Contact:
Room: 518
Position:
Full Professor
Research Vice-Head of Department
Research teams:
Nowe systemy głębokiego uczenia i ich zastosowania
Leader of: Nowe systemy głębokiego uczenia i ich zastosowania
Classes:
Eksploracja danych i Big Data lab
Eksploracja danych i Big Data wyk
Artificial neural networks lab
Artificial neural networks wyk
Prof. PhD DSc Eng
Rafał Scherer
Office hours: środa 11-14
Papers (182)
2024 (5)
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
De Magistris G., Grycuk R., Mandelli L., Scherer R., New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations. (0)
New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations
, New Approaches Based on PRNU-CNN for Image Camera Source Attribution in Forensic Investigations, CEUR Workshop Proceedings, 3870, 3870, 67-72, 2024, Cites: 0
Osowski M., Krasnodebska A., Drozda P., Scherer R., Professionally Diverse: AI-Generated Faces for Targeted Advertising. (0)
Professionally Diverse: AI-Generated Faces for Targeted Advertising
, Professionally Diverse: AI-Generated Faces for Targeted Advertising, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14795 LNAI, 14795 LNAI, 171-183, 2024, Cites: 0
Grycuk R., De Magistris G., Napoli C., Scherer R., Toward Real-Time Solar Content-Based Image Retrieval. (0)
Toward Real-Time Solar Content-Based Image Retrieval
, Toward Real-Time Solar Content-Based Image Retrieval, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14832 LNCS, 14832 LNCS, 107-120, 2024, Cites: 0
Lucas T.J., Passos L.A., Rodrigues D., Jodas D., Papa J.P., Da Costa K.A.P., Scherer R., Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems. (0)
Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems
, Ensemble Diversity Pruning on Cybersecurity: Optimizing Intrusion Detection Systems, International Conference on Systems, Signals, and Image Processing, 2024, Cites: 02023 (20)
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
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos. (17)
YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos
, YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos, Sensors (Basel, Switzerland), 23, 23, 2023, Cites: 17
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
Walczak J., Najgebauer P., Wojciechowski A., Scherer R., Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented]. (1)
Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented]
, Ultrasmall fully-convolution GVA-net for point cloud processing[Formula presented], Applied Soft Computing, 132, 132, 2023, Cites: 1
Lucas T.J., De Figueiredo I.S., Tojeiro C.A.C., De Almeida A.M.G., Scherer R., Brega J.R.F., Papa J.P., Da Costa K.A.P., A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks. (8)
A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks
, A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks, IEEE Access, 11, 11, 122638-122676, 2023, Cites: 8
Osowski M., Lorenc K., Drozda P., Scherer R., Szalapak K., Komar-Komarowski K., Szymanski J., Sobecki A., Previous Opinions is All You Need—Legal Information Retrieval System. (0)
Previous Opinions is All You Need—Legal Information Retrieval System
, Previous Opinions is All You Need—Legal Information Retrieval System, Communications in Computer and Information Science, 1864 CCIS, 1864 CCIS, 57-67, 2023, Cites: 0
Grycuk R., Korytkowski M., Scherer R., Fuzzy-Based Solar Magnetogram Image Retrieval. (0)
Fuzzy-Based Solar Magnetogram Image Retrieval
, Fuzzy-Based Solar Magnetogram Image Retrieval, IEEE International Conference on Fuzzy Systems, 2023, Cites: 0
Korytkowski M., Nowak J., Scherer R., Detecting Sensitive Data with GANs and Fully Convolutional Networks. (0)
Detecting Sensitive Data with GANs and Fully Convolutional Networks
, Detecting Sensitive Data with GANs and Fully Convolutional Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13995 LNAI, 13995 LNAI, 273-283, 2023, Cites: 0
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
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
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
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
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
Wei W., Li X., Zhang B., Li L., Damasevicius R., Scherer R., LSTM-SN: complex text classifying with LSTM fusion social network. (6)
LSTM-SN: complex text classifying with LSTM fusion social network
, LSTM-SN: complex text classifying with LSTM fusion social network, Journal of Supercomputing, 79, 79, 9558-9583, 2023, Cites: 6
Chen G., Zou W., Jing W., Wei W., Scherer R., Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data. (6)
Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data
, Improving the Efficiency of the EMS-Based Smart City: A Novel Distributed Framework for Spatial Data, IEEE Transactions on Industrial Informatics, 19, 19, 594-604, 2023, Cites: 6
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
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study. (24)
Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study
, Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study, Sensors, 23, 23, 2023, Cites: 24
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
Korytkowski M., Nowak J., Scherer R., Wei W., Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks. (1)
Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks
, Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 277-285, 2023, Cites: 1
