Research
The research interests of the employees of the Artificial Intelligence include the broadly understood methods of computational intelligence and their application.
Papers (818)
2024 (39)
Lin L., Cao J., Lam J., Zhu S., Azuma S.-I., Rutkowski L., Leader-Follower Consensus Over Finite Fields. (7)
Leader-Follower Consensus Over Finite Fields
, Leader-Follower Consensus Over Finite Fields, IEEE Transactions on Automatic Control, 69, 69, 4718-4725, 2024, Cites: 7
Xin Y., Cheng Z., Cao J., Rutkowski L., Wang Y., Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks. (6)
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: 6
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. (6)
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: 6
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. (5)
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: 5
Lin L., Cao J., Lu J., Rutkowski L., Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy. (4)
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: 4
Lv X., Cao J., Rutkowski L., Duan P., Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies. (4)
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: 4
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. (3)
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: 3
Huang Z., Lv W., Liu C., Xu Y., Rutkowski L., Huang T., Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks. (2)
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: 2
Tao M., Guo L., Cao J., Rutkowski L., A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems. (2)
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: 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
Szmidt E., Kacprzyk J., Bujnowski P., Starczewski J.T., Siwocha A., RANKING OF ALTERNATIVES DESCRIBED BY ATANASSOV'S INTUITIONISTIC FUZZY SETS – RECONCILING SOME MISUNDERSTANDINGS. (1)
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: 1
Cheng H., Xiao M., Yu W., Rutkowski L., Cao J., How to regulate pattern formations for malware propagation in cyber-physical systems. (1)
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: 1
Starzec G., Starzec M., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Ant colony optimization using two-dimensional pheromone for single-objective transport problems. (1)
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: 1
Duda P., Wojtulewicz M., Rutkowski L., Accelerating deep neural network learning using data stream methodology. (1)
Accelerating deep neural network learning using data stream methodology
, Accelerating deep neural network learning using data stream methodology, Information Sciences, 669, 669, 2024, Cites: 1
Wu J., Cheng J., Yan H., Rutkowski L., Cao J., Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems With Stochastic Communication Protocol. (0)
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, 2024, Cites: 0
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. (0)
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, 2024, Cites: 0
Slowik A., Cpalka K., Xue Y., Hapka A., An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm. (0)
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: 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
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
Ju Y., Xiao M., Huang C., Rutkowski L., Cao J., Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus. (0)
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: 0
Chen G., Xu G., He F., Hong Y., Rutkowski L., Tao D., Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games. (0)
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: 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
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
Dong S., Tang J., Abbas K., Hou R., Kamruzzaman J., Rutkowski L., Buyya R., Task offloading strategies for mobile edge computing: A survey. (0)
Task offloading strategies for mobile edge computing: A survey
, Task offloading strategies for mobile edge computing: A survey, Computer Networks, 254, 254, 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
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
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
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
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. (0)
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: 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
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
Sevastjanov P., Kaczmarek K., Rutkowski L., A multi-model approach to the development of algorithmic trading systems for the Forex market. (0)
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: 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
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. (0)
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: 0
Lapa K., Increasing the explainability and trustiness of Wang–Mendel fuzzy system for classification problems. (0)
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: 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
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
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. (0)
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: 0
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. (0)
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: 02023 (73)
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. (41)
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: 41
Bilski J., Smolag J., Kowalczyk B., Grzanek K., Izonin I., Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks. (23)
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: 23
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. (20)
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: 20
Alfarano A., De Magistris G., Mongelli L., Russo S., Starczewski J., Napoli C., A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection. (17)
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: 17
Shen H., Zhang Y., Wang J., Cao J., Rutkowski L., Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems. (15)
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: 15
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. (13)
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: 13
Zhu S., Cao J., Lin L., Rutkowski L., Lu J., Lu G., Observability and Detectability of Stochastic Labeled Graphs. (13)
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: 13
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. (13)
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: 13
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control. (12)
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: 12
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos. (8)
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: 8
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
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. (6)
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: 6
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. (6)
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: 6
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. (6)
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: 6
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. (6)
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: 6
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. (5)
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: 5
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
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. (5)
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: 5
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. (4)
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: 4
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. (4)
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: 4
Guan H., Liu Y., Kou K.I., Cao J., Rutkowski L., Collaborative neurodynamic optimization for solving nonlinear equations. (3)
Collaborative neurodynamic optimization for solving nonlinear equations
, Collaborative neurodynamic optimization for solving nonlinear equations, Neural Networks, 165, 165, 483-490, 2023, Cites: 3
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. (3)
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: 3
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
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
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
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
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
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
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
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. (1)
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: 1
Zhu W., Cao J., Shi X., Rutkowski L., Leader-following consensus of finite-field networks with time-delays. (1)
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: 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
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
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
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
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
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
Wu X., Zhu X., Baralis E., Lu R., Kumar V., Rutkowski L., Tang J., On Computing Paradigms - Where Will Large Language Models Be Going. (0)
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: 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
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
Jing W., Kuang Z., Scherer R., Wozniak M., Editorial: Big data and artificial intelligence technologies for smart forestry. (0)
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: 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
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
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
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
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
