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[null,null,"Titolo",null,"Computational Science – ICCS 2023 : 23rd International Conference, Prague, Czech Republic, July 3–5, 2023, Proceedings, Part IV / / edited by Jiří Mikyška, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M.A. Sloot"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"3-031-36027-3"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (687 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 10476"]
[null,null,"Disciplina",null,"511.3"]
[{"$nulls":4},"004"]
[null,null,"Soggetti",null,"Computer science"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Software Engineering"]
[{"$nulls":4},"Mathematics of Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part IV -- Computational Social Complexity -- The Social Graph Based on Real Data -- 1 Introduction and Model -- 2 Results and Conclusions -- References -- Longitudinal Analysis of the Topology of Criminal Networks Using a Simple Cost-Benefit Agent-Based Model -- 1 Introduction -- 2 Methodology -- 2.1 Agent-Based Modelling -- 2.2 Network Initialisation -- 2.3 Determine Topological Changes -- 2.4 Data -- 2.5 Measurement -- 3 Results and Discussion -- 4 Conclusion -- References -- Manifold Analysis for High-Dimensional Socio-Environmental Surveys -- 1 Introduction -- 2 Methods -- 2.1 Dimension Reduction Algorithms -- 2.2 Simulation Framework -- 2.3 Bangladesh Climate Change Adaptation Survey -- 3 Results -- 3.1 "]
[{"$nulls":4},"Simulation Framework Results -- 3.2 Bangladesh Climate Change Adaptation Survey Results -- 4 Discussion -- References -- Toxicity in Evolving Twitter Topics -- 1 Introduction -- 2 Related Work -- 3 Data and Methods -- 3.1 Topic Modelling and DAG Lineage -- 3.2 Transition Types -- 4 Studying Toxicity in Topic Evolution -- 4.1 Toxicity per Transition Type -- 4.2 Relationship Topic Popularity - Toxicity -- 5 Conclusion -- References -- Structural Validation of Synthetic Power Distribution Networks Using the Multiscale Flat Norm -- 1 Introduction -- 2 Methods -- 2.1 Multiscale Flat Norm -- 2.2 Proposed Algorithm -- 2.3 Normalized Flat Norm -- 3 Results and Discussion -- 3.1 Comparing Network Geometries -- 3.2 Comparison of Flat Norm and Hausdorff Distance Metrics -- 4 Conclusions -- References -- OptICS-EV: A Data-Driven Model for Optimal Installation of Charging Stations for Electric Vehicles -- 1 Introduction -- 1.1 Our Contributions -- 2 Related Work -- 3 Methodology -- 3.1 EV Charging Station Placement -- 3.2 Connecting EV Charging Stations -- 4 Experimental Results."]
[{"$nulls":4},"4.1 EV Charging Station Placement -- 4.2 Optimal Routing Problem -- 5 Discussions and Conclusion -- References -- Computer Graphics, Image Processing and Artificial Intelligence -- Radial Basis Function Neural Network with a Centers Training Stage for Prediction Based on Dispersed Image Data -- 1 Introduction -- 2 Model and Methods -- 3 Datasets and Results -- 4 Conclusion -- References -- Database of Fragments of Medieval Codices of the 11th-12th Centuries - The Uniqueness of Requirements and Data -- 1 Introduction -- 2 Existing Databases -- 3 Dataset Description and Analysis -- 4 Conclusion and Future Research -- References -- Global Optimisation for Improved Volume Tracking of Time-Varying Meshes -- 1 Introduction -- 2 Related Work -- 3 As-Rigid-as-Possible Volume Tracking -- 4 Maximum Distance Based Affinity -- 5 Irregular Center Detection -- 6 Global Optimisation -- 6.1 Global Tracking Energy -- 6.2 Optimisation Strategy -- 6.3 Global Movement-Based Affinity -- 7 Experimental Results -- 7.1 Influence of the Proposed Affinity -- 7.2 Irregular Center Removal -- 8 Conclusions -- References -- Detection of Objects Dangerous for the Operation of Mining Machines -- 1 Introduction -- 2 Preparing a Dataset -- 3 Applied and Tested Models of Neural Networks -- 3.1 RetinaNet -- 3.2 Mask RCNN -- 3.3 YOLOv5 -- 4 Detection System -- 5 Results -- 5.1 Evaluation of Networks -- 5.2 Evaluation of Created Detectors -- 6 Conclusion -- References -- A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Recognition to Enhance Learning Experience -- 1 Introduction -- 2 Related Works -- 2.1 Complete Solutions for Face Expression Recognition -- 2.2 Applying Image Pre-processing Prior to CNNs Classification -- 2.3 Features Extraction Using CNNs Coupled with Other Machine Learning Classifiers -- 3 DAAM-DCNNs Hybrid Approach to Facial Expression Recognition."]
