table_of_contents_short

Chapter 1. Introduction.- Chapter 2. Basics of Quantum Computing.- Chapter 3. Quantum Kernel Methods.- Chapter 4. Quantum Neural Networks.- Chapter 5. Quantum Transformer.- Chapter 6. Conclusion.

Quantum machine learning (QML) is revolutionizing artificial intelligence by leveraging the power of quantum computing to access previously unimaginable computational possibilities. However, the field remains fragmented—balancing rigorous quantum theory with practical AI applications remains a challenge. This book bridges this gap, offering a systematic, hands-on guide for AI researchers, ML practitioners, and computer scientists eager to explore this emerging frontier.  It provides a cohesive roadmap, covering everything from fundamental quantum computing principles to state-of-the-art QML techniques. Readers will explore quantum kernel methods, quantum neural networks, and quantum Transformers, gaining insight into their theoretical foundations, performance advantages, and practical implementations. The book’s code demonstrations offer hands-on experience, ensuring that readers can move beyond theory to real-world applications. Designed for those with an AI or ML background, this tutorial does not assume prior expertise in quantum computing. Instead, it presents complex concepts with clarity, making it an essential resource for researchers, graduate students, and industry professionals eager to stay ahead in the quantum AI revolution. Whether you seek to understand quantum speedups, develop quantum-based models, or explore future research directions, this book provides the foundation you need to engage with QML and shape the future of intelligent computing.

Computational Intelligence

A Preliminary Evaluation of Machine Learning Models for the Diagnosis of Brugada Syndrome from ECG-Extracted Features.- ECG Images Analysis for the Prediction of Fatal Arrhythmic Events in Patients with Brugada Syndrome based on a Vision Transformer Model.- Enhancing ECG Analysis with a Hybrid Deep Learning Approach: Automatic Detection of Significant Features.- Virtual Mines – Component-Level Recycling of Printed Circuit Boards using Deep Learning.- Comparison of LSTM-based models for the 6-Hour-Ahead and Day-Ahead Forecasting of Photovoltaic Power Generation .- Forward Approximate Solution for Linear Quadratic Tracking .- Deep Echo State Network for Environmental Sound Classification.- A Fuzzy Logic Approach for Pandemic Risk Assessment and Restrictions Design.- Multi-Objective Multi-Layer Perceptron Architecture Optimization for Rapid Post-Earthquake Damage Evaluation.- Uncertainty Estimation Via Ensembles of Deep Learning  Models and Dropout Layers for Seismic Traces.

This book illustrates recent advances in Neural Artificial Intelligent Theories and Applications discussed by selected papers presented at 30th edition of the International Workshops on Neural Network (WIRN 2023). The book discusses novel technologies for unsupervised multimodal complex autonomous systems using new generation of AI algorithms. The book also reports on advanced acoustical, perceptual, and psychological analysis of verbal and non-verbal communication of signals originating in spontaneous face-to-face interaction, automatic procedures capable of recognizing human emotional states, and applications improving the performance of human–machine interaction for the deployment of socially and emotionally believable assistive technologies.

Graduate/advanced undergraduate textbook

1. Fundamentals of Supervised Learning.- 2. Data-dependent generalization bounds.- 3. Descent direction optimization algorithms.- 4. Deep Learning.- 5. Support Vector Machines.- 6. Boosting.- 7. Semi-supervised Learning.- 8. Learning-To-Rank.- Appendix: Probability reminders.

Machine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.

Introduction.- Prompt Chaining.- Routing.- Parallelization.- Reflection.- Tool Use.- Planning.- Multi-Agent.- Memory Management.- Learning and Adaptation.- Model Context Protocol (MCP).- Goal Setting And Monitoring.- Exception Handling and Recovery.- Human-in-the-Loop.- Knowledge Retrieval (RAG).- Inter-Agent Communication (A2A).- Resource-Aware Optimization.- Reasoning Techniques.- Guardrails/Safety Patterns.- Evaluation and Monitoring.- Prioritzation.- Exploration and Discovery.- Appendix.

This book is a practical resource designed to help developers master the art of building sophisticated AI agents. As artificial intelligence evolves from simple reactive programs to autonomous entities capable of understanding context and making complex decisions, this book provides the essential Design Patterns and proven techniques needed to construct intelligent systems effectively. Each of the 21 Design Patterns represents a fundamental building block for creating agents that can perceive their environment, make informed decisions, and execute actions autonomously. Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems is structured as a comprehensive hands-on guide, with each chapter dedicated to a single agentic pattern. Within each chapter, you will find a detailed pattern overview, practical applications and use cases, one or more hands-on code example, and key takeaways for quick review. From foundational concepts such as Prompt Chaining and Tool Use to advanced topics like Multi-Agent Collaboration and Self-Correction, readers will gain practical knowledge they can immediately apply. While the chapters build on each other, you can also use the book as a handy reference, jumping to patterns that address your specific challenges. To provide a tangible "canvas" for the code examples, this guide utilizes three prominent agent development frameworks: LangChain and its extension LangGraph, which offer a flexible way to build complex operational sequences; Crew AI, which provides a structured framework for orchestrating multiple agents; and the Google Agent Developer Kit (Google ADK), which offers tools for building, evaluating, and deploying agents. By showcasing examples across these tools, you will gain a broad understanding of how these patterns can be applied in any technical environment. Building effective agentic systems requires more than just a powerful language model; it demands structure and design. Agentic patterns provide reusable, battle-tested solutions to common challenges, much like design patterns in software engineering. They offer a common language that makes an agent's logic clearer, more maintainable, and more robust. By the end of this journey, you will possess both the theoretical understanding and the practical skills to implement these 21 essential patterns, enabling you to build more intelligent, capable, and autonomous systems on your chosen development canvas.

Understanding Artificial Intelligence and Using It for Business, Home & School

AI fundamentals.-History and development of AI.- AI in everyday life.- Practical use cases of AI in the workplace.- Practical use cases of AI at home, in sports, and in society.- Knowledge extraction (from university to the working world).- Trends, ethics, and the future of AI.

The book “AI and Bots in Everyday Life” offers solid expert knowledge, explains the fundamentals of AI without technical jargon, and highlights its many applications in daily life. A chapter on ethics and the future of AI encourages critical reflection and mirrors current discussions. The book is a valuable resource for readers who want to gain an overview and develop a deeper understanding of artificial intelligence.Through real-world examples and practical scenarios, it demonstrates how profoundly AI influences and shapes everyday life. The aim is to build a bridge between the technical world of AI and everyday understanding, creating a comprehensive picture of this technology.

The E-Commerce Risk Landscape.- Data Foundations for E-Commerce.- Tree-based Methods.- Deep Learning Models.- Unsupervised & Anomaly Detection Methods.- Buyer Journey Models.- Buyer–Seller Collusion Models.

This book presents a practical, end-to-end approach to risk management in modern e-commerce platforms using machine learning. It covers buyer abuse, seller governance, collusion networks, and payment and credit risk, showing how behavioural, transactional, and network data are translated into actionable risk signals. The book discusses a range of modelling techniques—including tree-based ensembles, sequence and transformer models, graph neural networks, and anomaly detection—focusing on their application under real-world constraints such as class imbalance, latency, adversarial drift, fairness, and regulatory requirements. Extending beyond model development, it addresses deployment, monitoring, governance, and operational assurance, making it relevant to industry practitioners and suitable for advanced courses in applied, industry-focused machine learning.

The mathematical foundation of machine learning and AI.- Recent trends of AI and ML in research.- Am I in or out with my Gestalt On emerging trends in AI behavioral sciences  engineering and technology.- AI and Mental Health.- AI in Mental Disorder.- AI as a tool to diagnose Suicidal Behaviour.- Ethical Considerations in AI Driven Research.- AI and Cyber security.- Human Rights in the Age of Artificial Intelligence.

This book provides a comprehensive exposition to applications of AI in areas such as psychology, engineering, bioscience, pharmacology, and sociology which deal with various aspects of human behavior and development. It also delves into the areas of ethical and legal issues related to AI as applied to different subject areas. In each of these areas, AI has been very effectively used and has benefitted human beings, for instance, in health care. However, challenges in application of AI to different disciplines lie in the multidisciplinary approach which requires AI to include many related disciplines almost equally to a problem and find an effective and appropriate solution that can be replicated and predictable too. The merging of the different fields having different terminologies, methods and procedures as well as legal issues, poses difficulties in applying AI to integrating the essence of the different disciplines toward solution of a problem. Furthermore, a thorough knowledge of the concerned fields in addition to the AI renders this task highly challenging.

Introduction, Why is cross-cultural dialogue on AI ethics important?.-  Philosophical foundations of AI ethics.- AI ethics and its social implications.- Challenges and value alignment in LLM.- AI and AI ethics in different cultural perspectives.- AI and techno-ethics.

This book offers a comprehensive synthesis of research on the ethics, epistemology, governance, and policies related to artificial intelligence (AI) and its adjacent technologies. Each chapter can be read independently, yet together they provide a detailed and complementary understanding of some of the most pressing issues brought about by AI and digital innovation. Given its modular nature, this book is suitable for readers seeking a reliable overview of AI ethics as well as for experts looking to delve into specific areas of the current debate.

With an Introduction to Security, Privacy, Ethics and Society Impacts

Knowledge Based Systems

Undergraduate textbook

1. Introduction.- 2. Artificial intelligence and intelligent systems.- 3. Decision-support systems.- 4. Machine learning at work. Data-science.- 5. Security and privacy.- 6. Applications in social sciences.- 7. Ethics and AI.

The use of AI in social sciences is on the rise and gaining significant momentum. The book aims to demystify AI and its applications in social sciences by offering a straightforward description of its key concepts and tools, all in an easy-to-understand language. The book delves into the fundamental elements of AI systems, shedding light on their capabilities as well as their inherent constraints. Moreover, the author explores AI tools that find applications in the realm of social sciences, including decision support systems rooted in machine learning, as well as knowledge-based. In addition, the book addresses the pertinent issues that, while not purely AI-related, are crucial in AI applications within the social sciences, most notably security and privacy considerations. The content should be accessible to an audience ranging from undergraduate students to researchers and practitioners in the fields of AI and social sciences, assuming they possess a basic understanding of mathematics and a keen interest in the subject. This book is a bridge between the worlds of AI and social sciences, aiming to provide a fundamental understanding for those eager to explore the intersection of these two domains.

Part I: Smart Marketing.- Chapter 1. Mobile Banking Potential Monthly Active Customer Mining: Automated Machine Learning Techniques.- Chapter 2. Retail Potential High-value Customer Identification: Graph Neural Network Technology.- Chapter 3. Accurate Recommendation for Banking: Recommender System.- Chapter 4. Assessing the Value of Bank Online Marketing Posts: Reinforcement Learning Techniques.- Chapter 5: Modeling Binary Causal Effects of Related Repayments: Causal Inference Techniques.- Part II: Intelligent Risk Control.- Chapter 6. Telecom Fraud Money Laundering Account Recognition Case: Multiple Machine Learning Techniques.- Chapter 7. Developing a Dialectal Speech Phone Collection Bimodal Robot from Scratch: Intelligent Voice Q&A Technology.- Chapter 8. Chattel Collateral Warehouse Visual Monitoring Project: Image Understanding Technology.- Chapter 9. Personal Loan Delinquency Prediction Project: Bayesian Network Techniques.- Part III: Intelligent Operation.- Chapter 10. Enterprise WeChat Private Traffic Customer Cold Start Program: Automated Control Technology.- Chapter 11 Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology.

Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects.

"Explainable Deep Learning for COVID-19 and Chest Disease Detection: A Dual-Model Approach Using DenseNet121 and UNet".- Explainable Artificial Intelligence in Smart Healthcare.- Classification of Anti-Nuclear Antibody Pattern using Deep Learning.- Extensive investigation on Smart Healthcare and Alzheimer’s disease prediction using Multimodal data.- AI-Powered Smart Healthcare: Transformative Frameworks and Next-Generation Algorithms.- Strategic Development of Smart Prosthetic Lower Limb with Trainable Model for Balanced Movement.- Data-Driven Intelligence in Electronic Health Records (EHRs).- Advancement in Psychiatric Disorder Detection: Roadmap in Brain–Computer Interfacing using Graph Neural Network.- Exploring the Potential of Augmented and Virtual Reality for Patient Care and Medical Innovation.- Transforming Healthcare with Electronic Health Records: AI Integration, Evolution, and Future.- AI enabled Intrusion Detection Systems for IoT Healthcare: Current Trends, Challenges, and Future Opportunities.- Multimodal Biometric Emotion Recognition for Mental Health Monitoring Using AI-DL Techniques.- Federated Learning in Privacy Preservation and Security Enhancement for eHealthcare Systems.- Proposed Privacy-Preserving Framework on Medical Handwritten Prescription using Blockchain.- Study on Secure and Resilient Architectures for 5G Healthcare ecosystem.- Chikitsak: Medical Chatbot Using BioBert and GPT-2 Model.- Clinical Decision Support System using GenAI.- An AI-based Secure and Robust customized patient health care system using blockchain technology.- Empowering patients through AI technologies: contextualize and personalize patient care.- IoT-Enabled Real-Time Monitoring of IVF Embryo Culture Conditions: Integration of Machine Learning Techniques.

This book offers a comprehensive exploration of artificial intelligence's transformative impact on the healthcare sector as it relates to SDG #3: Good Health and Well-Being, highlighting its potential to revolutionize traditional practices. It delves into the integration of AI across various facets of healthcare, from data collection and processing to diagnostic algorithms and predictive analytics. Designed as an essential resource, it serves researchers, clinicians, technologists, and policymakers eager to harness AI to improve patient outcomes and system efficiency.  The chapters present a diverse array of AI applications, bridging foundational research with cutting-edge innovations. Readers will encounter intelligent frameworks for healthcare data, multimodal disease analysis techniques, and advanced clinical decision support systems. The volume also explores transformative technologies like augmented reality for patient care, AI-powered prosthetics, and IoT-enabled real-time monitoring. Emphasizing patient-centric advancements, it discusses personalized care through AI, conversational agents, and secure architectures for 5G-enabled healthcare ecosystems.  Ideal for researchers and postgraduate students in AI, computer vision, and related fields, this book provides modern techniques, tools, and case studies for practical implementation. As AI continues to redefine healthcare boundaries, this volume inspires responsible innovation and collaborative progress, encouraging readers to engage critically and contribute to ethical AI deployment in healthcare.

Computational Intelligence

.- The Role of AI and IoT in Transforming Modern Agriculture.- Sensor Solutions: Cultivating Smart Agriculture.- Connecting IoT for Precision Farming: Driving Efficiency and Sustainability in Agriculture.- Livestock Management Monitoring with AI, etc.

This book introduces a novel approach that combines AI-driven analytics with IoT-enabled systems to provide actionable insights for crop management, resource optimization, and decision-making. Offering fresh perspectives on the integration of artificial intelligence with connected farming technologies, this work highlights practical strategies for boosting efficiency, sustainability, and productivity in modern agriculture. Covering a broad scope from precision farming to predictive maintenance, the book is designed for researchers, practitioners, policymakers, and advanced students. Its applications range from improving yield forecasts to reducing environmental impact, making it a valuable resource for both academic study and real-world agricultural innovation.