Jing W., Kuang Z., Scherer R., Wozniak M., Editorial: Big data and artificial intelligence technologies for smart forestry. (1)
Editorial: Big data and artificial intelligence technologies for smart forestry
, Editorial: Big data and artificial intelligence technologies for smart forestry, Frontiers in Plant Science, 14, 14, 2023, Cites: 12022 (17)
Lucas T.J., Da Costa K.A.P., Scherer R., Papa J.P., An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance. (2)
An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance
, An Ensemble Pruning Approach to Optimize Intrusion Detection Systems Performance, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2022-October, 2022-October, 1173-1179, 2022, Cites: 2
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
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
Nguyen H.-C., Nguyen T.-H., Scherer R., Le V.-H., Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications. (19)
Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications
, Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications, Sensors, 22, 22, 2022, Cites: 19
Zhou Y., Jing W., Wang J., Chen G., Scherer R., Damasevicius R., MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution. (16)
MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution
, MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution, IET Image Processing, 16, 16, 659-668, 2022, Cites: 16
Bernacki J., Scherer R., Digital forensics: a fast algorithm for a digital sensor identification. (1)
Digital forensics: a fast algorithm for a digital sensor identification
, Digital forensics: a fast algorithm for a digital sensor identification, Journal of Information and Telecommunication, 6, 6, 399-419, 2022, Cites: 1
Ma R., Angryk R., Scherer R., Special issue on deep learning for time series data. (1)
Special issue on deep learning for time series data
, Special issue on deep learning for time series data, Neural Computing and Applications, 34, 34, 13147-13148, 2022, Cites: 1
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
Bernacki J., Costa K.A.P., Scherer R., Individual Source Camera Identification with Convolutional Neural Networks. (3)
Individual Source Camera Identification with Convolutional Neural Networks
, Individual Source Camera Identification with Convolutional Neural Networks, Communications in Computer and Information Science, 1716 CCIS, 1716 CCIS, 45-55, 2022, Cites: 3
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
Grycuk R., Galkowski T., Scherer R., Rutkowski L., A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder. (7)
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
, A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2022-July, 2022-July, 2022, Cites: 7
Kordos M., Blachnik M., Scherer R., Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems. (27)
Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems
, Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems, Information Sciences, 587, 587, 23-40, 2022, Cites: 27
Kordos M., Kulka R., Steblik T., Scherer R., Local Search in Selected Crossover Operators. (0)
Local Search in Selected Crossover Operators
, Local Search in Selected Crossover Operators, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13352 LNCS, 13352 LNCS, 369-382, 2022, Cites: 0
Grycuk R., Korytkowski M., Scherer R., Drozda P., Wei W., Kordos M., Fast Solar Image Retrieval and Classification by Fuzzy Rules. (2)
Fast Solar Image Retrieval and Classification by Fuzzy Rules
, Fast Solar Image Retrieval and Classification by Fuzzy Rules, IEEE International Conference on Fuzzy Systems, 2022-July, 2022-July, 2022, Cites: 2
Utimura L., Costa K., Scherer R., Real-time application of OPF-based classifier in Snort IDS. (0)
Real-time application of OPF-based classifier in Snort IDS
, Real-time application of OPF-based classifier in Snort IDS, Optimum-Path Forest: Theory, Algorithms, and Applications, 55-93, 2022, Cites: 0
Junior P.R.G.H., Scherer R., Januario L.B., Rodrigues D., Papa J.P., Costa K.A.P., From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks. (0)
From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks
, From Network Package Flow to Images: How to Accurately Detect Anomalies in Computer Networks, International Conference on Systems, Signals, and Image Processing, 2022-June, 2022-June, 2022, Cites: 0
Song Y., Wang L., Xiao L., Wei W., Scherer R., Qin G., Wang J., Hypergraph-partitioning-based online joint scheduling of tasks and data. (0)
Hypergraph-partitioning-based online joint scheduling of tasks and data
, Hypergraph-partitioning-based online joint scheduling of tasks and data, Journal of Supercomputing, 78, 78, 16088-16117, 2022, Cites: 02021 (20)
Wei W., Gao F., Scherer R., Damasevicius R., Polap D., Design and implementation of autonomous path planning for intelligent vehicle. (8)
Design and implementation of autonomous path planning for intelligent vehicle
, Design and implementation of autonomous path planning for intelligent vehicle, Journal of Internet Technology, 22, 22, 957-965, 2021, Cites: 8
Kulikajevas A., Maskeliunas R., Damasevicius R., Scherer R., Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction. (12)
Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction
, Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction, Sensors, 21, 21, 2021, Cites: 12
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
Tao T., Yang J., Wei W., Wozniak M., Scherer R., Damasevicius R., Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements. (4)
Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements
, Design of a MEMS sensor array for dam subsidence monitoring based on dual-sensor cooperative measurements, KSII Transactions on Internet and Information Systems, 15, 15, 3554-3570, 2021, Cites: 4
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
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
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
Wei W., Sun Z.-G., Zhang Z.-H., Scherer R., Damasevicius R., Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection. (6)
Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection
, Improved fisher MAP filter for despeckling of high-resolution SAR images based on structural information detection, Journal of Internet Technology, 22, 22, 413-421, 2021, Cites: 6
Wei W., Ke Q., Gao F., Scherer R., Fan S., Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs. (0)
Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs
, Sufficient conditions analysis of coverage algorithm constructed positive definite tridiagonal matrices in WSNs, Journal of Internet Technology, 22, 22, 735-741, 2021, Cites: 0
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
Ding X., Wei W., Zhang B., Scherer R., Damasevicius R., Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging. (2)
Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging
, Apple Packaging Redesign in LuochuanBased on the Concept of Sustainable Packaging, 2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021, 614-627, 2021, Cites: 2
Walczak J., Wojciechowski A., Najgebauer P., Scherer R., Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification. (2)
Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification
, Vicinity-Based Abstraction: VA-DGCNN Architecture for Noisy 3D Indoor Object Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12744 LNCS, 12744 LNCS, 229-241, 2021, Cites: 2
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
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
Li M., Jiang Z., Liu Y., Chen S., Wozniak M., Scherer R., Damasevicius R., Wei W., Li Z., Li Z., Sitsen: Passive sitting posture sensing based on wireless devices. (16)
Sitsen: Passive sitting posture sensing based on wireless devices
, Sitsen: Passive sitting posture sensing based on wireless devices, International Journal of Distributed Sensor Networks, 17, 17, 2021, Cites: 16
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
Bian J., Wang L., Scherer R., Wozniak M., Zhang P., Wei W., Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism. (35)
Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism
, Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory with the Attention Mechanism, IEEE Access, 9, 9, 47252-47265, 2021, Cites: 35
Le V.-H., Scherer R., Human segmentation and tracking survey on masks for mads dataset. (6)
Human segmentation and tracking survey on masks for mads dataset
, Human segmentation and tracking survey on masks for mads dataset, Sensors, 21, 21, 2021, Cites: 6
Grycuk R., Scherer R., Solar Image Hashing by Intermediate Descriptor and Autoencoder. (1)
Solar Image Hashing by Intermediate Descriptor and Autoencoder
, Solar Image Hashing by Intermediate Descriptor and Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2021-July, 2021-July, 2021, Cites: 1
Shi H., Wang L., Scherer R., Wozniak M., Zhang P., Wei W., Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network. (53)
Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network
, Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network, IEEE Access, 9, 9, 66965-66981, 2021, Cites: 532020 (29)
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
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images. (7)
Fully Convolutional Network for Removing DCT Artefacts from Images
, Fully Convolutional Network for Removing DCT Artefacts from Images, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 7
Scherer R., Image indexing techniques. (0)
Image indexing techniques
, Image indexing techniques, Studies in Computational Intelligence, 821, 821, 33-82, 2020, Cites: 0
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
Scherer R., Preface. (0)
Preface
, Preface, Studies in Computational Intelligence, 821, 821, v, 2020, Cites: 0
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
Nowak J., Korytkowski M., Scherer R., Discovering Sequential Patterns by Neural Networks. (3)
Discovering Sequential Patterns by Neural Networks
, Discovering Sequential Patterns by Neural Networks, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 3
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
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
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
Wei W., Ke Q., Nowak J., Korytkowski M., Scherer R., Wozniak M., Accurate and fast URL phishing detector: A convolutional neural network approach. (158)
Accurate and fast URL phishing detector: A convolutional neural network approach
, Accurate and fast URL phishing detector: A convolutional neural network approach, Computer Networks, 178, 178, 2020, Cites: 158
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
Ke Q., Zeng-Guo S., Liu Y., Wei W., Wozniak M., Scherer R., High-resolution SAR image despeckling based on nonlocal means filter and modified AA model. (11)
High-resolution SAR image despeckling based on nonlocal means filter and modified AA model