[{"$nulls":4},"3.1 Convolutional Neural Network -- 4 Experimental Setup and Results -- 4.1 Datasets -- 4.2 Investigated Parameters -- 4.3 Results and Discussion -- 5 Conclusion -- References -- Champion Recommendation in League of Legends Using Machine Learning*-1pc -- 1 Introduction -- 1.1 Gameplay Overview -- 1.2 Pick and Ban Phase -- 2 Related Work -- 3 Methodology -- 3.1 Formulation of Machine Learning Problem -- 3.2 Datasets -- 3.3 Machine Learning Models for Solving the Problem -- 4 Results and Discussion -- 4.1 Pre-made Datasets -- 4.2 Riot API Datasets -- 4.3 Execution Time -- 5 Conclusions -- References -- Classification Performance of Extreme Learning Machine Radial Basis Function with K-means, K-medoids and Mean Shift Clustering Algorithms -- 1 Introduction -- 2 Extreme Learning Machine -- 3 Extreme Learning Machine Radial Basis Function "]
[{"$nulls":4},"-- 4 Clustering Methods -- 4.1 Mean Shift -- 4.2 K-means -- 4.3 K-medoids -- 5 Experiments and Results -- 6 Conclusions -- References -- Impact of Text Pre-processing on Classification Accuracy in Polish -- 1 Introduction -- 2 Related Works in Text Pre-processing on Classification Accuracy -- 3 Machine Translation Model for English-Polish Translation -- 3.1 Chosen Model Architecture -- 3.2 The Dataset Used for Machine Translation Task -- 3.3 Machine Translation Task Results -- 3.4 Summary of Machine Translation Task -- 4 Text Pre-processing Impact on Text -- 4.1 Polish Sentences Dataset Used in Classification -- 4.2 Development Tools Used for Performing Experiments -- 4.3 Experiments Verifying the Impact of Noise Removal -- 4.4 Results of the Noise Reduction Experiments -- 5 Conclusions -- References -- A Method of Social Context Enhanced User Preferences for Conversational Recommender Systems*-1pc -- 1 Introduction -- 2 Related Work -- 2.1 Recommender Systems \"026E30F Conversational Recommender Systems."]