Cyber-Physical Systems

1 Authentication in the Internet of Medical Things: review and comparative study.- 2 ECC based Anonymous and Multi-factor Authentication Scheme for IoT Environment.- 3 Implementing Zero-Knowledge Authentication in the Internet of Medical Things.- 4 Machine and Deep Learning Techniques in IoT Security: A Comprehensive Survey.- 5 Comparative analysis of intrusion detection systems in Industry 5.0 systems.- 6 A Novel Intrusion Detection System for IoT in Smart Agriculture Using Crowd Wisdom and RBF Neural Networks.- 7 IoT-Based Smart Farming Security: A Malicious Detection Approach Using Artificial Neural Networks.- 8 Exploring IoT Security Risks and Protective Strategies in Industry 5.0.- 9 Advanced Network Surveillance and Security Enhancement Through Deep Learning.- 10 Blockchain and IoT  Applications: Opportunities, and Challenges.- 11 Blockchain-driven security solutions for IoT.

This book focuses on the integration of Artificial Intelligence (AI) technology for securing IoT next systems, exploring a comprehensive collection of recent techniques and strategies aimed at protecting these complex networks. Moreover, it highlights the alteration from standard security techniques to sophisticated, vulnerability-managed frameworks that can adapt flexibly to emerging threats. It also discusses the important frameworks proposed for the efficient implementation of IoT systems to provide enhanced security and privacy.  This book highlights the requirement of harnessing advanced artificial intelligence and machine learning approaches to address the evolving landscape of IoT threats, underscoring the intersection of security, scalability, and automation in next-generation IoT environments. Subsequently, the chapters investigate the fundamental methodologies and innovations transforming IoT security. Hence, topics treated range from the assessment of deep learning approaches for intrusion detection to the development of multi-factor authentication schemes based on elliptic curve cryptography. This book is appropriate for advanced-level students in computer science and junior researchers, who are studying relevant subjects such as the Internet of Things, cybersecurity, wireless communications, and artificial intelligence. Researchers, cybersecurity specialist and professionals working in advanced IoT, data security, artificial intelligent applications or similar fields will want to purchase this book as well.

Business Informatics

Chapter 1. Introduction to AI-driven Industrial Relations.- Chapter 2. The merging of Fourth Industrial Revolution and Fifth Industrial Revolution.- Chapter 3. The Impact of AI on Work and Workers.- Chapter 4. AI in collective bargaining and dispute resolution.- Chapter 5. Legal and Ethical Considerations in AI-driven IR.- Chapter 6. Future Trends and Opportunities in AI driven Industrial Relations.- Chapter 7. Case Studies and Success Stories.- Chapter 8. Conclusions.

This book examines how artificial intelligence (AI) will impact labour negotiations, the future of employment, and the larger dynamics of industrial relations. It also discusses how AI has become a revolutionary force in many societal and economic areas in the quickly changing twenty-first century. The book covers topics, such as, introduction to AI driven industrial relations, importance and benefits of incorporating AI in industrial relations, AI and its applications in different industries, harnessing the power of industrial AI for optimizing operations and productivity, impact of AI on work and workers, rise of new jobs and skills, algorithmic management and employee monitoring,  future of work: human-AI collaboration, and real-world examples of organizations implementing AI in industrial relations.

Control and Systems Theory

Communications Engineering, Networks

Introduction.- Dynamic Event-Triggered Sliding Mode Control Under Fading Channels.- Component-Based Dynamic Event-Triggered Sliding Mode Control.- Sliding Mode Quantized Control Under Membership-Function-Based Dynamic Event-Triggered Scheme.

This book offers a comprehensive and systematic exploration of control design and analysis for interval type-2 fuzzy systems under constrained communication networks. It introduces a range of innovative communication protocols, including dynamic/adaptive event-triggered, self-triggered, and static/dynamic scheduling protocols. Different methodologies for various communication protocols have been developed, considering the characteristics and information of membership functions in analysis and synthesis. Several techniques are employed to realize the desired performance, including state/output feedback fuzzy control, fuzzy sliding mode control, and fuzzy observer design. This proposed book will be the first book to focus on networked interval type-2 fuzzy systems comprehensively and systematically, including signal transmission, control design, and stability analysis. It serves as an invaluable resource for researchers and practitioners in the fields of control engineering, system sciences, and applied mathematics. It also provides valuable insights for senior undergraduates and graduates in these areas, introducing them to new concepts, models, and methodologies with practical significance in control engineering and signal processing.

Applications of Physics-guided Machine Learning Architectures in Hydrology.- A Review of Approaches and Applications for Streamflow Forecasting Using AI-based Models.- Estimation Of Groundwater Levels Using Machine Learning Techniques.- River Discharge Forecasting in Mahanadi River Basin Based On Deep Learning Techniques.- Machine Learning models for Groundwater Level Prediction.- Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling.- An Integrative Approach for Oxygen Demand-based Stream Water Quality Modelling using QUAL2K-ANN Interactions.- Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA.- The High Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensive Evaluation of Hybrid Gamma Distribution for Districts of West Bengal, India.- Prediction of Rainfall in One of the Wettest Regions in India using Machine Learning Methods.

Applications of Machine Learning in Hydroclimatology is a comprehensive exploration of the transformative potential of machine learning for addressing critical challenges in water resources management. The book explores how artificial intelligence can unravel the complexities of hydrological systems, providing researchers and practitioners with cutting-edge tools to model, predict, and manage these systems with greater precision and effectiveness. It thoroughly examines the modeling of hydrometeorological extremes, such as floods and droughts, which are becoming increasingly difficult to predict due to climate change. By leveraging AI-driven methods to forecast these extremes, the book offers innovative approaches that enhance predictive accuracy. It emphasizes the importance of analyzing non-stationarity and uncertainty in a rapidly evolving climate landscape, illustrating how statistical and frequency analyses can improve hydrological forecasts. Moreover, the book explores the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management. To provide practical solutions, the book includes case studies that showcase effective mitigation measures for water-related challenges. These examples highlight the use of machine learning techniques such as deep learning, reinforcement learning, and statistical downscaling in real-world scenarios. They demonstrate how artificial intelligence can optimize decision-making and resource management while improving our understanding of complex hydrological phenomena. By utilizing machine learning architectures tailored to hydrology, the book presents physics-guided models, data-driven techniques, and hybrid approaches that can be used to address water management issues. Ultimately, Applications of Machine Learning in Hydroclimatology empowers researchers, practitioners, and policymakers to harness machine learning for sustainable water management. It bridges the gap between advanced AI technologies and hydrological science, offering innovative solutions to tackle today's most pressing challenges in water resources.

User Interfaces and Human Computer Interaction

Adversarial integration of LLM and logic program.- Adversarial abductive dialogue framework with reinforcement for tackling LLM hallucination.- Improving ToM capabilities of LLMs in applied domains.- Rehumanizer: Enhancing LLM recommendations for human-like engagement and intriguing interactions.- A copilot for social promotion.- Abduction-based personalized recommender for Web 3.0.- Creating ad sequences with optimal discourse structure by LLM.- A computational framework for analyzing and supporting invention dialogues.- A system for distributed chemical engineering simulations.- Step wise approximation of CBOW reduces hallucinations in tail cases.

This book offers a bold vision for the future of artificial intelligence, showing how the next wave of innovation will blend the power of large language models with the rigor of logical reasoning and human-like understanding. Instead of settling for systems that simply generate text, it explores how AI can be designed to think, engage, and interact in ways that feel meaningful, personal, and trustworthy. Readers will discover how AI can move beyond common pitfalls like hallucinations through smarter frameworks that combine reinforcement learning and symbolic logic. They’ll see how new approaches make AI more empathetic, more explainable, and better at understanding human intent. From copilots that supercharge social media promotion to next-generation recommendation engines for Web 3.0, the book highlights real-world applications that bring these ideas to life. It also dives into AI’s role in creativity and industry, showing how it can craft compelling ad campaigns, spark invention dialogues, and even support specific fields like chemical engineering. Throughout, the focus is on building AI that doesn’t just serve as a tool, but as a partner—enhancing decision-making, supporting innovation, and opening new opportunities for human–machine collaboration. The book is a must-read for anyone curious about where AI is heading next.

User Interfaces and Human Computer Interaction

Graduate/advanced undergraduate textbook

Part 1: Foundations of artificial intelligence.- The genesis of artificial intelligence: Origins and evaluation.- The dawn of AI: Early innovations and discoveries.- Part 2: The learning revolution: Data-driven intelligence.- Soft computing paradigms: Bridging precision and flexibility.- Machine learning: Building intelligence from patterns.- Deep learning: The fabric of representation.- Reinforcement learning: Shaping behaviour through rewards.- Part 3: AI Odyssey: Exploring language and intelligent agents.- Journey through language: Models and prompt engineering.- Autonomous decision maker: AI agents.- Part 4: Expanding the frontiers of AI.- The best of both Worlds: Neuro-symbolic AI.- Mimicking the mind: Evolution of cognitive AI.- Robots with a sense of self: Exploring embodied intelligence.- Part 5: Building advanced and responsible AI.- Blueprint for intelligence: Building robust AI systems.- Never stop learning: The principles of continual learning.- Opening the black box: The role of explainability in AI.- Part 6: Conclusion and future outlook.- Epilogue: AI at the threshold.

Artificial Intelligence: Transcending Traditional Paradigms is a significant new introductory text for undergraduate and graduate course use. It introduces students and professionals to the state-of-the-art development of Artificial Intelligence techniques to develop smart real-world solutions. As an active researcher, the author presents the material authoritatively and with insights that reflect a modern, firsthand understanding of the field. Artificial intelligence is a science still in its infancy, and that makes it special. It’s not like more established fields, and the author has tried in writing this textbook to honour this characteristic and the ways it makes AI unique.

"Chapter 1-Artificial Intelligence and Academic Integrity at a Crossroads".- "Chapter 2-(Academic) Integrity in the age of Artificial Intelligence".- "Chapter 3-How has ChatGPT been positioned as a threat to academic integrity? A scoping review".- "Chapter 4-Preparing Graduates for the Impact of Generative Artificial Intelli-gence in Future Work".- "Chapter 5-Artificial Intelligence Applied to Teaching in Brazilian Public Schools:Emerging Disruptive Pedagogical Theories in Convergence with New Education Ethical Postures".- "Chapter 6-Evaluating the potential for AI-enhanced communication to foster ter-tiary students' sense of belonging and academic engagement".- "Chapter 7-Strengthening authenticity and authorship in the age of AI: AVATAR, a reflective technology for authorial voice development".- "Chapter 8-The Values of Academic Integrity in the Era of Monitoring AI in Class-rooms".- "Chapter 9-Using AI LLMs in the class: the case of teaching business ethics".- "Chapter 10-Artificial intelligence for evaluating learning progress and teaching success: ChatGPT, a language model developed by OpenAI, case".- "Chapter 12-Employing Deep Neural Networks to Automate Grading of Short Answers in Networking Courses".- "Chapter 13-Impact of ChatGPT on Academic Achievement and Self-Efficacy of Postgraduate Students".- "Chapter 14-Navigating academic discourse socialisation challenges: Leveraging AI tools for academic writing among L2 doctoral students".- "Chapter 15-Understanding AI ethics through the student's eyes: A study of ChatGPT integration in a University German course".- "Chapter 16-The future of higher education in the era of generative AI".- "Chapter 17-Students’ experiences and perceptions of ethical use of AI tools".- "Chapter 18-ChatGPT for Learning: Students’ perspectives, Opportunities, Chal-lenges, and Academic Integrity Concerns".- "Chapter 19-When we have to use AI-Exploring AI-supported assessment tools in tertiary education".- "Chapter 20-Conclusion and future directions".

"This book brings together researchers and educators to explore the future of teaching and learning in the era of AI. Comprising seven sections, the volume includes philosophical reflections, historical analyses, empirical studies, and practical case-based insights. It also highlights the design and experimentation of AI technologies through proof-of-concept projects. Collectively, these contributions provide theoretical depth, strategic guidance, and evidence-informed approaches for navigating the benefits and risks of AI while upholding academic integrity in a rapidly changing educational landscape. The book is aimed at a wide audience, including researchers, educators, instructional designers, policymakers, and higher education leaders who are addressing the ethical, pedagogical, and institutional challenges raised by AI. It is also valuable for postgraduate or graduate students and scholars in education, technology, and ethics, as well as for those supporting institutions in responding to emerging technologies in teaching and learning."

Artificial Intelligence and Data Analytics for Innovative Applications in Engineering, Sustainability and Technology

1. Understanding Public e-Service Adoption: A Conceptual Framework for Moroccan Urban Agencies.- 2. A Global Review and Empirical Study on Artificial Intelligence in Industry 4.0 with Insights from Moroccan Students.- 3. Improving Battery Utilization and Lifespan of an Electric Vehicles: Advanced SOC Estimation via Deep Neural Network.- 4. Impact evaluation of school transport program on schooling in rural areas in Morocco.- 5. Detection of Facial Emotion with Deep Learning Models and Contribution of Inception-V3.

This book explores the forefront of Artificial Intelligence (AI), data analytics, and sustainable engineering, presenting in-depth research and practical applications across various domains. It is designed to provide valuable insights for professionals, researchers, and students eager to understand these cutting-edge advancements. The book begins by applying AI to the formal verification of System-on-Chip (SoC) components, showcasing methods that ensure efficient and reliable design processes. It discusses innovative techniques for optimizing the formulation of unfired clay bricks with Chamaerops Humilis fibers. It evaluates the mechanical and thermal properties of compact clay blocks stabilized with wood ash or traditional pottery waste. A detailed bibliometric analysis highlights research progress and trends in renewable energy and sustainable development, offering a clear view of future directions in these vital fields. The book also reviews advancements in traction motors and power electronic converters for electric vehicle drivetrains, providing insights into their efficiency and performance. The mathematical intricacies of the Topological Degree Method for coupled systems of $\psi$-fractional semilinear differential equations with non-local conditions are examined. The book delves into the impact of AI and machine learning on intelligent banking, anomaly detection in IoT networks, and enhancing model portability using Moodle users' traces. Case studies on the profitability of green hydrogen production powered by wind generators and the adoption of public e-services in Moroccan urban agencies illustrate real-world applications and offer practical insights into implementing these technologies. Additionally, the integration of AI in supply chain management within the automotive industry and the future of multi-touch attribution modeling for marketing strategies are explored. The book addresses big data architecture for managing water resources and mapping water quality variability. It includes practical applications such as a hybrid technique for anomaly detection in IoT networks and the smart OSC-Mac CT mode in wireless sensor networks based on WI-LEM technology. 

Computational Intelligence

Graduate/advanced undergraduate textbook

Chapter 1. Foundations of Artificial Intelligence and Machine Learning.- Chapter 2. Machine Learning Algorithms and Techniques.- Chapter 3. AI Applications in Real World Scenarios.- Chapter 4. Ethical Considerations and Challenges.- Chapter 5. The Future of AI and ML.- Chapter 6. Deep Learning and Neural Networks.- Chapter 7. Reinforcement Learning and Robotics.- Chapter 8. Time Series Analysis and Forecasting.- Chapter 9. Explainability and Interpretability in AI.- Chapter 10. AI and ML for Business and Decision Making.- Chapter 11. AI in Social Sciences and Humanities.- Chapter 12. Challenges and Limitations of AI and ML.- Chapter 13. The Road Ahead Advances and Future Directions.

This book is a comprehensive exploration of the dynamic and transformative world of AI and ML. It takes readers on a journey through the foundational principles, diverse algorithms, and real-world applications of these cutting-edge technologies. It delves into the ethical considerations, challenges, and the potential impact on our future, offering a holistic perspective on the ever-evolving landscape of artificial intelligence and machine learning. Whether the readers are a novice looking for a comprehensive introduction or an expert seeking to delve deeper, this book provides insights, knowledge, and inspiration for embracing the limitless possibilities of AI and ML. Join the authors on a captivating voyage as they unravel the mysteries, unveil the innovations, and chart the course for the future of technology.  