, High-resolution SAR image despeckling based on nonlocal means filter and modified AA model, Security and Communication Networks, 2020, 2020, 2020, Cites: 11
Talun A., Drozda P., Bukowski L., Scherer R., FastText and XGBoost Content-Based Classification for Employment Web Scraping. (3)
FastText and XGBoost Content-Based Classification for Employment Web Scraping
, FastText and XGBoost Content-Based Classification for Employment Web Scraping, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 435-444, 2020, Cites: 3
Scherer R., Introduction. (0)
Introduction
, Introduction, Studies in Computational Intelligence, 821, 821, 1-5, 2020, Cites: 0
Lv Y., Liu Y., Jing W., Wozniak M., Damasevicius R., Scherer R., Wei W., Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis. (24)
Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis
, Quality control of the continuous hot pressing process of medium density fiberboard using fuzzy failure mode and effects analysis, Applied Sciences (Switzerland), 10, 10, 2020, Cites: 24
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
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
Dong L., Fang D., Wang X., Wei W., Damasevicius R., Scherer R., Wozniak M., Prediction of streamflow based on dynamic sliding window lstm. (38)
Prediction of streamflow based on dynamic sliding window lstm
, Prediction of streamflow based on dynamic sliding window lstm, Water (Switzerland), 12, 12, 1-11, 2020, Cites: 38
Scherer R., Concluding remarks and perspectives in computer vision. (1)
Concluding remarks and perspectives in computer vision
, Concluding remarks and perspectives in computer vision, Studies in Computational Intelligence, 821, 821, 137, 2020, Cites: 1
Hazra S., Roy P., Nandy A., Scherer R., A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications. (4)
A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications
, A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 4
Wei W., Hui M., Zhang B., Scherer R., Gao F., Damasevicius R., Research on variable scale algorithm. (0)
Research on variable scale algorithm
, Research on variable scale algorithm, Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020, 316-322, 2020, Cites: 0
Scherer R., Novel methods for image description. (0)
Novel methods for image description
, Novel methods for image description, Studies in Computational Intelligence, 821, 821, 83-105, 2020, Cites: 0
Khan M.A., Ashraf I., Alhaisoni M., Damasevicius R., Scherer R., Rehman A., Bukhari S.A.C., Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. (342)
Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists
, Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists, Diagnostics, 10, 10, 2020, Cites: 342
Scherer R., Image retrieval and classification in relational databases. (1)
Image retrieval and classification in relational databases
, Image retrieval and classification in relational databases, Studies in Computational Intelligence, 821, 821, 107-136, 2020, Cites: 1
Grycuk R., Scherer R., Novel Fast Binary Hash for Content-based Solar Image Retrieval. (1)
Novel Fast Binary Hash for Content-based Solar Image Retrieval
, Novel Fast Binary Hash for Content-based Solar Image Retrieval, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 1
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
Nasir I.M., Khan M.A., Yasmin M., Shah J.H., Gabryel M., Scherer R., Damasevicius R., Pearson correlation-based feature selection for document classification using balanced training. (104)
Pearson correlation-based feature selection for document classification using balanced training
, Pearson correlation-based feature selection for document classification using balanced training, Sensors (Switzerland), 20, 20, 1-18, 2020, Cites: 1042019 (7)
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
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
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
Dirvanauskas D., Maskeliunas R., Raudonis V., Damasevicius R., Scherer R., HEMIGEN: Human embryo image generator based on generative adversarial networks. (44)
HEMIGEN: Human embryo image generator based on generative adversarial networks
, HEMIGEN: Human embryo image generator based on generative adversarial networks, Sensors (Switzerland), 19, 19, 2019, Cites: 44
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
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
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: 12018 (5)
Nowak J., Korytkowski M., Nowicki R., Scherer R., Siwocha A., Random forests for profiling computer network users. (11)
Random forests for profiling computer network users
, Random forests for profiling computer network users, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 734-739, 2018, Cites: 11
Nowak J., Korytkowski M., Scherer R., Classification of computer network users with convolutional neural networks. (3)
Classification of computer network users with convolutional neural networks
, Classification of computer network users with convolutional neural networks, Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, 501-504, 2018, Cites: 3
Najgebauer P., Grycuk R., Scherer R., Fast Two-Level Image Indexing Based on Local Interest Points. (2)
Fast Two-Level Image Indexing Based on Local Interest Points
, Fast Two-Level Image Indexing Based on Local Interest Points, 2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018, 613-617, 2018, Cites: 2
Nowicki R.K., Korytkowski M., Scherer R., Rough neural network ensemble for interval data classification. (3)
