Note: Intro -- Preface -- Contents -- About the Editors -- Sustainable Clean Energy System -- Adaptive Neuro-Fuzzy Inference Based Modeling of Wind Energy Harvesting System for Remote Areas -- Abstract -- 1 Introduction -- 2 Motivation and Objective of Research -- 3 Related Work -- 4 Wind Energy Harvesting System -- 4.1 Modelling of Wind Turbine -- 4.2 Modeling of the PMSG -- 5 Adaptive Neuro-Fuzzy Inference System -- 6 Simulation Results and Discussion -- 6.1 Limitation of Research -- 7 Conclusion and Future Scope -- Acknowledgments -- References -- Features of Distributed Energy Integration in Agriculture -- Abstract -- 1 Introduction -- 2 Problems of Integration of Distributed Energy Projects in Agriculture -- 3 Discussion and Results -- 4 Conclusions -- References -- Concept of Multi-contact Switching System -- Abstract -- 1 Introduction -- 2 MCSS -- 3 Discussion -- 4 Conclusions -- References -- The Design of Optimum Modes of Grain Drying in Microwave-Convective Effect -- Abstract -- 1 Introduction -- 2 Main Part -- 2.1 Research Method -- 2.2 Experiment -- 2.3 Results and Discussion -- 3 Conclusions -- References -- Isolated Agroecosystems as a Way to Solve the Problems of Feed, Ecology and Energy Supply of Livestock Farming -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 3 Conclusions -- References -- Laboratory-Scale Implementation of Ethereum Based Decentralized Application for Solar Energy Trading -- Abstract -- 1 Introduction -- 2 Implement of DApp Using the Ethereum Open-Source Platform -- 2.1 Set Up the Ethereum Environment -- 2.2 Set Up React -- 2.3 Set Up Identities with Ganache and Wallets with Metamask for Participants -- 3 Implement of ERC20 Token -- 4 Implement Energy Trading -- 4.1 The User Uses ET Token to Do Both Sell Orders and Buy Orders -- 4.2 Trading -- 5 Deployment and Experimental Result -- 6 Conclusion. , References -- Solar Module with Photoreceiver Combined with Concentrator -- Abstract -- 1 Introduction -- 2 Creation of the Geometry of Working Profile of the Paraboloid Type Solar Radiation Concentrator -- 3 Results of Calculating the Profile of the Concentrator Paraboloid Type -- 4 Conclusion -- Acknowledgment -- References -- Modeling of Bilateral Photoreceiver of the Concentrator Photovoltaic Thermal Module -- Abstract -- 1 Introduction -- 2 Three-Dimensional Modeling of Bilateral Photoreceivers of Photovoltaic Thermal Solar Modules -- 3 Modeling and Visualization of the Thermal State of the Bilateral Photoreceiver of the Concentrator Photovoltaic Thermal Module -- 4 Conclusion -- Acknowledgment -- References -- Formation of Surface of the Paraboloid Type Concentrator of Solar Radiation by the Method of Orthogonal Parquetting -- Abstract -- 1 Introduction -- 2 Paraboloid Type Concentrators of the Solar Radiation -- 3 Method of the Orthogonal Parqueting of Surface of the Paraboloid Type Concentrator of the Solar Radiation -- 4 Paraboloid Type Concentrators of the Solar Radiation for Various Photoreceivers -- 5 Conclusion -- Acknowledgment -- References -- Determination of the Efficiency of Photovoltaic Converters Adequate to Solar Radiation by Using Their Spectral Characteristics -- Abstract -- 1 Introduction -- 2 Comparison of the Spectral Densities of Current Photoresponses of a Reference Silicon Photovoltaic Converter Under Various Exposure Conditions -- 3 Averaged Spectral Densities of Power of the Photoactive Part of the Spectrum for Silicon -- 4 Comparison of the Spectral Densities of Current Photoresponses of the Silicon Photovoltaic Converters -- 5 Dependence of the Efficiency of the Silicon Photovoltaic Converter Versus the Wavelength -- 6 Conclusion -- Acknowledgment -- References. , Modeling of the Thermal State of Systems of Solar-Thermal Regeneration of Adsorbents -- Abstract -- 1 Introduction -- 2 Research Problem Statement -- 3 Research Results -- 4 Conclusion -- References -- Economic Aspects and Factors of Solar Energy Development in Ukraine -- Abstract -- 1 Conclusions -- References -- A Method for Ensuring Technical Feasibility of Distributed Balancing in Power Systems, Considering Peer-to-Peer Balancing Energy Trade -- Abstract -- 1 Introduction -- 2 Notion of Power Flow and Its Feasibility -- 3 Proposed Method -- 3.1 Optimal Power Flow Problem (OPF) -- 3.2 Formulation (2) - Identification of Most Problematic Unit -- 3.2.1 Addition of New Constraint -- 3.3 Formulation (3) - Operator's Corrective Actions -- 4 Assessment of the Method -- 4.1 Measure of Market Effectiveness -- 4.2 Simulation Results -- 4.2.1 Numerical Results -- 5 Conclusions and Discussion -- References -- Sustainable Optimization, Metaheuristics and Computing for Expert System -- The Results of a Compromise Solution, Which Were Obtained on the Basis of the Method of Uncertain Lagrange Multipliers to Determine the Influence of Design Factors of the Elastic-Damping Mechanism in the Tractor Transmission -- Abstract -- 1 Introduction -- 2 Purpose of Research -- 3 Materials and Methods -- 4 Discussion Results -- 5 Conclusions -- Acknowledgments -- References -- Multiobjective Lévy-Flight Firefly Algorithm for Multiobjective Optimization -- Abstract -- 1 Introduction -- 2 Multiobjective Lévy-Flight Firefly Algorithm -- 2.1 Original FA Algorithm -- 2.2 LFFA Algorithm -- 2.3 mLFFA Algorithm -- 3 Multiobjective Test Functions -- 4 Results and Discussions -- 5 Conclusions -- References -- Cooperative FPA-ATS Algorithm for Global Optimization -- Abstract -- 1 Introduction -- 2 Cooperative FPA-ATS Algorithm -- 2.1 FPA Algorithm -- 2.2 ATS Algorithm. , 2.3 Cooperative FPA-ATS Algorithm -- 3 Benchmark Functions -- 4 Results and Discussions -- 5 Conclusions -- References -- Bayesian Optimization for Reverse Stress Testing -- Abstract -- 1 Introduction -- 1.1 Problem Statement -- 2 Supporting Literature -- 2.1 Other Optimization Methods -- 3 Bayesian Optimization: Overview -- 3.1 The LCB Acquisition Function -- 4 Changes to LCB Acquisition -- 4.1 Rationalization of the ZERO Acquisition Function -- 4.2 Definition: Run Length -- 5 Results -- 5.1 Discussion -- 6 Conclusion and Future Scope -- Appendix A -- References -- Modified Flower Pollination Algorithm for Function Optimization -- Abstract -- 1 Introduction -- 2 FPA and MoFPA Algorithms -- 2.1 Original FPA Algorithm -- 2.2 Proposed MoFPA Algorithm -- 3 Selected Benchmark Functions -- 4 Results and Discussions -- 5 Conclusions -- References -- Improved Nature-Inspired Algorithms for Numeric Association Rule Mining -- 1 Introduction -- 2 Background Information -- 2.1 Association Rule Mining -- 2.2 Differential Evolution for ARM Using Mixed Attributes -- 3 Improved DE for NARM -- 4 Experiments and Results -- 4.1 Discussion -- 5 Conclusion -- References -- Verification of the Adequacy of the Topological Optimization Method of the Connecting Rod Shaping by the BESO Method in ANSYS APDL System -- Abstract -- 1 Introduction -- 2 Analysis of Publications -- 3 Description of the Developed Optimization Method -- 4 Assessment of the Adequacy of the Developed Method -- 5 Conclusion -- References -- Method for Optimizing the Maintenance Process of Complex Technical Systems of the Railway Transport -- Abstract -- 1 Introduction -- 2 Block Diagram of the Method for Optimizing the Parameters of the Maintenance System -- 3 Monitoring of the