, 131-162, 2017, Cites: 2
Grycuk R., Scherer M., Voloshynovskiy S., Local keypoint-based image detector with object detection. (2)
Local keypoint-based image detector with object detection
, Local keypoint-based image detector with object detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 507-517, 2017, Cites: 2
Przybyl A., Er M.J., A method for design of hardware emulators for a distributed network environment. (1)
A method for design of hardware emulators for a distributed network environment
, A method for design of hardware emulators for a distributed network environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 318-336, 2017, Cites: 1
Starczewski A., Krzyzak A., A study of cluster validity indices for real-life data. (1)
A study of cluster validity indices for real-life data
, A study of cluster validity indices for real-life data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 148-158, 2017, Cites: 1
Cpalka K., Improving fuzzy systems interpretability by appropriate selection of their structure. (1)
Improving fuzzy systems interpretability by appropriate selection of their structure
, Improving fuzzy systems interpretability by appropriate selection of their structure, Studies in Computational Intelligence, 684, 684, 37-60, 2017, Cites: 1
Galkowski T., Pawlak M., The novel method of the estimation of the fourier transform based on noisy measurements. (1)
The novel method of the estimation of the fourier transform based on noisy measurements
, The novel method of the estimation of the fourier transform based on noisy measurements, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 52-61, 2017, Cites: 1
Cierniak R., Bilski J., Smolag J., Pluta P., Shah N., Parallel realizations of the iterative statistical reconstruction algorithm for 3D computed tomography. (1)
Parallel realizations of the iterative statistical reconstruction algorithm for 3D computed tomography
, Parallel realizations of the iterative statistical reconstruction algorithm for 3D computed tomography, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 473-484, 2017, Cites: 1
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, v-vi, 2017, Cites: 0
Petraitis T., Maskeliunas R., Damasevicius R., Polap D., Wozniak M., Gabryel M., Environment Recognition based on Images using Bag-of-Words. (0)
Environment Recognition based on Images using Bag-of-Words
, Environment Recognition based on Images using Bag-of-Words, International Joint Conference on Computational Intelligence, 1, 1, 166-176, 2017, Cites: 0
Lapa K., Cpalka K., Weighted fuzzy genetic programming algorithm for structure and parameters selection of fuzzy systems for nonlinear modelling. (0)
Weighted fuzzy genetic programming algorithm for structure and parameters selection of fuzzy systems for nonlinear modelling
, Weighted fuzzy genetic programming algorithm for structure and parameters selection of fuzzy systems for nonlinear modelling, Advances in Intelligent Systems and Computing, 521, 521, 157-174, 2017, Cites: 0
Vladymyrska N., Wrobel M., Starczewski J.T., Hnatushenko V., Narx neural network for prediction of refresh timeout in PIM–DM multicast routing. (0)
Narx neural network for prediction of refresh timeout in PIM–DM multicast routing
, Narx neural network for prediction of refresh timeout in PIM–DM multicast routing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 199-205, 2017, Cites: 0
Cpalka K., Concluding remarks and future perspectives. (0)
Concluding remarks and future perspectives
, Concluding remarks and future perspectives, Studies in Computational Intelligence, 684, 684, 191-193, 2017, Cites: 0
Cpalka K., Interpretability of fuzzy systems designed in the process of gradient learning. (0)
Interpretability of fuzzy systems designed in the process of gradient learning
, Interpretability of fuzzy systems designed in the process of gradient learning, Studies in Computational Intelligence, 684, 684, 61-90, 2017, Cites: 0
Lapa K., A new algorithm for online management of fuzzy rules base for nonlinear modeling. (0)
A new algorithm for online management of fuzzy rules base for nonlinear modeling
, A new algorithm for online management of fuzzy rules base for nonlinear modeling, Advances in Intelligent Systems and Computing, 521, 521, 15-28, 2017, Cites: 0
Cpalka K., Selected topics in fuzzy systems designing. (0)
Selected topics in fuzzy systems designing
, Selected topics in fuzzy systems designing, Studies in Computational Intelligence, 684, 684, 11-25, 2017, Cites: 0
Cpalka K., Case study: Interpretability of fuzzy systems applied to identity verification. (0)
Case study: Interpretability of fuzzy systems applied to identity verification
, Case study: Interpretability of fuzzy systems applied to identity verification, Studies in Computational Intelligence, 684, 684, 163-189, 2017, Cites: 0
Cpalka K., Introduction. (0)
Introduction
, Introduction, Studies in Computational Intelligence, 684, 684, 1-10, 2017, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, V-VI, 2017, Cites: 02016 (65)
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers. (142)
Fast image classification by boosting fuzzy classifiers
, Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 142
Cpalka K., Zalasinski M., Rutkowski L., A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. (92)
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
, A new algorithm for identity verification based on the analysis of a handwritten dynamic signature, Applied Soft Computing Journal, 43, 43, 47-56, 2016, Cites: 92
Bartczuk L., Przybyl A., Cpalka K., A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. (37)
A new approach to nonlinear modelling of dynamic systems based on fuzzy rules
, A new approach to nonlinear modelling of dynamic systems based on fuzzy rules, International Journal of Applied Mathematics and Computer Science, 26, 26, 603-621, 2016, Cites: 37
Zalasinski M., Cpalka K., Rakus-Andersson E., An idea of the dynamic signature verification based on a hybrid approach. (28)
An idea of the dynamic signature verification based on a hybrid approach
, An idea of the dynamic signature verification based on a hybrid approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 232-246, 2016, Cites: 28
Zalasinski M., Cpalka K., New algorithm for on-line signature verification using characteristic hybrid partitions. (27)
New algorithm for on-line signature verification using characteristic hybrid partitions
, New algorithm for on-line signature verification using characteristic hybrid partitions, Advances in Intelligent Systems and Computing, 432, 432, 147-157, 2016, Cites: 27
Bernacki J., Blazejczyk I., Indyka-Piasecka A., Kopel M., Kukla E., Trawinski B., Responsive web design: Testing usability of mobile web applications. (26)
Responsive web design: Testing usability of mobile web applications
, Responsive web design: Testing usability of mobile web applications, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9621, 9621, 257-269, 2016, Cites: 26
Wozniak M., Marszalek Z., Gabryel M., Nowicki R.K., Preprocessing large data sets by the use of quick sort algorithm. (23)
Preprocessing large data sets by the use of quick sort algorithm
, Preprocessing large data sets by the use of quick sort algorithm, Advances in Intelligent Systems and Computing, 364, 364, 111-121, 2016, Cites: 23
Bartczuk L., Gene expression programming in correction modelling of nonlinear dynamic objects. (19)
Gene expression programming in correction modelling of nonlinear dynamic objects
, Gene expression programming in correction modelling of nonlinear dynamic objects, Advances in Intelligent Systems and Computing, 429, 429, 125-134, 2016, Cites: 19
Kapuscinski T., Nowicki R.K., Napoli C., Application of genetic algorithms in the construction of invertible substitution boxes. (18)
Application of genetic algorithms in the construction of invertible substitution boxes
, Application of genetic algorithms in the construction of invertible substitution boxes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 380-391, 2016, Cites: 18
Zalasinski M., Cpalka K., Hayashi Y., A new approach to the dynamic signature verification aimed at minimizing the number of global features. (18)
A new approach to the dynamic signature verification aimed at minimizing the number of global features
, A new approach to the dynamic signature verification aimed at minimizing the number of global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 218-231, 2016, Cites: 18
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., A method for automatic adjustment of ensemble size in stream data mining. (18)
A method for automatic adjustment of ensemble size in stream data mining
, A method for automatic adjustment of ensemble size in stream data mining, Proceedings of the International Joint Conference on Neural Networks, 2016-October, 2016-October, 9-15, 2016, Cites: 18
Gabryel M., A bag-of-features algorithm for applications using a NoSQL database. (17)
A bag-of-features algorithm for applications using a NoSQL database
, A bag-of-features algorithm for applications using a NoSQL database, Communications in Computer and Information Science, 639, 639, 332-343, 2016, Cites: 17
Grycuk R., Gabryel M., Nowicki R., Scherer R., Content-based image retrieval optimization by differential evolution. (17)
Content-based image retrieval optimization by differential evolution
, Content-based image retrieval optimization by differential evolution, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 86-93, 2016, Cites: 17
Lapa K., Cpalka K., On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection. (16)
On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection
, On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection, Advances in Intelligent Systems and Computing, 429, 429, 111-123, 2016, Cites: 16
Wozniak M., Gabryel M., Nowicki R.K., Nowak B.A., An application of firefly algorithm to position traffic in NoSQL database systems. (16)
An application of firefly algorithm to position traffic in NoSQL database systems
, An application of firefly algorithm to position traffic in NoSQL database systems, Advances in Intelligent Systems and Computing, 416, 416, 259-272, 2016, Cites: 16
Gabryel M., The bag-of-features algorithm for practical applications using the MySQL database. (15)
The bag-of-features algorithm for practical applications using the MySQL database
, The bag-of-features algorithm for practical applications using the MySQL database, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 635-646, 2016, Cites: 15
Dziwinski P., Avedyan E.D., A new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points. (15)
A new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points
, A new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 293-305, 2016, Cites: 15
Zalasinski M., New algorithm for on-line signature verification using characteristic global features. (14)
New algorithm for on-line signature verification using characteristic global features
, New algorithm for on-line signature verification using characteristic global features, Advances in Intelligent Systems and Computing, 432, 432, 137-146, 2016, Cites: 14
Bilski J., Kowalczyk B., Zurada J.M., Application of the givens rotations in the neural network learning algorithm. (14)
Application of the givens rotations in the neural network learning algorithm
, Application of the givens rotations in the neural network learning algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 46-56, 2016, Cites: 14
Nowicki R.K., Nowak B.A., Wozniak M., Application of rough sets in k nearest neighbours algorithm for classification of incomplete samples. (13)
Application of rough sets in k nearest neighbours algorithm for classification of incomplete samples
, Application of rough sets in k nearest neighbours algorithm for classification of incomplete samples, Advances in Intelligent Systems and Computing, 416, 416, 243-257, 2016, Cites: 13
Starczewski A., Krzyzak A., A modification of the Silhouette index for the improvement of cluster validity assessment. (13)
A modification of the Silhouette index for the improvement of cluster validity assessment
, A modification of the Silhouette index for the improvement of cluster validity assessment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 114-124, 2016, Cites: 13
Bilski J., Wilamowski B.M., Parallel learning of feedforward neural networks without error backpropagation. (13)
Parallel learning of feedforward neural networks without error backpropagation
, Parallel learning of feedforward neural networks without error backpropagation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 57-69, 2016, Cites: 13
Starczewski J.T., Pabiasz S., Vladymyrska N., Marvuglia A., Napoli C., Wozniak M., Self organizing maps for 3D face understanding. (12)
Self organizing maps for 3D face understanding
, Self organizing maps for 3D face understanding, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 210-217, 2016, Cites: 12
Bartczuk L., Lapa K., Koprinkova-Hristova P., A new method for generating of fuzzy rules for the nonlinear modelling based on semantic genetic programming. (12)
A new method for generating of fuzzy rules for the nonlinear modelling based on semantic genetic programming
, A new method for generating of fuzzy rules for the nonlinear modelling based on semantic genetic programming, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 262-278, 2016, Cites: 12
Saito T., Yasuda K., Ishikawa T., Hosoi R., Takahashi K., Chen Y., Zalasinski M., Estimating CPU features by browser fingerprinting. (11)
Estimating CPU features by browser fingerprinting
, Estimating CPU features by browser fingerprinting, Proceedings - 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2016, 587-592, 2016, Cites: 11
Przybyl A., Joo Er M., The method of hardware implementation of fuzzy systems on FPGA. (9)
The method of hardware implementation of fuzzy systems on FPGA
, The method of hardware implementation of fuzzy systems on FPGA, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 284-298, 2016, Cites: 9
Grycuk R., Gabryel M., Scherer M., Voloshynovskiy S., Image descriptor based on edge detection and crawler algorithm. (9)
Image descriptor based on edge detection and crawler algorithm
, Image descriptor based on edge detection and crawler algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 647-659, 2016, Cites: 9
Cierniak R., Lorent A., Comparison of algebraic and analytical approaches to the formulation of the statistical model-based reconstruction problem for X-ray computed tomography. (9)
