Computer science Books

Book SynopsisThis concise yet thorough textbook presents an active-learning model for the teaching of computer science. Offering both a conceptual framework and detailed implementation guidelines, the work is designed to support a Methods of Teaching Computer Science (MTCS) course, but may be applied to the teaching of any area of computer science at any level, from elementary school to university. This text is not limited to any specific curriculum or programming language, but instead suggests various options for lesson and syllabus organization.Fully updated and revised, the third edition features more than 40 new activities, bringing the total to more than 150, together with new chapters on computational thinking, data science, and soft concepts and soft skills. This edition also introduces new conceptual frameworks for teaching such as the MERge model, and new formats for the professional development of computer science educators.Topics and features: includes an extensive set of activities, to further support the pedagogical principles outlined in each chapter; discusses educational approaches to computational thinking, how to address soft concepts and skills in a MTCS course, and the pedagogy of data science (NEW); focuses on teaching methods, lab-based teaching, and research in computer science education, as well as on problem-solving strategies; examines how to recognize and address learners’ misconceptions, and the different types of questions teachers can use to vary their teaching methods; provides coverage of assessment, teaching planning, and designing a MTCS course; reviews high school teacher preparation programs, and how prospective teachers can gain experience in teaching computer science.This easy-to-follow textbook and teaching guide will prove invaluable to computer science educators within all frameworks, including university instructors and high school teachers, as well as to instructors of computer science teacher preparation programs.Table of ContentsIntroduction – What Is This Guide About? Active Learning and the Active-Learning-Based Teaching Model Overview of the Discipline of Computer Science Computational Thinking Computer Science Soft Concepts and Soft Skills Data Science and Computer Science Education Research in Computer Science Education Problem-Solving Strategies Learners’ Alternative Conceptions Teaching Methods in Computer Science Education Lab-Based Teaching Types of Questions in Computer Science Education Assessment Teaching Planning Design of Methods of Teaching Computer Science Courses Getting Experience in Computer Science Education High School Computer Science Teacher Preparation Programs Epilogue

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Book SynopsisThis book constitutes the proceedings of the 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020, held in Padua, Italy, in May 2020. The 13 regular and 24 short papers presented were carefully reviewed and selected from 206 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.