Pluta P., Cierniak R., A New Rebinning Reconstruction Method for the Low Dose CT Scanners with Flying Focal Spot. (0)
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: 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
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
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
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
Sevastjanov P., Kaczmarek K., Rutkowski L., A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization. (0)
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: 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
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. (0)
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: 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
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
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
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
Korytkowski M., Nowak J., Scherer R., Wei W., Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks. (0)
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: 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
Walczak J., Najgebauer P., Wojciechowski A., Scherer R., Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented]. (0)
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: 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
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
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
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
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
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
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
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: 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
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
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
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: 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. (62)
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: 62
Yu T., Cao J., Rutkowski L., Luo Y.-P., Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control. (46)
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: 46
Song Y., Cao J., Rutkowski L., A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy. (43)
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: 43
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. (37)
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: 37
Luo Y., Zhu W., Cao J., Rutkowski L., Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems. (30)
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: 30
Kordos M., Blachnik M., Scherer R., Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems. (24)
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: 24
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. (20)
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: 20
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. (20)
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: 20
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. (19)
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: 19
Slowik A., Cpalka K., Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications. (19)
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: 19
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control. (16)
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: 16
Feng Y., Zhang W., Xiong J., Li H., Rutkowski L., Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes. (15)
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: 15
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. (15)
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: 15
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. (14)
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: 14
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. (13)
General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching
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Hammad M., Meshoul S., Dziwinski P., Plawiak P., Elgendy I.A., Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification. (12)
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: 12
Zhu W., Cao J., Shi X., Rutkowski L., Synchronization of Finite-Field Networks With Time Delays. (10)
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: 10
Bilski J., Kowalczyk B., Kisiel-Dorohinicki M., Siwocha A., Zurada J., Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm. (10)
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: 10
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
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
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. (9)
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: 9
Hu J., Cao J., Rutkowski L., Xue C., Yu J., Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators. (9)
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: 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. (7)
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: 7
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
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. (6)
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
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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
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
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Bernacki J., Digital camera identification by fingerprint’s compact representation. (4)
Digital camera identification by fingerprint’s compact representation
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A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems
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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
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Individual Source Camera Identification with Convolutional Neural Networks
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An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance
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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
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Grycuk R., Korytkowski M., Scherer R., Drozda P., Wei W., Kordos M., Fast Solar Image Retrieval and Classification by Fuzzy Rules. (1)
Fast Solar Image Retrieval and Classification by Fuzzy Rules
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Fast Estimation of Multidimensional Regression Functions
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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
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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
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A Real-time Hand Gesture Recognition System for Human-Computer and Human-Robot Interaction
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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
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
Kordos M., Kulka R., Steblik T., Scherer R., Local Search in Selected Crossover Operators. (0)
Local Search in Selected Crossover Operators
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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
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
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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
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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
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: 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
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Wang J., Yang C., Shen H., Cao J., Rutkowski L., Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters. (118)
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: 118
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. (98)
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: 98
Xia Z., Liu Y., Lu J., Cao J., Rutkowski L., Penalty method for constrained distributed quaternion-variable optimization. (47)
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: 47
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. (43)
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: 43
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. (42)
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: 42
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. (31)
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: 31
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks. (21)
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: 21
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
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
Lv X., Cao J., Rutkowski L., Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control. (16)
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: 16
Xu S., Cao J., Liu Q., Rutkowski L., Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method. (16)
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: 16
Starczewski A., Scherer M.M., Ksiek W., Debski M., Wang L., A Novel Grid-Based Clustering Algorithm. (15)
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: 15
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. (14)
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: 14
Bilski J., Kowalczyk B., Marjanski A., Gandor M., Zurada J., A Novel Fast Feedforward Neural Networks Training Algorithm. (14)
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: 14
Kulikajevas A., Maskeliunas R., Damasevicius R., Scherer R., Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction. (11)
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: 11
Dziwinski P., Przybyl A., Trippner P., Paszkowski J., Hayashi Y., Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm. (11)
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: 11
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