Comparison of algebraic and analytical approaches to the formulation of the statistical model-based reconstruction problem for X-ray computed tomography
, Comparison of algebraic and analytical approaches to the formulation of the statistical model-based reconstruction problem for X-ray computed tomography, Computerized Medical Imaging and Graphics, 52, 52, 19-27, 2016, Cites: 9
Blazejczyk I., Trawinski B., Indyka-Piasecka A., Kopel M., Kukla E., Bernacki J., Usability testing of a mobile friendly web conference service. (8)
Usability testing of a mobile friendly web conference service
, Usability testing of a mobile friendly web conference service, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9875 LNCS, 9875 LNCS, 565-579, 2016, Cites: 8
Laskowski ., Laskowska M., Jelonkiewicz J., Galkowski T., Pawlik P., Piech H., Doskocz M., Iron Doped SBA-15 Mesoporous Silica Studied by Mössbauer Spectroscopy. (8)
Iron Doped SBA-15 Mesoporous Silica Studied by Mössbauer Spectroscopy
, Iron Doped SBA-15 Mesoporous Silica Studied by Mössbauer Spectroscopy, Journal of Nanomaterials, 2016, 2016, 2016, Cites: 8
Jaworski M., Rutkowski L., Pawlak M., Hybrid splitting criterion in decision trees for data stream mining. (7)
Hybrid splitting criterion in decision trees for data stream mining
, Hybrid splitting criterion in decision trees for data stream mining, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 60-72, 2016, Cites: 7
Galkowski T., Pawlak M., Nonparametric estimation of edge values of regression functions. (7)
Nonparametric estimation of edge values of regression functions
, Nonparametric estimation of edge values of regression functions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 49-59, 2016, Cites: 7
Lapa K., Szczypta J., Saito T., Aspects of evolutionary construction of new flexible PID-fuzzy controller. (6)
Aspects of evolutionary construction of new flexible PID-fuzzy controller
, Aspects of evolutionary construction of new flexible PID-fuzzy controller, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 450-464, 2016, Cites: 6
Kempa W.M., Wozniak M., Nowicki R.K., Gabryel M., Damasevicius R., Transient solution for queueing delay distribution in the GI/M/1/K-type mode with “queued” waking up and balking. (6)
Transient solution for queueing delay distribution in the GI/M/1/K-type mode with “queued” waking up and balking
, Transient solution for queueing delay distribution in the GI/M/1/K-type mode with “queued” waking up and balking, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 340-351, 2016, Cites: 6
Dziwinski P., Avedyan E.D., A new approach for using the fuzzy decision trees for the detection of the significant operating points in the nonlinear modeling. (6)
A new approach for using the fuzzy decision trees for the detection of the significant operating points in the nonlinear modeling
, A new approach for using the fuzzy decision trees for the detection of the significant operating points in the nonlinear modeling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 279-292, 2016, Cites: 6
Przybyl A., Szczypta J., Method of evolutionary designing of FPGA-based controllers. (5)
Method of evolutionary designing of FPGA-based controllers
, Method of evolutionary designing of FPGA-based controllers, Przeglad Elektrotechniczny, 92, 92, 174-179, 2016, Cites: 5
Lapa K., Cpalka K., Koprinkova-Hristova P., New method for fuzzy nonlinear modelling based on genetic programming. (5)
New method for fuzzy nonlinear modelling based on genetic programming
, New method for fuzzy nonlinear modelling based on genetic programming, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 432-449, 2016, Cites: 5
Nowicki R.K., Scherer R., Rutkowski L., Novel rough neural network for classification with missing data. (5)
Novel rough neural network for classification with missing data
, Novel rough neural network for classification with missing data, 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016, 820-825, 2016, Cites: 5
Bartczuk L., Galushkin A.I., A new method for generating nonlinear correction models of dynamic objects based on semantic genetic programming. (5)
A new method for generating nonlinear correction models of dynamic objects based on semantic genetic programming
, A new method for generating nonlinear correction models of dynamic objects based on semantic genetic programming, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 249-261, 2016, Cites: 5
Olas T., Mleczko W.K., Nowicki R.K., Wyrzykowski R., Adaptation of deep belief networks to modern multicore architectures. (5)
Adaptation of deep belief networks to modern multicore architectures
, Adaptation of deep belief networks to modern multicore architectures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9573, 9573, 459-472, 2016, Cites: 5
Lapa K., Cpalka K., Nonlinear pattern classification using fuzzy system and hybrid genetic-imperialist algorithm. (4)
Nonlinear pattern classification using fuzzy system and hybrid genetic-imperialist algorithm
, Nonlinear pattern classification using fuzzy system and hybrid genetic-imperialist algorithm, Advances in Intelligent Systems and Computing, 432, 432, 159-171, 2016, Cites: 4
Staszewski P., Woldan P., Korytkowski M., Scherer R., Wang L., Query-by-example image retrieval in Microsoft SQL server. (4)
Query-by-example image retrieval in Microsoft SQL server
, Query-by-example image retrieval in Microsoft SQL server, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 746-754, 2016, Cites: 4
Bilski J., Galushkin A.I., A new proposition of the activation function for significant improvement of neural networks performance. (4)
A new proposition of the activation function for significant improvement of neural networks performance
, A new proposition of the activation function for significant improvement of neural networks performance, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 35-45, 2016, Cites: 4
Przybyl A., Er M.J., A new approach to designing of intelligent emulators working in a distributed environment. (4)
A new approach to designing of intelligent emulators working in a distributed environment
, A new approach to designing of intelligent emulators working in a distributed environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 546-558, 2016, Cites: 4
Laskowski L., Laskowska M., Piech H., Galkowski T., Boullanger A., The concept of molecular neurons. (4)
The concept of molecular neurons
, The concept of molecular neurons, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 494-501, 2016, Cites: 4
Przybyl A., Lapa K., Szczypta J., Wang L., The method of the evolutionary designing the elastic controller structure. (3)
The method of the evolutionary designing the elastic controller structure
, The method of the evolutionary designing the elastic controller structure, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 476-492, 2016, Cites: 3
Scherer M., Smolag J., Gaweda A., Predicting success of bank direct marketing by neuro-fuzzy systems. (3)
Predicting success of bank direct marketing by neuro-fuzzy systems
, Predicting success of bank direct marketing by neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 570-576, 2016, Cites: 3
Lapa K., Cpalka K., Wang L., New approach for interpretability of neuro-fuzzy systems with parametrized triangular norms. (3)
New approach for interpretability of neuro-fuzzy systems with parametrized triangular norms
, New approach for interpretability of neuro-fuzzy systems with parametrized triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 248-265, 2016, Cites: 3
Lagiewka M., Scherer R., Angryk R., Color-based large-scale image retrieval with limited hardware resources. (2)
Color-based large-scale image retrieval with limited hardware resources
, Color-based large-scale image retrieval with limited hardware resources, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 689-699, 2016, Cites: 2
Korytkowski M., A novel convolutional neural network with glial cells. (2)
A novel convolutional neural network with glial cells
, A novel convolutional neural network with glial cells, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 670-679, 2016, Cites: 2
Grycuk R., Knop M., Neural video compression based on surf scene change detection algorithm. (2)
Neural video compression based on surf scene change detection algorithm
, Neural video compression based on surf scene change detection algorithm, Advances in Intelligent Systems and Computing, 389, 389, 105-112, 2016, Cites: 2
Korytkowski M., Staszewski P., Woldan P., Scherer R., Fast computing framework for convolutional neural networks. (2)
Fast computing framework for convolutional neural networks
, Fast computing framework for convolutional neural networks, Proceedings - 2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud 2016, Social Computing and Networking, SocialCom 2016 and Sustainable Computing and Communications, SustainCom 2016, 118-123, 2016, Cites: 2
Duda P., Pietruczuk L., Jaworski M., Krzyzak A., On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions. (2)
On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions
, On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 37-48, 2016, Cites: 2
Mleczko W.K., Nowicki R.K., Angryk R., Rough restricted Boltzmann machine -New architecture for incomplete input data. (2)
Rough restricted Boltzmann machine -New architecture for incomplete input data
, Rough restricted Boltzmann machine -New architecture for incomplete input data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 114-125, 2016, Cites: 2
Najgebauer P., Korytkowski M., Barranco C.D., Scherer R., Novel image descriptor based on color spatial distribution. (2)
Novel image descriptor based on color spatial distribution
, Novel image descriptor based on color spatial distribution, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 712-722, 2016, Cites: 2
Bernacki J., Kolaczek G., RFID security: A method for tracking prevention. (1)
RFID security: A method for tracking prevention
, RFID security: A method for tracking prevention, Communications in Computer and Information Science, 659, 659, 241-249, 2016, Cites: 1
Lapa K., Cpalka K., Hayashi Y., New approach for nonlinear modelling based on online designing of the fuzzy rule base. (1)
New approach for nonlinear modelling based on online designing of the fuzzy rule base
, New approach for nonlinear modelling based on online designing of the fuzzy rule base, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 230-247, 2016, Cites: 1
Duda P., Jaworski M., Pietruczuk L., Korytkowski M., Gabryel M., Scherer R., On the application of orthogonal series density estimation for image classification based on feature description. (1)
On the application of orthogonal series density estimation for image classification based on feature description
, On the application of orthogonal series density estimation for image classification based on feature description, Advances in Intelligent Systems and Computing, 364, 364, 529-540, 2016, Cites: 1
Najgebauer P., Scherer R., Fast image search by trees of keypoint descriptors. (0)
Fast image search by trees of keypoint descriptors
, Fast image search by trees of keypoint descriptors, Advances in Intelligent Systems and Computing, 364, 364, 541-552, 2016, Cites: 0
Cierniak R., Analytical statistical approach for fan-beam scanners. (0)
Analytical statistical approach for fan-beam scanners
, Analytical statistical approach for fan-beam scanners, Advances in Intelligent Systems and Computing, 471, 471, 231-243, 2016, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, V-VI, 2016, Cites: 0
Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M., Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I. (0)
Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I
, Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 2016, Cites: 0
Cierniak R., Lorent A., Pluta P., Shah N., Regularization methods for the analytical statistical reconstruction problem in medical computed tomography. (0)
Regularization methods for the analytical statistical reconstruction problem in medical computed tomography
, Regularization methods for the analytical statistical reconstruction problem in medical computed tomography, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 147-158, 2016, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, V-VI, 2016, Cites: 0
Szczypta J., Lapa K., Aspects of structure selection and parameters tuning of control systems using hybrid genetic-fruit fly algorithm. (0)
Aspects of structure selection and parameters tuning of control systems using hybrid genetic-fruit fly algorithm
, Aspects of structure selection and parameters tuning of control systems using hybrid genetic-fruit fly algorithm, Advances in Intelligent Systems and Computing, 429, 429, 101-110, 2016, Cites: 02015 (42)
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., A new method for data stream mining based on the misclassification error. (105)
A new method for data stream mining based on the misclassification error
, A new method for data stream mining based on the misclassification error, IEEE Transactions on Neural Networks and Learning Systems, 26, 26, 1048-1059, 2015, Cites: 105
Starczewski A., Krzyzak A., Performance evaluation of the silhouette index. (78)
Performance evaluation of the silhouette index
, Performance evaluation of the silhouette index, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 49-58, 2015, Cites: 78
Wozniak M., Napoli C., Tramontana E., Capizzi G., Lo Sciuto G., Nowicki R.K., Starczewski J.T., A multiscale image compressor with RBFNN and Discrete Wavelet decomposition. (43)
A multiscale image compressor with RBFNN and Discrete Wavelet decomposition
, A multiscale image compressor with RBFNN and Discrete Wavelet decomposition, Proceedings of the International Joint Conference on Neural Networks, 2015-September, 2015-September, 2015, Cites: 43
Wozniak M., Polap D., Nowicki R.K., Napoli C., Pappalardo G., Tramontana E., Novel approach toward medical signals classifier. (42)
Novel approach toward medical signals classifier
, Novel approach toward medical signals classifier, Proceedings of the International Joint Conference on Neural Networks, 2015-September, 2015-September, 2015, Cites: 42
Wozniak M., Polap D., Gabryel M., Nowicki R.K., Napoli C., Tramontana E., Can we process 2D images using artificial bee colony?. (42)
Can we process 2D images using artificial bee colony?