Rough neural network ensemble for interval data classification
, Rough neural network ensemble for interval data classification, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 3
Grycuk R., Najgebauer P., Scherer R., Siwocha A., Architecture of database index for content-based image retrieval systems. (1)
Architecture of database index for content-based image retrieval systems
, Architecture of database index for content-based image retrieval systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 36-47, 2018, Cites: 12017 (5)
Najgebauer P., Rutkowski L., Scherer R., Interest point localization based on edge detection according to gestalt laws. (2)
Interest point localization based on edge detection according to gestalt laws
, Interest point localization based on edge detection according to gestalt laws, 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, 2017-January, 2017-January, 349-353, 2017, Cites: 2
Najgebauer P., Rutkowski L., Scherer R., Novel method for joining missing line fragments for medical image analysis. (3)
Novel method for joining missing line fragments for medical image analysis
, Novel method for joining missing line fragments for medical image analysis, 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017, 861-866, 2017, Cites: 3
Nowak J., Taspinar A., Scherer R., LSTM recurrent neural networks for short text and sentiment classification. (134)
LSTM recurrent neural networks for short text and sentiment classification
, LSTM recurrent neural networks for short text and sentiment classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 553-562, 2017, Cites: 134
Lagiewka M., Korytkowski M., Scherer R., Distributed image retrieval with color and keypoint features. (3)
Distributed image retrieval with color and keypoint features
, Distributed image retrieval with color and keypoint features, Proceedings - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, 45-50, 2017, Cites: 3
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: 72016 (10)
Najgebauer P., Scherer R., Fast image search by trees of keypoint descriptors. (0)
Fast image search by trees of keypoint descriptors
, Fast image search by trees of keypoint descriptors, Advances in Intelligent Systems and Computing, 364, 364, 541-552, 2016, Cites: 0
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
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
Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M., Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I. (0)
Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I
, Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 2016, Cites: 0
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
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
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers. (142)
Fast image classification by boosting fuzzy classifiers
, Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 142
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
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., 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: 12015 (7)
Romanowski J., Korytkowski M., Scherer R., Efficient bone detector and geometric descriptor for x-ray imaging. (2)
Efficient bone detector and geometric descriptor for x-ray imaging
, Efficient bone detector and geometric descriptor for x-ray imaging, Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 1337-1342, 2015, Cites: 2
Grycuk R., Gabryel M., Scherer R., Voloshynovskiy S., Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database. (19)
Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database
, Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 715-726, 2015, Cites: 19
Kostadinov D., Voloshynovskiy S., Ferdowsi S., Diephuis M., Scherer R., Supervised transform learning for face recognition. (1)
Supervised transform learning for face recognition
, Supervised transform learning for face recognition, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 737-746, 2015, Cites: 1
Najgebauer P., Rygal J., Nowak T., Romanowski J., Rutkowski L., Voloshynovskiy S., Scherer R., Fast dictionary matching for content-based image retrieval. (2)
Fast dictionary matching for content-based image retrieval
, Fast dictionary matching for content-based image retrieval, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 747-756, 2015, Cites: 2
Korytkowski M., Scherer R., Staszewski P., Woldan P., Bag-of-features image indexing and classification in microsoft SQL server relational database. (10)
Bag-of-features image indexing and classification in microsoft SQL server relational database
, Bag-of-features image indexing and classification in microsoft SQL server relational database, Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015, 478-482, 2015, Cites: 10
Ferdowsi S., Voloshynovskiy S., Kostadinov D., Korytkowski M., Scherer R., Secure representation of images using multi-layer compression. (3)
Secure representation of images using multi-layer compression
, Secure representation of images using multi-layer compression, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 696-705, 2015, Cites: 3
Nowicki R.K., Korytkowski M., Nowak B.A., Scherer R., Design methodology for rough neuro-fuzzy classification with missing data. (5)
Design methodology for rough neuro-fuzzy classification with missing data
, Design methodology for rough neuro-fuzzy classification with missing data, Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 1650-1657, 2015, Cites: 52014 (8)