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
Przybyl A., Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems. (8)
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: 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
Bernacki J., Robustness of digital camera identification with convolutional neural networks. (7)
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: 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
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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
Wei W., Gao F., Scherer R., Damasevicius R., Polap D., Design and implementation of autonomous path planning for intelligent vehicle. (6)
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: 6
Le V.-H., Scherer R., Human segmentation and tracking survey on masks for mads dataset. (5)
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: 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
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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
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
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
He J., Liu Y., Lu J., Cao J., Rutkowski L., Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays. (4)
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: 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
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
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. (3)
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: 3
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
Wrobel M., Starczewski J.T., Fijalkowska J., Siwocha A., Napoli C., Handwritten word recognition using fuzzy matching degrees. (3)
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: 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
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
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
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
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
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
Lapa K., Cpalka K., Slowik A., Population Management Approaches in the OPn Algorithm. (1)
Population Management Approaches in the OPn Algorithm
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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
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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
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
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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
Bartczuk L., A New Interval Type-2 Fuzzy PRISM Algorithm. (0)
A New Interval Type-2 Fuzzy PRISM Algorithm
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Gabryel M., Kocic M., Fingerprint Device Parameter Stability Analysis. (0)
Fingerprint Device Parameter Stability Analysis
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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
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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: 0
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Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms
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Preface
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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
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
Starczewski A., A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm. (0)
A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm
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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. (314)
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: 314
Wei W., Ke Q., Nowak J., Korytkowski M., Scherer R., Wozniak M., Accurate and fast URL phishing detector: A convolutional neural network approach. (142)
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: 142
Yang X., Liu Y., Cao J., Rutkowski L., Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching. (112)
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: 112
Liu Y., Zheng Y., Lu J., Cao J., Rutkowski L., Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach. (96)
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: 96
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. (87)
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: 87
Dziwinski P., Bartczuk L., A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic. (83)
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: 83
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. (75)
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: 75
Tan X., Cao J., Rutkowski L., Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay. (73)
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: 73
Bilski J., Kowalczyk B., Marchlewska A., Zurada J.M., Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks. (71)
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: 71
Starczewski A., Goetzen P., Er M.J., A New Method for Automatic Determining of the DBSCAN Parameters. (47)
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: 47
Lin L., Cao J., Rutkowski L., Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks. (44)
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: 44
Lin L., Cao J., Zhu S., Rutkowski L., Lu G., Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model. (35)
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: 35
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
Chen B., Cao J., Luo Y., Rutkowski L., Asymptotic Output Tracking of Probabilistic Boolean Control Networks. (34)
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: 34
Dong L., Fang D., Wang X., Wei W., Damasevicius R., Scherer R., Wozniak M., Prediction of streamflow based on dynamic sliding window lstm. (33)
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: 33
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
Bernacki J., A survey on digital camera identification methods. (28)
A survey on digital camera identification methods
, A survey on digital camera identification methods, Forensic Science International: Digital Investigation, 34, 34, 2020, Cites: 28
Korytkowski M., Senkerik R., Scherer M.M., Angryk R.A., Kordos M., Siwocha A., Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic. (27)
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: 27
Duda P., Jaworski M., Cader A., Wang L., On Training Deep Neural Networks Using a Streaming Approach. (24)
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: 24
Chen B., Cao J., Lu G., Rutkowski L., Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks. (23)
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: 23
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. (22)
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: 22
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. (21)
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: 21
Starczewski J.T., Goetzen P., Napoli C., Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems. (21)
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: 21
Gabryel M., Grzanek K., Hayashi Y., Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic. (21)
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: 21
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
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
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
Nowicki R.K., Grzanek K., Hayashi Y., Rough Support Vector Machine for Classification with Interval and Incomplete Data. (15)
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: 15
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
Bernacki J., Automatic exposure algorithms for digital photography. (15)
Automatic exposure algorithms for digital photography
, Automatic exposure algorithms for digital photography, Multimedia Tools and Applications, 79, 79, 12751-12776, 2020, Cites: 15
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
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
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
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
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
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
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
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
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. (9)
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: 9
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
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. (8)
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: 8
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
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining. (7)
Decision Trees in Data Stream Mining
, Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 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. (6)
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: 6
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images. (6)
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: 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
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
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
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
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
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
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
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
Hazra S., Roy P., Nandy A., Scherer R., A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications. (3)
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: 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
Bernacki J., Digital camera identification based on analysis of optical defects. (3)
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: 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