, Can we process 2D images using artificial bee colony?, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 660-671, 2015, Cites: 42
Napoli C., Pappalardo G., Tramontana E., Nowicki R.K., Starczewski J.T., Wozniak M., Toward work groups classification based on probabilistic neural network approach. (42)
Toward work groups classification based on probabilistic neural network approach
, Toward work groups classification based on probabilistic neural network approach, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 79-89, 2015, Cites: 42
Nowak B.A., Nowicki R.K., Wozniak M., Napoli C., Multi-class nearest neighbour classifier for incomplete data handling. (42)
Multi-class nearest neighbour classifier for incomplete data handling
, Multi-class nearest neighbour classifier for incomplete data handling, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 469-480, 2015, Cites: 42
Cpalka K., Lapa K., Przybyl A., A new approach to design of control systems using genetic programming. (40)
A new approach to design of control systems using genetic programming
, A new approach to design of control systems using genetic programming, Information Technology and Control, 44, 44, 433-442, 2015, Cites: 40
Bilski J., Smolag J., Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks. (40)
Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks
, Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks, IEEE Transactions on Parallel and Distributed Systems, 26, 26, 2561-2570, 2015, Cites: 40
Zalasinski M., Cpalka K., Hayashi Y., New fast algorithm for the dynamic signature verification using global features values. (34)
New fast algorithm for the dynamic signature verification using global features values
, New fast algorithm for the dynamic signature verification using global features values, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 175-188, 2015, Cites: 34
Bilski J., Smolag J., Zurada J.M., Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks. (24)
Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks
, Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 3-14, 2015, Cites: 24
Lapa K., Szczypta J., Venkatesan R., Aspects of structure and parameters selection of control systems using selected multi-population algorithms. (20)
Aspects of structure and parameters selection of control systems using selected multi-population algorithms
, Aspects of structure and parameters selection of control systems using selected multi-population algorithms, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 247-260, 2015, Cites: 20
Grycuk R., Gabryel M., Scherer R., Voloshynovskiy S., Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database. (19)
Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database
, Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 715-726, 2015, Cites: 19
Dziwinski P., Avedyan E.D., A new approach to nonlinear modeling based on significant operating points detection. (19)
A new approach to nonlinear modeling based on significant operating points detection
, A new approach to nonlinear modeling based on significant operating points detection, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 364-378, 2015, Cites: 19
Gabryel M., Wozniak M., Damasevicius R., An application of differential evolution to positioning queueing systems. (19)
An application of differential evolution to positioning queueing systems
, An application of differential evolution to positioning queueing systems, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 379-390, 2015, Cites: 19
Mleczko W.K., Kapuscinski T., Nowicki R.K., Rough deep belief network - application to incomplete handwritten digits pattern classification. (18)
Rough deep belief network - application to incomplete handwritten digits pattern classification
, Rough deep belief network - application to incomplete handwritten digits pattern classification, Communications in Computer and Information Science, 538, 538, 400-411, 2015, Cites: 18
Gabryel M., Grycuk R., Korytkowski M., Holotyak T., Image indexing and retrieval using GSOM algorithm. (17)
Image indexing and retrieval using GSOM algorithm
, Image indexing and retrieval using GSOM algorithm, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 706-714, 2015, Cites: 17
Zalasinski M., Cpalka K., Er M.J., A new method for the dynamic signature verification based on the stable partitions of the signature. (16)
A new method for the dynamic signature verification based on the stable partitions of the signature
, A new method for the dynamic signature verification based on the stable partitions of the signature, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 161-174, 2015, Cites: 16
Lapa K., Cpalka K., Galushkin A.I., A new interpretability criteria for neuro-fuzzy systems for nonlinear classification. (12)
A new interpretability criteria for neuro-fuzzy systems for nonlinear classification
, A new interpretability criteria for neuro-fuzzy systems for nonlinear classification, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 448-468, 2015, Cites: 12
Korytkowski M., Scherer R., Staszewski P., Woldan P., Bag-of-features image indexing and classification in microsoft SQL server relational database. (10)
Bag-of-features image indexing and classification in microsoft SQL server relational database
, Bag-of-features image indexing and classification in microsoft SQL server relational database, Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015, 478-482, 2015, Cites: 10
Grycuk R., Knop M., Mandal S., Video key frame detection based on SURF algorithm. (10)
Video key frame detection based on SURF algorithm
, Video key frame detection based on SURF algorithm, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 566-576, 2015, Cites: 10
Bartczuk L., Przybyl A., Koprinkova-Hristova P., New method for non-linear correction modelling of dynamic objects with genetic programming. (9)
New method for non-linear correction modelling of dynamic objects with genetic programming
, New method for non-linear correction modelling of dynamic objects with genetic programming, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 318-329, 2015, Cites: 9
Olas T., Mleczko W.K., Nowicki R.K., Wyrzykowski R., Krzyzak A., Adaptation of RBM learning for intel MIC architecture. (7)
Adaptation of RBM learning for intel MIC architecture
, Adaptation of RBM learning for intel MIC architecture, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 90-101, 2015, Cites: 7
Pabiasz S., Starczewski J.T., Marvuglia A., SOM vs FCM vs PCA in 3D face recognition. (6)
SOM vs FCM vs PCA in 3D face recognition
, SOM vs FCM vs PCA in 3D face recognition, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 120-129, 2015, Cites: 6
Knop M., Dobosz P., Neural Video Compression Algorithm. (5)
Neural Video Compression Algorithm
, Neural Video Compression Algorithm, Advances in Intelligent Systems and Computing, 313 AISC, 313 AISC, 59-66, 2015, Cites: 5
Nowicki R.K., Korytkowski M., Nowak B.A., Scherer R., Design methodology for rough neuro-fuzzy classification with missing data. (5)
Design methodology for rough neuro-fuzzy classification with missing data
, Design methodology for rough neuro-fuzzy classification with missing data, Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 1650-1657, 2015, Cites: 5
Bernacki J., Kozierkiewicz-HetmaNska A., The comparison of creating homogeneous and heterogeneous collaborative learning groups in intelligent tutoring systems. (5)
The comparison of creating homogeneous and heterogeneous collaborative learning groups in intelligent tutoring systems
, The comparison of creating homogeneous and heterogeneous collaborative learning groups in intelligent tutoring systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9011, 9011, 46-55, 2015, Cites: 5
Galkowski T., Starczewski A., Fu X., Improvement of the multiple-view learning based on the self-organizing maps. (4)
Improvement of the multiple-view learning based on the self-organizing maps
, Improvement of the multiple-view learning based on the self-organizing maps, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 3-12, 2015, Cites: 4
Galkowski T., Pawlak M., Orthogonal series estimation of regression functions in nonstationary conditions. (3)
Orthogonal series estimation of regression functions in nonstationary conditions
, Orthogonal series estimation of regression functions in nonstationary conditions, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 427-435, 2015, Cites: 3
Ferdowsi S., Voloshynovskiy S., Kostadinov D., Korytkowski M., Scherer R., Secure representation of images using multi-layer compression. (3)
Secure representation of images using multi-layer compression
, Secure representation of images using multi-layer compression, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 696-705, 2015, Cites: 3
Romanowski J., Korytkowski M., Scherer R., Efficient bone detector and geometric descriptor for x-ray imaging. (2)
Efficient bone detector and geometric descriptor for x-ray imaging
, Efficient bone detector and geometric descriptor for x-ray imaging, Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 1337-1342, 2015, Cites: 2
Najgebauer P., Rygal J., Nowak T., Romanowski J., Rutkowski L., Voloshynovskiy S., Scherer R., Fast dictionary matching for content-based image retrieval. (2)
Fast dictionary matching for content-based image retrieval
, Fast dictionary matching for content-based image retrieval, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 747-756, 2015, Cites: 2
Knas M., Cierniak R., Computed Tomography Images Denoising with Markov Random Field Model Parametrized by Prewitt Mask. (2)
Computed Tomography Images Denoising with Markov Random Field Model Parametrized by Prewitt Mask
, Computed Tomography Images Denoising with Markov Random Field Model Parametrized by Prewitt Mask, Advances in Intelligent Systems and Computing, 313 AISC, 313 AISC, 53-58, 2015, Cites: 2
Przybyl A., Szczypta J., Wang L., Optimization of controller structure using evolutionary algorithm. (2)
Optimization of controller structure using evolutionary algorithm
, Optimization of controller structure using evolutionary algorithm, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 261-271, 2015, Cites: 2
Kozierkiewicz-Hetmanska A., Bernacki J., A conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems. (1)
A conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems
, A conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems, Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015, 97-101, 2015, Cites: 1
Kostadinov D., Voloshynovskiy S., Ferdowsi S., Diephuis M., Scherer R., Supervised transform learning for face recognition. (1)
Supervised transform learning for face recognition
, Supervised transform learning for face recognition, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 737-746, 2015, Cites: 1
Szarek A., Korytkowski M., Rutkowski L., Scherer M., Szyprowski J., Kostadinov D., Customization of joint articulations using soft computing methods. (1)
Customization of joint articulations using soft computing methods
, Customization of joint articulations using soft computing methods, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 151-160, 2015, Cites: 1
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, V-VII, 2015, Cites: 0
Kozierkiewicz-Hetmanska A., Bernacki J., Modeling Learning Group's Communication in Intelligent Tutoring Systems. (0)
Modeling Learning Group's Communication in Intelligent Tutoring Systems
, Modeling Learning Group's Communication in Intelligent Tutoring Systems, Proceedings - 2nd European Network Intelligence Conference, ENIC 2015, 140-144, 2015, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, V-VII, 2015, Cites: 0
Knas M., Cierniak R., Rebrova O., An edge detection using 2D Gaussian function in computed tomography. (0)
An edge detection using 2D Gaussian function in computed tomography
, An edge detection using 2D Gaussian function in computed tomography, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 586-593, 2015, Cites: 0
Korytkowski M., Scherer M., Ferdowsi S., Software framework for modular machine learning systems. (0)
Software framework for modular machine learning systems
, Software framework for modular machine learning systems, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 760-767, 2015, Cites: 02014 (43)
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., The CART decision tree for mining data streams. (275)
The CART decision tree for mining data streams
, The CART decision tree for mining data streams, Information Sciences, 266, 266, 1-15, 2014, Cites: 275
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., Decision trees for mining data streams based on the gaussian approximation. (144)
Decision trees for mining data streams based on the gaussian approximation
, Decision trees for mining data streams based on the gaussian approximation, IEEE Transactions on Knowledge and Data Engineering, 26, 26, 108-119, 2014, Cites: 144
Cpalka K., Zalasinski M., On-line signature verification using vertical signature partitioning. (88)
On-line signature verification using vertical signature partitioning
, On-line signature verification using vertical signature partitioning, Expert Systems with Applications, 41, 41, 4170-4180, 2014, Cites: 88
Cpalka K., Zalasinski M., Rutkowski L., New method for the on-line signature verification based on horizontal partitioning. (88)
New method for the on-line signature verification based on horizontal partitioning
, New method for the on-line signature verification based on horizontal partitioning, Pattern Recognition, 47, 47, 2652-2661, 2014, Cites: 88
Cpalka K., Lapa K., Przybyl A., Zalasinski M., A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. (82)
A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects