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Book SynopsisThis book approaches economic problems from a systems thinking and feedback perspective. By introducing system dynamics methods (including qualitative and quantitative techniques) and computer simulation models, the respective contributions apply feedback analysis and dynamic simulation modeling to important local, national, and global economics issues and concerns. Topics covered include: an introduction to macro modeling using a system dynamics framework; a system dynamics translation of the Phillips machine; a re-examination of classical economic theories from a feedback perspective; analyses of important social, ecological, and resource issues; the development of a biophysical economics module for global modelling; contributions to monetary and financial economics; analyses of macroeconomic growth, income distribution and alternative theories of well-being; and a re-examination of scenario macro modeling.The contributions also examine the philosophical differences between the economics and system dynamics communities in an effort to bridge existing gaps and compare methods. Many models and other supporting information are provided as online supplementary files. Consequently, the book appeals to students and scholars in economics, as well as to practitioners and policy analysts interested in using systems thinking and system dynamics modeling to understand and improve economic systems around the world."Clearly, there is much space for more collaboration between the advocates of post-Keynesian economics and system dynamics! More generally, I would like to recommend this book to all scholars and practitioners interested in exploring the interface and synergies between economics, system dynamics, and feedback thinking." Comments in the Foreword by Marc Lavoie, Emeritus Professor, University of Ottawa and University of Sorbonne Paris NordTrade Review“This volume is a phenomenal achievement. It offers something to both the neophyte and the experienced SD researcher … . Weighing in at almost 600 pages, it is a wealth of information, and the bibliographies are extensive. The fact that the illustrations are full color is a tremendous help. If we are to make SD more widely acceptable, this is the sort of effort that is necessary. I hope it is widely read. I highly recommend this volume.” (John T. Harvey, System Dynamics Review, Vol. 37 (4), 2021)Table of ContentsChapter 1. Introduction to Feedback Economics.- Part I: Macroeconomics.- Chapter 2. Get Started with Macro Modeling.- Chapter 3. Equilibrium, Instability, Growth and Feedback in Economics.- Chapter 4. Accounting System Dynamics Modeling of Money Stock as Debts: Theory and Case Analysis of Japan.- Chapter 5. A System Dynamics Translation of the Phillips Machine.- Part II: Microeconomics.- Chapter 6. Economic Origins of the Sicilian Mafia: A Simulation Feedback Model.- Chapter 7. Comparison of System Dynamics Calibration and Econometric Estimation.- Chapter 8. Modeling the “Paradox of Redistribution”.- Part III: Resource Economics.- Chapter 9. Limits to Growth Concepts in Classical Economics.- Chapter 10. Development of a Biophysical Economics Module for the Global Integrated Assessment Model WORLD7.- Chapter 11. System Dynamics Modelling of Ecological-Economic Systems.- Chapter 12. Blessing or Burden? Another Look at the National Resource Curse.- Part IV: Policy Analysis.- Chapter 13. Policy Responses to Sovereign Debt Induced Banking Crises: A Model-Based Evaluation of Alternatives.- Chapter 14. Use of System Dynamics for Macro-Financial Scenario Assessment: Debt and Currency Crises in Russia.- Chapter 15. Economic Impact Assessment of Raw Material Efficiency.- Chapter 16. Insights on Profit Sharing and Jobs Creation in Bi-dimensional Goodwinian Models.- Part V: Variations and Reflections.- Chapter 17. Behavioral Expectation Formation and the Knife Edge: Another Look at Harrod.- Chapter 18. Burying Samuelson’s Multiplier-Accelerator and resurrecting Goodwin’s Growth Cycle in Minsky.- Chapter 19. Extending the Boundaries of Feedback Economics to Well-Being: An Interlinked Thinking Approach.- Chapter 20: A System Dynamics Scenario Model of the New Zealand Economy: Review and Reflections after 25 Years

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Book SynopsisThis unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Develops many new exercises (most with MATLAB code and instructions) Includes review summaries at the end of each chapter Analyses state-of-the-art models, algorithms, and procedures for image mining Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing Demonstrates how features like color, texture, and shape can be mined or extracted for image representation Applies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision trees Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.Table of Contents1. Fourier Transform.- 2. Windowed Fourier Transform.- 3. Wavelet Transform.- 4. Color Feature Extraction.- 5. Texture Feature Extraction.- 6. Shape Representation.- 7. Bayesian Classification.- Support Vector Machines.- 8. Artificial Neural Networks.- 9. Image Annotation with Decision Trees.-10. Image Indexing.- 11. Image Ranking.- 12. Image Presentation.- 13. Appendix.

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Book SynopsisIntroduction.- Developing a framework for integrating chatbots with personality in organizations.- Ethical foundations of AI with personality.- Engineering AI societies.- Creating AI persona.- Designing the appearance of conversational agents with personality.- Conclusion.

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Book SynopsisIntroduction.- Analysis.- Detection.- Mitigation.- Challenges and the Road Ahead.

