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[null,"1.","Record Nr.",null,"UNINA9910767562003321"]
[null,null,"Autore",null,"Burduk Robert"]
[null,null,"Titolo",null,"Progress on Pattern Classification, Image Processing and Communications : Proceedings of the CORES and IP&C Conferences 2023"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"3-031-41630-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (241 pages)"]
[null,null,"Collana",null,"Lecture Notes in Networks and Systems Series ; ; v.766"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Advanced Data Analysis and Machine Learning -- A Novel Approach of Multi-string Parsing for Syntactic Pattern Recognition -- 1 Introduction -- 2 Methodological Motivation -- 3 Preliminaries -- 4 Multi-string Parsing -- 5 Concluding Remarks -- References -- Hollow n-grams Vectorizer for Natural Language Processing Problems -- 1 Introduction -- 2 Hollow n-grams -- 3 Experiments Design -- 4 Experimental Evaluation -- 4.1 Experiment 1 - Max Features Threshold -- 4.2 Experiment 2 - Different nn values -- 5 Conclusions and Future Works -- References -- A Non-deep Approach to Classifying Movie Genres Based on Multimodal Data -- 1 Introduction -- 2 Related Works -- 3 Data Preprocessing and Extraction -- 4 Experimental Evaluation -- 4.1 Set-Up -- 4.2 Experiment 1 - Classification Comparison for Each Modality Separately -- 4.3 Experiment 2 - Classification Comparison for Multi-modal Data -- 4.4 Experiment 3 - Impact of Preprocessing on Distance-Based Classifiers Performance -- 5 Conclusion -- References -- Incremental Extreme Learning Machine for Binary Data Stream Classification -- 1 Introduction -- 2 Methods -- 3 Evaluation of Data Stream Classifiers -- 3.1 Experimental Setup -- 4 Experimental Evaluation -- 4.1 ELMi Hyperparameters -- 4.2 Comparison of Classifiers -- 5 Conclusions -- References -- Interpreting Intrusions - The Role of Explainability in AI-Based Intrusion Detection Systems -- 1 Introduction, Motivation and Rationale -- 2 Materials and Methods -- 2.1 Advantages and Limitations of xAI Techniques -- 2.2 Overview of xAI Techniques Applicable in Intrusion Detection -- 3 Experimental "]
[{"$nulls":4},"Setup and Results -- 3.1 xAI Interpretation -- 4 Conclusions -- References -- Real-Time Deformation of Three-Dimensional Volumetric Models Using Mesh Models -- 1 Introduction -- 2 Motivations."]
[{"$nulls":4},"3 Deformation Simulation -- 3.1 Obi Based Simulation -- 3.2 3D Volume Deformation -- 4 Results and Discussion -- References -- Hyperparameters Optimization Using GridSearchCV Method for TinyML Models -- 1 Introduction -- 2 GridSearchCV Method -- 3 TGrid Algorithm -- 4 Experiments -- 5 Conclusions -- References -- Analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy -- 1 Introduction -- 1.1 Related Works -- 1.2 Contribution -- 2 Purposes of the Experiment -- 2.1 Experiment 1: Single Criterion Optimization -- 2.2 Experiment 2: Dependency Research for Resampling Methods -- 3 Experimental Evaluation -- 3.1 Experiment 1: Single Criterion Optimization -- 3.2 Experiment 2: Dependency Research for Resampling Methods -- 4 Conclusion -- References -- Image Processing -- Deep Learning for Pit Detection in Cherries -- 1 Introduction -- 2 Image Acquisition System -- 3 Datasets -- 4 Image Analysis Method -- 5 Experiments -- 5.1 Polarization Parameters in Classification -- 5.2 Combining Information for Better Prediction -- 6 Summary -- References -- Image Analysis System for Augmented Reality Games -- 1 Introduction -- 2 General Concept -- 2.1 Detection and Tracking Module -- 2.2 Global Re-ID -- 2.3 Action Recognition -- 3 Experiments -- 4 Conclusions -- References -- Coregistration of Selected Sequences in MRI Imaging Based on Edge Analysis of Image Difference -- 1 Introduction -- 2 Materials and Methods -- 2.1 The Dataset -- 2.2 Designed Procedure -- 2.3 Reference Measures of Image Alignment -- 2.4 Results -- 3 Summary -- References -- Actinic Keratosis Prediction Based on Deep Learning Methods -- 1 Introduction -- 1.1 Deep Leaning for AK Classification -- 1.2 Deep Leaning for AK Detection -- 2 Methods -- 2.1 Data Collection -- 2.2 Data Preprocessing and Augmentation."]