, A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neurocomputing, 135, 135, 203-217, 2014, Cites: 82
Wozniak M., Kempa W.M., Gabryel M., Nowicki R.K., A finite-buffer queue with a single vacation policy: An analytical study with evolutionary positioning. (46)
A finite-buffer queue with a single vacation policy: An analytical study with evolutionary positioning
, A finite-buffer queue with a single vacation policy: An analytical study with evolutionary positioning, International Journal of Applied Mathematics and Computer Science, 24, 24, 887-900, 2014, Cites: 46
Lapa K., Cpalka K., Wang L., New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. (39)
New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability
, New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 217-232, 2014, Cites: 39
Starczewski J.T., Centroid of triangular and Gaussian type-2 fuzzy sets. (38)
Centroid of triangular and Gaussian type-2 fuzzy sets
, Centroid of triangular and Gaussian type-2 fuzzy sets, Information Sciences, 280, 280, 289-306, 2014, Cites: 38
Zalasinski M., Cpalka K., Hayashi Y., New method for dynamic signature verification based on global features. (36)
New method for dynamic signature verification based on global features
, New method for dynamic signature verification based on global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 231-245, 2014, Cites: 36
Zalasinski M., Cpalka K., Er M.J., New method for dynamic signature verification using hybrid partitioning. (33)
New method for dynamic signature verification using hybrid partitioning
, New method for dynamic signature verification using hybrid partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 216-230, 2014, Cites: 33
Dziwinski P., Bartczuk L., Przybyl A., Avedyan E.D., A new algorithm for identification of significant operating points using swarm intelligence. (33)
A new algorithm for identification of significant operating points using swarm intelligence
, A new algorithm for identification of significant operating points using swarm intelligence, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 349-362, 2014, Cites: 33
Wozniak M., Kempa W.M., Gabryel M., Nowicki R.K., Shao Z., On applying evolutionary computation methods to optimization of vacation cycle costs in finite-buffer queue. (32)
On applying evolutionary computation methods to optimization of vacation cycle costs in finite-buffer queue
, On applying evolutionary computation methods to optimization of vacation cycle costs in finite-buffer queue, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 480-491, 2014, Cites: 32
Grycuk R., Gabryel M., Korytkowski M., Scherer R., Voloshynovskiy S., From single image to list of objects based on edge and blob detection. (32)
From single image to list of objects based on edge and blob detection
, From single image to list of objects based on edge and blob detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 605-615, 2014, Cites: 32
Bilski J., Smolag J., Galushkin A.I., The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. (28)
The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks
, The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 12-21, 2014, Cites: 28
Grycuk R., Gabryel M., Korytkowski M., Scherer R., Content-Based Image Indexing by Data Clustering and Inverse Document Frequency. (26)
Content-Based Image Indexing by Data Clustering and Inverse Document Frequency
, Content-Based Image Indexing by Data Clustering and Inverse Document Frequency, Communications in Computer and Information Science, 424, 424, 374-383, 2014, Cites: 26
Bartczuk L., Przybyl A., Koprinkova-Hristova P., New method for nonlinear fuzzy correction modelling of dynamic objects. (26)
New method for nonlinear fuzzy correction modelling of dynamic objects
, New method for nonlinear fuzzy correction modelling of dynamic objects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 169-180, 2014, Cites: 26
Cierniak R., An analytical iterative statistical algorithm for image reconstruction from projections. (17)
An analytical iterative statistical algorithm for image reconstruction from projections
, An analytical iterative statistical algorithm for image reconstruction from projections, International Journal of Applied Mathematics and Computer Science, 24, 24, 7-17, 2014, Cites: 17
Szczypta J., Lapa K., Shao Z., Aspects of the selection of the structure and parameters of controllers using selected population based algorithms. (17)
Aspects of the selection of the structure and parameters of controllers using selected population based algorithms
, Aspects of the selection of the structure and parameters of controllers using selected population based algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 440-454, 2014, Cites: 17
Duda P., Jaworski M., Pietruczuk L., Rutkowski L., A novel application of Hoeffding's inequality to decision trees construction for data streams. (16)
A novel application of Hoeffding's inequality to decision trees construction for data streams
, A novel application of Hoeffding's inequality to decision trees construction for data streams, Proceedings of the International Joint Conference on Neural Networks, 3324-3330, 2014, Cites: 16
Grycuk R., Gabryel M., Korytkowski M., Romanowski J., Scherer R., Improved digital image segmentation based on stereo vision and mean shift algorithm. (13)
Improved digital image segmentation based on stereo vision and mean shift algorithm
, Improved digital image segmentation based on stereo vision and mean shift algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8384 LNCS, 8384 LNCS, 433-443, 2014, Cites: 13
Szczypta J., Przybyl A., Wang L., Evolutionary approach with multiple quality criteria for controller design. (12)
Evolutionary approach with multiple quality criteria for controller design
, Evolutionary approach with multiple quality criteria for controller design, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 455-467, 2014, Cites: 12
Przybyl A., Er M.J., The idea for the integration of neuro-fuzzy hardware emulators with real-time network. (12)
The idea for the integration of neuro-fuzzy hardware emulators with real-time network
, The idea for the integration of neuro-fuzzy hardware emulators with real-time network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 279-294, 2014, Cites: 12
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., The Parzen kernel approach to learning in non-stationary environment. (12)
The Parzen kernel approach to learning in non-stationary environment
, The Parzen kernel approach to learning in non-stationary environment, Proceedings of the International Joint Conference on Neural Networks, 3319-3323, 2014, Cites: 12
Nowicki R.K., Nowak B.A., Starczewski J.T., Cpalka K., The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features. (10)
The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features
, The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features, Proceedings of the International Joint Conference on Neural Networks, 3759-3766, 2014, Cites: 10
Nowak B.A., Nowicki R.K., Starczewski J.T., Marvuglia A., The learning of neuro-fuzzy classifier with fuzzy rough sets for imprecise datasets. (10)
The learning of neuro-fuzzy classifier with fuzzy rough sets for imprecise datasets
, The learning of neuro-fuzzy classifier with fuzzy rough sets for imprecise datasets, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 256-266, 2014, Cites: 10
Galkowski T., Pawlak M., Nonparametric function fitting in the presence of nonstationary noise. (9)
Nonparametric function fitting in the presence of nonstationary noise
, Nonparametric function fitting in the presence of nonstationary noise, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 531-538, 2014, Cites: 9
Starczewski J.T., Nowicki R.K., Nowak B.A., Genetic fuzzy classifier with fuzzy rough sets for imprecise data. (8)
Genetic fuzzy classifier with fuzzy rough sets for imprecise data
, Genetic fuzzy classifier with fuzzy rough sets for imprecise data, IEEE International Conference on Fuzzy Systems, 1382-1389, 2014, Cites: 8
Pabiasz S., Starczewski J.T., Marvuglia A., A new three-dimensional facial landmarks in recognition. (7)
A new three-dimensional facial landmarks in recognition
, A new three-dimensional facial landmarks in recognition, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 179-186, 2014, Cites: 7
Knop M., Cierniak R., Shah N., Video compression algorithm based on neural network structures. (7)
Video compression algorithm based on neural network structures
, Video compression algorithm based on neural network structures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 715-724, 2014, Cites: 7
Galkowski T., Pawlak M., Nonparametric extension of regression functions outside domain. (6)
Nonparametric extension of regression functions outside domain
, Nonparametric extension of regression functions outside domain, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 518-530, 2014, Cites: 6
Bernacki J., Kozierkiewicz-Hetmanska A., Creating collaborative learning groups in intelligent tutoring systems. (5)
Creating collaborative learning groups in intelligent tutoring systems
, Creating collaborative learning groups in intelligent tutoring systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8671, 8671, 184-193, 2014, Cites: 5
Najgebauer P., Nowak T., Romanowski J., Gabryel M., Korytkowski M., Scherer R., Content-based image retrieval by dictionary of local feature descriptors. (5)
Content-based image retrieval by dictionary of local feature descriptors
, Content-based image retrieval by dictionary of local feature descriptors, Proceedings of the International Joint Conference on Neural Networks, 512-517, 2014, Cites: 5
Nowak T., Najgebauer P., Romanowski J., Gabryel M., Korytkowski M., Scherer R., Kostadinov D., Spatial keypoint representation for visual object retrieval. (4)
Spatial keypoint representation for visual object retrieval
, Spatial keypoint representation for visual object retrieval, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 639-650, 2014, Cites: 4
Lorent A., Cierniak R., Regularized Image Reconstruction from Projections Method. (3)
Regularized Image Reconstruction from Projections Method
, Regularized Image Reconstruction from Projections Method, Proceedings - 2014 International Conference on Engineering and Telecommunication, EnT 2014, 82-86, 2014, Cites: 3
Nowak T., Gabryel M., Korytkowski M., Scherer R., Comparing images based on histograms of local interest points. (3)
Comparing images based on histograms of local interest points
, Comparing images based on histograms of local interest points, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8384 LNCS, 8384 LNCS, 423-432, 2014, Cites: 3
Cierniak R., Knas M., Ultrafast iterative model-based statistical 3d reconstruction algorithm for x-ray computed tomography. (2)
Ultrafast iterative model-based statistical 3d reconstruction algorithm for x-ray computed tomography
, Ultrafast iterative model-based statistical 3d reconstruction algorithm for x-ray computed tomography, Advances in Intelligent Systems and Computing, 283, 283, 187-196, 2014, Cites: 2
Kostadinov D., Voloshynovskiy S., Ferdowsi S., Diephuis M., Scherer R., Robust face recognition by group sparse representation that uses samples from list of subjects. (2)
Robust face recognition by group sparse representation that uses samples from list of subjects
, Robust face recognition by group sparse representation that uses samples from list of subjects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 616-626, 2014, Cites: 2
Ferdowsi S., Voloshynovskiy S., Gabryel M., Korytkowski M., Multi-class classification: A coding based space partitioning. (2)
Multi-class classification: A coding based space partitioning
, Multi-class classification: A coding based space partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 593-604, 2014, Cites: 2
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (1)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 2014, Cites: 1
Rygal J., Romanowski J., Scherer R., Ferdowsi S., Novel algorithm for translation from image content to semantic form. (1)
Novel algorithm for translation from image content to semantic form
, Novel algorithm for translation from image content to semantic form, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 783-792, 2014, Cites: 1
Lorent A., Cierniak R., Dobosz P., Rebrova O., A new image reconstruction from projections algorithm. (1)
A new image reconstruction from projections algorithm
, A new image reconstruction from projections algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 725-732, 2014, Cites: 1
Knas M., Cierniak R., A modification of the parallel spline interpolation algorithms. (0)
A modification of the parallel spline interpolation algorithms
, A modification of the parallel spline interpolation algorithms, Advances in Intelligent Systems and Computing, 233, 233, 185-190, 2014, Cites: 0
Dobosz P., Cierniak R., Using of EM algorithm to image reconstruction problem with tomography noises. (0)
Using of EM algorithm to image reconstruction problem with tomography noises
, Using of EM algorithm to image reconstruction problem with tomography noises, Advances in Intelligent Systems and Computing, 233, 233, 37-43, 2014, Cites: 02013 (25)
Rutkowski L., Pietruczuk L., Duda P., Jaworski M., Decision trees for mining data streams based on the mcdiarmid's bound. (169)
Decision trees for mining data streams based on the mcdiarmid's bound
, Decision trees for mining data streams based on the mcdiarmid's bound, IEEE Transactions on Knowledge and Data Engineering, 25, 25, 1272-1279, 2013, Cites: 169
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On design of flexible neuro-fuzzy systems for nonlinear modelling. (67)
On design of flexible neuro-fuzzy systems for nonlinear modelling
