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[null,"1.","Record Nr.",null,"UNINA9910691321703321"]
[null,null,"Autore",null,"Hudson Rex A"]
[null,null,"Titolo",null,"The sociology and psychology of terrorism [[electronic resource] ] : who becomes a terrorist and why? : a report / / prepared under an interagency agreement by the Federal Research Division, Library of Congress ; author, Rex A. Hudson ; editor, Marilyn Majeska"]
[null,null,"Pubbl/distr/stampa",null,"Washington, D.C. : , : The Division, , [1999]"]
[null,null,"Soggetti",null,"Terrorists - Psychology"]
[{"$nulls":4},"Terrorists - Social conditions"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from title screen."]
[null,"2.","Record Nr.",null,"UNINA9910869173803321"]
[null,null,"Titolo",null,"Computational Science – ICCS 2024 : 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part IV / / edited by Leonardo Franco, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-63772-0"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (420 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14835"]
[null,null,"Disciplina",null,"511.3"]
[null,null,"Soggetti",null,"Computer science"]
[{"$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"]
[{"$nulls":4},"Mathematics of Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part IV -- Biomedical and Bioinformatics Challenges for Computer Science -- Exploiting Medical-Expert Knowledge Via a Novel Memetic Algorithm for the Inference of Gene Regulatory Networks -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experimentation -- 4.1 Parameter Settings -- 4.2 Comparison with GENECI -- 5 Conclusions and Future Work -- References -- Human Sex Recognition Based on Dimensionality and Uncertainty of Gait Motion Capture Data -- 1 Introduction -- 2 Related Work -- 3 Correlation Dimension and Sample Entropy -- 4 Dataset -- 5 Experimental Setup -- 6 Results -- 7 Summary and Conclusions -- References -- A Multi-domain Multi-task Approach for Feature Selection from Bulk RNA Datasets -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Data -- 5 "]
[{"$nulls":4},"Experiment -- 6 Results -- 7 Conclusions -- References -- Neural Dynamics in Parkinson's Disease: Integrating Machine Learning and Stochastic Modelling with Connectomic Data -- 1 Introduction -- 2 Methods -- 2.1 Discrete Brain Network Model of CBGTH -- 2.2 Stochastic Brain Network Model of CBGTH -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for ``Green'' Fetal Brain Segmentation -- 1 Introduction -- 1.1 Fetal Brain Segmentation -- 1.2 ``Green'' Deep Learning -- 1.3 Contribution -- 2 Methods -- 2.1 Setup and Hardware -- 2.2 Used Datasets -- 2.3 Energy Usage and Performance Measure -- 2.4 Experimental Design -- 2.5 Evaluated Techniques -- 3 Results and Discussion -- 3.1 Final Model Choice -- 3.2 Conclusions and Recommendations -- References -- Negation Detection in Medical Texts -- 1 Introduction -- 2 Basic Definition and Open Problems -- 3 Main Approaches to Negation Detection -- 3.1 Rule-Based Approaches."]
[{"$nulls":4},"3.2 Machine Learning Approaches -- 3.3 Deep Learning Approaches -- 4 Discussions and Results -- References -- EnsembleFS: an R Toolkit and a Web-Based Tool for a Filter Ensemble Feature Selection of Molecular Omics Data -- 1 Introduction -- 2 Methods -- 2.1 Feature Selection and Classification Algorithms -- 2.2 Ensemble Feature Selection -- 3 EnsembleFS an R Toolkit -- 3.1 Web Application -- 3.2 R Package -- 4 Use Case -- 5 Computational Aspects -- 6 Summary -- References -- A Method for Inferring Candidate Disease-Disease Associations -- 1 Background -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Gene-Disease Associations -- 2.3 Disease-Disease Associations -- 2.4 Score Evaluation -- 3 Results and Discussion -- 4 Conclusion -- References -- Network Model with Application to Allergy Diseases -- 1 Introduction -- 2 Hierarchical Logistic Network Models -- 2.1 Generative Model -- 2.2 Misspecified Model -- 3 Application to Modelling Allergic Diseases -- 3.1 The Structure of the Model -- 3.2 Generative and Misspecified Models of Allergy Diseases -- 3.3 Comparison of Two Versions of Our Model -- 3.4 Estimation of Parameters and Evaluation of the Model -- 4 Discussion -- 5 Conclusions -- References -- TM-MSAligner: A Tool for Multiple Sequence Alignment of Transmembrane Proteins -- 1 Introduction -- 2 Software Description -- 2.1 Software Architecture -- 2.2 Transmembrane Proteins Features -- 2.3 Execution Modes -- 2.4 Output Results -- 3 Illustrative Example -- 4 Discussion -- 5 Conclusions -- References -- Determining Mouse Behavior Based on Brain Neuron Activity Data -- 1 Introduction -- 2 Background and Related Work -- 3 Classification of Mouse Position on a Circular Track -- 4 Regression of Mouse Position on a Circular Track -- 5 Conclusions -- References."]