[{"$nulls":4},"2.2 Social Context Information -- 3 Preliminaries -- 3.1 Problem Formulation -- 4 Methodology -- 4.1 Model Overview -- 4.2 Representation Learning -- 4.3 Social-Enhanced User Preference Estimation -- 4.4 Item and Attribute Scoring -- 4.5 Model Training -- 5 Experiments Setups -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baselines -- 5.4 Implementation Details -- 6 Results and Discussion -- 6.1 Performance Comparison for Multi-round CRS -- 6.2 Performance Comparison at Different Conversation Turns -- 6.3 Ablation Study -- 6.4 Performance Comparison for User Preference Estimation -- 7 Conclusion -- References -- Forest Image Classification Based on Deep Learning and XGBoost Algorithm*-1pc -- 1 Introduction -- 2 Related Studies -- 3 Proposed Model -- 3.1 Multi-label Image Classification -- 3.2 Pre-processing -- 4 Overview of the Model Architecture -- 4.1 The XGBOOST Algorithm -- 4.2 ResNet50 Network Architecture -- 5 Metrics for the Study -- 6 Results and Discussion -- 7 Conclusion -- References -- Radius Estimation in Angiograms Using Multiscale Vesselness Function -- 1 Introduction -- 2 Methods -- 2.1 Vesselness-Radius Relationship -- 2.2 Curve Fitting to Estimate Vessel Radius -- 2.3 Reference Methods -- 3 Results -- 3.1 Radius Estimation Results in Images of Cylinders -- 3.2 Radius Estimation Results in Bifurcation Image -- 3.3 Radius Estimation in MRA -- 4 Summary and Conclusions -- References -- 3D Tracking of Multiple Drones Based on Particle Swarm Optimization -- 1 Introduction -- 2 A Method for Tracking Multiple Drones -- 2.1 Dataset -- 2.2 Particle Swarm Optimization -- 2.3 Fitness Function -- 3 Experiment Results -- 3.1 Simulation Dataset -- 3.2 Real Dataset -- 4 Conclusions -- References -- Sun Magnetograms Retrieval from Vast Collections Through Small Hash Codes -- 1 Introduction."]
[{"$nulls":4},"2 Solar Magnetic Intensity Hash for Solar Image Retrieval -- 2.1 Magnetic Region Detection -- 2.2 Calculation of Solar Magnetic Intensity Descriptor -- 2.3 Hash Generation -- 2.4 Retrieval -- 3 Experimental Results -- 4 Conclusions -- References -- Cerebral Vessel Segmentation in CE-MR Images Using Deep Learning and Synthetic Training Datasets -- 1 Introduction -- 2 Related Work -- 2.1 State-of-the-Art Methods -- 2.2 Current Contribution -- 3 Methods and Materials -- 3.1 Vessel Segmentation Model -- 3.2 MR Angiography Simulation -- 4 Experimental Results -- 4.1 Simulated Training Images -- 4.2 Tests of the Segmentation Model -- 5 Conclusions -- References -- Numerical Method for 3D Quantification of Glenoid Bone Loss -- 1 Introduction -- 2 Statement of the Problem -- 3 Volume of a Polyhedron via the Gauss Formula -- 4 The Voxelization Approach -- 5 Description of Our Numerical Method -- "]
[{"$nulls":4},"5.1 Randomized Sampling of the Surface -- 5.2 Downsampling the Point Cloud -- 5.3 Distance Function and Closest Point Map -- 5.4 Indicator Function -- 5.5 Denoising -- 5.6 Test of Accuracy -- 6 Numerical Illustration -- 7 Conclusion -- References -- Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps*-1pc -- 1 Introduction -- 1.1 Motivation -- 1.2 Aims and Structure of this Paper -- 2 Previous Work -- 3 The Optimization Problem -- 4 The Proposed Method: ClonalG Algorithm -- 5 Experimental Results -- 5.1 Example I -- 5.2 Example II -- 5.3 Example III -- 5.4 Implementation Issues -- 6 Conclusions and Future Work -- References -- Machine Learning and Data Assimilation for Dynamical Systems -- Clustering-Based Identification of Precursors of Extreme Events in Chaotic Systems -- 1 Introduction -- 2 Methodology -- 2.1 Preparatory Steps -- 2.2 Transition Probability Matrix and Graph Interpretation -- 2.3 Modularity-Based Clustering."]
[{"$nulls":4},"2.4 Extreme and Precursor Clusters Identification."]
[null,null,null,"Sommario/riassunto","The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023. The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks. The theme for 2023, \"Computation at the Cutting Edge of Science\", highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational andsystems biology, environmental systems, finance, and others."]