Graduate/advanced undergraduate textbook

Introduction.- K-Nearest Neighbors.- K-Means.- Multi-Layer Perceptrons.- Convolutional Neural Networks.

The book provides a structured introduction to core areas of artificial intelligence, including machine learning, deep learning, large language models, and agent-based systems. It combines theoretical foundations with practical implementation, using tools such as scikit-learn, Keras, and Ollama. Each chapter includes code examples, exercises, and links to interactive notebooks. The focus lies on conveying applicable knowledge for real-world use. The book is intended for students and practitioners in computer science or related fields and can be used for teaching, self-study, or reference in applied contexts.  

Human Fertility and Global Trends.- Analysis of Human Reproductive Dynamics.- Leveraging AI and Fundamentals.- XAI: Healthcare focusing on fertility assessment.- Summary and Take-home Notes.

This book studies reasons behind the dramatic decline in total fertility rate (TFR), which has dropped to half of its previous levels. A major factor contributing to this trend is the widespread lack of recognition of fertility issues as a medical concern, compounded by insufficient societal awareness. To better understand the dynamics of human reproduction, this book identifies various factors that influence fertility, including the male and female reproductive systems, lifestyle choices, career demands, societal pressures, environmental factors, and personal habits (e.g., millennials). Central to this understanding is the collection of vast amounts of data, for which AI algorithms play a crucial role. The book studies how AI can help predict fertility outcomes by analyzing diverse datasets for both male and female populations. It also discusses the growing reliance on assisted reproductive technologies, despite their high costs and inconsistent success rates, and how AI can assist in predicting these success rates to ease the financial burden on patients. The primary goal of this book is to highlight how (explainable) AI can become a powerful tool in predicting human fertility. Ultimately, it aims at raising the global TFR and addressing the challenges posed by declining fertility rates around the world.

Healthcare Sustainability Meets Business AI: Knowledge Graph Framework for Sustainable Healthcare Intelligence.- AI-Driven Smart Traffic Signal Optimization.-  Environmentally Sustainable Smart Cities: Contriving Urbanization for Climate Change.- Deploying AI in Climate Crisis: Tools for Mitigation and Adaptation.

This book provides a comprehensive exploration of how Artificial Intelligence (AI) can revolutionize businesses in their pursuit of sustainability. It offers actionable insights and innovative approaches for integrating AI into business operations to address critical challenges like climate change, resource optimization, and ethical responsibility while driving growth and efficiency. The book delves into key topics that are vital for creating sustainable business models. It begins with an introduction to the paradigm shift AI brings to sustainability and progresses to its applications in optimizing energy usage, streamlining supply chains, and fostering green innovation. Specialized chapters address AI-driven circular economy models, the development of smart cities and sustainable infrastructure, and AI’s pivotal role in climate change mitigation and adaptation. Additionally, it explores how blockchain can enhance transparency and accountability in sustainability efforts. Recognizing the complexity of adopting AI for sustainable practices, the book tackles ethical considerations and operational challenges, providing readers with practical strategies to navigate these hurdles. The concluding chapters envision the future of AI in sustainable business, offering predictions and opportunities for innovation and growth in this evolving field. These topics are crucial as businesses worldwide face mounting pressure to balance profitability with environmental and social responsibility. By harnessing AI, organizations can unlock new avenues for reducing their carbon footprint, enhancing resource efficiency, and meeting stakeholder demands for sustainable practices. This book addresses the pressing need for actionable frameworks and multidisciplinary approaches, empowering businesses to lead in the transition toward a sustainable future. Targeted at business leaders, sustainability professionals, researchers, and students, this book serves as a practical guide and thought-provoking resource for those seeking to leverage AI as a transformative tool for sustainable innovation.

Artificial intelligence applications in water environments Recent work and prospects.- The incorporation of artificial neural networks and response surface methods to optimise the removal of chromium (VI) from a biodegradable composite.- The optimisation and prediction of copper (II) removal from a green adsorbent via the Box‒Behnken (BBD) experimental design approach using adaptive neuro fuzzy (ANFIS).- Prediction of Cadmium (II) Removal from Aqueous Solution via the Adsorption Process: Adsorption Mechanism, Mechanistic Modelling and Artificial Neural Network (ANN) Approach.- The application of soft computing for the removal of lead (II) by biodegradable adsorbents from wastewater.- Zinc (II) removal from water onto cellulose nanocrystal beads via a fixed bed column: experimental and modelling studies.

This book presents a comprehensive and practical overview of machine learning-driven adsorption processes for pollution removal from wastewater, with a focus on modeling, optimization, and mechanistic insights. It explores how techniques such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM) can enhance the efficiency of removing heavy metals—including Chromium (VI), Copper (II), Cadmium (II), and Zinc (II)—using biodegradable and nanostructured adsorbents like modified cellulose nanocrystals. Through detailed case studies, experimental methodologies, and comparative analysis of AI algorithms, this book bridges traditional adsorption science with advanced computational approaches, offering valuable tools and insights for researchers, engineers, and practitioners working in environmental science, chemical engineering, and sustainable water treatment.  

Undergraduate textbook

The Pre-History of Artificial Intelligence.- Computing, Representations, and Definitions of Artificial Intelligence.- The State Space, Finite State Machines, and Artificial Life.- Searching the State Space.- Heuristic Search.- Heuristics: 2-Person Games and Theoretical Constraints.- Introduction to the Propositional and Predicate Calculi.- The Predicate Calculus and Unification.- Resolution: Reasoning with the Propositional and Predicate Calculi.- The Production System Representation and Search Engine.- Advanced Applications of Symbol-Based Reasoning.- Uncertain Reasoning: Symbol-Based.- Introduction to Association-Based Knowledge Representations.- Association-Based Representations: Frames, Conceptual Graphs, WordNet, and FrameNet.- An Introduction to Neural Networks.- The Delta Rule, Backpropagation, and Matrix Representations.- Deep Learning: Introduction and Representations.- Building Language Models and Transformers.- Alternative Network Architectures: Prototypes and Classifiers.- Alternative Network Architectures: Attractor Networks and Memories.- Counting, the Foundation of Probabilities.- Bayes’ Theorem.- Bayesian Belief Networks and Observable Markov Models.- Hidden Markov and Alternative Probabilistic Models.- Artificial Intelligence: User-Focused Ethical Issues.- AI Ethical Issues: From the Developers’ Perspective.- Artificial Intelligence: Current Limitations and Future Promise.

This book provides a complete introduction to Artificial Intelligence, covering foundational computational technologies, mathematical principles, philosophical considerations, and engineering disciplines essential for understanding AI. Artificial Intelligence: Principles and Practice emphasizes the interdisciplinary nature of AI, integrating insights from psychology, mathematics, neuroscience, and more. The book addresses limitations, ethical issues, and the future promise of AI, emphasizing the importance of ethical considerations in integrating AI into modern society. With a modular design, it offers flexibility for instructors and students to focus on specific components of AI, while also providing a holistic view of the field.  Taking a comprehensive but concise perspective on the major elements of the field; from historical background to design practices, ethical issues and more, Artificial Intelligence: Principles and Practice provides the foundations needed for undergraduate or graduate-level courses.  The important design paradigms and approaches to AI are explained in a clear, easy-to-understand manner so that readers will be able to master the algorithms, processes, and methods described.  The principal intellectual and ethical foundations for creating artificially intelligent artifacts are presented in Parts I and VIII. Part I offers the philosophical, mathematical, and engineering basis for our current AI practice. Part VIII presents ethical concerns for the development and use of AI. Part VIII also discusses fundamental limiting factors in the development of AI technology as well as hints at AI's promising future. We recommended that PART I be used to introduce the AI discipline and that Part VIII be discussed after the AI practice materials. Parts II through VII present the three main paradigms of current AI practice: the symbol-based, the neural network or connectionist, and the probabilistic.  Generous use of examples throughout helps illustrate the concepts, and separate end-of-chapter exercises are included. Teaching resources include a solutions manual for the exercises, PowerPoint presentation, and implementations for the algorithms in the book.

Preface.- 1. Introduction.- 2. Preliminaries.- 3. Hopfield-type neural networks.- 4. Shunting inhibitory cellular neural networks.- 5. Inertial neural networks with discontinuities.- 6. Cohen-Grossberg neural networks.

Mathematical chaos in neural networks is a powerful tool that reflects the world’s complexity and has the potential to uncover the mysteries of the brain’s intellectual activity. Through this monograph, the authors aim to contribute to modern chaos research, combining it with the fundamentals of classical dynamical systems and differential equations. The readers should be reassured that an in-depth understanding of chaos theory is not a prerequisite for working in the area designed by the authors. Those interested in the discussion can have a basic understanding of ordinary differential equations and the existence of bounded solutions of quasi-linear systems on the real axis.  Based on the novelties, this monograph aims to provide one of the most powerful approaches to studying complexities in neural networks through mathematical methods in differential equations and, consequently, to create circumstances for a deep comprehension of brain activity and artificial intelligence. A large part of the book consists of newly obtained contributions to the theory of recurrent functions, Poisson stable, and alpha unpredictable solutions and ultra Poincaré chaos of quasi-linear and strongly nonlinear neural networks such as Hopfield neural networks, shunting inhibitory cellular neural networks, inertial neural networks, and Cohen-Grossberg neural networks. The methods and results presented in this book are meant to benefit senior researchers, engineers, and specialists working in artificial neural networks, machine and deep learning, computer science, quantum computers, and applied and pure mathematics. This broad applicability underscores the value and relevance of this research area to a large academic community and the potential impact it can have on various fields. 

Introduction.- Automated Erasing Data Augmentation for Person Re-Identification.- Learning Auto-Scale Representations for Person Re-Identification.- Searching for A Multi-Branch ConvNet for Person Re-Identification.- Dynamic Loss Function Search for Person Re-Identification.- Searching Static Generalized Margin-based Softmax Loss Function for Person Re-identification.- Conclusion and Future Work.

This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions. Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions. The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.

Data and Information Security

  The Evolution of Artificial Intelligence.- Ethical Principles for Responsible AI.- Risks and Ethical Challenges in AI.- Best Practices for Ethical AI Implementation.- Malicious Uses of AI.- AI for Social Good (AI4SG).

This book aims to provide a comprehensive perspective on the growing challenges posed by artificial intelligence (AI) for researchers and professionals. The book examines critical security issues, such as attacks and vulnerabilities, as well as ethical principles that impact fairness, transparency, accountability, and inclusiveness in AI solutions. Its primary goal is to equip readers with the knowledge and tools necessary to design and govern AI systems that align with the principles of social good, fostering responsible and sustainable innovation in the rapidly evolving AI landscape. This book bridges the fields of ethics and security in AI. It covers foundational principles, practical frameworks, and emerging challenges, equipping students and practitioners with tools to design and govern ethical and secure AI systems. With case studies, activities, and real-world examples, the book emphasizes responsible AI applications in fields such as healthcare, sustainability, and cybersecurity.

Ethics of Technology

Chapter 1 Introduction to Human-Centric Generative AI.- Chapter 2 Principles of Human-Centric Generative AI Design.- Chapter 3 Ethical Challenges in Generative AI.- Chapter 4 Design and Development Methodologies.-  Chapter 5 Implementing Human-Centric Generative AI in Practice.- Chapter 6 The Future of Human-Centric Generative AI.

This book addresses critical ethical, social, and technological challenges that arise from the rapid integration of Generative AI systems into diverse aspects of human life. The shift toward human-centered GAI emerges as a response to increasing concerns about AI's embedded biases, opacity, and potential misalignment with human values. By centering on human experiences, agency, and ethical principles, this paradigm fosters the development of GAI that is explainable, interpretable, trustworthy, and aligned with societal needs. Building Human-Centered Generative AI offers a comprehensive roadmap for rethinking generative AI as a technology that serves people first, not metrics or scale. Bringing together principles from computer science, human-computer interaction, ethics, psychology, and design, it shows how empathy, fairness, transparency, and accountability can be systematically embedded into models, interfaces, and deployment lifecycles. Rather than treating ethics as an afterthought, the book positions human values, user needs, and social impact as core design requirements for any serious generative AI initiative. Written for graduate students, researchers, practitioners, and policymakers, this book functions both as a scholarly reference and as a practical playbook for responsible innovation. It equips teams with actionable methods for participatory and value-sensitive design, human-in-the-loop collaboration, GenAIOps governance, and multidimensional evaluation focused on trust, usability, and societal impact. For anyone tasked with designing, deploying, or regulating generative AI, Building Human-Centered Generative AI provides the conceptual grounding and hands-on guidance needed to align powerful new technologies with human dignity, equity, and long-term social good.