[{"$nulls":4},"2.3 Object Detection Model -- 2.4 Performance Metrics -- 2.5 Model Interpretation -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- A New Assessment of Convolutional Neural Networks for Texture Directionality Detection -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Enhanced Training Data -- 3.2 Novel CNN Architectures and Additional Activation Functions -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Knowledge Distillation with Relative Representations for Image Representation Learning -- 1 Introduction -- 2 Related Work -- 3 Background: Relative Representations -- 4 Relative Representation Distillation -- 5 Experiments -- 5.1 Distillation Compared to Training from Scratch -- 5.2 Comparison to Other Relation-Based Distillation Methods -- 5.3 Transfer Learning -- 5.4 Anchor Selection -- 6 Conclusion -- References -- An Approach for CT Image Conversion Using Filtering Based on Quaternion Mathematics -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Bilateral Filter -- 3.2 Quaternion Mathematics -- 4 Quaternion Bilateral Filter (QBF) -- 5 Experimental Results -- 5.1 Evaluation Metrics -- 5.2 Results -- 6 Conclusion -- References -- Exploring Text-Guided Synthetic Distribution Shifts for Robust Image Classification -- 1 Introduction -- 2 Related Work -- 3 Distribution-Shifted Data Generation -- 4 Experiments -- 4.1 Implementation -- 4.2 Measuring Distribution Shift -- 4.3 Probing Adversarial Ability -- 4.4 Use as Training Data -- 5 Conclusion -- References -- Artificial Intelligence: Various Applications and Problems -- Color Tracking Application Using AI-Based Docker "]
[{"$nulls":4},"Container Scheduling in Fog Computing -- 1 Introduction -- 2 FogBus2 Framework -- 2.1 Multiplatform Support -- 2.2 Containerization Support -- 2.3 Heterogeneous IoT Applications -- 2.4 Distributed Multi-database."]
[{"$nulls":4},"2.5 Integration with Cloud Infrastructure and VPN -- 2.6 Dynamic Scheduling Mechanism and Policy Integration -- 3 Communications Among Components -- 4 Proposed Solution for Container Scheduling in Cloud Computing -- 5 Experimental Results -- 5.1 Developed Edge Computing Network -- 5.2 Experimental Environment -- 5.3 Experimental Results -- 6 Conclusions and Future Actions -- References -- Air Pollution Monitoring and Information Distribution System -- 1 Introduction -- 2 Proposed System -- 2.1 System Structure -- 2.2 Vehicle -- 2.3 Instruments -- 2.4 Results -- 3 Conclusion -- References -- Detection of Conspiracy Theories from Tweets Using NLI-Based Zero Shot Text Classification Models -- 1 Introduction -- 2 Related Works -- 3 Proposed Data Pipeline -- 3.1 Data Collection -- 3.2 Models -- 4 Analysis -- 4.1 Data Analysis -- 4.2 Controversy Analysis -- 4.3 Sentiment Analysis -- 5 Conclusions -- References -- Battery Management System for Polish Hybrid Residential Photovoltaic Power Plants -- 1 Introduction -- 2 Self-consumption Battery Management System -- 3 Time-Based Battery Management System -- 4 Experiments -- 5 Conclusions -- References -- Document Annotation Tool for News Content Analysis -- 1 Introduction -- 2 Proposed Tool for Content Annotation -- 3 Results Analysis -- 4 Conclusions -- References -- Dynamic Time Warping Technique Applied to the User's Intent Recognition for Myoelectric-Based Control of Upper Limb Prosthesis -- 1 Introduction -- 2 Methods -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Experiment 1 -- 4.2 Experiment 2 -- 5 Conclusions -- References -- A Hybrid Long Short-Term Memory Network Based on Wind and Rain Sensitive and Its Application to PM2.5 Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Air Pollution Forecasting and Air Pollution Factors -- 2.2 Time Series Problem -- 3 Proposed Approach."]
[{"$nulls":4},"3.1 Overview -- 3.2 Data Preprocessing -- 3.3 Wind-Sensitive LSTM and Rain-Sensitive LSTM -- 4 Experiment and Evaluation -- 4.1 Dataset and Experiment Setup -- 4.2 Evaluation Metric -- 4.3 Compared Models -- 4.4 Evaluation of the Number of Past Time Periods for PM2.5 -- 4.5 Evaluation of All Comparison Models -- References -- Author Index."]