Nowak J., Korytkowski M., Scherer R., Discovering Sequential Patterns by Neural Networks. (2)
Discovering Sequential Patterns by Neural Networks
, Discovering Sequential Patterns by Neural Networks, Proceedings of the International Joint Conference on Neural Networks, 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
Talun A., Drozda P., Bukowski L., Scherer R., FastText and XGBoost Content-Based Classification for Employment Web Scraping. (2)
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: 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
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 L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification. (2)
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: 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
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
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
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
Scherer R., Feature detection. (1)
Feature detection
, Feature detection, Studies in Computational Intelligence, 821, 821, 7-32, 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., 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
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
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
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
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., 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
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
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
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
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
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
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
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
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
Scherer R., Preface. (0)
Preface
, Preface, Studies in Computational Intelligence, 821, 821, v, 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., Regression. (0)
Regression
, Regression, Studies in Big Data, 56, 56, 309-322, 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
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
Scherer R., Concluding remarks and perspectives in computer vision. (0)
Concluding remarks and perspectives in computer vision
, Concluding remarks and perspectives in computer vision, Studies in Computational Intelligence, 821, 821, 137, 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
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., 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
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
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
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
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
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
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: 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
Scherer R., Image indexing techniques. (0)
Image indexing techniques
, Image indexing techniques, Studies in Computational Intelligence, 821, 821, 33-82, 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: 02019 (47)
Dirvanauskas D., Maskeliunas R., Raudonis V., Damasevicius R., Scherer R., HEMIGEN: Human embryo image generator based on generative adversarial networks. (41)
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: 41
Rutkowski T., Lapa K., Nielek R., On Explainable Fuzzy Recommenders and their Performance Evaluation. (25)
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: 25
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
Starczewski A., Cader A., Determining the eps parameter of the DBSCAN algorithm. (15)
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: 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
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
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
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
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
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
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
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
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
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. (2)
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: 2
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
Bilski J., Kowalczyk B., Cader A., Modifications of the Givens Training Algorithm for Artificial Neural Networks. (1)
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: 1
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
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
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., 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
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
Nowicki R.K., Final Remarks. (0)
Final Remarks
, Final Remarks, Studies in Computational Intelligence, 802, 802, 185-188, 2019, Cites: 0
Korytkowski M., Nowak J., Nowicki R., Milkowska K., Scherer M., Goetzen P., Sequential Data Mining of Network Traffic in URL Logs. (0)
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: 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
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
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
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
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
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
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
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: 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., Introduction. (0)
Introduction
, Introduction, Studies in Computational Intelligence, 802, 802, 1-6, 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
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
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: 02018 (43)
Jaworski M., Duda P., Rutkowski L., New Splitting Criteria for Decision Trees in Stationary Data Streams. (89)
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: 89
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. (27)
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: 27
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
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
Gabryel M., Damasevicius R., Przybyszewski K., Application of the bag-of-words algorithm in classification the quality of sales leads. (15)
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: 15
Jaworski M., Duda P., Rutkowski L., Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine. (13)
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: 13
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
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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
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Bilski J., Kowalczyk B., Grzanek K., The parallel modification to the levenberg-marquardt algorithm. (11)
The parallel modification to the levenberg-marquardt algorithm
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Lapa K., Cpalka K., Przybyl A., Grzanek K., Negative space-based population initialization algorithm (NSPIA). (8)
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A fuzzy measure for recognition of handwritten letter strokes
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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
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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
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A fuzzy SOM for understanding incomplete 3D faces
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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
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Classification of computer network users with convolutional neural networks
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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
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On ensemble components selection in data streams scenario with gradual concept-drift
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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
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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
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Estimation of probability density function, differential entropy and other relative quantities for data streams with concept drift
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Starczewski A., Przybyszewski K., Improvement of the simplified silhouette validity index. (1)
Improvement of the simplified silhouette validity index
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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
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Lapa K., Cpalka K., Evolutionary approach for automatic design of PID controllers. (1)
Evolutionary approach for automatic design of PID controllers
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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
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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
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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
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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
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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
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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
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Performance evaluation of DBN learning on intel multi- and manycore architectures
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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