, On design of flexible neuro-fuzzy systems for nonlinear modelling, International Journal of General Systems, 42, 42, 706-720, 2013, Cites: 67
Lapa K., Przybyl A., Cpalka K., A new approach to designing interpretable models of dynamic systems. (46)
A new approach to designing interpretable models of dynamic systems
, A new approach to designing interpretable models of dynamic systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 523-534, 2013, Cites: 46
Zalasinski M., Cpalka K., Novel algorithm for the on-line signature verification using selected discretization points groups. (40)
Novel algorithm for the on-line signature verification using selected discretization points groups
, Novel algorithm for the on-line signature verification using selected discretization points groups, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 493-502, 2013, Cites: 40
Zalasinski M., Cpalka K., New approach for the on-line signature verification based on method of horizontal partitioning. (39)
New approach for the on-line signature verification based on method of horizontal partitioning
, New approach for the on-line signature verification based on method of horizontal partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 342-350, 2013, Cites: 39
Lapa K., Zalasinski M., Cpalka K., A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling. (38)
A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling
, A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 329-344, 2013, Cites: 38
Zalasinski M., Lapa K., Cpalka K., New algorithm for evolutionary selection of the dynamic signature global features. (38)
New algorithm for evolutionary selection of the dynamic signature global features
, New algorithm for evolutionary selection of the dynamic signature global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 113-121, 2013, Cites: 38
Szczypta J., Przybyl A., Cpalka K., Some aspects of evolutionary designing optimal controllers. (36)
Some aspects of evolutionary designing optimal controllers
, Some aspects of evolutionary designing optimal controllers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 91-100, 2013, Cites: 36
Gabryel M., Nowicki R.K., Wozniak M., Kempa W.M., Genetic cost optimization of the GI/M/1/N finite-buffer queue with a single vacation policy. (29)
Genetic cost optimization of the GI/M/1/N finite-buffer queue with a single vacation policy
, Genetic cost optimization of the GI/M/1/N finite-buffer queue with a single vacation policy, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 12-23, 2013, Cites: 29
Wozniak M., Marszalek Z., Gabryel M., Nowicki R.K., Modified merge sort algorithm for large scale data sets. (28)
Modified merge sort algorithm for large scale data sets
, Modified merge sort algorithm for large scale data sets, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 612-622, 2013, Cites: 28
Bilski J., Smolag J., Parallel approach to learning of the recurrent Jordan neural network. (28)
Parallel approach to learning of the recurrent Jordan neural network
, Parallel approach to learning of the recurrent Jordan neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 32-40, 2013, Cites: 28
Gabryel M., Korytkowski M., Scherer R., Rutkowski L., Object detection by simple fuzzy classifiers generated by boosting. (24)
Object detection by simple fuzzy classifiers generated by boosting
, Object detection by simple fuzzy classifiers generated by boosting, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 540-547, 2013, Cites: 24
Pietruczuk L., Duda P., Jaworski M., Adaptation of decision trees for handling concept drift. (22)
Adaptation of decision trees for handling concept drift
, Adaptation of decision trees for handling concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 459-473, 2013, Cites: 22
Bartczuk L., Przybyl A., Dziwinski P., Hybrid state variables - Fuzzy logic modelling of nonlinear objects. (19)
Hybrid state variables - Fuzzy logic modelling of nonlinear objects
, Hybrid state variables - Fuzzy logic modelling of nonlinear objects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 227-234, 2013, Cites: 19
Starczewski J.T., Advanced concepts in fuzzy logic and systems with membership uncertainty. (19)
Advanced concepts in fuzzy logic and systems with membership uncertainty
, Advanced concepts in fuzzy logic and systems with membership uncertainty, Studies in Fuzziness and Soft Computing, 284, 284, 1-319, 2013, Cites: 19
Galkowski T., Kernel estimation of regression functions in the boundary regions. (16)
Kernel estimation of regression functions in the boundary regions
, Kernel estimation of regression functions in the boundary regions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 158-166, 2013, Cites: 16
Nowak B.A., Nowicki R.K., Mleczko W.K., A new method of improving classification accuracy of decision tree in case of incomplete samples. (8)
A new method of improving classification accuracy of decision tree in case of incomplete samples
, A new method of improving classification accuracy of decision tree in case of incomplete samples, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 448-458, 2013, Cites: 8
Pabiasz S., Starczewski J.T., A new approach to determine three-dimensional facial landmarks. (7)
A new approach to determine three-dimensional facial landmarks
, A new approach to determine three-dimensional facial landmarks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 286-296, 2013, Cites: 7
Cierniak R., Knop M., Video compression algorithm based on neural networks. (5)
Video compression algorithm based on neural networks
, Video compression algorithm based on neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 524-531, 2013, Cites: 5
Najgebauer P., Nowak T., Romanowski J., Rygal J., Korytkowski M., Representation of edge detection results based on graph theory. (4)
Representation of edge detection results based on graph theory
, Representation of edge detection results based on graph theory, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 588-601, 2013, Cites: 4
Starczewski A., A clustering method based on the modified RS validity index. (4)
A clustering method based on the modified RS validity index
, A clustering method based on the modified RS validity index, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 242-250, 2013, Cites: 4
Rygal J., Najgebauer P., Romanowski J., Scherer R., Extraction of objects from images using density of edges as basis for GrabCut algorithm. (3)
Extraction of objects from images using density of edges as basis for GrabCut algorithm
, Extraction of objects from images using density of edges as basis for GrabCut algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 613-623, 2013, Cites: 3
Nowak T., Najgebauer P., Rygal J., Scherer R., A novel graph-based descriptor for object matching. (2)
A novel graph-based descriptor for object matching
, A novel graph-based descriptor for object matching, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 602-612, 2013, Cites: 2
Romanowski J., Nowak T., Najgebauer P., Litwinski S., Improved X-ray edge detection based on background extraction algorithm. (1)
Improved X-ray edge detection based on background extraction algorithm
, Improved X-ray edge detection based on background extraction algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 309-319, 2013, Cites: 1
Lorent A., Knas M., Dobosz P., Neuronal model-based image reconstruction from projections method. (0)
Neuronal model-based image reconstruction from projections method
, Neuronal model-based image reconstruction from projections method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 580-587, 2013, Cites: 02012 (43)
Rutkowski L., Przybyl A., Cpalka K., Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. (91)
Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation
, Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation, IEEE Transactions on Industrial Electronics, 59, 59, 1238-1247, 2012, Cites: 91
Zalasinski M., Cpalka K., Novel algorithm for the on-line signature verification. (45)
Novel algorithm for the on-line signature verification
, Novel algorithm for the on-line signature verification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 362-367, 2012, Cites: 45
Przybyl A., Cpalka K., A new method to construct of interpretable models of dynamic systems. (43)
A new method to construct of interpretable models of dynamic systems
, A new method to construct of interpretable models of dynamic systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 697-705, 2012, Cites: 43
Bartczuk L., Dziwinski P., Starczewski J.T., A new method for dealing with unbalanced linguistic term set. (27)
A new method for dealing with unbalanced linguistic term set
, A new method for dealing with unbalanced linguistic term set, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 207-212, 2012, Cites: 27
Bilski J., Smolag J., Parallel realisation of the recurrent multi layer perceptron learning. (26)
Parallel realisation of the recurrent multi layer perceptron learning
, Parallel realisation of the recurrent multi layer perceptron learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 12-20, 2012, Cites: 26
Gabryel M., Wozniak M., K. Nowicki R., Creating learning sets for control systems using an evolutionary method. (26)
Creating learning sets for control systems using an evolutionary method
, Creating learning sets for control systems using an evolutionary method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7269 LNCS, 7269 LNCS, 206-213, 2012, Cites: 26
Dziwinski P., Bartczuk L., Starczewski J.T., Fully controllable ant colony system for text data clustering. (26)
Fully controllable ant colony system for text data clustering
, Fully controllable ant colony system for text data clustering, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7269 LNCS, 7269 LNCS, 199-205, 2012, Cites: 26
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Application of neural networks in assessing changes around implant after total hip arthroplasty. (23)
Application of neural networks in assessing changes around implant after total hip arthroplasty
, Application of neural networks in assessing changes around implant after total hip arthroplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 335-340, 2012, Cites: 23
Duda P., Jaworski M., Pietruczuk L., On pre-processing algorithms for data stream. (18)
On pre-processing algorithms for data stream
, On pre-processing algorithms for data stream, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 56-63, 2012, Cites: 18
Pietruczuk L., Duda P., Jaworski M., A new fuzzy classifier for data streams. (18)
A new fuzzy classifier for data streams
, A new fuzzy classifier for data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 318-324, 2012, Cites: 18
Jaworski M., Duda P., Pietruczuk L., On fuzzy clustering of data streams with concept drift. (18)
On fuzzy clustering of data streams with concept drift
, On fuzzy clustering of data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 82-91, 2012, Cites: 18
Duda P., Hayashi Y., Jaworski M., On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment. (17)
On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment
, On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 47-54, 2012, Cites: 17
Jaworski M., Pietruczuk L., Duda P., On resources optimization in fuzzy clustering of data streams. (17)
On resources optimization in fuzzy clustering of data streams
, On resources optimization in fuzzy clustering of data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 92-99, 2012, Cites: 17
Er M.J., Duda P., On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. (14)
On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment
, On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 443-450, 2012, Cites: 14
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Forecasting wear of head and acetabulum in hip joint implant. (14)
Forecasting wear of head and acetabulum in hip joint implant
, Forecasting wear of head and acetabulum in hip joint implant, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 341-346, 2012, Cites: 14
Scherer R., Preface. (10)
Preface
, Preface, Studies in Fuzziness and Soft Computing, 288, 288, 2012, Cites: 10
Scherer R., Takagi-Sugeno fuzzy systems. (6)
Takagi-Sugeno fuzzy systems
, Takagi-Sugeno fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 73-79, 2012, Cites: 6
Rygal J., Najgebauer P., Nowak T., Romanowski J., Gabryel M., Scherer R., Properties and structure of fast text search engine in context of semantic image analysis. (5)
Properties and structure of fast text search engine in context of semantic image analysis
, Properties and structure of fast text search engine in context of semantic image analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 592-599, 2012, Cites: 5
Cierniak R., Lorent A., A neuronal approach to the statistical image reconstruction from projections problem. (5)
A neuronal approach to the statistical image reconstruction from projections problem
, A neuronal approach to the statistical image reconstruction from projections problem, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7653 LNAI, 7653 LNAI, 344-353, 2012, Cites: 5
Krupa T., Majewski P., Kowalczyk B., Turek W., On-demand web search using browser-based volunteer computing. (5)
On-demand web search using browser-based volunteer computing
, On-demand web search using browser-based volunteer computing, Proceedings - 2012 6th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2012, 184-190, 2012, Cites: 5
Pabiasz S., Starczewski J.T., Meshes vs. depth maps in face recognition systems. (5)
Meshes vs. depth maps in face recognition systems
, Meshes vs. depth maps in face recognition systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 567-573, 2012, Cites: 5
Najgebauer P., Nowak T., Romanowski J., Rygal J., Korytkowski M., Scherer R., Novel method for parasite detection in microscopic samples. (4)