Grycuk R., Gabryel M., Korytkowski M., Scherer R., Voloshynovskiy S., From single image to list of objects based on edge and blob detection. (32)
From single image to list of objects based on edge and blob detection
, From single image to list of objects based on edge and blob detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 605-615, 2014, Cites: 32
Grycuk R., Gabryel M., Korytkowski M., Romanowski J., Scherer R., Improved digital image segmentation based on stereo vision and mean shift algorithm. (13)
Improved digital image segmentation based on stereo vision and mean shift algorithm
, Improved digital image segmentation based on stereo vision and mean shift algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8384 LNCS, 8384 LNCS, 433-443, 2014, Cites: 13
Grycuk R., Gabryel M., Korytkowski M., Scherer R., Content-Based Image Indexing by Data Clustering and Inverse Document Frequency. (26)
Content-Based Image Indexing by Data Clustering and Inverse Document Frequency
, Content-Based Image Indexing by Data Clustering and Inverse Document Frequency, Communications in Computer and Information Science, 424, 424, 374-383, 2014, Cites: 26
Nowak T., Gabryel M., Korytkowski M., Scherer R., Comparing images based on histograms of local interest points. (3)
Comparing images based on histograms of local interest points
, Comparing images based on histograms of local interest points, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8384 LNCS, 8384 LNCS, 423-432, 2014, Cites: 3
Najgebauer P., Nowak T., Romanowski J., Gabryel M., Korytkowski M., Scherer R., Content-based image retrieval by dictionary of local feature descriptors. (5)
Content-based image retrieval by dictionary of local feature descriptors
, Content-based image retrieval by dictionary of local feature descriptors, Proceedings of the International Joint Conference on Neural Networks, 512-517, 2014, Cites: 5
Rygal J., Romanowski J., Scherer R., Ferdowsi S., Novel algorithm for translation from image content to semantic form. (1)
Novel algorithm for translation from image content to semantic form
, Novel algorithm for translation from image content to semantic form, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 783-792, 2014, Cites: 1
Kostadinov D., Voloshynovskiy S., Ferdowsi S., Diephuis M., Scherer R., Robust face recognition by group sparse representation that uses samples from list of subjects. (2)
Robust face recognition by group sparse representation that uses samples from list of subjects
, Robust face recognition by group sparse representation that uses samples from list of subjects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 616-626, 2014, Cites: 2
Nowak T., Najgebauer P., Romanowski J., Gabryel M., Korytkowski M., Scherer R., Kostadinov D., Spatial keypoint representation for visual object retrieval. (4)
Spatial keypoint representation for visual object retrieval
, Spatial keypoint representation for visual object retrieval, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 639-650, 2014, Cites: 42013 (3)
Rygal J., Najgebauer P., Romanowski J., Scherer R., Extraction of objects from images using density of edges as basis for GrabCut algorithm. (3)
Extraction of objects from images using density of edges as basis for GrabCut algorithm
, Extraction of objects from images using density of edges as basis for GrabCut algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 613-623, 2013, Cites: 3
Gabryel M., Korytkowski M., Scherer R., Rutkowski L., Object detection by simple fuzzy classifiers generated by boosting. (24)
Object detection by simple fuzzy classifiers generated by boosting
, Object detection by simple fuzzy classifiers generated by boosting, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 540-547, 2013, Cites: 24
Nowak T., Najgebauer P., Rygal J., Scherer R., A novel graph-based descriptor for object matching. (2)
A novel graph-based descriptor for object matching
, A novel graph-based descriptor for object matching, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 602-612, 2013, Cites: 22012 (15)
Scherer R., Ensemble techniques. (0)
Ensemble techniques
, Ensemble techniques, Studies in Fuzziness and Soft Computing, 288, 288, 29-37, 2012, Cites: 0
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Forecasting wear of head and acetabulum in hip joint implant. (14)
Forecasting wear of head and acetabulum in hip joint implant
, Forecasting wear of head and acetabulum in hip joint implant, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 341-346, 2012, Cites: 14
Rygal J., Najgebauer P., Nowak T., Romanowski J., Gabryel M., Scherer R., Properties and structure of fast text search engine in context of semantic image analysis. (5)
Properties and structure of fast text search engine in context of semantic image analysis
, Properties and structure of fast text search engine in context of semantic image analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 592-599, 2012, Cites: 5
Scherer R., Introduction to fuzzy systems. (0)
Introduction to fuzzy systems
, Introduction to fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 7-28, 2012, Cites: 0
Scherer R., Ensembles of the Mamdani fuzzy systems. (0)
Ensembles of the Mamdani fuzzy systems
, Ensembles of the Mamdani fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 51-59, 2012, Cites: 0
Scherer R., Relational modular fuzzy systems. (1)