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Book Synopsis.- Natural gas consumption forecasting based on weighted ensemble learning..- Machine learning trust prediction using localized nodes characteristics..- A Data-Driven Wildfire Forecasting: Case study of Algeria..- Multi-Aggregation Strategies in Ensemble-Based Machine Learning and Deep Learning Models for Cough-Based COVID-19 Detection..- Benchmark Analysis of Time Series Models for Malaria Trends in the Adamawa Region (Cameroon)..- A low-ressource language dataset: Moore Natural Emotions Speech Dataset..- Automatic pure speech files detection using MFCC+F0 features and LSTM classifier in multilingual context for low-resource languages..- ShipFPN: New Feature Pyramid Network Architecture for Object Detection in High-Resolution Satellite Images: Application to Ship Detection..- New approach based on alliances in graphs for image segmentation: application to breast MR images..- Identification of Non-Plant Elements in Herbarium Images Using YOLO..- A Service Composition Engine for Incremental Computation..- Expressiveness of the LSAWfP Workflow Language Compared to BPMN..- Formal Verification of Deep Learning Matrix Calculus..- Addressing Input/Output composition through Open Automata representation..- Clustering for Performance Optimization in Vehicular Ad-hoc NETworks: A Comprehensive Survey..- Functional Sparse Data Clustering using Conditional Expectation PACE Method..- Hierarchical Sparse Subspace K-Means..- HW/SW Co-design Implementation of Elliptic Curve Digital Signature Algorithm for Blockchain Network 29..- Intrusion recognition coupled with a heuristic attributes selection method using neural networks..- New heuristic order based on tree-decomposition for solving CSPs..- The fair OFDMA-FD MAC for 802.11be and 802.11ax..- GPU-based Support Vector Machine using Artificial Orca Algorithm for Intrusion Detection Systems..- Improved photometric stereo based on Bee Swarm Optimization BSO algorithm..- Modelling Behavior of Microbiota Metabolic Network Subject to Diets..- Imbalanced Data Classification using Synthetic Minority Oversampling Technique in stages for a Rice Dataset..- Adversarial Attacks and Defense Mechanisms in Computer Vision: a Comprehensive Survey..- Early Diabetic Retinopathy Detection with Vision Transformers and Optimized Data Preprocessing..- Enhanced TSCH Protocol for IoT and Industry 4.0 Applications..- A modified remora optimisation for optimal localisation of weather radars network over an airspace..- A Low-Resource Language Translation: French To Moor´..- A Hybrid Model Based on State-ANFIS And Particle Swarm Optimization Algorithm For Short-term Load Forecasting.

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Book Synopsis.- Machine Learning..- Weakly Supervised Learning for Textual Propaganda Detection on Twitter and a Multi label Tweet Propaganda Dataset..- Interpretability of a Joint Learning Problem..- Development of an Intrusion Detection Model Using A Support Vector Machine To Identify Cyberattacks..- Deep Learning..- VectorQuest: A Faster and Better Open-Domain Question Answering System..- Deep Learning Approach to Indian Sign Language Recognition..- False Data Injection Attack Prediction using Federated Deep Learning Approach..- Block chain..- Blockchain Ballot : A Secure and Transparent E Voting System..- Combination of IoT and Blockchain for Safe Data Management..- A Prototype for Secure and Efficient Real Estate Transactions using IPFS and Blockchain Technology.

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Book Synopsis.- Enhancing Personalized Nutrition: A Hybrid Intelligence Approach with LLM-Powered Meal Planning..- Generating Explanations for Molecular Property Predictions in Graph Neural Networks..- Balancing (Normative) Reasons for the Intelligent Human-input-based Blockchain Oracle..- Feature Generation Using LLMs: An Evolutionary Algorithm Approach..- Augmenting Dark Patterns Text Data by Leveraging Large Language Models: a Multi-Agent Framework and Parameter-Efficient Fine-Tuning..- Assessing the Robustness of LLMs in Predicting Supports and Attacks..- Enhancing accuracy and explainability in anomaly classification with large language models..- Agent-Based Hate Speech Moderation Approach.