[{"$nulls":4},"Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification -- 1 Introduction -- 2 Methods -- 2.1 Prompt-Engineering ChatGPT for Simulated-Articles Generation -- 2.2 Feature Extraction and Biological Graph Construction -- 2.3 Fact-Checking ChatGPT Biological Graphs -- 3 Results -- 4 Discussion -- 5 Conclusion and Future Direction -- References -- Identification of Domain Phases in Selected Lipid Membrane Compositions -- 1 Introduction -- 2 Methods -- 2.1 System Preparation and Simulation -- 2.2 Lipid Features and Machine Learning Techniques -- 3 Results and Discussion -- 4 Conclustions -- References -- MonoWeb: Cardiac Electrophysiology Web Simulator -- 1 Introduction -- 2 Methods -- 2.1 Monodomain Model -- 2.2 Trame -- 2.3 Paraview -- 3 MonoWeb -- 3.1 Configuring the Cellular Model -- 3.2 Configuring the Stimuli -- 4 Case Studies -- 5 Conclusion -- References -- Enhancing Breast "]
[{"$nulls":4},"Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Datasets of Breast Ultrasound Images and Preprocessing -- 3.2 Convolutional Neural Networks (CNNs) Architecture -- 3.3 Proposed Method -- 4 Results and Discussion -- 4.1 Breast Cancer Segmentation Results -- 4.2 Breast Cancer Classification Results -- 5 Conclusions -- References -- Integration of Self-supervised BYOL in Semi-supervised Medical Image Recognition -- 1 Introduction -- 2 Related Work -- 3 A Brief on BYOL -- 4 Proposed Method -- 4.1 Pre-training -- 4.2 Fine-Tuning -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Hyperparameter Tuning -- 5.3 Results -- 6 Conclusion -- References -- Computational Health -- Local Sensitivity Analysis of a Closed-Loop in Silico Model of the Human Baroregulation -- 1 Introduction -- 2 Methods -- 2.1 Baroreflex Model."]
[{"$nulls":4},"2.2 Cardiovascular Circulation Model -- 2.3 Sensitivity and Orthogonality Analysis -- 3 Results -- 3.1 Local Sensitivity Analysis -- 3.2 Orthogonality Analysis -- 4 Discussion -- 5 Conclusions and Further Work -- References -- Healthcare Resilience Evaluation Using Novel Multi-criteria Method -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Conclusions -- References -- Plasma-Assisted Air Cleaning Decreases COVID-19 Infections in a Primary School: Modelling and Experimental Data -- 1 Introduction -- 2 Methods -- 2.1 Experiment Description and Experimental Results -- 2.2 Statistical Model for Data Interpretation -- 2.3 SIR Model Assumptions and Equations -- 2.4 Sensitivity Analysis on SIR Model -- 3 Results -- 3.1 Statistical Modelling Results -- 3.2 SIR Modelling Results -- 4 Uncertainty Quantification -- 5 Conclusion -- References -- Modelling Information Perceiving Within Clinical Decision Support Using Inverse Reinforcement Learning -- 1 Introduction -- 2 Related Works -- 3 Modelling Perceiving in Clinical Decision Process with CDSS Interaction -- 3.1 CDSS Data -- 3.2 Initializing MDP for CDSS Data -- 4 Case Study: T2DM Risk Prediction Perceiving -- 4.1 Simulating Risk Prediciton Using MDP -- 4.2 Inferring Reward Functions for Trajectories -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Modelling of Practice Sharing in Complex Distributed Healthcare System -- 1 Introduction -- 2 Modelling Practice Sharing in Complex Healthcare System -- 2.1 Quantitative Medical Practice -- 2.2 Physician Practice Sharing Activity -- 2.3 Distributed Medical Network Structure. -- 2.4 Simulation -- 2.5 Evaluation Analysis Methods -- 3 Practice Sharing in Vertigo Treatment -- 3.1 Data Set and Processes -- 3.2 Model Identification, Validation, and Sensitivity Analysis Based on Actual Data."]
[{"$nulls":4},"3.3 Simulation Scenario Expansion: Long-Term, Large-Scale, and Variant Studies -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data -- 1 Introduction -- 2 Related Work -- 3 Data Generation -- 3.1 Inpatient Claims Modeling -- 3.2 Evaluation of the Simulation Results -- 4 Fraud Detection -- 4.1 Results -- 4.2 Discussion -- 5 Conclusion -- References -- The Past Helps the Future: Coupling Differential Equations with Machine Learning Methods to Model Epidemic Outbreaks -- 1 Introduction -- 2 Methods -- 2.1 SIRD Model -- 2.2 Physics-Informed Neural Network -- 2.3 Data -- 3 Results -- 3.1 Forecasting of Post-peak Incidence -- 3.2 Peak Prediction -- 4 Discussion -- References -- Combining Convolution and Involution for the Early Prediction of Chronic Kidney Disease -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Model -- 3.3 Cohort Selection -- 3.4 Feature Engineering -- 3.5 Pre-processing -- 3.6 Machine Learning Model -- 4 Results and "]
[{"$nulls":4},"Discussion -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion -- References -- Segmentation of Cytology Images to Detect Cervical Cancer Using Deep Learning Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Collection -- 2.2 Pre-processing of the Data -- 2.3 Implementation Details -- 2.4 Training the Model -- 3 Results and Discussion -- 4 Conclusions -- References -- Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Concept -- 3.2 Data Sets -- 3.3 Model Architectures -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Model Quality -- 4.3 Data Quality and Privacy -- 4.4 Computational Resources -- 5 Conclusions -- References."]
[{"$nulls":4},"Visual Explanations and Perturbation-Based Fidelity Metrics for Feature-Based Models."]
[null,null,null,"Sommario/riassunto","The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024. The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions. They were organized in topical sections as follows: Part I: ICCS 2024 Main Track Full Papers; Part II: ICCS 2024 Main Track Full Papers; Part III: ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Part IV: Biomedical and Bioinformatics Challenges for Computer Science; Computational Health; Part V: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation; Part VI: Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Science; Quantum Computing; Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks, and Artificial Intelligence; Solving Problems with Uncertainties; Teaching Computational Science."]