Relational modular fuzzy systems
, Relational modular fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 39-50, 2012, Cites: 1
Scherer R., Concluding remarks and challenges for future research. (0)
Concluding remarks and challenges for future research
, Concluding remarks and challenges for future research, Studies in Fuzziness and Soft Computing, 288, 288, 129-130, 2012, Cites: 0
Scherer R., Preface. (10)
Preface
, Preface, Studies in Fuzziness and Soft Computing, 288, 288, 2012, Cites: 10
Scherer R., Introduction. (0)
Introduction
, Introduction, Studies in Fuzziness and Soft Computing, 288, 288, 1-5, 2012, Cites: 0
Scherer R., Rough-neuro-fuzzy ensembles for classification with missing data. (0)
Rough-neuro-fuzzy ensembles for classification with missing data
, Rough-neuro-fuzzy ensembles for classification with missing data, Studies in Fuzziness and Soft Computing, 288, 288, 81-127, 2012, Cites: 0
Scherer R., Takagi-Sugeno fuzzy systems. (6)
Takagi-Sugeno fuzzy systems
, Takagi-Sugeno fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 73-79, 2012, Cites: 6
Najgebauer P., Nowak T., Romanowski J., Rygal J., Korytkowski M., Scherer R., Novel method for parasite detection in microscopic samples. (4)
Novel method for parasite detection in microscopic samples
, Novel method for parasite detection in microscopic samples, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 551-558, 2012, Cites: 4
Scherer R., Logical type fuzzy systems. (0)
Logical type fuzzy systems
, Logical type fuzzy systems, Studies in Fuzziness and Soft Computing, 288, 288, 61-71, 2012, Cites: 0
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Application of neural networks in assessing changes around implant after total hip arthroplasty. (23)
Application of neural networks in assessing changes around implant after total hip arthroplasty
, Application of neural networks in assessing changes around implant after total hip arthroplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 335-340, 2012, Cites: 23
Rutkowski L., Cpalka K., Nowicki R., Pokropinska A., Scherer R., Neuro-fuzzy systems. (2)
Neuro-fuzzy systems
, Neuro-fuzzy systems, Computational Complexity: Theory, Techniques, and Applications, 9781461418009, 9781461418009, 2069-2081, 2012, Cites: 22011 (3)
Scherer R., An ensemble of logical-type neuro-fuzzy systems. (11)
An ensemble of logical-type neuro-fuzzy systems
, An ensemble of logical-type neuro-fuzzy systems, Expert Systems with Applications, 38, 38, 13115-13120, 2011, Cites: 11
Korytkowski M., Rutkowski L., Scherer R., Rule base normalization in Takagi-Sugeno ensemble. (3)
Rule base normalization in Takagi-Sugeno ensemble
, Rule base normalization in Takagi-Sugeno ensemble, IEEE SSCI 2011 - Symposium Series on Computational Intelligence - HIMA 2011: 2011 IEEE Workshop on Hybrid Intelligent Models and Applications, 1-5, 2011, Cites: 3
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., AdaBoost ensemble of DCOG rough-neuro-fuzzy systems. (25)
AdaBoost ensemble of DCOG rough-neuro-fuzzy systems
, AdaBoost ensemble of DCOG rough-neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6922 LNAI, 6922 LNAI, 62-71, 2011, Cites: 252010 (8)
Scherer R., Designing boosting ensemble of relational fuzzy systems. (32)
Designing boosting ensemble of relational fuzzy systems
, Designing boosting ensemble of relational fuzzy systems, International Journal of Neural Systems, 20, 20, 381-388, 2010, Cites: 32
Korytkowski M., Rutkowski L., Scherer R., Szarek A., Neural network-based assessment of femur stress after hip joint alloplasty. (0)
Neural network-based assessment of femur stress after hip joint alloplasty
, Neural network-based assessment of femur stress after hip joint alloplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 621-626, 2010, Cites: 0
Korytkowski M., Scherer R., Negative correlation learning of neuro-fuzzy system ensembles. (6)
Negative correlation learning of neuro-fuzzy system ensembles
, Negative correlation learning of neuro-fuzzy system ensembles, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 114-119, 2010, Cites: 6
Scherer R., Starczewski J.T., Relational type-2 interval fuzzy systems. (3)
Relational type-2 interval fuzzy systems
, Relational type-2 interval fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6067 LNCS, 6067 LNCS, 360-368, 2010, Cites: 3
Korytkowski M., Scherer R., Modular neuro-fuzzy systems based on generalized parametric triangular norms. (2)
Modular neuro-fuzzy systems based on generalized parametric triangular norms
, Modular neuro-fuzzy systems based on generalized parametric triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6067 LNCS, 6067 LNCS, 332-339, 2010, Cites: 2
Gabryel M., Korytkowski M., Pokropinska A., Scherer R., Drozda S., Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms. (1)
Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms
, Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 74-79, 2010, Cites: 1
Korytkowski M., Nowicki R.K., Scherer R., Rutkowski L., MICOG defuzzification rough-neuro-fuzzy system ensemble. (6)
MICOG defuzzification rough-neuro-fuzzy system ensemble
, MICOG defuzzification rough-neuro-fuzzy system ensemble, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010, Cites: 6
Scherer R., Neuro-fuzzy systems with relation matrix. (23)
Neuro-fuzzy systems with relation matrix
, Neuro-fuzzy systems with relation matrix, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 210-215, 2010, Cites: 232009 (2)