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Book SynopsisBioinformatics.- Clustering-based Negative Sampling Approaches for Protein-Protein Interaction Prediction.- Proteins transcription factor prediction using Graph Neural Networks.- Identification of Differential Alternative Splicing Events: Assessing Tools Performance with Different Sequencing Parameters.- Methods and tools to facilitate RE:IN modeling and analysis of GRNs.- Gene set-focused analysis of RNA-seq data with MIEP (Make-It-Easy-Pipeline).- Cross sequencing integration of compositional microbiome data in cancer.- Medical Informatics.- Private, Efficient and Scalable Kernel Learning for Medical Image Analysis.- Toward a Unified Graph-Based Representation of Medical Data for Precision Oncology Medicine.- FP-Elegans M1: feature pyramid reservoir connectome transformers and multi-backbone feature extractors for MEDMNIST2D-V2.- Natural language processing (NLP) and large language models (LLM) for unstructured data in health informatics.- Driver Gene Detection via Causal Inference on Single Cell Embeddings.- Assessing and Comparing Free Large Language Models’ Responses to a Clinical Case: Accuracy, Safety, and Reliability.- Three-stage Data Science methodology to explore genetic heterogeneity of diseases.- Functional data analysis and clustering of haematological parameters in SARS-CoV-2 patients.- Modeling and simulation methods for computational biology and systems medicine.- Gene set optimization for single cell transcriptomics.- MicroRNAs as biomarkers for Ulcerative Colitis.- PHeP: TrustAlert Open-Source Platform for Enhancing Predictive Healthcare with Deep Learning.- Cutting Slices of Complexity in Cancer Therapy Design: An Agent-Based Model of Dabrafenib in Melanoma.- Machine learning for structured data in clinical informatics and medical biology.- Forward and backward feature selection guided by prior biological knowledge for enhanced interpretability.- The impact of mis-labeled artefacts on deep learning models for EEG analysis: a case study.- Benchmark study on supervised Relevance-Redundancy assessment for feature selection in genomic data.- Computational Intelligence in Personalized Medicine.- Group discovery in a clinical database of patients with psychosis who have undergone Metacognitive Training.- Hierarchical Clustering with an Ensemble of Principle Component Trees for Interpretable Patient Stratification.- Computational Structural Bioinformatics.- ESMCrystal : Enhancing Protein Crystallization Prediction through Protein Embeddings.- TARNAS, a TrAnslator for RNA Secondary structure formats.- Short papers.- Novel Approaches for Spatially Resolving Gene Responses and Injection Site Localization in Transcriptomic Data.- Deep Learning Approaches for Forensics DNA Profiling: a Replication Study.

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Book Synopsis.- A Network Approach to Aquatic Food Web Dynamics..- Leveraging Diffuser Data Augmentation to enhance ViT-based performance on Dermatoscopic Melanoma Images Classification..- Thyroid Nodule Diagnosis Using a New Supervised Autoencoder Neural net work with multi-categorical medical data..- Can smoothing methods recognize the patterns of the hazard function in complex clinical scenarios? A simulation study using discrete-time survival models..- Nested Named Entity Recognition in Chinese Electronic Medical Records..- Transformers for Interpretable Classification of Histopathological Images..- Breast Cancer Malignancy Prediction Through Explainable Models based on a Multimodal Signature of Features..- Exploring the Conformational Odorant Space in the Olfactory Re-ceptor Binding Region..- Synergy between mechanistic modelling and Ensemble Feature Selection ap proaches to explore multiscale biological Heterogeneity..- Homophily of large weighted networks in a data streaming setting..- Living along COVID-19: assessing contention policies through Agent-Based Models..- Stochastic modeling and dosage optimization of a cancer vaccine exploiting the EpiMod Framework..- Extension of the GreatMod modeling framework to simulate non-Markovian processes with general-distributed events..- Identifying Damage-Related Features in scRNA-seq Data..- A benchmark study of gene fusion prioritization tools..- Improving the reliability of tree-based feature importance via consensus signals..- Interpretable Machine Learning for Automated Cellular Population Analysis in Flow Cytometry..- Pre-trained Models Based on Primary Sequence to Classify Antibody Bind ing to Protein and Non-Protein Targets with 80% Accuracy..- Inferring breast cancer subtype associations using an original omics integra tion based on Non-negative Matrix Tri-Factorization..- Screening the bioactivity of the P450 enzyme by spiking neural networks..- Enhancing functional interpretability in gene expression analysis through biologically-guided feature selection..- Extraction of Attributes from Electrodermal Activity Signals Applying Time Series Fuzzy Granulation for Classification of Academic Stress Perception in Different Scenarios..- Transfer Learning and AutoML as a Support for the Pneumonia Diagnosis using Chest X-ray scan.