Technical Condition of the Technical System -- 4 Automatic Identification System "PALM". , 5 Automation of Obtaining the Necessary Initial Data to Optimize Maintenance Periods -- 6 Set of Operations for Maintenance -- 7 Conclusion -- References -- Optimization of Power Supply System of Agricultural Enterprise with Solar Distributed Generation -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 3 Results and Their Discussion -- 4 Conclusion -- References -- Crack Detection of Iron and Steel Bar Using Natural Frequencies: A CFD Approach -- Abstract -- 1 Introduction -- 2 Mathematical Modeling -- 2.1 Governing Equation -- 2.2 Equation of Free Vibration -- 2.3 Boundary Conditions -- 3 Computational Domain and Mesh Generation -- 4 Numerical Results and Discussion -- 5 Conclusion -- 6 Future Study -- Acknowledgement -- References -- Belief Rule-Based Expert System to Identify the Crime Zones -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Crime Zone Prediction -- 3.2 Crime Zone -- 3.3 Purpose of Identifying Crime Zone -- 3.4 Types of Crime Zone -- 3.5 Crime Zone Prediction Techniques -- 3.6 Factors Related to Crime Zone -- 3.7 Design of BRB for Identifying Crime Zone -- 4 Implementation of Expert System -- 4.1 Initial Rule Base for X -- 4.2 User Interface -- 5 Results and Discussion -- 6 Conclusion -- References -- Parameter Tuning of Nature-Inspired Meta-Heuristic Algorithms for PID Control of a Stabilized Gimbal -- 1 Introduction -- 2 Description of the Control System -- 2.1 The Plant -- 2.2 Fitness Function Used for Meta-Heuristic Algorithms -- 3 Nature-Inspired Algorithms -- 3.1 Flower Pollination Algorithm -- 3.2 Teaching-Learning-Based Optimization Algorithm -- 4 Computational Experimental Design -- 5 Results and Discussions -- 5.1 Parameter Optimization -- 5.2 Robustness Analysis -- 5.3 Suggestions for Future Practitioners -- 6 Conclusion -- References. , Solving an Integer Program by Using the Nonfeasible Basis Method Combined with the Cutting Plane Method.

Note: Intro -- Preface -- Organization -- Invited Talks -- Computing Useful Recommendations: Still Requires Knowledge -- Learning to Compare with Few Data for Personalised Human Activity Recognition -- Contents -- Invited Paper -- Learning to Compare with Few Data for Personalised Human Activity Recognition -- 1 Introduction -- 2 Reasoning with Sensor Data for HAR -- 2.1 HAR Using selfBACK Accelerometer Data -- 2.2 Multi-modal Exercise Recognition with MEx Data -- 2.3 Personalisation with Non-iid Data -- 3 Learning to Personalise with Few Data -- 3.1 Learning to Match -- 3.2 Learning to Relate -- 3.3 Learning to Adapt -- 4 Evaluations -- 4.1 HAR Comparative Study -- 4.2 Discussion -- 5 Conclusion -- References -- Technical Session: Workflows -- A*-Based Similarity Assessment of Semantic Graphs -- 1 Introduction -- 2 Background -- 2.1 Semantic Graphs -- 2.2 Approaches to Similarity Assessment of Multi-labeled Graphs -- 2.3 Semantic Graph Similarity Following the Local-Global Principle -- 2.4 A*-Based Similarity Search -- 3 Improving the A*-Based Similarity Search -- 3.1 Search Space Reduction -- 3.2 Adaptive Mapping Orientation -- 3.3 More Informed Heuristic -- 3.4 Heuristic-Based Element Selection -- 4 Experimental Evaluation -- 4.1 Similarity Computation with Ensured Optimality -- 4.2 Similarity Computation Without Ensured Optimality -- 5 Conclusion and Future Work -- References -- A Time-Series Similarity Measure for Case-Based Deviation Management to Support Flexible Workflow Execution -- 1 Introduction -- 2 Deviation Management for Flexible Workflows -- 2.1 Workflow Flexibility -- 2.2 Constraint-Based Workflow Engine -- 2.3 