Novel method for parasite detection in microscopic samples
, Novel method for parasite detection in microscopic samples, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 551-558, 2012, Cites: 4
Nowak B.A., Nowicki R.K., Learning in rough-neuro-fuzzy system for data with missing values. (3)
Learning in rough-neuro-fuzzy system for data with missing values
, Learning in rough-neuro-fuzzy system for data with missing values, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 501-510, 2012, Cites: 3
Starczewski A., A new hierarchical clustering algorithm. (3)
A new hierarchical clustering algorithm
, A new hierarchical clustering algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 175-180, 2012, Cites: 3
Starczewski A., A cluster validity index for hard clustering. (2)
A cluster validity index for hard clustering
, A cluster validity index for hard clustering, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 168-174, 2012, Cites: 2
Rutkowski L., Cpalka K., Nowicki R., Pokropinska A., Scherer R., Neuro-fuzzy systems. (2)
Neuro-fuzzy systems
, Neuro-fuzzy systems, Computational Complexity: Theory, Techniques, and Applications, 9781461418009, 9781461418009, 2069-2081, 2012, Cites: 2
Dobosz P., An analytical approach to the image reconstruction problem using em algorithm. (1)
An analytical approach to the image reconstruction problem using em algorithm
, An analytical approach to the image reconstruction problem using em algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 495-500, 2012, Cites: 1
Duda P., Zurada J.M., On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise. (1)
On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise
, On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 435-442, 2012, Cites: 1
Scherer R., Relational modular fuzzy systems. (1)
Relational modular fuzzy systems
, Relational modular fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 39-50, 2012, Cites: 1
Galkowski T., Starczewski A., An application of the self-organizing map to multiple view unsupervised learning. (1)
An application of the self-organizing map to multiple view unsupervised learning
, An application of the self-organizing map to multiple view unsupervised learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 181-187, 2012, Cites: 1
Scherer R., Ensemble techniques. (0)
Ensemble techniques
, Ensemble techniques, Studies in Fuzziness and Soft Computing, 288, 288, 29-37, 2012, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 2012, Cites: 0
Duda P., Korytkowski M., On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise. (0)
On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise
, On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 55-62, 2012, Cites: 0
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (0)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7269 LNCS, 7269 LNCS, 2012, Cites: 0
Er M.J., Duda P., On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment. (0)
On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment
, On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 39-46, 2012, Cites: 0
Duda P., Hayashi Y., On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment. (0)
On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment
, On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 427-434, 2012, Cites: 0
Scherer R., Introduction to fuzzy systems. (0)
Introduction to fuzzy systems
, Introduction to fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 7-28, 2012, Cites: 0
Scherer R., Ensembles of the Mamdani fuzzy systems. (0)
Ensembles of the Mamdani fuzzy systems
, Ensembles of the Mamdani fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 51-59, 2012, Cites: 0
Scherer R., Concluding remarks and challenges for future research. (0)
Concluding remarks and challenges for future research
, Concluding remarks and challenges for future research, Studies in Fuzziness and Soft Computing, 288, 288, 129-130, 2012, Cites: 0
Scherer R., Introduction. (0)
Introduction
, Introduction, Studies in Fuzziness and Soft Computing, 288, 288, 1-5, 2012, Cites: 0
Scherer R., Rough-neuro-fuzzy ensembles for classification with missing data. (0)
Rough-neuro-fuzzy ensembles for classification with missing data
, Rough-neuro-fuzzy ensembles for classification with missing data, Studies in Fuzziness and Soft Computing, 288, 288, 81-127, 2012, Cites: 0
Scherer R., Logical type fuzzy systems. (0)
Logical type fuzzy systems
, Logical type fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 61-71, 2012, Cites: 0
Jaworski M., Gabryel M., On learning in a time-varying environment by using a probabilistic neural network and the recursive least squares method. (0)
On learning in a time-varying environment by using a probabilistic neural network and the recursive least squares method
, On learning in a time-varying environment by using a probabilistic neural network and the recursive least squares method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 99-110, 2012, Cites: 02011 (7)
Cierniak R., X-ray computed tomography in biomedical engineering. (53)
X-ray computed tomography in biomedical engineering
, X-ray computed tomography in biomedical engineering, X-Ray Computed Tomography in Biomedical Engineering, 1-319, 2011, Cites: 53
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., AdaBoost ensemble of DCOG rough-neuro-fuzzy systems. (25)
AdaBoost ensemble of DCOG rough-neuro-fuzzy systems
, AdaBoost ensemble of DCOG rough-neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6922 LNAI, 6922 LNAI, 62-71, 2011, Cites: 25
Scherer R., An ensemble of logical-type neuro-fuzzy systems. (11)
An ensemble of logical-type neuro-fuzzy systems
, An ensemble of logical-type neuro-fuzzy systems, Expert Systems with Applications, 38, 38, 13115-13120, 2011, Cites: 11
Cierniak R., Neural network algorithm for image reconstruction using the "grid-friendly" projections. (9)
Neural network algorithm for image reconstruction using the "grid-friendly" projections
, Neural network algorithm for image reconstruction using the "grid-friendly" projections, Australasian Physical and Engineering Sciences in Medicine, 34, 34, 375-389, 2011, Cites: 9
Korytkowski M., Rutkowski L., Scherer R., Rule base normalization in Takagi-Sugeno ensemble. (3)
Rule base normalization in Takagi-Sugeno ensemble
, Rule base normalization in Takagi-Sugeno ensemble, IEEE SSCI 2011 - Symposium Series on Computational Intelligence - HIMA 2011: 2011 IEEE Workshop on Hybrid Intelligent Models and Applications, 1-5, 2011, Cites: 3
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On designing of flexible neuro-fuzzy systems for nonlinear modelling. (2)
On designing of flexible neuro-fuzzy systems for nonlinear modelling
, On designing of flexible neuro-fuzzy systems for nonlinear modelling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6743 LNAI, 6743 LNAI, 147-154, 2011, Cites: 2
Rutkowski L., Foreword. (0)
Foreword
, Foreword, Intelligent Systems Reference Library, 6, 6, 2011, Cites: 02010 (28)
Li X., Er M.J., Lim B.S., Zhou J.H., Gan O.P., Rutkowski L., Fuzzy regression modeling for tool performance prediction and degradation detection. (67)
Fuzzy regression modeling for tool performance prediction and degradation detection
, Fuzzy regression modeling for tool performance prediction and degradation detection, International Journal of Neural Systems, 20, 20, 405-419, 2010, Cites: 67
Rutkowski L., Przybyl A., Cpalka K., Er M.J., Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. (54)
Online speed profile generation for industrial machine tool based on neuro-fuzzy approach
, Online speed profile generation for industrial machine tool based on neuro-fuzzy approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 645-650, 2010, Cites: 54
Nowicki R.K., On classification with missing data using rough-neuro-fuzzy systems. (36)
On classification with missing data using rough-neuro-fuzzy systems
, On classification with missing data using rough-neuro-fuzzy systems, International Journal of Applied Mathematics and Computer Science, 20, 20, 55-67, 2010, Cites: 36
Scherer R., Designing boosting ensemble of relational fuzzy systems. (32)
Designing boosting ensemble of relational fuzzy systems
, Designing boosting ensemble of relational fuzzy systems, International Journal of Neural Systems, 20, 20, 381-388, 2010, Cites: 32
Bilski J., Smolag J., Parallel realisation of the recurrent Elman neural network learning. (25)
Parallel realisation of the recurrent Elman neural network learning
, Parallel realisation of the recurrent Elman neural network learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 19-25, 2010, Cites: 25
Scherer R., Neuro-fuzzy systems with relation matrix. (23)
Neuro-fuzzy systems with relation matrix
, Neuro-fuzzy systems with relation matrix, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 210-215, 2010, Cites: 23
Starczewski J.T., Bartczuk L., Dziwinski P., Marvuglia A., Learning methods for type-2 FLS based on FCM. (20)
Learning methods for type-2 FLS based on FCM
, Learning methods for type-2 FLS based on FCM, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 224-231, 2010, Cites: 20
PrzybyL A., Smolag J., Kimla P., Distributed control system based on real time ethernet for computer numerical controlled machine tool. (16)
Distributed control system based on real time ethernet for computer numerical controlled machine tool
, Distributed control system based on real time ethernet for computer numerical controlled machine tool, Przeglad Elektrotechniczny, 86, 86, 342-346, 2010, Cites: 16
Bartczuk L., Dziwinski P., Starczewski J.T., New method for generation type-2 fuzzy partition for FDT. (16)
New method for generation type-2 fuzzy partition for FDT
, New method for generation type-2 fuzzy partition for FDT, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 275-280, 2010, Cites: 16
Cierniak R., A three-dimentional neural network based approach to the image reconstruction from projections problem. (14)
A three-dimentional neural network based approach to the image reconstruction from projections problem
, A three-dimentional neural network based approach to the image reconstruction from projections problem, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 505-514, 2010, Cites: 14
Dziwinski P., Starczewski J.T., Bartczuk L., New linguistic hedges in construction of interval type-2 FLS. (14)
New linguistic hedges in construction of interval type-2 FLS
, New linguistic hedges in construction of interval type-2 FLS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 445-450, 2010, Cites: 14
Starczewski J.T., General type-2 FLS with uncertainty generated by fuzzy rough sets. (7)
General type-2 FLS with uncertainty generated by fuzzy rough sets
, General type-2 FLS with uncertainty generated by fuzzy rough sets, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010, Cites: 7
Nowicki R.K., Starczewski J.T., On non-singleton fuzzification with DCOG defuzzification. (7)
On non-singleton fuzzification with DCOG defuzzification
, On non-singleton fuzzification with DCOG defuzzification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 168-174, 2010, Cites: 7
Du J., Er M.J., Rutkowski L., Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach. (6)
Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach
, Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 58-65, 2010, Cites: 6
Korytkowski M., Scherer R., Negative correlation learning of neuro-fuzzy system ensembles. (6)
Negative correlation learning of neuro-fuzzy system ensembles
, Negative correlation learning of neuro-fuzzy system ensembles, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 114-119, 2010, Cites: 6
Korytkowski M., Nowicki R.K., Scherer R., Rutkowski L., MICOG defuzzification rough-neuro-fuzzy system ensemble. (6)
MICOG defuzzification rough-neuro-fuzzy system ensemble
, MICOG defuzzification rough-neuro-fuzzy system ensemble, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010, Cites: 6
Cierniak R., A statistical appraoch to image reconstruction from projections problem using recurrent neural network. (5)
A statistical appraoch to image reconstruction from projections problem using recurrent neural network
, A statistical appraoch to image reconstruction from projections problem using recurrent neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6353 LNCS, 6353 LNCS, 138-141, 2010, Cites: 5
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (5)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 2010, Cites: 5
Gabryel M., Rutkowski L., Evolutionary designing of logic-type fuzzy systems. (5)
Evolutionary designing of logic-type fuzzy systems
, Evolutionary designing of logic-type fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 143-148, 2010, Cites: 5
Du J., Er M.J., Rutkowski L., An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting. (3)
An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting
, An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 49-57, 2010, Cites: 3
Scherer R., Starczewski J.T., Relational type-2 interval fuzzy systems. (3)
Relational type-2 interval fuzzy systems
, Relational type-2 interval fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6067 LNCS, 6067 LNCS, 360-368, 2010, Cites: 3
Cierniak R., A statistical tailored image reconstruction from projections method. (3)
A statistical tailored image reconstruction from projections method
, A statistical tailored image reconstruction from projections method, Smart Innovation, Systems and Technologies, 4, 4, 181-190, 2010, Cites: 3
Cierniak R., A neural network optimization-based method of image reconstruction from projections. (2)
A neural network optimization-based method of image reconstruction from projections