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Book Synopsis.- A Fine-grained Actor-Based Autoencoder Model..- Advanced IoT-AI Integration for Predictive Management of Positive Temperature Cold Storage Systems..- AI-Driven Artistry - Transforming Guitar Design with Generative Models..- An Integrated Convolutional & Transformer Architecture for Word-Based Handwriter Identification..- Assessing Deep Learning Challenges in Lightweight AES Cryptanalysis..- Border Gateway Protocol Hijacks and Anomalies Detection: A Graph-based Deep Learning Approach..- Collaborative Split Federated Learning with Parallel Training and Aggregation..- Comparison of Machine Learning and Deep Learning Models for Change of Direction Classification Using Engineered Features..- Declarative, generic definition and effective implementation of transfer learning algorithms..- Disaster Data Driven Forecasting for Preemptive Crisis Response..- Domain Adaptation for Automated Tag Prediction in Competitive Programming..- Enhancing Agricultural Disease Management: An Application of Deep Learning for Fusarium Head Blight Detection in Wheat Crops..- Facial Expression Recognition Using Dual Direction Attention and Diffusion Models with Self-Supervised Learning..- Imbalanced Graph Learning via Graph Attention Network and Variational Autoencoder..- Insurance Claim Prediction Using Unbiased Confidence Guarantees..- Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach..- Leveraging Incremental Domain Adaptation in Olive Disease Detection..- Machine Learning modeling of the impact of masonry infills’ in-plan irregularities’ on the R/C buildings’ damage response..- Machine learning-powered customizable urban land use classification for high-resolution satellite imagery..- MAtCHelper: An Automated Tool for Guiding in the Acceleration and Compression of Neural Networks..- Multi-Model Data Transfer by Knowledge Distillation for Enhancing Precipitation Nowcasting..- Reinforcement Learning for UPI Fraud Detection: Actor Critic Optimization Approach..- Robust Zero-Watermarking of Medical Images based ResNet-50..- Shoreline and Sea Level Changes Detection from Satellite Images Using Deep Learning..- Towards Cross-Domain Anomaly Detection in Cyber-Physical Systems: A Hybrid Approach..- A Content-Aware Collaborative Filtering Framework for Video Recommender Systems..- A Personalised Music Recommendation System based on User’s Emotions..- ColBic : a New Biclustering-based Collaborative Filtering..- Cross-domain recommendations using attention and multitask learning..- Enhancing Explainability in AI-Powered Data Retrieval Systems..- Improving the Diversity and Fairness in Job Recommendations using the Stable Matching Algorithm..- Personalized Hotel Recommendation System.

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Book Synopsis.- A Multi-Agent System Approach for Dynamic and Adaptive Daily Profile Detection in Building Data..- Agile MLOps: Bridging the Gap Between Agility and Machine Learning Operations..- Artificial Intelligence in Finnish and Swedish Workflows: A Comparative Survey of Micro- and Large-Scale Enterprises..- Clustering by Limit-Cycles in Hopfield Neural Network..- Enhancing Ransomware Detection Using Storage Access Pattern..- Fault Detection in Inverter-Driven Permanent Magnet Synchronous Motor Systems using Semi-Supervised Learning..- Fingerprint Minutiae-Based Cryptographic Key Generation: Towards Secure and Usable Authentication..- Handling Anonymized Non-Numerical Features in Data using Transformations for Regression Models..- Higher-Order Multi-Adaptive Neural Network  Modeling of the Role of Epigenetics in Epilepsy..- Historical Map Understanding Using End-to-end Methods..- ICU Mechanical Ventilation Data Imputation..- Immigration Detection in Multilanguage Tweets Using Machine Learning Algorithms..- Integrating Artificial Intelligence and Digital Tools to Enhance Learning and Accessibility in Music Education..- Interest-Based Pattern Mining For Deforestation  Analysis in the Kroumiria Region, Tunisia..- Large-to-Small: Energy Consumption Reduction of Training the Fully Connected Layers..- Malware Detection through API Call Frequency: Assessing Early-Stage versus Full-Time Classification Potential..- Metaheuristics-based Long Short-Term Memory Optimization for Emotion Classification..- Multi-order Adaptive Dynamical System Modeling of  the Role of Epigenetics in the Development of Hypomania..- Off-Policy Inspired Imitation Learning for Generation of Adversarial Malware..- PixelPath: Predicting UAV Trajectories in GPS-Restricted Environments Using Image Feature Extraction and Machine Learning..- Predicting Vessel Arrival Time Using Machine Learning for Enhanced Port Efficiency and Optimal Berth Allocation..- Semi-Supervised Learning for Early Certification Prediction in MOOCs..- So-Connect: recommending layer-wise connections in social networks using SNN-DBSCAN..- Time Series analysis using Memory Enhanced Liquid Neural Network..- A Web-Based Information System For Medical Forensics Incorporating Generative AI in Reports Compilation..- Explainable AI-Driven Feature Selection for Improved Intrusion Detection Systems in the Internet of Medical Things..- Explainable Surface-Level Malware Analysis through Scalable and Accurate Feature Selection..- Integrating AI and RDF Knowledge Graphs with MITRE ATT&CK for CTI Insights..- Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models..- Merged LIME and SHAP eXplanation (MLSX): BERT case in NER Task..- OPJUSTICE - A Multi-label Text Classification Model..- SHAKTI: A 2.5 BILLION PARAMETER SMALL LANGUAGE MODEL OPTIMIZED FOR EDGE AI AND LOW-RESOURCE ENVIRONMENTS..- Unified Mental Health Disorder Detection on Social Media.