Case-Based Deviation Management -- 3 Similarity Measures for Time Series -- 3.1 Dynamic Time Warping -- 3.2 Smith-Waterman-Algorithm -- 3.3 Weighted Vector Similarity -- 4 Similarity Measure for Deviating Workflows. , 4.1 Characteristics of Similarity for Deviating Workflows -- 4.2 Pre-processing of the Case Base -- 4.3 Similarity Assessment -- 5 Evaluation -- 6 Conclusion and Future Work -- References -- Process Trace Classification for Stroke Management Quality Assessment -- 1 Introduction -- 2 Related Work -- 3 Medical Process Trace Classification -- 3.1 Classification Through Semantic Trace Comparison -- 3.2 Deep Learning Classification -- 4 Experimental Results -- 4.1 Semantic Trace Comparison: Classification Results -- 4.2 Deep Learning: Classification Results -- 5 Discussion and Conclusions -- References -- Technical Session: Applications in Health -- Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans -- 1 Introduction -- 2 Related Work -- 3 A CBR Approach to Marathon Training -- 3.1 From Training/Workout Sessions to Cases -- 3.2 Task 1: Predicting Goal Race-Times -- 3.3 Task 2: Recommending Tailored Training Programmes -- 3.4 From Single Weeks to Multiple Weeks -- 4 Evaluation -- 4.1 Setup -- 4.2 Prediction Error by Training Week -- 4.3 Prediction Stability -- 4.4 Prediction Error by Ability -- 4.5 Evaluating Training Plan Recommendations -- 5 Conclusions -- References -- Classifying Breast Cancer Tissue Through DNA Methylation and Clinical Covariate Based Retrieval -- 1 Introduction -- 2 Background -- 2.1 Research Background -- 3 Methods -- 3.1 Data Preprocessing -- 3.2 System Overview -- 3.3 Distance Measures -- 3.4 Retrieval Framework -- 3.5 Feature Selection -- 4 Results -- 4.1 Test for Confounding Clinical Variables -- 4.2 Classification Tasks -- 4.3 First Task: Cancer Classification -- 4.4 Second Task: Cancer Subtype -- 4.5 Initial Findings -- 4.6 Effect of Clinical Covariate Integration -- 4.7 Case Refinement for Subtype Classification -- 5 Discussion -- References. , A New Adaptation Phase for Thresholds in a CBR System Associated to a Region Growing Algorithm to Segment Tumoral Kidneys -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 COLISEUM-3D -- 3.2 CBR System for Segmentation of Pathological Kidney -- 3.3 Adaptation Phase -- 4 Results -- 4.1 Database, Initial Hypothesis and Conditions, and Evaluation Process -- 4.2 Determination of Optimal Weights -- 4.3 Comparaison with OV2ASSION Approach for Segmentation of Tumoral Kidneys -- 5 Discussion and Future Work -- 6 Conclusion -- References -- Predicting the Personal-Best Times of Speed Skaters Using Case-Based Reasoning -- 1 Introduction -- 2 An Introduction to Speed Skating -- 2.1 The Anatomy of Speed Skating: Skaters and Races -- 2.2 The Importance of Pacing -- 2.3 The Dutch Speed Skating Data Set -- 2.4 Research Questions -- 3 Predicting Track-Specific Race-Times -- 3.1 From Races to Cases -- 3.2 Adapting for Track Variations -- 3.3 Generating Predictions -- 4 Evaluation -- 4.1 Data and Methodology -- 4.2 Prediction Error vs k -- 4.3 Best Performers -- 4.4 On Gender and Age -- 5 Conclusions -- References -- Technical Session: New Paradigms -- Clood CBR: Towards Microservices Oriented Case-Based Reasoning -- 1 Introduction -- 2 Related Work in CBR Development Architectures -- 3 Microservices Design Paradigm for CBR -- 3.1 Clood Architecture -- 3.2 The Casebase -- 3.3 Local Similarity -- 3.4 Global Similarity -- 3.5 Implication for CBR Cycle -- 4 Clood CBR System -- 4.1 Casebase Using Elasticsearch -- 4.2 Clood Similarity Functions -- 4.3 REST API -- 4.4 Clood CBR Dashboard -- 5 Evaluation -- 5.1 Experimental Setup