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Lapa K., Population-based algorithm with selectable evolutionary operators for nonlinear modeling. (0)
Population-based algorithm with selectable evolutionary operators for nonlinear modeling
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Outliers detection in regressions by nonparametric parzen kernel estimation
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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: 126
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?. (65)
How to adjust an ensemble size in stream data mining?
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A new validity index for crisp clusters
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The image classification with different types of image features
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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
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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
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Korytkowski M., Novel visual information indexing in relational databases. (17)
Novel visual information indexing in relational databases
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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
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Comparison of effectiveness of multi-objective genetic algorithms in optimization of invertible S-boxes
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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
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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
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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
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
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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
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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
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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
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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
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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
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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
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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
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
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
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Zalasinski M., Cpalka K., Hayashi Y., A method for genetic selection of the most characteristic descriptors of the dynamic signature. (4)
A method for genetic selection of the most characteristic descriptors of the dynamic signature
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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
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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
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Lapa K., Elastic FOPID+FIR controller design using hybrid population-based algorithm. (3)
Elastic FOPID+FIR controller design using hybrid population-based algorithm
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Lapa K., Cpalka K., Wang L., A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators. (3)
A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators
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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
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Lagiewka M., Korytkowski M., Scherer R., Distributed image retrieval with color and keypoint features. (3)
Distributed image retrieval with color and keypoint features
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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
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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
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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
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
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
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Cpalka K., Introduction to fuzzy system interpretability. (2)
Introduction to fuzzy system interpretability
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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
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Cpalka K., Improving fuzzy systems interpretability by appropriate selection of their structure. (1)
Improving fuzzy systems interpretability by appropriate selection of their structure
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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
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
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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
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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
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Cpalka K., Concluding remarks and future perspectives. (0)
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Introduction
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Selected topics in fuzzy systems designing
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Cpalka K., Case study: Interpretability of fuzzy systems applied to identity verification. (0)
Case study: Interpretability of fuzzy systems applied to identity verification
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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
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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: 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
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Preface
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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
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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: 02016 (65)
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers. (141)
Fast image classification by boosting fuzzy classifiers
, Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 141
Cpalka K., Zalasinski M., Rutkowski L., A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. (91)
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: 91
Bartczuk L., Przybyl A., Cpalka K., A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. (36)
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: 36
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. (25)
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: 25
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
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
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
Kapuscinski T., Nowicki R.K., Napoli C., Application of genetic algorithms in the construction of invertible substitution boxes. (17)
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: 17
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
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
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
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
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
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
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
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
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
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
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
Saito T., Yasuda K., Ishikawa T., Hosoi R., Takahashi K., Chen Y., Zalasinski M., Estimating CPU features by browser fingerprinting. (10)
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: 10
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. (8)
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: 8
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
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
Laskowski ., Laskowska M., Jelonkiewicz J., Galkowski T., Pawlik P., Piech H., Doskocz M., Iron Doped SBA-15 Mesoporous Silica Studied by Mössbauer Spectroscopy. (7)
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: 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
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
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
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., 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
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., 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
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., 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
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
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
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
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
Bilski J., Galushkin A.I., A new proposition of the activation function for significant improvement of neural networks performance. (3)
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: 3
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., 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
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
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., 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
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
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
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
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
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: 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)