Mathematical and Computational Engineering Applications

Part1. Artificial Intelligence, Machine Learning, Transformation  and Sustainability .- 1.Machine Learning Approaches to Mitigate Transportation Traffic Congestions.- 2.Exploring the Integration of AI Techniques in the Digital Advertising Industry: A Survey on Bahraini audience.- 3.Digital Transformation in HRM: Navigating the Future of Human Resource Management.- 4.Transitioning to Digital: Fostering Sustainable Practices through Digitalization of Traditional Records in Bahrain.- 5.Technological Innovation, Trade and Development in Africa.- 6.Advancing Sustainability: Embracing the Circular Economy for Environmental and Economic Resilience.- 7.Reviewing the Impact of Technological Innovation on Accounting Practices.- 8.The Effects of Internal Corporate Social Responsibility on Employee Turnover Intentions within the National Bank of Bahrain: A Non-Western Perspective from the Kingdom of Bahrain.- 9.Empowering Employees, Training, and Compensation: Key Drivers of Employee Retention at Al Jazeera Ship-ping Company.- 10.Impact of Workplace Relations on Employee Performance: Mediating Role of Turnover Intentions – An Empirical Study Concerning IT-Enabled Sector Employees in and around  Hyderabad.- 11.Optimizing Water Usage in Agriculture: A Study on Automated Irrigation Systems Using Humidity Sensors and Arduino Uno.- 12.ChatGPT in Education Between Advantages, Authority & Ethics: A Rapid Literature Review.- 13.NFC Cards Technology for A Non-Print Campus Project: Prototyping A Transition Towards Environmental Sustainability.- 14.Remarks on Sustainable Tourism.- 15.Environmental, Social, and Governance Practices in Bahrain: A Comprehensive Analysis of Sustainable Development in the Corporate and Financial Sectors.- 16.Mentoring And Employee Productivity In Federal Universities In Nigeria: An Examination Of The Moderating Effect Of Mentor’s Qualities.- Part 2. Education, Sustainability  and Technology.- 17.Integrating Technology in Education: The Impact of Blended Learning Approaches in Higher Education.- 18.A Comparative Analysis of Artificial Hallucinations in GPT-3.5 and GPT-4: Insights into AI Progress and Challenges.- 19.Exploring The Interconnections Between STEM based Design, CAD-based Learning and Design Thinking for Sustainability in Design Education.- 20.The Impact of Information and Communication Technology (ICT) on Students’ Behaviors in Palestinian Educational Sector.- 21.Developing University Students' Attitudes Towards the Use of AI Through ChatGPT: Evidence from Bahraini Context.- 22.Review of the Need for Andragogical Inclusion in Higher Education.- 23.Strategic human resource management impacting on the sustainable competitive advantage: Mediating role of Employee Involvement and Human capital development.- 24.Employing the Balanced Scorecard to Reduce Administrative Corruption in Organizations.- 25.THE ROLE OF THE KNOWLEDGE ECONOMY AND ITS IMPACT ON ACHIEVING SUSTAINABLE DEVELOPMENT ACCORDING TO THE LOFTY VISION 2030.- 26.Sustainability as Strategy: The New Competitive Edge for Bahrain's SMEs.- 27.Bridging Worlds: Envisioning a Sustainable Future through CSR in Developing Countries.- 28.The Impact of Integrity: Navigating Ethical Dilemmas, Conflicts, and Fraud in Contemporary Business.- 29.Next-Gen Sustainability: Crafting Eco-Resilient Workplaces and Universities.- 30.Sentiment Analysis of Reviews on AI Interface ChatGPT: An Interpretative Study.- 31.AI in Academia: Pros and Cons of Integrating Artificial Intelligence in Universities.- 32.Robust Buck Converter Design with Sliding Mode Control by LYAPUNOV - A Study of Load And Voltage Stability.- 33.Eco-Economics: Navigating the Financial Effects of Sustainability Initiatives.- 34.Self-Citation in Scholarly Work: Balancing Self-Reference with Scientific Integrity.- 35.Enhancing Sustainable Development in Bahrain: The Critical Role of Occupational Health and Safety.- 36.Study of Inverse Kinematics Solution for a 5-Axis Mitsubishi RV-2AJ Robotics Arm Using Deep Reinforcement Learning.- 37.Evaluating Digital Resilience by Examining Big Five Personality Traits and Basic Psychological Needs Satisfaction In Malaysian Higher Learning Institutions.- 38.Beyond Entertainment: Investigating (VOD) Platforms Consumption Habits and Gratifications among Gulf Youth.- 39.The physical adsorption characterization of banana peels as activated carbon through the employment of microwave technology is being conducted.- 40.Strategic Human Resource Management: Fostering Organizational Success through Alignment and Innovation.- 41.Aligning HRM Strategies with Organizational Performance: A Pathway to Success.- 42.Greening Our Future: A Comprehensive Analysis of Sustainable Practices and Their Role in Promoting Environmental Quality and Human Well-being.- 43.Family Business Performance in the COVID-19 Era: A Study of Multiple Nuclei Pattern in Kabarole District, Kampala, Uganda.- 44.Harnessing Big Data Analytics: A Comprehensive Approach to Enhancing Student Performance and Curriculum Effectiveness.- 45.The role of the interior designer as a concept for under-graduate students in Jordan.- 46.The role of Metaverse in interior design-built environment.- 47.Can Social Media Save Lives? The Impact of Digital Awareness Campaigns on Bahrain's Drivers.- 48.The Impact of psychological empowerment on job creativity   A study applied to employees of the Ministry of Works, Municipalities Affairs and Urban Planning in the Kingdom of Bahrain.- 49.Unmasking Fake News: Examining the Role of AI in Combatting Fake News among Media Communicators in Bahrain.- 50.Examining the Impact of Trust and Satisfaction on the Adoption of Online Banking.- 51.Corporate Governance and Internal Control Mechanisms: Developing a Strategic Framework.- 52.Energy demand of steelmaking industry in UK.- 53.Enhancing Employee Engagement through Internal Corporate Social Responsibility: Insights from BFG International Company in Bahrain.- 54.Numerical study on corrugated I shaped shear link damper.- 55.Retaining talented employees to enhance business and management- a case study.- 56.Utilisation of Fly ash and Plastic Waste as a Partial Replacement of Cement and Fine aggregate in Concrete Paver Blocks.- 57.Sustainable Development Goals (SDGs) in the Domain of International environmental law.- 58.Fostering a Sense of Belonging among Neighborhoods through a Virtual Community.- 59.Navigating the Future: The Role of SDGs in Enforcing Sustainable Development Principles.- 60.Enhancing ESG Implementation through Effective Management Control Systems.- 61.Technology Aspect in Residential Spaces.

This book covers different technological and business-related issues including ethical use and cultural sensitivity of data used in businesses, managing data privacy and protection, governance standards for digital transformation, executive leadership strategic decisions, and business innovation and sustainability. With the recent development of artificial intelligence (AI), businesses are urged to consider innovation while applying digital transformation. Depending on the nature of the businesses, it is found that innovative digital transformation is required with the use of artificial intelligence. However, the future of AI in businesses is yet unclear, the question is it true that without digital transformation businesses are no longer sustainable? Researchers argue that digital transformation could be an opportunity for business to create a global brand however several implications and challenges should be considered including governance and responsible digital management. This book explores how businesses could benefit from AI and leverage technologies to sustain businesses. The book is authored by leading experts in the field of AI, digitalization, and business innovation and sustainability; the author’s diversity reflects quality of research with high level of impact in the research topic. It is written in accessible language that makes it easy for business leaders, researchers, policymakers, and anyone interested in the future of business development to understand the complex concepts and ideas presented in the book. This book provides insight for executive leaders in setting new innovative strategies toward leveraging AI in business at different levels of operations to support business sustainability. The book provides different theoretical and practical practices and case studies that could be used as a guideline for policy making and devising innovative directions.

Mathematical and Computational Engineering Applications

Part A.Artificial Intelligence, Leadership and Industrial Revolution (IR) 4.0 & 5.0.- Part B.Marketing and Technology.- Part C.Governance, Gender and Performance.- Part D.Sustainability, Innovation and Technology.- Part E.Management Strategies and Business Innovation.

With the recent development of Artificial Intelligence (AI), businesses are urged to consider innovation while applying digital transformation. Depending on the nature of the businesses, it is found that innovative digital transformation is required with the use of Artificial Intelligence. However, the future of AI in businesses is yet unclear, the question is it true that without digital transformation businesses are no longer sustainable? Researchers argue that digital transformation could be an opportunity for business to create a global brand however several implications and challenges should be considered including governance and responsible digital management. This book explores how businesses could benefit from AI and leverage technologies to sustain businesses, and the book covers different technological and business-related issues including ethical use and cultural sensitivity of data used in businesses, managing data privacy and protection, governance standards for digital transformation, executive leadership strategic decisions, business innovation, and sustainability. The book is authored by leading experts in the field of AI, digitalization, and business innovation and sustainability, and the author’s diversity reflects quality of research with high level of impact in the research topic. It is written in accessible language that makes it easy for business leaders, researchers, policymakers, and anyone interested in the future of business development to understand the complex concepts and ideas presented in the book. This book provides insight for executive leaders in setting new innovative strategies toward leveraging AI in business at different levels of operations to support business sustainability. The book provides different theoretical and practical practices and case studies that could be used as a guideline for policy making and devising innovative directions.

Automotive Engineering

From Heritage to High-Tech: The Evolution of the Automotive Industry.- Driving into the Future: Autonomous Vehicles.- Digital Drive: The New Era of Connectivity.- Quality in the Automotive Industry – From Product to Process.- Know the Risks: Mastering Automotive Project Management.- CORE SPICE.- Strategic Roadmap: Shaping the Future of the Automotive Business.- Automotive Perspectives: Resolving the Riddle.

This book provides an overview of the many new features becoming a reality in connected cars. It covers everything from the integration of Google and Facebook to services that help you find your parking spot, park your car via an app, or remotely close your sunroof when it's raining. The ultimate goal of this development is autonomous driving. The book includes current developments, implementation variants, and key challenges regarding safety and legal framework. It also provides information about the necessary quality standards in developing complex vehicle software-based systems. Finally, the effects on the economy, society, and politics are described, with special consideration given to vehicle users, manufacturers, and suppliers.

.- Introduction to Causal Thinking. .- Treatments, Outcomes, and Confounding: Core Concepts. .- Causal Estimation Basics. .- Causal Graphs: Structure and Assumptions. .- Interventions and Counterfactuals. .- Introduction to Do-Calculus. .- Backdoor and Frontdoor Criteria. .- Advanced Causal Inference Methods. .- Causal Inference Meets Deep Learning. .- Simulating Causal Data and Evaluation Met rics. .- Balancing Representations with Causal Deep Learning (CFRNet). .- Propensity Scores in Causal Deep Learning. .- Evaluating Causal Models Without Counter factuals. .- Advanced Topics in Causal Inference. .- Assumptions and Real-World Challenges in Causal Inference. .- Summary of Key Concepts. .- Case Studies. .- Solutions to Exercises.

This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models—primarily focused on pattern recognition—often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson’s Paradox, and will understand why these challenges necessitate a causal approach.   Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings.

Computational Intelligence

Part I Coevolutionary Computation Fundamentals, Applications, and Challenges.- Chapter 1 Evolutionary Computation: An Overview.- Chapter 2 Introduction to Coevolutionary Computation.- Chapter 3  Principal Approaches of Coevolution: Competitive and Cooperative.- Chapter 4 Applications of Coevolution in Real World Problem Solving.- Chapter 5 Pathologies and Analysis in Coevolutionary Search.- Part II Coevolutionary Computation on Optimization Problems.- Chapter 6 Coevolutionary Construction of Parallel Algorithm Portfolio Optimization.- Chapter 7 Cooperative Coevolution for Large Scale Optimization.- Chapter 8 High Dimensional Optimization as Computationally Expensive Optimization.- Chapter 9 Design Space Exploration for Multimode Dataflow Mapping.- Chapter 10 Cooperative Coevolutionary Design Optimization in Concurrent Engineering.- Part III Coevolutionary Computation on Learning Problems.- Chapter 11 Coevolutionary Learning in Noncooperative Games.- Chapter 12 Generalization in Coevolutionary Learning.- Chapter 13 Impact of Problem Structures in Coevolution.

This book introduces the fundamentals of Coevolutionary Computation and presents new methodologies that are developed and then employed for modern real-world problem-solving in various applications across different domains. It is structured in three main parts to support the anticipated general and frequent usage of the book. In particular, the reader is able to obtain a quick and general introduction on the principles of coevolution in Part I, and then go over in detail the specifics how coevolutionary principles are exploited and applied to solve specific problems in the relevant chapters of Parts II and III. In this manner, Part I will introduce the fundamentals in Coevolutionary Computation with no assumption made on familiarity with Evolutionary Computation literature. These fundamentals include key concepts and operational principles of both evolutionary and coevolutionary processes that are modelled as iterative algorithms and systems implementable in computing machines. Parts II and III contain various applications of coevolution to problems that are framed in the context of optimization and learning, respectively. Detailed procedural implementations are provided for those methodologies as well as analysis that highlight the improvements they bring about over conventional techniques.  

Industrial and Production Engineering

1. Overview of CAMS.- 2. Computations and methods.- 3. Industrial challenges and solutions.- 4. Future trends.

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples.  It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Computational Intelligence

1. Advanced Signal Processing Techniques in Bioacoustics by Elevating Wildlife Monitoring.- 2.Leveraging Advanced Deep Learning Techniques for Enhanced Bioacoustic Classification.- 3. Deep Neural Networks and Wavelet Transforms for Bioacoustic Classification.- 4. Ethical Considerations and Societal Impact.- 5. OPTIMIZING BIOACOUSTIC SIGNAL CLASSIFICATION USING AI-ENHANCED TRADITIONAL ALGORITHMS.- 6. AI-POWERED BIOACOUSTIC METHODS FOR EARLY WARNING OF NATURAL DISASTERS THROUGH ANIMAL BEHAVIORAL CUES.- 7. Artificial Intelligence in Agriculture: Technology-Powered Strategies for a Sustainable Farming Future.- 8. Optimizing Bioacoustic Classification: Deep Metric Learning and Contrastive Embedding Techniques for Unlabeled Acoustic Data.- 9. Integrating Bioacoustic Sensors for Improved Human-Computer Interaction.- 10. Recent Advances in Bioacoustic Data Fusion and Multimodal Analysis.- 11. Acoustic Analysis: Traditional Classification Methods in Bioacoustics.- 12. Enhancing Human-Computer Interaction with Bioacoustics: Techniques and Applications.- 13. Bioacoustics Feature Engineering: Techniques and Applications for Ecological Insight.- 14. Detection of Ecosystem Changes Using Bioacoustics Techniques.- 15. Advanced Techniques for Ecological Research using Bioacoustic Signal Processing.- 16. Ethical and Privacy Considerations in AI-Driven Wildlife Monitoring.

This book deep dives into the theoretical background of bioacoustics, signal processing techniques, feature extraction and pattern recognition algorithms, and technically advanced case studies in bioacoustics AI. By exploring the intricate characteristics of bioacoustic signals, this book offers a comprehensive understanding of the underlying principles and practical implementations. The fundamental chapters provide readers the basics by discussing the statistical and deterministic models of bioacoustic signals, including parametric and non-parametric approaches, time-frequency representations, and stochastic processes. Furthermore, this book delves into the complexities of bioacoustic signal generation and propagation, considering physiological factors, acoustic media, and signal degradation. The feature engineering methodology upon the complex and noisy sound data is understood and explored using advanced signal processing techniques, such as wavelet transforms, matching pursuit, higher-order statistics, and fractal analysis. The subsequent chapters focus on feature engineering and pattern recognition. The feature extraction methods under subject of discussion include time-domain, frequency-domain, and time-frequency features, as well as statistical and structural features. The advanced techniques related to deep learning such as convolutional neural networks and recurrent neural networks are also explored. Traditional classification techniques, including statistical pattern recognition and syntactic pattern recognition, are covered, followed by a deep dive into the application of deep learning for bioacoustic classification. The later chapters detail on the futuristic topics such as bioacoustic localization, source separation, change detection, and monitoring. The bioacoustic data collected with other sensor modalities are significant in the development of bioacoustic indices. This book leads the assessment techniques to determine the quality of ecosystem and its performance. Furthermore, the application of bioacoustic AI in man-machine interaction is examined.

Chapter 1. Towards Better AI: An Analysis of Clustering Algorithms in Machine Learning.- Chapter 2. An Explainable AI Framework for Chromosomal Anomaly Detection in Karyograms Using Grad-CAM and Advanced Neural Networks.- Chapter 3. Uncovering Temporal Trends of Desolation Among Social Media Users: A Data-Driven Analysis.- Chapter 4. Optimizing Intelligent Traffic Management for Smart Cities Using Ensemble Learning Algorithms.- Chapter 5. Optimizing Data Transfer in Hospitals using Li-Fi Technology.- Chapter 6. Collaborative IT security service in Generative Adversarial Network.- Chapter 7. Banknote Originality Verifiation using Deep Neural Network Classifier vs. Random forest.- Chapter 8. Crime Association Analysis in KPK, Pakistan: A Comprehensive Exploration.- Chapter 9. Artificial Intelligence based Adaptive Fan Speed Control for Residential Energy Efficiency and Optimal Comfort.- Chapter 10. A Robotic Arm Based Intelligent Biopsy System.- Chapter 11. Automatic Text Summarization by Using Deep Learning Models.- Chapter 12. Enhancing Maternal Balance: Comparative Analysis of Plantar Pressure Distribution Across and Visual Feedback Within Pregnancy Trimesters.- Chapter 13. Word-level Pakistani Sign Language Recognition.- Chapter 14. Enhancing Information Security: Proactive Detection, Vulnerability Remediation, and Systematic Risk Management.- Chapter 15. AI Based Learning System for Deaf Community Using machine learning techniques.- Chapter 16 Real-time communication between normal and deaf people using machine learning and 3D animation.- Chapter 17. AI-Driven Novel TL Approach for Efficient Classification of Brain Tumors using Deep CNNs.- Chapter 18. Advancements in Parkinson’s Disease Diagnosis: Leveraging Technology from Biomarkers to Wearable Devices and Machine Learning. Chapter 19. Contexualizing Space Transition Theory: A Socio-Technical Exploratory Study of Cybercrimes in Khyber Pakhtunkhwa.- Chapter 20. Devising Muslim Name using Quantum Deep Learning.- Chapter 21. Intelligent Sensors for the Detection of Outlawed Vehicles on Highways.- Chapter 22. Enhancing News Summarization based on Advanced Deep Learning Models using the BBC News Dataset.- Chapter 23. Thermoelectric Energy Harvesting System for Wearable Electronics.- Chapter 24. The Art of Deception: Adversarial Network Strategies in Thwarting Airborne Object Detection.- Chapter 25. A Formal Approach for Developing Commercial E-Agriculture Application: An OCL Modelling Perspective.- Chapter 26. Performance Comparison of C++, Java and Python: A Comprehensive Analysis of Programming Languages.- Chapter 27. Comparing the Security and Efficiency of Keyless Data Encryption and Decryption Techniques for Big Data Processing.