, A neural network optimization-based method of image reconstruction from projections, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5990 LNAI, 5990 LNAI, 241-250, 2010, Cites: 2
Korytkowski M., Scherer R., Modular neuro-fuzzy systems based on generalized parametric triangular norms. (2)
Modular neuro-fuzzy systems based on generalized parametric triangular norms
, Modular neuro-fuzzy systems based on generalized parametric triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6067 LNCS, 6067 LNCS, 332-339, 2010, Cites: 2
Gabryel M., Korytkowski M., Pokropinska A., Scherer R., Drozda S., Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms. (1)
Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms
, Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 74-79, 2010, Cites: 1
Korytkowski M., Rutkowski L., Scherer R., Szarek A., Neural network-based assessment of femur stress after hip joint alloplasty. (0)
Neural network-based assessment of femur stress after hip joint alloplasty
, Neural network-based assessment of femur stress after hip joint alloplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 621-626, 2010, Cites: 0
Cpalka K., Rutkowski L., Er M.J., On automatic design of neuro-fuzzy systems. (0)
On automatic design of neuro-fuzzy systems
, On automatic design of neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 43-48, 2010, Cites: 0
Liu F., Er M.J., Rutkowski L., An online approach towards self-generating fuzzy neural networks with applications. (0)
An online approach towards self-generating fuzzy neural networks with applications
, An online approach towards self-generating fuzzy neural networks with applications, Proceedings of the International Joint Conference on Neural Networks, 2010, Cites: 02009 (12)
Starczewski J.T., Efficient triangular type-2 fuzzy logic systems. (73)
Efficient triangular type-2 fuzzy logic systems
, Efficient triangular type-2 fuzzy logic systems, International Journal of Approximate Reasoning, 50, 50, 799-811, 2009, Cites: 73
Cpalka K., On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. (67)
On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification
, On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification, Nonlinear Analysis, Theory, Methods and Applications, 71, 71, 2009, Cites: 67
Nowicki R., Rough neuro-fuzzy structures for classification with missing data. (62)
Rough neuro-fuzzy structures for classification with missing data
, Rough neuro-fuzzy structures for classification with missing data, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39, 39, 1334-1347, 2009, Cites: 62
Cpalka K., A new method for design and reduction of neuro-fuzzy classification systems. (61)
A new method for design and reduction of neuro-fuzzy classification systems
, A new method for design and reduction of neuro-fuzzy classification systems, IEEE Transactions on Neural Networks, 20, 20, 701-714, 2009, Cites: 61
Cierniak R., New neural network algorithm for image reconstruction from fan-beam projections. (47)
New neural network algorithm for image reconstruction from fan-beam projections
, New neural network algorithm for image reconstruction from fan-beam projections, Neurocomputing, 72, 72, 3238-3244, 2009, Cites: 47
Starczewski J.T., Extended triangular norms. (34)
Extended triangular norms
, Extended triangular norms, Information Sciences, 179, 179, 742-757, 2009, Cites: 34
Scherer R., Neuro-fuzzy relational systems for nonlinear approximation and prediction. (29)
Neuro-fuzzy relational systems for nonlinear approximation and prediction
, Neuro-fuzzy relational systems for nonlinear approximation and prediction, Nonlinear Analysis, Theory, Methods and Applications, 71, 71, 2009, Cites: 29
Korytkowski M., Nowicki R., Scherer R., Neuro-fuzzy rough classifier ensemble. (27)
Neuro-fuzzy rough classifier ensemble
, Neuro-fuzzy rough classifier ensemble, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5768 LNCS, 5768 LNCS, 817-823, 2009, Cites: 27
Bartczuk L., Rutkowska D., Medical diagnosis with type-2 fuzzy decision trees. (24)
Medical diagnosis with type-2 fuzzy decision trees
, Medical diagnosis with type-2 fuzzy decision trees, Advances in Intelligent and Soft Computing, 65, 65, 11-21, 2009, Cites: 24
Nowicki R., Nonlinear modelling and classification based on the MICOG defuzzification. (16)
Nonlinear modelling and classification based on the MICOG defuzzification
, Nonlinear modelling and classification based on the MICOG defuzzification, Nonlinear Analysis, Theory, Methods and Applications, 71, 71, 2009, Cites: 16
Starczewski J.T., A type-1 approximation of interval type-2 FLS. (7)
A type-1 approximation of interval type-2 FLS
, A type-1 approximation of interval type-2 FLS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5571 LNAI, 5571 LNAI, 287-294, 2009, Cites: 7
Cpalka K., Rebrova O., Rutkowski L., A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis. (2)
A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis
, A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5769 LNCS, 5769 LNCS, 435-444, 2009, Cites: 22008 (23)
Rutkowski L., Computational intelligence: Methods and techniques. (309)
Computational intelligence: Methods and techniques
, Computational intelligence: Methods and techniques, Computational Intelligence: Methods and Techniques, 1-514, 2008, Cites: 309
Korytkowski M., Rutkowski L., Scherer R., From ensemble of fuzzy classifiers to single fuzzy rule base classifier. (62)
From ensemble of fuzzy classifiers to single fuzzy rule base classifier
, From ensemble of fuzzy classifiers to single fuzzy rule base classifier, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 265-272, 2008, Cites: 62
Nowicki R., On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data. (45)
On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data
, On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data, IEEE Transactions on Knowledge and Data Engineering, 20, 20, 1239-1253, 2008, Cites: 45
Cierniak R., A new approach to image reconstruction from projections using a recurrent neural network. (43)
A new approach to image reconstruction from projections using a recurrent neural network
, A new approach to image reconstruction from projections using a recurrent neural network, International Journal of Applied Mathematics and Computer Science, 18, 18, 147-157, 2008, Cites: 43
Cierniak R., A 2D approach to tomographic image reconstruction using a Hopfield-type neural network. (35)
A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
, A 2D approach to tomographic image reconstruction using a Hopfield-type neural network, Artificial Intelligence in Medicine, 43, 43, 113-125, 2008, Cites: 35
Bilski J., Smolag J., Parallel realisation of the recurrent RTRN neural network learning. (28)
Parallel realisation of the recurrent RTRN neural network learning
, Parallel realisation of the recurrent RTRN neural network learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 11-16, 2008, Cites: 28
Starczewski J., Scherer R., Korytkowski M., Nowicki R., Modular type-2 neuro-fuzzy systems. (21)
Modular type-2 neuro-fuzzy systems
, Modular type-2 neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4967 LNCS, 4967 LNCS, 570-578, 2008, Cites: 21
Bartczuk L., Rutkowska D., Type-2 fuzzy decision trees. (20)
Type-2 fuzzy decision trees
, Type-2 fuzzy decision trees, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 197-206, 2008, Cites: 20
Korytkowski M., Nowicki R., Scherer R., Rutkowski L., Ensemble of rough-neuro-fuzzy systems for classification with missing features. (14)
Ensemble of rough-neuro-fuzzy systems for classification with missing features
, Ensemble of rough-neuro-fuzzy systems for classification with missing features, IEEE International Conference on Fuzzy Systems, 1745-1750, 2008, Cites: 14
Dziwinski P., Rutkowska D., Ant focused crawling algorithm. (14)
Ant focused crawling algorithm
, Ant focused crawling algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 1018-1028, 2008, Cites: 14
Korytkowski M., Gabryel M., Rutkowski L., Drozda S., Evolutionary methods to create interpretable modular system. (13)
Evolutionary methods to create interpretable modular system
, Evolutionary methods to create interpretable modular system, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 405-413, 2008, Cites: 13
Cierniak R., A novel approach to image reconstruction problem from fan-beam projections using recurrent neural network. (12)
A novel approach to image reconstruction problem from fan-beam projections using recurrent neural network
, A novel approach to image reconstruction problem from fan-beam projections using recurrent neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 752-761, 2008, Cites: 12
Starczewski J.T., On defuzzification of interval type-2 fuzzy sets. (11)
On defuzzification of interval type-2 fuzzy sets
, On defuzzification of interval type-2 fuzzy sets, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 333-340, 2008, Cites: 11
Cpalka K., Rutkowski L., Evolutionary learning of flexible neuro-fuzzy systems. (10)
Evolutionary learning of flexible neuro-fuzzy systems
, Evolutionary learning of flexible neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 969-975, 2008, Cites: 10
Gabryel M., Rutkowski L., Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm. (10)
Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm
, Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 398-404, 2008, Cites: 10
Jelonkiewicz J., Przybyl A., Accuracy improvement of neural network state variable estimator in induction motor drive. (5)
Accuracy improvement of neural network state variable estimator in induction motor drive
, Accuracy improvement of neural network state variable estimator in induction motor drive, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 71-77, 2008, Cites: 5
Pokropinska A., Scherer R., Financial prediction with neuro-fuzzy systems. (5)
Financial prediction with neuro-fuzzy systems
, Financial prediction with neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 1120-1126, 2008, Cites: 5
Scherer R., Korytkowski M., Nowicki R., Rutkowski L., Modular rough neuro-fuzzy systems for classification. (3)
Modular rough neuro-fuzzy systems for classification
, Modular rough neuro-fuzzy systems for classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4967 LNCS, 4967 LNCS, 540-548, 2008, Cites: 3
Starczewski A., A new approach to creating multisegment fuzzy systems. (1)
A new approach to creating multisegment fuzzy systems
, A new approach to creating multisegment fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 324-332, 2008, Cites: 1
Scherer R., Regression modeling with fuzzy relations. (1)
Regression modeling with fuzzy relations
, Regression modeling with fuzzy relations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 317-323, 2008, Cites: 1
Cpalka K., Rutkowski L., An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems. (1)
An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems
, An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 207-216, 2008, Cites: 1
Cpalka K., Rebrova O., Galkowski T., Rutkowski L., On differential stroke diagnosis by neuro-fuzzy structures. (1)
On differential stroke diagnosis by neuro-fuzzy structures
, On differential stroke diagnosis by neuro-fuzzy structures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 974-980, 2008, Cites: 1
Morkowski M., Nowicki R., Information theory inspired weighted immune classification algorithm. (0)
Information theory inspired weighted immune classification algorithm
, Information theory inspired weighted immune classification algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 652-660, 2008, Cites: 02007 (3)
Cpalka K., Nowicki R., Rutkowski L., Rough-neuro-fuzzy systems for classification. (3)
Rough-neuro-fuzzy systems for classification
, Rough-neuro-fuzzy systems for classification, Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 1-8, 2007, Cites: 3
Korytkowski M., Rutkowski L., Scherer R., Drozda G., On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems. (1)
On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems
, On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, 234-237, 2007, Cites: 1
Korytkowski M., Rutkowski L., Scherer R., On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm. (0)
On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm
, On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm, Advances in Soft Computing, 45, 45, 319-326, 2007, Cites: 02006 (14)
Korytkowski M., Rutkowski L., Scherer R., On combining backpropagation with boosting. (58)
On combining backpropagation with boosting
, On combining backpropagation with boosting, IEEE International Conference on Neural Networks - Conference Proceedings, 1274-1277, 2006, Cites: 58
Cpalka K., A method for designing flexible neuro-fuzzy systems. (40)
A method for designing flexible neuro-fuzzy systems
, A method for designing flexible neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 212-219, 2006, Cites: 40
Scherer R., Boosting ensemble of relational neuro-fuzzy systems. (34)
Boosting ensemble of relational neuro-fuzzy systems
, Boosting ensemble of relational neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 306-313, 2006, Cites: 34
Nowicki R., Rough-neuro-fuzzy system with MICOG defuzzification. (24)
Rough-neuro-fuzzy system with MICOG defuzzification
, Rough-neuro-fuzzy system with MICOG defuzzification, IEEE International Conference on Fuzzy Systems, 1958-1965, 2006, Cites: 24
Bartczuk L., Rutkowska D., A new version of the fuzzy-ID3 algorithm. (23)
A new version of the fuzzy-ID3 algorithm
, A new version of the fuzzy-ID3 algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 1060-1070, 2006, Cites: 23
Gabryel M., Rutkowski L., Evolutionary learning of mamdani-type neuro-fuzzy systems. (19)
Evolutionary learning of mamdani-type neuro-fuzzy systems