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Book Synopsis.- An Ensemble Approach to Predict Immunotherapy Efficacy in Non-Small Cell Lung Cancer using Digital Pathology..- An Integrated System for the Texture Analysis of Prostate Ultrasound Images Based on Different Pre-processing Schemes..- Automated Prostate Segmentation in Ultrasound Images Based on Different Pre-processing Schemes..- Balancing Accuracy and Energy: the Impact of Window Size on Sensor-based Human Activity Recognition..- Classifying cognitive states of Alzheimer’s Disease with machine learning using digital biomarkers from the Bio-Hermes study cohort..- Convolutional Autoencoder-based Dimensionality Reduction for EEG Microstate Analysis..- DADA-MED: Data-Augmented Dual Attention Model for Enhanced Medication Recommendations..- Diaphragmatic Motion Analysis in Neonates using Ultrasound Simulated Videos..- Enhancing Sound-Based Sleep Quality Assessment by Multi-modal Knowledge Distillation..- Exploring FIWARE-based Data Model for Data Sharing Platforms..- Functional Connectivity Disruptions in Alzheimer’s Disease: A Resting-State fMRI Classification Study..- Incorporating Medical Assistants in eHealth Environments Using an Agentic RAG Approach..- Normative Modeling of Functional Connectivity Alterations in Psychiatric Disorders and Chronic Pain: A Resting-State fMRI Approach..- On the Interplay of Epigenetics, ADHD, Decision Making and Digital Media Usage: an Adaptive Dynamical System Modelling Approach..- Optimized Machine Learning Models and Stacking Hybrid Approach for Chronic Kidney Disease Prediction..- Optimizing Alzheimer's Detection: A Comparative Analysis of Pre-Processing Techniques and FreeSurfer Integration with EfficientNetV2S..- Optimizing Life Insurance Risk Prediction: A Comparative Analysis of Traditional, Ensemble, and Deep Learning Models..- Proteomics, Neuropsychological and Demographics Multi-Modal Machine Learning Approach to Alzheimer's Disease Prediction on the Bio-Hermes Study Cohort..- Automating Industrial Quality Control: A Multimodal LLM and RAG Framework for Anomaly Detection..- Benchmarking Vision Language Models on German Factual Data..- Comparative Analysis and Evaluation of SLMs and LLMs for Stock Price Movement Prediction..- Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models..- Enhancing Cryptocurrency Sentiment Analysis with GPT-4: A Comparative Study..- Large Language Model-based Federated Zero-Shot Learning for Intrusion Detection in Smart Grids..- Leveraging Large Language Models for Emotion Recognition: Case of Employee Satisfaction Surveys..- LLM-Based Automated Hallucination Detection in Multilingual Customer Service RAG Applications..- Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval..- Summarizing Online Discussions with Prototype Relation Networks Using Large Language Models..- SurveyBot: A New Era of Web Survey Pretesting..- Toward Safer and Trustworthy Chatbots in E-Commerce: An LLM-as-a-Judge Approach for Ethical Evaluation..- Vulnerability Patch Prediction using LLM based Bert Model with Trustworthy AI Practice for Cyber Security Enhancement.