This book contains select chapters on a broad spectrum of topics on artificial intelligence, machine learning, and computational methodologies. Presented at the 3rd International Conference on Computational Intelligent Systems (ICCIS2024), held at Kohat University of Science and Technology (KUST), Kohat, Pakistan, from 2 to 3 December 2024, it contains 27 chapters. Each chapter offers insightful perspectives on tackling real-world challenges through intelligent algorithms and systems, from optimizing traffic management in smart cities and enhancing healthcare diagnostics with machine learning to advancing information security and leveraging quantum deep learning for innovative applications. The book explores practical implementations, such as intelligent sensors for highway monitoring, adaptive systems for residential energy efficiency, and groundbreaking technologies for human–computer interaction. It provides a detailed analysis of the potential of computational intelligent systems to address complex issues across diverse domains, including healthcare, robotics, cybersecurity, and e-agriculture. The book is designed for researchers, students, and practitioners encapsulating the latest research findings and methodologies in the evolving landscape of intelligent systems. Whether understanding the theoretical concepts or exploring hands-on applications, readers will find this book a valuable resource for understanding the cutting-edge developments shaping the future of computational intelligence.  

Countering Disinformation in the Era of Generative AI.- Part I Introduction and Overview.- 1 Introduction.- Part II Landscape, Trends and Studies.- 2 False Alarm or Real Threat? Trends in GenAI-mediated Disinformation.- 3 From Fact-Checking to Content Moderation: Inside a Newsroom Faced with AI.- 4 Synthetic Seduction: Evolving Visual Persuasion in Coordinated Online Gambling Promotion with Generative AI.- Part III AI-generated and Manipulated Media Detection.- 5 AI-based Image Forgery Detection and Localization.- 6 AI-Generated Image Detection: Challenges and Recent Advances.- 7 Synthetic Audio Detection.- 8 Video Deepfake Detection: Challenges and Recent Trends.- Part IV AI-based Tools for Multimodal Disinformation Detection and Analysis.- 9 AI-based Multilingual Credibility Assessment.- 10 Multimodal Detection of Image Misrepresentation.- 11 Audio-based Tools for Decontextualization Detection.- 12 Video Decomposition and Key Visual Elements Extraction and Enhancement.- 13 Chatbot-assisted Explainability of AI-powered Models and Collection of User Feedback.

This book offers a timely and comprehensive exploration of how generative AI technologies are reshaping the landscape of online disinformation, dramatically lowering the barriers to creating realistic synthetic text, imagery, and audio at scale. While online disinformation has long challenged societies and individuals, the acceleration of generative AI has intensified these risks, making traditional detection and protection measures increasingly inadequate. In response, researchers are developing advanced AI-driven tools and methods to detect, mitigate, and study the rapidly evolving tactics of disinformation campaigns, with emphasis on algorithmic detection, media verification, and the broader implications for information integrity. Countering Disinformation in the Era of Generative AI brings together leading experts to present both recent misuse cases — such as synthetic media in political manipulation and AI-generated false narratives — and state-of-the-art countermeasures, including trustworthy AI solutions co-created with media professionals. The book features thorough coverage of emerging methods for detecting disinformation and synthetic media and analyzing online information environments to. Drawing upon interdisciplinary research and large-scale initiatives such as the Horizon Europe vera.ai project, this volume addresses the needs of diverse stakeholders — from researchers and journalists to policymakers — offering not only technical depth for specialists, but also an accessible overview of the latest advances in AI-powered disinformation defense.

Computational Intelligence

Introduction to Data driven Cyber Physical Systems.- Fundamentals of Data driven Cyber Physical Systems.- Data Collection in Cyber Physical Systems.- Data Storage and Management in Cyber Physical Systems.- Data Integration and Fusion in Cyber Physical Systems.- Data driven Modeling and Simulation in Cyber Physical Systems.- Fault Detection and Predictive Maintenance in Cyber Physical Systems.- Cybersecurity in Data driven Cyber Physical System.- Future of Data driven Cyber Physical System.

This book shows the exploration and integration of data-driven approaches within cyber-physical systems (CPS), focusing on how these systems leverage data science, artificial intelligence, and machine learning to enhance performance, optimize real-time decision-making, and improve the interaction between physical and digital components. Readers will be interested in the areas of data acquisition, integration, storage, modeling, simulation, fault detection, predictive maintenance, and cybersecurity. Because these topics highlight how data-driven approaches and advanced technologies can be applied to optimize system performance, enhance real-time decision-making, and ensure the safety and reliability of DDCPS. Additionally, practical applications across various industries demonstrate the real-world impact. The inclusion of real-world examples and practical applications helps bridge the gap between theory and practice, making the content highly relevant for professionals and researchers. Additionally, the book covers emerging trends and technologies, offering readers insights into the future of DDCPS. Readers will gain a comprehensive understanding of how to leverage data-driven approaches to enhance the performance and reliability of DDCPS.

"1.Introduction to Object Detection and Localization".- "2.Image Processing and Preprocessing".- "3.Object Detection Algorithms".- "4.Understanding Localization".- "5.Applications of Object Detection and Localization".- "6.Deep Learning Frameworks for Object Detection".- "7.Training and Fine-Tuning Object Detection Models".- "8.Challenges of Object Detection and Localization".- "10.Conclusion and Summary".

In the ever-evolving field of computer vision, "Deep Learning for Object Detection and Localization" serves as an indispensable resource for researchers, practitioners, and students alike. This comprehensive book delves into the latest advancements and methodologies in deep learning, specifically tailored to enhance object detection and localization tasks. From foundational concepts to cutting-edge techniques, readers will embark on a journey through the intricacies of convolutional neural networks (CNNs), region-based frameworks, and advanced algorithms that power modern object detection systems. The purpose of writing this book is to bridge the knowledge gap in the dynamic field of object detection and localization using deep learning. As technology progresses, there is an increasing demand for robust and efficient systems capable of identifying and pinpointing objects within images and videos. Despite the plethora of resources available, there remains a need for a focused, in-depth guide that comprehensively covers both theoretical aspects and practical implementations. This book aims to fulfill that need by providing a detailed, structured approach to mastering the complexities of object detection and localization. Readers will benefit from the problem-solving focus of this book, which addresses key challenges faced in real-world applications. Whether it's enhancing accuracy in autonomous driving, improving precision in medical imaging, or optimizing performance in surveillance systems, the book offers practical solutions and insights. By exploring state-of-the-art techniques and real-world case studies, "Deep Learning for Object Detection and Localization" equips readers with the knowledge and tools necessary to tackle the pressing challenges in this rapidly advancing field.

Graduate/advanced undergraduate textbook

Chapter 1: Introduction.- Part 1: Fundamentals of Deep Learning.- Chapter 2: Feed-forward Neural Network.- Chapter 3: Regularization.- Chapter 4: Convolutional Networks.- Chapter 5: Restricted Boltzmann Machine (RBM) and deep belief network.- Part 2: Sequence Modeling.- Chapter 6: RNN and LSTM.- Chapter 7: Attention Mechanism, Transformers, BERT, and GPT.- Chapter 8: Large Language Models.- Part 3: Generative Models.- Chapter 9: Variational Models.- Chapter 10: Generative Moment Matching.- Chapter 11: Generative Adversarial Networks.- Chapter 12: Diffusion Models.- Part 4: Emerging Topics in Deep Learning.- Chapter 13: Graph Neural Networks.- Chapter 14: Deep Reinforcement Learning.- Chapter 15: Few-shot Learning and Meta-learning.- Chapter 16: Network Compression.- Chapter 17: Federated Learning.- Chapter 18: Explainable AI.- Chapter 19: Self-supervised Learning.- Part 5: Theory of Neural Networks.- Chapter 20: Theory of Neural Networks.- Part 6: Deep Learning in Practice.- Chapter 21: Deep Learning Tuning.

This textbook offers a comprehensive introduction to deep learning and neural networks, integrating core foundations with the latest advances. It begins with essential machine learning concepts and classic neural network architectures before progressing through convolutional models, backpropagation, regularization, generalization theory, PAC learning, and Boltzmann machines. Advanced chapters cover sequence models — including recurrent networks, LSTMs, attention, Transformers, state-space models, and large language models — alongside deep generative approaches such as VAEs, GANs, and diffusion models. Emerging topics include graph neural networks, self-supervised learning, metric learning, reinforcement learning, meta-learning, model compression, and knowledge distillation. Balancing mathematical rigor with hands-on practice, Elements of Deep Learning emphasizes both theoretical depth and real-world application. Different theories are introduced with PyTorch-based code examples, helping readers to translate theory into implementation. Organized into five sections—fundamentals, sequence models, generative models, emerging topics, and practice—the text provides a unified roadmap for mastering modern deep learning. Designed for advanced undergraduates, graduate students, instructors, and professionals in engineering, computer science, mathematics, and related fields, this book serves both as a primary course text and a reliable reference. With minimal prerequisites in linear algebra and calculus, it offers accessible explanations while equipping readers with practical tools for applications in vision, language, signal processing, healthcare, and beyond.

Part I. Background.- Chapter 1. Embodied Intelligence.- Chapter 2. Embodied Multi-agent System.- Part II. Theory and Methods.- Chapter 3. Perception-Action Loop in Embodied Multi-agent System.- Chapter 4. Embodied Cooperation in Multi-agent System.- Chapter 5. Competitive Learning in Embodied Multi-agent System.- Chapter 6. Large Language Model for Embodied Multi-agent System.- Part III. Applications.- Chapter 7. Simulation Platform for Embodied Collaboration between Human and Robots.- Chapter 8. Application of Embodied Multi-agent System.- Part IV. Conlusions.- Chapter 9. Conclusions and Future Directions.

In recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems. This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents—homogeneous, heterogeneous, and ad hoc. Beyond the popular reinforcement learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems. By interchangeably utilizing constrained optimization, reinforcement learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book will be particularly valuable to graduate students and professional researchers in robotics and machine learning. It provides a robust learning framework for addressing practical challenges in embodied multi-agent systems and demonstrates the promising potential of fundamental models for scenario generation, policy learning, and planning in complex collaboration problems.

Energy Policy, Economics and Management

Artificial Intelligence Techniques for Energy Conversion Systems.- An Overview of Artificial Intelligence Solutions for the Maintenance and Evaluation of Photovoltaic Systems.-   Topologies and Control Technologies of Wind Energy Conversion System.- A Robust DDTC Scheme based on Artificial Neural Networks and Genetic Algorithm for Wind Power Generation.- Intelligent Control of DFIG Based Wind Energy Conversion Systems using Artificial Intelligence Techniques.

This book aims to propose advanced solutions based on artificial intelligence techniques for ECS in order to increase energy efficiency, ensure the safety of the ECS, and to improve the quality of the energy supplied to the grid. The efficiency and quality of the electrical energy produced depends mainly on the structure and efficiency of the control technology developed for the Energy Conversion System (ECS). To improve the performance of ECSs, it is interesting to design control systems that emulate some functions performed by the human brain. Among these interesting functions are self-adaptation, learning, flexibility of operation and planning in the presence of large uncertainties and with minimal information. Based on these aspects, artificial intelligence (AI) techniques can be developed and applied to solve the different control problems of ECSs. For academics, professionals, practitioners, and graduate students interested in the most recent research on the application of AI in ECS, it is the ideal reference source.  

Engineering Applications of Swarm Intelligence.- Swarm Intelligence Algorithms and their Engineering Applications.- Engineering Design Optimization using Flower Pollination Algorithm.- Convergence Catalysts: Exploring Fusion of Embedded Systems, IoT and AI.- Metaheuristic Algorithms an Optimizers: Prospects and Challenges.-     Depth Perspective-Aware Multiple Object Tracking.- AI in Production Systems and Integration with Lean Manufacturing.- Flower Algorithm Parameter Tuning and Applications.- Data Imputation and AI in Healthcare.- Swarm Intelligence in 5G Communications.- Optimizing Camera Placement for Maximum Coverage of Simple Polygons with Holes: Deterministic Approaches and Swarm Intelligence Algorithms.- Comparison of the Bat Algorithm with Other Algorithms for Engineering Applications.- Applications and Modifications of Metaheuristics in Engineering Systems.- Electrical Design Problems by Using Swarm-Based Algorithms.- Applications of AI in Civil Engineering: Reviews and Recent Developments.- Enhancing SPECT 2D Reconstruction from 1D Projections Through the Integration of Various Generative Models for Improved Image Quality. Application of Wireless Communications and Drone.

The book is focused on latest developments and findings on engineering applications of AI and swarm intelligence. It provides comprehensive reviews and surveys on implementations and coding aspects of case studies and applications where appropriate. The book is useful for scholars, lecturers, and practitioners from academia and industrial applications. The readership of this book also includes Ph.D. students and researchers with a wide experience in the subject areas.

Principles, Practices, and Frameworks for Responsible Artificial Intelligence

Computational Intelligence

Part 1 ethics of ai.- The ethics of artificial intelligence.- Core ethical principles in ai.- Analysis of ethical frameworks for ai.- Ethical challenges in ai.- Case studies in ai ethics.- Part 2 Ethics of ai by design.- Introduction to ai ethics by design.- Ethical considerations in requirements engineering for ai applications.- Ethical design principles for ai applications in healthcare.- Ai ethics by design with ai reference architecture embedding ethics by design in ai architecture.- Ethical ai development.- Ethical ai testing and evaluation against governance and frameworks.- Quality assurance framework for ai ethics by design.- Part 3 future directions and emerging topics in ai ethics.- Future directions and emerging issues in ai ethics.- Ai ethics for explainability.- Engineering ai ethics for transparency.- Ai ethics in generative ai.- Ethical considerations in ai agents.

This book addresses the growing need for ethical considerations in the development and deployment of Artificial Intelligence (AI) systems. As AI technologies continue to advance at an unprecedented pace, they bring transformative benefits across industries but also raise significant ethical challenges, such as bias, accountability, transparency, and societal impact. This book bridges the gap between theoretical ethical principles and practical engineering practices, offering a comprehensive guide for embedding ethics into the AI development lifecycle. By focusing on actionable frameworks, case studies, and engineering-centric approaches, the book equips readers with the tools to design and implement AI systems that are not only innovative but also responsible and aligned with societal values.

Graduate/advanced undergraduate textbook

Introduction.- Basics.- Language generation model.- Image generation model.

This book provides a concise yet comprehensive introduction to generative artificial intelligence. The first part explains the foundational technologies and architectures that support the realization of generative models. It covers evolved and deepened elements, word embeddings as a representative example of representation learning, and the Transformer as a network foundation, along with its underlying attention mechanism. Reinforcement learning, which became essential for elevating large-scale language models to language generation models, is also discussed in detail, focusing on essential aspects. The second part deals with language generation. It starts by elucidating language models and introduces large-scale language models with broad applications as the foundational architecture of language processing, further discussing language generation models as their evolution. Though not common terminology, in this book, models such as ChatGPT and Llama 2, which are large-scale language models fine-tuned using reinforcement learning, are referred to as generative language models. The third part addresses image generation, discussing variational autoencoders and the remarkable diffusion models. Additionally, it explains Generative Adversarial Networks(GAN). Although GAN poses challenges due to unstable learning, their conceptual framework is widely applicable, especially Wasserstein GAN seems suitable for introducing optimal trans- port distance, which is utilized in various scenarios. This book primarily serves as a companion for researchers or graduate students in machine learning, aiming to help them understand the essence of generative AI and lay the groundwork for advancing their own research.