and Dataset -- 5.2 Results and Discussion -- 6 Conclusion -- References -- Holographic Case-Based Reasoning -- 1 Introduction -- 2 Holographic Systems -- 3 Holographic CBR -- 4 Realization of a Holographic Reasoner. , 5 Discussion and Related Work -- 6 Conclusion -- References -- Technical Session: Explanations -- Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI) -- 1 Introduction -- 2 Counterfactual Explanation: Promise, Problems and Prospects -- 2.1 Counterfactual Promise -- 2.2 Counterfactual Problems -- 2.3 CBR's Prospects for Counterfactuals -- 3 Experiment 1: Plotting Counterfactual Potential -- 3.1 Method: Data Sets and Procedure -- 3.2 Results and Discussion -- 4 A Case-Based Technique for Good Counterfactuals -- 4.1 Explanatory Competence -- 4.2 Leveraging Counterfactual Cases for Explanation -- 4.3 A Case-Based Approach to Generating Good Counterfactuals -- 5 Experiment 2: Evaluating Explanation Competence -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Explanatory Competence -- 5.3 Results and Discussion: Counterfactual Distance -- 6 Conclusions and Future Directions -- References -- CBR-LIME: A Case-Based Reasoning Approach to Provide Specific Local Interpretable Model-Agnostic Explanations -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 The CBR-LIME Method -- 4.1 Case Base Elicitation -- 4.2 Case-Based Explanation -- 5 Evaluation -- 5.1 Offline Evaluation -- 5.2 Online Evaluation with Users -- 6 Conclusions and Future Work -- References -- A User-Centric Evaluation to Generate Case-Based Explanations Using Formal Concept Analysis -- 1 Introduction -- 2 Related Work -- 3 Explanation System -- 3.1 Matrix Factorization Recommender System -- 3.2 Retrieving Explanatory Items -- 3.3 Generating Explanations Using FCA -- 4 User-Centric Evaluation -- 4.1 Data -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusions and Future Work -- References -- Technical Session: Deep learning. , Improved and Visually Enhanced Case-Based Retrieval of Room Configurations for Assistancepg in Architectural Design Education -- 1 Introduction -- 2 Concepts and Foundations of the System -- 2.1 Artificial Intelligence Methods -- 2.2 Room Configuration -- 2.3 Architectural Room Zones -- 2.4 Zoned Connection Map -- 3 Combined Retrieval + UI System -- 3.1 Data Augmentation -- 3.2 Context-Based Preselection of Cases -- 3.3 Graph Matching -- 3.4 User Interface: RoomConf Editor -- 4 Evaluation -- 4.1 Quantitative Analysis -- 4.2 User Study -- 4.3 General Results -- 4.4 Feedback on Use in Education -- 5 Conclusion and Future Work -- References -- Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Previous Work -- 2.1 Semantic Workflow Representation -- 2.2 Similarity Assessment -- 2.3 MAC/FAC Retrieval for POCBR -- 3 Similarity Learning for Workflow Graphs with Siamese Graph Neural Networks -- 3.1 Encoding Semantic Graphs for Similarity Learning -- 3.2 Similarity Learning for Workflow Graphs -- 3.3 Siamese GNN-Based Workflow Retrieval -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Applying Class-to-Class Siamese Networks to Explain Classifications with Supportive and Contrastive Cases -- 1 Introduction -- 2 Background -- 2.1 The Class-to-Class (C2C) Approach -- 2.2 Siamese Networks -- 2.3 Explanation by Cases in CBR -- 3 Class-to-Class Siamese Networks -- 3.1 Benefits of the C2C-SN Approach -- 3.2 Building a C2C-SN -- 4 Experiments -- 4.1 Classification Accuracy of Pairs -- 4.2 One-Shot Learning -- 4.3 Explanation by Cases -- 4.4 Finding Prototypical Cases -- 4.5 Using Prototypical Cases in Classification of a Case -- 5 Additional Results and Future Directions -- 6 Conclusion. , References.