, Evolutionary learning of mamdani-type neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 354-359, 2006, Cites: 19
Starczewski J.T., A triangular type-2 fuzzy logic system. (19)
A triangular type-2 fuzzy logic system
, A triangular type-2 fuzzy logic system, IEEE International Conference on Fuzzy Systems, 1460-1467, 2006, Cites: 19
Dziwinski P., Rutkowska D., Algorithm for generating fuzzy rules for WWW document classification. (17)
Algorithm for generating fuzzy rules for WWW document classification
, Algorithm for generating fuzzy rules for WWW document classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 1111-1119, 2006, Cites: 17
Cpalka K., Rutkowski L., A new method for designing and reduction of neuro-fuzzy systems. (16)
A new method for designing and reduction of neuro-fuzzy systems
, A new method for designing and reduction of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1851-1857, 2006, Cites: 16
Cierniak R., A novel approach to image reconstruction from discrete projections using hopfield-type neural network. (10)
A novel approach to image reconstruction from discrete projections using hopfield-type neural network
, A novel approach to image reconstruction from discrete projections using hopfield-type neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 890-898, 2006, Cites: 10
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Merging ensemble of neuro-fuzzy systems. (7)
Merging ensemble of neuro-fuzzy systems
, Merging ensemble of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1954-1957, 2006, Cites: 7
Pokropinska A., Nowicki R., Scherer R., Isolines of statistical information criteria for relational neuro-fuzzy system design. (1)
Isolines of statistical information criteria for relational neuro-fuzzy system design
, Isolines of statistical information criteria for relational neuro-fuzzy system design, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 288-296, 2006, Cites: 1
Cpalka K., Rutkowski L., A new method for complexity reduction of neuro-fuzzy systems. (1)
A new method for complexity reduction of neuro-fuzzy systems
, A new method for complexity reduction of neuro-fuzzy systems, WSEAS Transactions on Systems, 5, 5, 2514-2521, 2006, Cites: 1
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Combining logical-type neuro-fuzzy systems. (1)
Combining logical-type neuro-fuzzy systems
, Combining logical-type neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 240-249, 2006, Cites: 12005 (5)
Rutkowski L., Cpalka K., Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems. (69)
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
, Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems, IEEE Transactions on Fuzzy Systems, 13, 13, 140-151, 2005, Cites: 69
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno fuzzy systems. (57)
Flexible Takagi-Sugeno fuzzy systems
, Flexible Takagi-Sugeno fuzzy systems, Proceedings of the International Joint Conference on Neural Networks, 3, 3, 1764-1769, 2005, Cites: 57
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation. (42)
Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation
, Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation, WSEAS Transactions on Systems, 4, 4, 1450-1458, 2005, Cites: 42
Starczewski J.T., Extended triangular norms on gaussian fuzzy sets. (21)
Extended triangular norms on gaussian fuzzy sets
, Extended triangular norms on gaussian fuzzy sets, Proceedings - 4th Conference of the European Society for Fuzzy Logic and Technology and 11th French Days on Fuzzy Logic and Applications, EUSFLAT-LFA 2005 Joint Conference, 872-877, 2005, Cites: 21
Cpalka K., Rutkowski L., Flexible neuro-fuzzy structures for pattern classification. (7)
Flexible neuro-fuzzy structures for pattern classification
, Flexible neuro-fuzzy structures for pattern classification, WSEAS Transactions on Computers, 4, 4, 679-688, 2005, Cites: 72004 (20)
Rutkowski L., Cpalka K., Neuro-fuzzy systems derived from quasi-triangular norms. (39)
Neuro-fuzzy systems derived from quasi-triangular norms
, Neuro-fuzzy systems derived from quasi-triangular norms, IEEE International Conference on Fuzzy Systems, 2, 2, 1031-1036, 2004, Cites: 39
Scherer R., Rutkowski L., Neuro-fuzzy relational classifiers. (25)
Neuro-fuzzy relational classifiers
, Neuro-fuzzy relational classifiers, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 376-380, 2004, Cites: 25
Nowicki R., Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications. (22)
Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications
, Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 518-525, 2004, Cites: 22
Bilski J., Litwinski S., Smolag J., Parallel realisation of QR algorithm for neural networks learning. (21)
Parallel realisation of QR algorithm for neural networks learning
, Parallel realisation of QR algorithm for neural networks learning, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 158-165, 2004, Cites: 21
Nowicki R., Rough sets in the neuro-fuzzy architectures based on monotonic fuzzy implications. (19)
Rough sets in the neuro-fuzzy architectures based on monotonic fuzzy implications
, Rough sets in the neuro-fuzzy architectures based on monotonic fuzzy implications, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 510-517, 2004, Cites: 19
Nowicki R., Pokropinska A., Information criterions applied to neuro-fuzzy architectures design. (19)
Information criterions applied to neuro-fuzzy architectures design
, Information criterions applied to neuro-fuzzy architectures design, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 332-337, 2004, Cites: 19
Cierniak R., An image compression algorithm based on neural networks. (12)
An image compression algorithm based on neural networks
, An image compression algorithm based on neural networks, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 706-711, 2004, Cites: 12
Bilski J., Momentum modification of the RLS algorithms. (11)
Momentum modification of the RLS algorithms
, Momentum modification of the RLS algorithms, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 151-157, 2004, Cites: 11
Gaweda A.E., Scherer R., Fuzzy number-based hierarchical fuzzy system. (8)
Fuzzy number-based hierarchical fuzzy system
, Fuzzy number-based hierarchical fuzzy system, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 302-307, 2004, Cites: 8
Starczewski J.T., What differs interval type-2 FLS from type-1 FLS?. (7)
What differs interval type-2 FLS from type-1 FLS?
, What differs interval type-2 FLS from type-1 FLS?, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 381-387, 2004, Cites: 7
Scherer R., Starczewski J., Gaweda A., New methods for uncertainty representations in neuro-fuzzy systems. (4)
New methods for uncertainty representations in neuro-fuzzy systems
, New methods for uncertainty representations in neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 659-667, 2004, Cites: 4
Korytkowski M., Gabryel M., Nowicki R., Scherer R., Genetic algorithm for database indexing. (4)
Genetic algorithm for database indexing
, Genetic algorithm for database indexing, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 1142-1147, 2004, Cites: 4
Korytkowski M., Gabryel M., Gaweda A., Recursive probabilistic neural networks. (3)
Recursive probabilistic neural networks
, Recursive probabilistic neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 626-631, 2004, Cites: 3
Cpalka K., A flexible connectionist fuzzy system. (2)
A flexible connectionist fuzzy system
, A flexible connectionist fuzzy system, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 618-625, 2004, Cites: 2
Nowicki R., Pokropinska A., Hayashi Y., On designing of neuro-fuzzy systems. (1)
On designing of neuro-fuzzy systems
, On designing of neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 641-649, 2004, Cites: 1
Cierniak R., Image compression based on soft computing techniques. (1)
Image compression based on soft computing techniques
, Image compression based on soft computing techniques, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 609-617, 2004, Cites: 1
Nowicki R., Neuro-fuzzy versus non-parametric approach to system modeling and classification. (0)
Neuro-fuzzy versus non-parametric approach to system modeling and classification
, Neuro-fuzzy versus non-parametric approach to system modeling and classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 632-640, 2004, Cites: 0
Jelonkiewicz J., Przybyl A., Influence of the training set selection on the performance of the neural network state variables estimators in the induction motor. (0)
Influence of the training set selection on the performance of the neural network state variables estimators in the induction motor
, Influence of the training set selection on the performance of the neural network state variables estimators in the induction motor, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 966-971, 2004, Cites: 0
Bilski J., Smolag J., Zurada J., Systolic architectures for soft computing algorithms. (0)
Systolic architectures for soft computing algorithms
, Systolic architectures for soft computing algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 601-608, 2004, Cites: 0
Cpalka K., Rutkowski L., Fuzzy modelling with a compromise fuzzy reasoning. (0)
Fuzzy modelling with a compromise fuzzy reasoning
, Fuzzy modelling with a compromise fuzzy reasoning, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 284-289, 2004, Cites: 02003 (4)
Rutkowski L., Cpalka K., Flexible neuro-fuzzy systems. (183)
Flexible neuro-fuzzy systems
, Flexible neuro-fuzzy systems, IEEE Transactions on Neural Networks, 14, 14, 554-574, 2003, Cites: 183
Nowicki R., Scherer R., Rutkowski L., A Hierarchical Neuro-Fuzzy System Based on S-Implications. (5)
A Hierarchical Neuro-Fuzzy System Based on S-Implications
, A Hierarchical Neuro-Fuzzy System Based on S-Implications, Proceedings of the International Joint Conference on Neural Networks, 1, 1, 321-325, 2003, Cites: 5
Rutkowski L., Cpalka K., Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574)). (0)
Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574))
, Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574)), IEEE Transactions on Neural Networks, 14, 14, 967, 2003, Cites: 0
Rutkowski L., Cpalka K., A new approach to designing fuzzy systems. (0)
A new approach to designing fuzzy systems
, A new approach to designing fuzzy systems, Recent Advances in Intelligent Systems and Signal Processing, 343-347, 2003, Cites: 02002 (4)
Starczewski J., Rutkowski L., Connectionist structures of type 2 fuzzy inference systems. (62)
Connectionist structures of type 2 fuzzy inference systems
, Connectionist structures of type 2 fuzzy inference systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2328, 2328, 634-642, 2002, Cites: 62
Rutkowski L., Cpalka K., A neuro - Fuzzy controller with a compromise fuzzy reasoning. (56)
A neuro - Fuzzy controller with a compromise fuzzy reasoning
, A neuro - Fuzzy controller with a compromise fuzzy reasoning, Control and Cybernetics, 31, 31, 297-308, 2002, Cites: 56
Rutkowski L., Cpalka K., Flexible weighted neuro-fuzzy systems. (36)
Flexible weighted neuro-fuzzy systems
, Flexible weighted neuro-fuzzy systems, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, 4, 4, 1857-1861, 2002, Cites: 36
Rutkowska D., Nowicki R., Hayashi Y., Parallel processing by implication-based neuro-fuzzy systems. (4)
Parallel processing by implication-based neuro-fuzzy systems
, Parallel processing by implication-based neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2328, 2328, 599-607, 2002, Cites: 42001 (2)
Rutkowski L., Cpalka K., A general approach to neuro-fuzzy systems. (47)
A general approach to neuro-fuzzy systems
, A general approach to neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 3, 3, 1428-1431, 2001, Cites: 47
Rutkowska D., Rutkowski L., Nowicki R., Neuro-fuzzy systems with inference based on bounded product. (5)
Neuro-fuzzy systems with inference based on bounded product
, Neuro-fuzzy systems with inference based on bounded product, Advances in Neural Networks and Applications, 104-109, 2001, Cites: 52000 (1)
Cierniak R., Rutkowski L., On image compression by competitive neural networks and optimal linear predictors. (51)
On image compression by competitive neural networks and optimal linear predictors
, On image compression by competitive neural networks and optimal linear predictors, Signal Processing: Image Communication, 15, 15, 559-565, 2000, Cites: 511998 (1)
Bilski J., Rutkowski L., A fast training algorithm for neural networks. (65)
A fast training algorithm for neural networks
, A fast training algorithm for neural networks, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 45, 45, 749-753, 1998, Cites: 651996 (1)
Cierniak Robert, Rutkowski Leszek, Neural networks and semi-closed-loop predictive vector quantization for image compression. (1)
Neural networks and semi-closed-loop predictive vector quantization for image compression
, Neural networks and semi-closed-loop predictive vector quantization for image compression, IEEE International Conference on Image Processing, 1, 1, 245-248, 1996, Cites: 11986 (1)
Galkowski T., Rutkowski L., Nonparametric Fitting of Multivariate Functions. (64)
Nonparametric Fitting of Multivariate Functions
, Nonparametric Fitting of Multivariate Functions, IEEE Transactions on Automatic Control, 31, 31, 785-787, 1986, Cites: 641985 (1)
Galkowski T., Rutkowski L., Nonparametric Recovery of Multivariate Functions with Applications to System Identification. (62)
Nonparametric Recovery of Multivariate Functions with Applications to System Identification
, Nonparametric Recovery of Multivariate Functions with Applications to System Identification, Proceedings of the IEEE, 73, 73, 942-943, 1985, Cites: 621983 (1)
Galkowski Tomasz, Rutkowski Leszek, NEW ALGORITHM FOR SHAPE ANALYSIS - THE MULTIDIMENSIONAL CASE.. (0)