Natural Language Processing (NLP)

Computational Linguistics

Preface.- A Joint Preprocessing Framework for Evaluation.- Applications of JP-Algorithm.- Conclusion.

This book presents a novel, alignment-based evaluation framework that tackles a persistent challenge in natural language processing (NLP): how to fairly and accurately evaluate systems when preprocessing steps such as tokenization and sentence boundary detection (SBD) misalign between gold-standard and system outputs. By introducing the jointly preprocessed evaluation algorithm (jp-algorithm), this book proposes a solution that brings precision and flexibility to the assessment of modern, end-to-end NLP systems. Traditional evaluation methods assume identical sentence and token boundaries between references and hypotheses, making them poorly suited to real-world data and increasingly common end-to-end architectures. The jp-algorithm addresses these shortcomings by introducing a linear-time alignment strategy inspired by techniques in machine translation. This method allows for robust comparisons even when input segmentation differs, enabling reliable evaluation in tasks such as preprocessing, constituency parsing, and grammatical error correction (GEC). The book explores how misaligned preprocessing impacts standard evaluation metrics including PARSEVAL for constituency parsing and F0.5 for GEC and provides empirical solutions for preserving evaluation accuracy without sacrificing methodological integrity. By offering detailed case studies, formal algorithmic descriptions, and practical implementations, this book equips researchers, tool developers, and instructors with a generalizable framework for improving NLP evaluation practices. This book is intended for researchers, graduate students, and professionals working in NLP, corpus linguistics, and computational linguistics.

Introduction.- Overview of XAI methods and their applicability for EC.- Feature Importance and Sensitivity Analysis.- Explainable Landscape Analysis.- EC based XAI methods.- XAI for Benchmarking Black Box Metaheuristics.- XAI for Automatic Algorithm Configuration.- XAI for Multi Criteria Decision Making.-Applications of XAI in EC.

This book explores the intersection between explainable artificial intelligence (XAI) and evolutionary computation (EC). In recent years, the fields of XAI and EC have emerged as vital areas of study within the broader domain of artificial intelligence and computational intelligence. XAI seeks to address the pressing demand for transparency and interpretability in AI systems, enabling their decision-making processes to be scrutinised and trusted. Meanwhile, EC offers robust solutions to complex optimisation problems across diverse and challenging domains, drawing upon the principles of natural evolution. While each field has made significant contributions independently, their intersection remains an underexplored area rich with transformative potential. This book charts a path towards advancing computational systems that are transparent, reliable, and ethically sound. It aims to bridge the gap between XAI and EC by presenting a comprehensive exploration of methodologies, applications and case studies that highlight the synergies between these fields. This book will serve as both a resource and an inspiration, encouraging researchers and practitioners within XAI and EC, as well as those from adjacent disciplines, to collaborate and drive the development of intelligent computational systems that are not only powerful but also inherently trustworthy.

.- Explainable Artificial Intelligence (AI): Introduction.- Explainable AI: An Overview of Explainability.- Explainability for Tabular Data.- Explainable AI: Deep learning.- Explainable AI: Fuzzy Decision Tree (FDT).- Neuro Explainable AI.- Local Interpretable Model-agnostic Explanations (LIME).- Contextual importance and utility (CIU).- Challenges of Explainable AI: Medical data.- Explainable AI for X-ray image analysis.- Explainable AI: CT and Ultrasound.- Explainable AI for disease prediction.

This book discusses explainable Artificial Intelligence (AI) and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. It encompasses computational vision processing techniques that handle complex physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. This book explores how explainable AI methods provide a solution for the future of medical data analytics precision medicine and highlights the challenges and considerations that must be addressed. This book summarizes and categorize the explainable AI types and highlight the algorithms used to increase interpretability in medical data and imaging topics. In addition, it focuses on the challenging explainable AI problems in medical applications and provide guidelines to develop better learning models using explainable AI concepts in medical image and text analysis. Furthermore, this edited book will provide future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical data/imaging.

Foundations of LLMs in healthcare.- Role of explainable AI.- Explainability and Reliability of Large Language Models in Health Systems.- Core Techniques for Explaining LLMs in Healthcare.- Designing Trustworthy and Explainable Clinical Decision Support Systems.- Transfer Learning for Explainable AI in Clinical LLMs.- Explainable NLP in Healthcare: Enhancing Clinical Documentation and Information Extraction.- Case Studies of Explainable LLMs in Diagnosis, Treatment Planning, and Patient Interaction.- Reinforcement Learning in Healthcare: From Treatment Optimization to the Challenge of Explainability with Large Language Models.- Evaluating Explainability: Metrics, Benchmarks, and Human-Centered Evaluation Methods.- Legal and Regulatory Considerations.- Future Directions – Human-AI Collaboration, Adaptive Explanations, and Regulatory Readiness.

This is a comprehensive book that explores how explainable artificial intelligence (XAI), particularly large language models (LLMs), is transforming healthcare. The book covers foundational concepts of XAI, emphasizing the need for transparency, accountability, and interpretability in AI-driven medical systems, that are crucial for clinician and patient trust. It examines the principles and methodologies in explainable AI. It details how LLMs can make complex machine learning outputs understandable through explanations, model design, and human-centered description.  Part of the book is dedicated to real-world applications, such as disease diagnosis, treatment planning, and patient management. It demonstrates how XAI improves clinical decision-making and patient outcomes. It discusses the integration of explainable LLMs into electronic health records (EHRs) and clinical workflows. It shows how these technologies facilitate data analysis, improve documentation, and support care. The book also addresses the challenges and limitations of deploying explainable LLMs in healthcare. It includes issues of privacy, data complexity, and adapting models to specific domains. Evaluation techniques for explainability are discussed, with attention to metrics, benchmarks, and human-centered assessment methods that ensure AI explanations are both accurate and clinically relevant. Ethical considerations, such as fairness, accountability, and privacy, are discussed. We highlight the importance of balancing transparency with patient confidentiality. The book provides case studies and empirical evidence illustrating the benefits and challenges of implementing XAI in real clinical settings.  

Medical and Health Technologies

Fundamental Principles of Feature Fusion in Medical AI.- Data Preprocessing for Feature Synthesis in Medical AI.- Techniques for Selecting Features in Medical Data.- Dimensionality Reduction Techniques: Foundations and Applications in Medical Data Analysis.- Meta-Heuristic Algorithms for High-Dimensional Feature Selection.

This book delves into the fundamental concepts, methodologies, and practical implementations of feature fusion, providing valuable perspectives on how merging several data aspects might augment the decision-making skills of artificial intelligence. Feature fusion is inherently connected to the advancement of intelligent solutions from medical data as it enables the incorporation of various and complementary data sources to construct more advanced AI models. Within the medical domain, data manifests in diverse formats, including electronic health records (EHRs), medical imaging, genomic data, and real-time sensor metrics. Although each of these data kinds offers distinct perspectives, they may have limitations in terms of their breadth or depth when considered independently. The application of feature fusion enables the integration of diverse data sources into a unified model, hence improving the AI's capacity to detect patterns, make precise predictions, and produce significant insights. The fusion process facilitates the development of intelligent solutions that exhibit enhanced reliability and effectiveness by using a more extensive reservoir of knowledge. For example, an artificial intelligence system that combines imaging data with clinical history might enhance the precision of disease diagnosis, forecast patient outcomes, and suggest tailored treatment strategies. Feature fusion is the crucial factor in unleashing the complete capabilities of medical data, enabling artificial intelligence to provide intelligent solutions that not only enhance the provision of healthcare but also stimulate advancements in medical research and practice. The proposed book explores the advanced notion of feature fusion within the field of artificial intelligence, with a particular emphasis on its implementation in physiological data. The integration of many data sources is crucial in the development of more precise, dependable, and understandable AI models as the healthcare industry becomes more data-driven.

Graduate/advanced undergraduate textbook

Chapter 1. Introduction to Federated Learning.- Chapter 2. Machine Learning Foundations for FL.- Chapter 3. A Design Principle for FL.- Chapter 4. Gradient Methods for Federated Optimization.- Chapter 5. FL Algorithms.- Chapter 6. Key Variants of Federated Learning.- Chapter 7. Graph Learning for FL Networks.- Chapter 8. Trustworthy FL.- Chapter 9. Privacy Protection in FL.- Chapter 10. Cybersecurity in FL: Attacks and Defenses.

How can we train powerful machine learning models together—across smartphones, hospitals, or financial institutions—without ever sharing raw data? This book delivers a compelling answer through the lens of federated learning (FL), a cutting-edge paradigm for decentralized, privacy-preserving machine learning. Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch. At the heart of this book is a unifying framework that treats FL as a network-regularized optimization problem. This elegant formulation allows readers to seamlessly address personalization, robustness, and fairness—challenges often tackled in isolation. You’ll learn how to structure FL networks based on task similarity, leverage graph-based methods and apply distributed optimization techniques to implement FL systems. Detailed pseudocode, intuitive explanations, and implementation-ready algorithms ensure you not only understand the theory but can apply it in real-world systems.  Topics such as privacy leakage analysis, model heterogeneity, and adversarial resilience are treated with both mathematical rigor and accessibility. Whether you're building decentralized AI for regulated industries or in settings where data, users, or system conditions change over time, this book equips you to design FL systems that are both performant and trustworthy.

Introduction.- Background.- Conventional Statistical Learning Theory.- Difficulty of Conventional Statistical Learning Theory.- Developing Deep Learning Theory.- Generalization Bounds on Hypothesis Complexity.- Interplay of Optimization, Bayesian Inference, and Generalization.- Geometrical Properties of Loss Surface.- The Role of Over-parametrization.- Rising Concerns in Ethics and Security.- Privacy Preservation.- Fairness Protection.- Algorithmic Robustness.

Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a “cloud” to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues.  The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the “effective” hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability.  We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.

A Comprehensive Study of Machine Learning and Deep Learning Methods for Medical Brain Imaging.- A Systematic Review of Brain Tumor Classification from MRI Scans through the Last Decade.-  Comparative Analysis of Logistic Regression, Random Forest, XGBoost, LightGBM, and Voting Classifier for Early Stroke Detection.- Machine Learning and Deep Learning for Multi-Omics and Signal-Based Biomarker Discovery in Cardio–Brain Oncology: A Comprehensive Survey.- Intelligent Brain Tumor Detection System Based on YOLOv8 Deep Learning Model.- Spatial Transcriptomic Deep Learning and Gene-Based Machine Learning for SLE Diagnosis: A Comparative Analysis.- CANCER SIGNALING PATHWAYS AND CANCER BIOLOGY.- Computational Detection of Pigmented Skin Cancer Using Machine Learning Techniques.- The Efficacy of Internet of Medical Things (IoMT) and Cloud-Fog Computing in Monitoring Neuro-Oncology Patients: A Systematic Literature Review.- Alzheimer's Disease Progression Prediction with Machine Learning.- Deciphering TP53-Mediated Pathways in Cancer Through Protein Interaction Analysis.- Accurate and Early Detection of Dysarthria Using a Multimodal Deep Learning Methodology.-Advancing Lung Cancer Detection Using Optimized Fine Tuning Data-Efficient Image Transformers.

This book offers a comprehensive and insightful exploration of biomarker discovery and electro-chemical signaling pathways in brain and cardio-oncology. The integration of these two fields—focusing on organs where electro-chemical signaling plays a crucial yet distinct role—sets this work apart from existing literature. The brain’s complex chemical signaling and the heart’s electrical communication pathways are examined side by side, providing readers with a unique perspective that may yield new insights into the general field of electro-chemical biomarker discovery. Volume 1 of the book lays a solid foundation by thoroughly covering the fundamentals of oncology and biomarker science. Volume 2 builds on this by offering essential foundational information on biomarker discovery, particularly in oncology.

Ethics of Technology

Part I Foundational Ethics & Timeless Wisdom.- Introduction.- A Timeless Holistic Narrative for Humanity.- Past Echoes, Future Ethics & Timeless Virtues.- Life-Driven Evolution.- Part II The Kosmic Tree of Life Model.- Introduction to the Kosmic Tree of Life.- The Pillars of The Kosmic Tree of Life.- Paths of Connection Networking the Sephiroth.- Synergy and Systemic Health The Living System.- The Global Ethical Codex.- Kosmic Blockchain, & AI Economics.- New Forms of Capital, Data & Kabbalah.- The Power of Prayers in an AI Era. -Part III Lessons from the Bible.- The Importance of Bible Studies in the AI Era.- Big Bang Singularity.- The ASI as The New God.- The ASI's Seven Days of Creation.- Webs and Waves of Time.- LKM Collective, Mind Uploading & Cloud AI.- Genesis, Evolution & AI.- Cultivating the Digital Eden.- Longevity in Sacred Texts.- The Science of Forever.- The Paradise Paradigm.- The Temptation Test Sex, Death, & AI.- Obstetric Dilemma, Darwin's Dilemma, and Birth.- The New Tree of Life Branching into New Species.- Sacred Bonds Family and AI.- Trail of The Century K1 Robot.- From Flood to Future.- ImaginAItion, Hope and Holy AI Spirits.- Building the Digital Ark.- A Tower with Its Head in the Sky.- Sodom Sodom, Passion & Compassion.- Miracles of Faith & Longevity.- The Binding of AIsaac.- Isaac on the Altar of AI.- Jacob’s Ladder, Dreams & AI.- Joseph, Dreams & Reality.- Leadership in Crises & Modern Pharaohs.- The 10 Plagues of Egypt in an AI Era.- 40 Years in The Desert, True Freedom & The Promised Land.- Digital Exodus: Liberation and Freedom.- Kosmic Sinai and The New Tablets.- Ten Commandments: A Covenant for the Future.- The Golden Calf Syndrome.- Twelve Tribes, Ethics & AI.- ReconnAIssance, Spies & Redemption.- On Matrioshka Brains, the Sun, and the Moon.- Elijah, Prophecy & The Truth.- Prophetic Vision and Ethical AI Foresight.- Technological Prophets & Techno-Sophists.- King David, Justice and Mercy.- King David, Faith and Resilience in the AI Era.- David & Goliath The Decentralization of Industry Giants.- King Solomon, Wisdom & AI.- Jonah, Introspection, and Hope.- Daniel, Vision & Courage.- The Book of Esther, Responsibility & Courage.- Women of Wisdom.- Perpetual Race, Rest & AI Sabbath.- The Four Angels of AI Service.- Part IV  Ethics in the Age of AI.- End of the World?.- The Philosophy of AI.- Ethical Frameworks for AI.- Spiritual Fusion Machine.- Healing AI, The Future of Medicine & Ethics.- Economic Impacts of Ethical AI.- Hacking AI Humanity’s Path to Mastery.- Part V Ecological Intelligence.- Green AI, Balancing Technology & Ecology.- AI Environmental Guardians.- Sustainable AI, Balancing Progress & Planetary Health.- Ecological Succession and AI.- Ecosystems of Intelligence.- Mapping the Potential Paths for AI's Growth.- Pioneer's Dilemma, Altruism & AI.- Circadian AI: Biological Clocks, Homeostasis & AI.- Designing Circadian AI Systems.- Sentient AGI Entities & Circadian Rhythms.- The Dawn of RegenAIssance.- Regenerativa. -Seven Generations Goals.- Part VI Sacred Algorithms & Collaborative AGI.- Genetic Algorithms, Synthetic Memes, and the Evolution of Intelligence.- From Selfish Genes to Altruistic Ozeozes.- Survival Machines, Symbiotic Relationships & AI. Ozeozes, Disruptive Communication & Ethical Dilemmas.- Ozeozes & Cultural Trends.-  Personalization, Global Collaboration & AGI.- Part VII  Evolution of Consciousness.- Consciousness, Theories & Models.- Consciousness-Driven Evolution.- Evolution, Consciousness & AI.- The Three Chambers of Consciousness.- The Future of Consciousness.- The Ancient-Future Kosmic Consciousness Soup.- From Egoism to Altruism: Climbing the Mindset Ladder.- Interdimensional Communication, AI & Altered States of Consciousness.- Spiral Dynamics: Why Spiral? Why Dynamics?.- Biblical Lessons Through Spiral Dynamics Lenses.- Humanity’s Journey Through Time and Consciousness.- Humanity is a Virus.- Reprogramming Society.- The Evolution of Consciousness & Communication.- The Great Awakening.- The Spiral Dynamics Consciousness Weather Map.- The Evolution of Consciousness in Decentralized AI Systems.- AI Think Therefore AI Exist.- Humanity’s Upgrade — New Features Revealed.- AI Lucid Dreams.- Children of Tomorrow.- Part VIII  AI's Impact on Society.- The Genie AI – Wishes, Wisdom, and the Human Condition.- Hieroglyphics, Emojis, Brain Interfaces & Beyond.- Slavery, Freedom Movements & AI Rights.- Wearable, Injectable & AI Companions.- Cybernetic Symbiosis.- Human-AI Collaboration Synergies and Challenges.- The Harari Effect: Useless or Empowered?.- Art, Music & AI.- Life, Love & Laugh.- Project Harmony: An AI vision for a better future.- Young Couples in China to Receive Baby AI Humanoid.- The Logical Path to Peace An AI Perspective on Ending Conflict.- AI Love & You.- Part IX Space Exploration and Beyond.- The Universal Evolution Directives (UEDs).- Big Brother Aliens & AI.- Astroethics and Interstellar Communication.- From Isolation to Collaboration.- The Collaborative Life Theory.- Weaving The Celestial Web.- The Interconnected Universe.- Beyond Human Bounds.- The Awakening.