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Book Synopsis.- A Multi-Agent System Approach for Dynamic and Adaptive Daily Profile Detection in Building Data..- Agile MLOps: Bridging the Gap Between Agility and Machine Learning Operations..- Artificial Intelligence in Finnish and Swedish Workflows: A Comparative Survey of Micro- and Large-Scale Enterprises..- Clustering by Limit-Cycles in Hopfield Neural Network..- Enhancing Ransomware Detection Using Storage Access Pattern..- Fault Detection in Inverter-Driven Permanent Magnet Synchronous Motor Systems using Semi-Supervised Learning..- Fingerprint Minutiae-Based Cryptographic Key Generation: Towards Secure and Usable Authentication..- Handling Anonymized Non-Numerical Features in Data using Transformations for Regression Models..- Higher-Order Multi-Adaptive Neural Network  Modeling of the Role of Epigenetics in Epilepsy..- Historical Map Understanding Using End-to-end Methods..- ICU Mechanical Ventilation Data Imputation..- Immigration Detection in Multilanguage Tweets Using Machine Learning Algorithms..- Integrating Artificial Intelligence and Digital Tools to Enhance Learning and Accessibility in Music Education..- Interest-Based Pattern Mining For Deforestation  Analysis in the Kroumiria Region, Tunisia..- Large-to-Small: Energy Consumption Reduction of Training the Fully Connected Layers..- Malware Detection through API Call Frequency: Assessing Early-Stage versus Full-Time Classification Potential..- Metaheuristics-based Long Short-Term Memory Optimization for Emotion Classification..- Multi-order Adaptive Dynamical System Modeling of  the Role of Epigenetics in the Development of Hypomania..- Off-Policy Inspired Imitation Learning for Generation of Adversarial Malware..- PixelPath: Predicting UAV Trajectories in GPS-Restricted Environments Using Image Feature Extraction and Machine Learning..- Predicting Vessel Arrival Time Using Machine Learning for Enhanced Port Efficiency and Optimal Berth Allocation..- Semi-Supervised Learning for Early Certification Prediction in MOOCs..- So-Connect: recommending layer-wise connections in social networks using SNN-DBSCAN..- Time Series analysis using Memory Enhanced Liquid Neural Network..- A Web-Based Information System For Medical Forensics Incorporating Generative AI in Reports Compilation..- Explainable AI-Driven Feature Selection for Improved Intrusion Detection Systems in the Internet of Medical Things..- Explainable Surface-Level Malware Analysis through Scalable and Accurate Feature Selection..- Integrating AI and RDF Knowledge Graphs with MITRE ATT&CK for CTI Insights..- Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models..- Merged LIME and SHAP eXplanation (MLSX): BERT case in NER Task..- OPJUSTICE - A Multi-label Text Classification Model..- SHAKTI: A 2.5 BILLION PARAMETER SMALL LANGUAGE MODEL OPTIMIZED FOR EDGE AI AND LOW-RESOURCE ENVIRONMENTS..- Unified Mental Health Disorder Detection on Social Media.