Korytkowski M., Nowicki R., Scherer R., Neuro-fuzzy rough classifier ensemble. (27)
Neuro-fuzzy rough classifier ensemble
, Neuro-fuzzy rough classifier ensemble, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5768 LNCS, 5768 LNCS, 817-823, 2009, Cites: 27
Scherer R., Neuro-fuzzy relational systems for nonlinear approximation and prediction. (29)
Neuro-fuzzy relational systems for nonlinear approximation and prediction
, Neuro-fuzzy relational systems for nonlinear approximation and prediction, Nonlinear Analysis, Theory, Methods and Applications, 71, 71, 2009, Cites: 292008 (6)
Pokropinska A., Scherer R., Financial prediction with neuro-fuzzy systems. (5)
Financial prediction with neuro-fuzzy systems
, Financial prediction with neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 1120-1126, 2008, Cites: 5
Scherer R., Regression modeling with fuzzy relations. (1)
Regression modeling with fuzzy relations
, Regression modeling with fuzzy relations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 317-323, 2008, Cites: 1
Korytkowski M., Nowicki R., Scherer R., Rutkowski L., Ensemble of rough-neuro-fuzzy systems for classification with missing features. (14)
Ensemble of rough-neuro-fuzzy systems for classification with missing features
, Ensemble of rough-neuro-fuzzy systems for classification with missing features, IEEE International Conference on Fuzzy Systems, 1745-1750, 2008, Cites: 14
Starczewski J., Scherer R., Korytkowski M., Nowicki R., Modular type-2 neuro-fuzzy systems. (21)
Modular type-2 neuro-fuzzy systems
, Modular type-2 neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4967 LNCS, 4967 LNCS, 570-578, 2008, Cites: 21
Scherer R., Korytkowski M., Nowicki R., Rutkowski L., Modular rough neuro-fuzzy systems for classification. (3)
Modular rough neuro-fuzzy systems for classification
, Modular rough neuro-fuzzy systems for classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4967 LNCS, 4967 LNCS, 540-548, 2008, Cites: 3
Korytkowski M., Rutkowski L., Scherer R., From ensemble of fuzzy classifiers to single fuzzy rule base classifier. (62)
From ensemble of fuzzy classifiers to single fuzzy rule base classifier
, From ensemble of fuzzy classifiers to single fuzzy rule base classifier, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 265-272, 2008, Cites: 622007 (2)
Korytkowski M., Rutkowski L., Scherer R., On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm. (0)
On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm
, On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm, Advances in Soft Computing, 45, 45, 319-326, 2007, Cites: 0
Korytkowski M., Rutkowski L., Scherer R., Drozda G., On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems. (1)
On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems
, On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, 234-237, 2007, Cites: 12006 (5)
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Merging ensemble of neuro-fuzzy systems. (7)
Merging ensemble of neuro-fuzzy systems
, Merging ensemble of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1954-1957, 2006, Cites: 7
Pokropinska A., Nowicki R., Scherer R., Isolines of statistical information criteria for relational neuro-fuzzy system design. (1)
Isolines of statistical information criteria for relational neuro-fuzzy system design
, Isolines of statistical information criteria for relational neuro-fuzzy system design, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 288-296, 2006, Cites: 1
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Combining logical-type neuro-fuzzy systems. (1)
Combining logical-type neuro-fuzzy systems
, Combining logical-type neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 240-249, 2006, Cites: 1
Korytkowski M., Rutkowski L., Scherer R., On combining backpropagation with boosting. (58)
On combining backpropagation with boosting
, On combining backpropagation with boosting, IEEE International Conference on Neural Networks - Conference Proceedings, 1274-1277, 2006, Cites: 58
Scherer R., Boosting ensemble of relational neuro-fuzzy systems. (34)
Boosting ensemble of relational neuro-fuzzy systems
, Boosting ensemble of relational neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 306-313, 2006, Cites: 342004 (4)
Scherer R., Rutkowski L., Neuro-fuzzy relational classifiers. (25)
Neuro-fuzzy relational classifiers
, Neuro-fuzzy relational classifiers, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 376-380, 2004, Cites: 25
Scherer R., Starczewski J., Gaweda A., New methods for uncertainty representations in neuro-fuzzy systems. (4)
New methods for uncertainty representations in neuro-fuzzy systems
, New methods for uncertainty representations in neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 659-667, 2004, Cites: 4
Korytkowski M., Gabryel M., Nowicki R., Scherer R., Genetic algorithm for database indexing. (4)
Genetic algorithm for database indexing
, Genetic algorithm for database indexing, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 1142-1147, 2004, Cites: 4
Gaweda A.E., Scherer R., Fuzzy number-based hierarchical fuzzy system. (8)
Fuzzy number-based hierarchical fuzzy system
, Fuzzy number-based hierarchical fuzzy system, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 302-307, 2004, Cites: 82003 (1)
Nowicki R., Scherer R., Rutkowski L., A Hierarchical Neuro-Fuzzy System Based on S-Implications. (5)