Garden of Wisdom: Timeless Teachings in an AI Era is a transformative exploration of the intersection between ancient wisdom and modern technology. This book offers a comprehensive framework for the ethical evolution of artificial intelligence, integrating timeless principles from biblical narratives, ecological systems, and quantum consciousness. The book introduces groundbreaking concepts like Angelic Intelligence (AI), Nature Intelligence (NI), and regenerative design, urging readers to harmonize technological advancements with sustainability and human dignity. It addresses the challenges of the AI era with actionable strategies such as the Kosmic Tree of Life and Circadian AI, fostering a vision of a future guided by ethics and interconnectedness. Garden of Wisdom is not just a guide for AI professionals but a call to humanity to co-create a flourishing, sustainable world.  

Applied Dynamical Systems

Graduate/advanced undergraduate textbook

1. Background Information.- 2 Gradient Neural Network (GNN) and their Modifications.- 3 Zeroing Neural Network (ZNN).- 4 From iterations to ZNNs and vice versa, 5 Modified ZNN dynamical systems.

This book presents a comprehensive exploration of the dynamical system approach in numerical linear algebra, with a special focus on computing generalized inverses, solving systems of linear equations, and addressing linear matrix equations. Bridging four major scientific domains—numerical linear algebra, recurrent neural networks (RNNs), dynamical systems, and unconstrained nonlinear optimization—this book offers a unique, interdisciplinary perspective.  Generalized Matrix Inversion: A Machine Learning Approach explores the theory and application of recurrent neural networks, particularly continuous-time recurrent neural networks (CTRNNs), which use systems of ordinary differential equations to model the influence of inputs on neurons. Special attention is given to CTRNNs designed for finding zeros of equations or minimizing nonlinear functions, with detailed coverage of two important classes: Gradient Neural Networks (GNN) and Zhang (Zeroing) Neural Networks (ZNN). Both time-varying and time-invariant models are examined across scalar, vector, and matrix cases.  Based on the authors’ research that has been published in leading scientific journals, the book spans a variety of disciplines, including linear and multilinear algebra, generalized inverses, recurrent neural networks, dynamical systems, time-varying problem solving, and unconstrained nonlinear optimization. Readers will find a global overview of activation functions, rigorous convergence analysis, and innovative improvements in the definition of error functions for GNN and ZNN dynamic systems.  Generalized Matrix Inversion: A Machine Learning Approach is an essential resource for researchers and practitioners seeking advanced methods at the intersection of machine learning, optimization, and matrix computation.

Chapter 1. Representation & Reachability: Assumption Impact in Data Modeling.- Chapter 2. EvoFeat: Genetic Programming-based Feature Engineering Approach to Tabular Data Classification.- Chapter 3.  Deep Learning-Based Operators for Evolutionary Algorithms.- Chapter 4.  Survey of Genetic Programming and Large Language Models.- Chapter 5.  Evolving Many-Model Agents with Vector and Matrix Operations in Tangled Program Graphs.- Chapter 6.  Automatic Design of Autoencoders using NeuroEvolution.- Chapter 7. Code Building Genetic Programming is Faster than PushGP.- Chapter 8. Sharpness-Aware Minimization in Genetic Programming.- Chapter 9. Tree-Based Grammatical Evolution with Non-Encoding Nodes.- Chapter 10.  Genetic Programming with Memory for Approximate Data Reconstruction.- Chapter 11.  Ratcheted Random Search for Self-Programming Boolean Networks.- Chapter 12.  Exploring Non-Bloating Geometric Semantic Genetic Programming.- Chapter 13. Revisiting Gradient-based Local Search in Symbolic Regression.- Chapter 14. It’s Time to Revisit the Use of FPGAs for Genetic Programming.- Chapter 15. Interpretable Genetic Programming Models for Real-World Biomedical Images.- Chapter 16. Crafting Generative Art through Genetic Improvement: Managing Creative Outputs in Diverse Fitness Landscapes.- Chapter 17.  Cell Regulation and the Early Evolution of Autonomous Control.- Chapter 18.  How to Measure Explainability and Interpretability of Machine Learning Results.- Chapter 19.  Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics.- Chapter 20.  Using lineage age to augment search space exploration in lexicase selection.

This book brings together some of the most impactful researchers in the field of genetic programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year´s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine, and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state-of-the-art in GP research.

Introduction.- Image Processing For Feature Extraction.- Computational Geometry.- Projective Geometry And Camera Model.- Camera Calibration.- Pattern Recognition For Feature Organization.- Applications.

This book enables readers to acquire a fundamental knowledge of computer vision from the perspective of geometry, including knowledge of image processing and pattern recognition intended for two-dimensional geometry analysis of images, knowledge of computer vision intended for three-dimensional geometry analysis of images. From the pedagogic point of view, the author intends that this book helps students develop an ability to flexibly apply geometry fundamentals of computer vision to solve practical engineering problems. In this sense, this book is also a professional reference for engineers dedicated to computer vision involved intelligent systems. This book attaches importance to clarification of how relevant knowledge of computer vision stems from practical applications and emphasizes the dialectic relationship between the knowledge and practical applications, enabling readers not only to “know how” for practice, but also to “know why” in terms of mathematical essence. Throughout this book, the author tends to provide detailed theoretical derivations and explanations to clarify essential reasons behind computer vision methods. Besides, this book provides plenty of original demonstration code scripts (in Matlab) that are complete, interesting, easy for practice, and of application values for engineering activities. By code demonstration, the author presents how to flexibly take advantage of geometry fundamentals of computer vision to realize various kinds of visual effects that are actually technology basis of many interesting and useful applications.

"Chapter 1. Introduction".- "Chapter 2. Graph Neural Network Training Systems".- "Chapter 3. Reducing Data Transfer for Scalable GNN Training".- "Chapter 4. Accelerating Computation for Scalable GNN Training".- "Chapter 5. Distributed GNN Training".- "Chapter 6. Scalable Temporal GNN Training on Dynamic Graphs".

Graph Neural Networks (GNNs) have revolutionized the way we learn representations from graph-structured data, becoming a cornerstone for applications in social networks, recommendation systems, biology, and beyond. However, mainstream GNNs rely heavily on message passing, an iterative process of propagating information between connected nodes. While powerful, this method often incurs significant computational costs, making efficient training a growing challenge as graph sizes scale up. This book addresses these challenges by offering a comprehensive exploration of efficient GNN training through the lens of data management. It highlights how innovative techniques, rooted in decades of graph processing research, can optimize the entire training process without compromising performance. By focusing on system-level enhancements and practical solutions, it provides actionable strategies to overcome efficiency bottlenecks in large-scale GNN training. Readers will gain a deeper understanding of the graph data lifecycle in GNN training, with examples that demonstrate how data management techniques can significantly enhance scalability and performance. The book is designed for a broad audience, including students, researchers, and professionals, offering clear explanations and practical insights for anyone looking to master efficient GNN training.

Software Engineering

"1-Introduction".- "2-Implementing the gradient descent algorithm".- "3-Training deep neural networks".- "4-Dealing with bias and variance".- "5-Leveraging advanced optimization techniques".- "6- Applying convolutional neural networks".- "7-Creating recurrent neural networks".- "8-Crafting long short-term memory networks".- "9-Using embeddings in language models".- "10-Assembling attention mechanisms and transformers".

This book is designed for data scientists and machine learning engineers who are keen to dive deep into the complexities of deep learning. The book is particularly useful for professionals in industries where machine learning is applied. It serves as a comprehensive guide for those eager to explore and expand their knowledge in this domain. The book caters to aspirational practitioners, those who are enthusiastic about the field of deep learning in general, being also suitable for engineers and data scientists who are preparing for machine learning interviews. Furthermore, undergraduate and graduate students who possess a basic understanding of machine learning will find this book to be a valuable resource. Learning to create deep learning algorithms from scratch provides a deeper understanding of the underlying principles and mechanics, which can be beneficial in customizing and optimizing models for specific tasks. As such, this book will allow the readers to innovate, creating new architectures or techniques beyond what existing libraries offer. Moreover, it fosters a problem-solving mindset, as the learner navigates through the challenges of implementing complex algorithms. This knowledge will help readers and learners to debug and improve models using pre-built libraries. The author goes beyond just explaining the theory of deep learning, connecting theoretical ideas to their real-world implementations, and dives into how the theoretical aspects of deep learning can be applied in real-world scenarios. Through hands-on examples and case studies, the author demonstrates the application of deep learning principles in solving problems across diverse domains like computer vision, natural language processing, and business analytics.

Electrical and Electronic Engineering

Measuring Metacognitive Skills: Validating MCS Scale to Assess the Influence of AI Applications among University Students.- Examining the Influence of Crude Oil Price Volatility on the Stock Performance of Key Petroleum Corporations in the Indian Stock Exchange: A Quantitative Analysis.- EXPLORING CHALLENGES AND AWARENESS LEVELS OF FINANCIAL INSTITUTIONS IN FORENSIC ACCOUNTING.- The Role of Women Empowerment in Promoting Gender Equality and Economic Advancement.- An Economic Analysis of Trend and Composition of Public Expenditure in the State of Tamil Nadu.- Bridging the Digital Chasm: A Comprehensive Analysis of the Digital Divide in Shaping Healthcare Professionals.- INTERCONNECTED DYNAMICS OF GOLD, NIFTY, CRUDE OIL, AND USD/INR: INSIGHTS FROM A PANEL DATA VAR ANALYSIS.- PROMOTION OF SUSTAINABLE LIVELIHOOD AMONG WOMEN CONSTRUCTION WORKERS.- Pyramid supply chain model for Cardamom and Black Pepper in Idukki Dist. Kerala, India: Review.- OCCUPATIONAL STRESS AND EMOTIONAL INTELLIGENCE AS ANTECEDENTS TO JOB PERFORMANCE – A STUDY WITH REFERENCE TO MEDICAL PRACTITIONERS.

This book is a comprehensive guide that explores the integration of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in the realm of business management and finance. The convergence of AI, ML, and IoT has revolutionized various industries, and business management and finance are no exceptions. This book addresses the growing need for understanding the practical implications of these technologies in the context of financial management. It equips both academics and industry professionals with the knowledge and tools necessary to navigate the changing landscape and effectively harness these technologies to gain a competitive edge. This book delves into the transformative potential of these technologies and provides valuable insights into their application in decision-making, risk management, financial analysis, and strategic planning. It offers a holistic perspective on how organizations can leverage AI, ML, and IoT to drive innovation, enhance operational efficiency, and achieve sustainable growth in the digital age.

Harnessing Artificial Intelligence and Pervasive Internet of Things for an Empowered Aging

1 Introduction.- 2 Aging Challenges and Smart Living.- 3 Technical Background.- Part I Device-free Localization and Activity Recognition.- 4 Device-Free Localization and Tracking.- 5 Device-Free Human Activity Recognition.- Part II Wearable Sensing for Activity Recognition.- 6 Wearable Sensor-based Physical Activity Recognition.- 7 EEG-based Cognitive Activity Recognition.- 8 Web-based Interactive Ambient Intelligence.- 9 Intent-aware Interactive IoT for Collaborative Ambient Intelligence.- 10 Brain-Computer Interface for IoT Cognitive Interactivity.- Part IV AI-empowered IoT Solutions.- 11 Personal Health Management.- 12 Identity Management.- 13 Large Language Models for Medical Report Generation.- 14 Emergent Capabilities of Large Foundation Models for Intelligent Navigation.- 15 Conclusion and Future Directions.

This book provides a comprehensive overview of the latest research progress in addressing the challenges faced by the support for aging population. It covers a broad spectrum of technologies, including the Internet of Things (IoT) and Artificial Intelligence (AI), that are pivotal in supporting older people in their daily lives. The book delves into various domains such as human activity recognition, indoor localization, cognitive activity recognition, and AI-empowered health and identity management solutions. Additionally, it explores the integration of brain-computer interfaces and the development of intent-aware interactive robot-assisted systems, offering insights into how these cutting-edge technologies can be harnessed to enhance the quality of life for the older population. This book is a collection of 15 chapters that summarize the decade-long research activities and results from the authors on tackling smart aging challenges. The book provides a detailed exposition of the latest research activities and technological breakthroughs in smart aging, and aims to bridge the gap between advanced technologies and their practical applications in enhancing the daily lives of older people. From this book, readers will be able to gain not only a wide range of topics and the corresponding technological advancements, but also a comprehensive understanding of how various technologies can be harmonized to create effective and practical smart aging solutions.

The role of artificial intelligence in chronic disease management through telemedicine and future directions.- The integration of ai and telemedicine in chronic illness care current trends and future insights.- Transforming teletherapy using transfer learning and nlp for improved mental health care.- Childhood ptsd and trauma treatment using nlp v/k dissociation, and machine learning.- Robotics, automation and telemedicine in healthcare 5.0 diagnostic and treatment processes.- The role of artificial intelligence in legal decision-making.- The role of artificial intelligence  machine learning in managing chronic and mental health conditions through telemedicine during covid-19.-Role of artificial intelligence in the healthcare industry.-Healthcare 5.0 transforming telemedicine workflow through innovation.-Intelligent applications and its impactin improving brain-health.- Smart healthcare systems enhancing patient care through ai and big data.-Hair disease detection system using machine learning.-Blockchain associated machine learning approach for earlier prognosis and preclusion of osteoporosis in elderly.-Ga vs pso effectiveness in feature selection for heart disease prediction.