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Book SynopsisArtificial IntelligenceAI-Based Solutions for Optimizing Lane Change Safety in Connected Automated Vehicles.- AI-Driven Platform Architecture for Business Plan Formulation in Indigenous Communities: A Gender-Focused Case Study in Colombia.- Automated Detection of Pulmonary Thromboembolism in Computed Tomography Pulmonary Angiography Images Using Convolutional Neural Networks: A Literature Review.- Development of Agentic Workflows with LangGraph for Software Development Life Cycle Automation.- Last-Mile Delivery in High-Density Emerging Economy Cities Using Crowd-Generated Data and Artificial Intelligence.- Systematic Review of AI and Non-AI Approaches for Personalized Learning Paths.-  Autonomous SystemsSmart Detection and Measurement: The Real-AODM System.- Tracking the Unseen: Autonomous Multi-Object Motion Analysis in Complex Environments.-  Data AnalysisBayesIntuit: A Neural Framework for Intuition-Based Reasoning.- Dynamic Simulation Tool for the Analysis of the Effects of Man-Machine Ratio on the Productivity of Test Manufacturing of a Semiconductor Company.-  Decision SystemsAn Analysis of Water Supply System Losses: A Case Study of Santa Catarina State, Brazil.- Comparison on the use of Hybrid and Plugin Hybrid Electric Buses for Sustainable Urban Transportation – Split Use Case.- Consumption and Runtime Automatic Software Performance Optimization on Android Smartphones.- RECO: An AI-Powered Chatbot System for Optimizing Reverse Agri-Food Logistics and Fighting Food Insecurity in Colombia.- Revamping Agile Logistics for Emergency Management.- Spiral Design and Simulation-Driven Deployment of a Drive-Through COVID-19 Testing and Housing Check-In System at a Large Public University.-  Learning Management SystemsEnhancing STEM Education with AI: Fostering Soft Skills and Inclusion.-  Machine LearningBridging the Black Box: Data Mining Enhanced Machine Learning Interpretability for Exoplanet Discovery.- Cost Optimization in Mold-Making: A Conceptual Industry 4.0 Framework.- Enhancing Multi-Echelon Home Improvement Supply Chain Responsiveness Through Machine Learning-Enabled Inventory Segmentation and Positioning.- Ensemble Machine Learning for Healthcare Data: A Comparative Analysis of Chronic Kidney Disease and Cardiovascular Risk Prediction with NHANES Data.-  Security SystemsPreliminary Modeling of Cyber Attackers in Microgrids: An Agent-Based and System Dynamics Approach Enhanced with Statecharts.

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Book Synopsis.- Design and assessment for joint systems and workflows..- Beyond Manual Annotation: A Human-AI Collaborative Framework for Medical Image Segmentation Using Only “Better or Worse” Expert Feedback..- A methodology for clinically driven interactive segmentation evaluation..- Interactive environments for clinical training, education,and human-AI teaming..- Explainable AI for Automated User-specific Feedback in Surgical Skill Acquisition..- Real-Time, Dynamic, and Highly Generalizable Ultrasound Image Simulation-Guided Procedure Training System for Musculoskeletal Minimally Invasive Treatment..- Human-in-the-loop model training..- Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi-modal Gross Tumor Volume Segmentation..- Guided Active Learning for Medical Image Segmentation..- Applications of human-AI interaction, collaboration, and human factor analysis..- User Perception of Attention Visualizations: Effects on Interpretability Across Evidence-Based Medical Documents..- Simulating Inter-observer Variability Across Clinical Experience Levels..- Boosting transparency, interpretability, and risk management..- Perceptual Evaluation of GANs and Diffusion Models for Generating X-rays.

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Book SynopsisThis book discusses questions of numerical solutions of applied problems on parallel computing systems. Nowadays, engineering and scientific computations are carried out on parallel computing systems, which provide parallel data processing on a few computing nodes. In the development of up-to-date applied software, this feature of computers must be taken into account for the maximum efficient usage of their resources. In constructing computational algorithms, we should separate relatively independent subproblems in order to solve them on a single computing node.

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Book SynopsisCryptography has become essential as bank transactions, credit card infor-mation, contracts, and sensitive medical information are sent through inse-cure channels. This book is concerned with the mathematical, especially algebraic, aspects of cryptography. It grew out of many courses presented by the authors over the past twenty years at various universities and covers a wide range of topics in mathematical cryptography. It is primarily geared towards graduate students and advanced undergraduates in mathematics and computer science, but may also be of interest to researchers in the area. Besides the classical methods of symmetric and private key encryption, the book treats the mathematics of cryptographic protocols and several unique topics such as Group-Based Cryptography Gröbner Basis Methods in Cryptography Lattice-Based Cryptography

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Book SynopsisThe amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.

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Book Synopsis

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