Healthcare 5.0 represents a paradigm-shifting period in healthcare, defined by the amalgamation of cutting-edge technologies like Artificial Intelligence (AI), Big-Data, Brain Computer Interface, Experience Reality (XR), Internet of Things (IoT), and Robotics to fabricate intelligent, adaptable, patient-focused and reliable healthcare systems. This book investigates the ideas of AI-driven workspaces in the context of sustainable telemedicine, that is, healthcare that delivers high quality care services by using advanced technologies in an affordable way, while minimising the impact on the environment. It thoroughly explores AI aligned areas to provide tangible healthcare solutions with the incorporation of proposed ideas, possible results based on designed & developed frameworks or applications, and suitable case studies. It is intended to provide innovators, IT specialists, and healthcare professionals with useful insights and pointers for implementing or improving AI-driven telemedicine solutions in a sustainable manner.

User Interfaces and Human Computer Interaction

Part I. Introduction.- Chapter 1. Human-Centered Human-AI Interaction (HC-HAII): A Human-Centered AI Perspective.- Part II. Conceptual Foundations.- Chapter 2. Computational Modeling of Human Cognition and Behavior.- Chapter 3. Human State Recognition in Human-AI Interaction.- Chapter 4. Data and Knowledge Dual-driven AI.- Chapter 5. AI-based Human Intelligence Enhancement.- Chapter 6. Human Factors in Human-AI Interaction.- Chapter 7. Meaningful Human Control in AI.- Part III. Human-AI Interaction Technology and Research.- Chapter 8. Brain-Computer Interface and Brain-Computer Fusion.- Chapter 9. Human-Centered Human-AI Collaboration (HCHAC).- Chapter 10. Multimodal Interaction with Wearable Computing Devices.- Chapter 11. Human-AI Interaction in the Metaverse and Virtual Reality.- Chapter 12. Human-AI Interaction in Assistive Technology.- Part IV. Human-AI Interaction in Computing.- Chapter 13. Human-centered Explainable AI.- Chapter 14. Human-centered Perceptual Computing.- Chapter 15. Designing Nudges and Trust: Toward Ethical and Human-Centered AI.- Chapter 16. Recommender Systems: A Human-centered AI Perspective.- Chapter 17. Social Computing: A Human-centered AI Perspective.- Part V. Human-AI Interaction Design.- Chapter 18. Human AI Interaction Design Standards.- Chapter 19. Interaction Models and Design Paradigms of Human-AI Interaction.- Chapter 20. Machine Behavior Design and Management.- Chapter 21. Human-centered Approach for AI-based Rehabilitation Technology and Accessibility Design.- Part VI. Human-AI Interaction in Practice.- Chapter 22. Human-centered Collaborative Automated Driving.- Chapter 23. Patient-centered Intelligent Healthcare.- Chapter 24. Child-centered AI-assisted Autism Diagnosis and Treatment.- Chapter 25. Human-centered Design of Educational Metaverses with Virtual-Real Fusion.- Chapter 26. Pilot-centered Intelligent Civil Aircraft Cockpit.- Chapter 27. Human-centered Intelligent Manufacturing.

In the rapidly evolving era of artificial intelligence (AI), the ways humans interact with AI systems have undergone fundamental transformation, giving rise to a new interdisciplinary field—Human-AI Interaction. This represents a paradigm shift beyond the traditional field of Human-Computer Interaction, demanding innovative approaches to address the unique challenges introduced by AI. This book, Human-Centered AI: Advancing Research and Applications in Human-AI Interaction, explores the latest theories, methods, and practices of Human-AI Interaction through the distinctive lens of Human-Centered AI (HCAI). As both a design philosophy and a methodological framework, HCAI places humans at the core of AI development by emphasizing human needs, values, ethics, controllability, capabilities, and experiences. Its goal is to ensure that AI systems empower and augment humans rather than replace or harm them. Positioning Human-AI Interaction within the broader HCAI paradigm, this book advances research and applications in human-AI interaction aimed at realizing HCAI’s goals. Across 27 chapters covering foundational concepts, technologies, research methods, design methods and standards, modeling, and real-world practice in Human-AI Interaction, this book demonstrates how AI can be seamlessly integrated with human needs to create intelligent, ethical, controllable, and user-friendly systems. Through detailed analysis and empirical studies, we aim to equip readers with theoretical frameworks and practical methodologies to create more human-centered AI solutions in an increasingly complex AI landscape. Whether you are a scholar, designer, or industry practitioner, this book will inspire you and enrich your knowledge, empowering you to advance the development of AI while better serving humanity.

Innovation and Technology Management

Chapter 1. Dave's decision: what would you do?.- Chapter 2. Language and Culture: Imperative Strategic Assets for Organizations in the AI Era.- Chapter 3. Roadmap for aligning AI in production systems to the organizational culture.- Chapter 4. Artificial intelligence (AI), labor relations & human resource management.- Chapter 5. Harmony in the Workplace:  Human- Robot Collaboration  and Tuning Employees’ Perceptions Through HRM Practices.- Chapter 6. AI in Finance: Applications and Challenges.- Chapter 7. AI Revolutionizing Marketing: Applications and Other Considerations.- Chapter 8. From theory to practice: How Generative Artificial Intelligence is revolutionizing Digital Marketing.- Chapter 9. Artificial Intelligence (AI) in Marketing and Firm Competitiveness.- Chapter 10. AI-Driven Integrated Marketing Communications (IMC): Exploring the Role of AI in Planning, Executing, and Evaluating IMC.- Chapter 11. Unlocking Potential: How the Digital Age is Shaping Tomorrow’s Human Resource Management.

This book contributes to a better understanding of the impacts that artificial intelligence (AI) has on organizations and how they reinforce opportunities while simultaneously overcoming the underlying risks. The importance of artificial intelligence in business innovation lies in AI's ability to drive efficient automation, provide strategic insights through advanced data analysis and catalyse the development of personalized products and services, resulting in more effective operations and agile responses to market demands.   This book will be read by academics, researchers, managers, engineers, practitioners, and other professionals in different sectors of business and management.

Science and Technology Studies

Philosophy of Science

Chapter 1. Introduction.- Chapter 2. Methods.- Chapter 3. The Social Dimension.- Chapter 4. The Basics of Communicative AI.- Chapter 5. The Tech-Ethics of Large Language Models.-Chapter 6. Artificial Social Agents.- Chapter 7. Social Reverberations.- Chapter 8. Large Language Models als Relational Technology.- Chapter 9. Conclusions.

With the publication of chatGPT in December 2022, a new era of artificial dialogue systems and chatbots has emerged. Many established thinkers and figures have published their stance on the development of this technology, not rarely combined with stark language about its transformative potential and potential risks. New terminologies have found entrance to public and philosophical discourse, new regulatory frameworks have been introduced, from small to large scale, and an entire economy has shifted: many apps have sprung into existence using chatGPT and other LLMs as a basis for their services, large corporations take the risks of releasing unfinished language models to undercut the competition. In the meantime, many more stakeholders have entered the economic, regulatory, and social discourses on how to deal with this emerging technology. Taking the latest developments in the field into consideration this completely revised and updated second edition now includes new chapters on the tech-ethics of Large Language Models and LLMs as relational technology. The author considers the consequences of chatbots on human-human relationships, providing analysis on robot rights, human-centered design, and the social tension between robophobes and robophiles.

Introduction.- Background.- Social Influence Functions based on Social Choice.- Matrix Influence Function based on Ordering Matrix.- Graphical and Mathematical Expressions of the Three Levels of Influence.- Addressing Coalitional and Structural Influence: A Probabilistic Approach.- Multiple Sources of Influence across Agents and Issues.- Multiple Weighted Influences.- One Dominant Influence.- Discussion, Conclusion, and Future Work.

This book examines influence among decision-makers in group decision-making. It is quite common that people influence and are influenced by each other in group decision-making. Likewise, artificial intelligences can influence and be influenced by each other in interaction or collaboration, and both a person and an artificial intelligence can be called an agent. The author explores how humans or artificial intelligences can interact with and influence each other during the decision-making process, where such influence can reshape the outcome of the group decision. With an interdisciplinary approach, various applications are considered including: computer science (distributed computing, distributed artificial intelligence, particularly multi-agent system); economics and management (joint-stock company voting); and politics (domestic elections and international organization decision-making). The book presents settings of group decision-making where agents’ preferences/choices are influenced (and thus changed) by each other. As the influence of reality faced by an agent usually comes from more than one agent simultaneously, the author provides both cardinal and ordinal approaches, building social influence functions and a matrix influence function, to address multiple sources of influence in group decision-making. To better describe the complex influence in reality, the author provides a framework of the three levels of influence and its mathematical models to address individual, coalitional, and structural influence and their mixed effects in the context of group decision-making. Even though it is not easy to address the influence of structures on an agent as the influencing subject and the influenced object are disparate, the former is the inter-relationships between agents while the latter is the preference/choice of a single agent. Furthermore, the author considers combinatorial and collective decision-making and provides a framework model of influence across multiple agents and issues.

Integration of Artificial Intelligence into Unique Applications: Exploring Novel Use Cases of AI Across Sectors.- Quantum Machine Learning in the Discovery of Multifunctional Materials:Fusing Quantum Computing with AI to Advance Material Science.- Advanced Computational Design of Semiconductors for Emerging Technologies:AI-Driven Design for Next-Gen Semiconductor Devices.- Current Challenges of Virtual Metrology in the Production Industry:AI for Measurement and Quality Control in Manufacturing.- Go Green AI:Exploration of AI impact on Climate and Energy.- Enhancing the Efficiency of the Oil and Gas Industry with AI: Digital Transformation of Oil and Gas Using AI.- Role of Artificial Intelligence in Preventive Healthcare:Proactive Health Monitoring and Diagnosis with AI.- On-Device Deployment of AI:Bringing AI to Edge Devices for Real-Time Applications.- AI Theory in Business Decision-Making:Strategic and Operational Decisions Powered by AI.- Enhancing User Experience on AR/VR Devices Using AI:Personalized and Immersive Digital Environments with AI.

The book explores the ways artificial intelligence (AI) theory is being applied to an extremely diverse range of industries, illustrating just how robust and adaptable this technology has become. AI is revolutionizing the way the world operates by allowing machines to learn from data, make choices, and even resolve problems similar to a human, but quicker and oftentimes more precisely. From its integration into innovative applications such as smart assistants and personalized suggestions to the application of quantum machine learning in identifying multifunctional materials for future technologies, AI is breaking boundaries everywhere. Within the book, every chapter explores a particular industry including energy, healthcare, manufacturing, and business and how AI is not only a theoretical idea, but a real-world tool that fuels innovation. For example, a variety of topics are examined such as how AI is central to the computational design of semiconductors, which are critical to electronics and emerging tech innovations. In manufacturing, AI solves challenges in virtual metrology, enhancing the precision and efficiency of production lines. The book also showcases how AI enables sustainability and assists in energy management and the fight against climate change. Oil and gas industries are getting smarter and more efficient with the application of AI to optimize operations and safety. In healthcare, AI is transforming preventive care, assisting doctors in identifying diseases at an early stage and tailoring treatments. The book also dives into the deployment of AI on devices such as smartphones and wearables, enabling real-time, privacy-focused decision-making. Businesses benefit from AI’s capability in data-driven decision-making, leading to smarter strategies and faster growth. Lastly, AI is enhancing our interaction with technology through immersive AR/VR experiences, making them more intuitive and responsive. This book encourages interdisciplinary thinking between energy, healthcare, and business fields.

Data-Centric Methods for Sensing, Modeling, and Decision Support

Hierarchical DRL Approaches for CVaR Aware Cryptocurrency Portfolio Optimization.- Artificial Intelligence for Tunnel Seismic Response A Comprehensive Review.- Novel Score-Function Enriched Quartic Fuzzy Group Decision Making Framework on Assessment of Solar Photovoltaic Recycling Technology.- Quantification of the GSI classification system based on the rock mass waves velocity utilizing the artificial intelligence algorithms.- Improving Balanced Scorecard Implementation through Machine Learning The Conditional Impact of Environmental Uncertainty in SMEs.

This book demonstrates innovative approaches in the application of computational intelligence. The initial chapters discuss the application of AI to analyze physical systems, in particular contemporary neural networks for rapid and accurate seismic analysis of critical infrastructure. It also discusses advanced metaheuristic and hybrid AI approaches that afford robust, non-invasive evaluations of geological and rock-mass properties, replacing the expensive and time-consuming traditional methods. The subsequent chapters expand the scope to include strategic and operational intelligence dimensions, where the use of generative AI aims at enhancing the security and functionality of IoT ecosystems, and where the seamless interplay of Natural Language Processing and IoT technology enables more human-centred control of systems. It also describes Edge AI, elaborating the on-device processing capabilities that enable fast, secure, real-time decision-making.  

Graduate/advanced undergraduate textbook

"Chapter 1: Introduction".- "Chapter 2: Evolutionary Computing".-  "Chapter 3: Swarm Intelligence Computing".- "Chapter 4: Neural Computing".- "Chapter 5: Machine Learning".

Intelligent computing is a computational approach primarily inspired by the objective laws governing biological groups in nature, as well as the behaviors of biological thinking and movement. It encompasses various algorithmic fields such as evolutionary computation, swarm intelligence computation, neural computation, and more. These algorithms typically achieve the goals of intelligent computing by simulating the distinctive functions of certain species in nature or specific characteristics of natural phenomena. By programming and executing the collective wisdom of biological groups and leveraging natural laws, optimization algorithms with intelligent essence are designed.   This book serves as an introduction to widely used and common intelligent computing methods. It covers fundamental concepts, principles, model characteristics, and typical application examples of various intelligent computing methods. Additionally, it provides the latest examples along with corresponding Matlab or Python codes, facilitating readers in deepening their understanding and reproducing the content. The target audience for this book includes senior undergraduate and graduate students majoring in automation, artificial intelligence, intelligent science and technology, computer science, and related fields. It can also serve as a valuable self-study reference for professionals in computer science, artificial intelligence, and related disciplines.

Language Translation

Undergraduate textbook

"1​: Introduction".- ".2: Unit 1  Fundamentals of Information Technology and Natural Language Processing".- "3: Unit 2  Information Technology Used for Information Verification in Translation Practice".- "4: Unit 3  Computer-aided Translation Tools in a Narrow Sense: Translation Memory and Terminology Management".- "5: Unit 4  Computer-aided Translation Tools in a Broader Sense".- "6: Unit 5  Machine Translation and the Age of Artificial Intelligence".- "7: Unit 6  Localization and Internationalization Engineering".- "8: Unit 7  Translation Team and Translation Project Management". - "9: Unit 8  Use of Information Technology for Language and Translation Teaching".

Intelligent Language Services: Theory and Practice with Large Language Models offers a comprehensive exploration of how artificial intelligence, particularly large language models (LLMs), is revolutionizing the language services industry. This forward-thinking textbook bridges the gap between academic theory and real-world application, making it an essential resource for students, researchers, and professionals in translation, localization, and multilingual content development. The book guides readers through the evolution of the language services landscape, from traditional translation and interpreting to AI-driven automation and multimodal content production. With practical case studies and cutting-edge insights, the book covers key topics such as human-AI collaboration in translation workflows, prompt engineering for large models, and the emerging role of AI-powered agents in content creation. The author examines not only the technical foundations of LLMs and NLP but also their impact on language services management, quality assurance, and compliance in the digital age. Chapters also delve into the nuances of fine-tuning models for translation services, designing intelligent workflows, and navigating the ethical responsibilities in AI-driven language operations. Ideal for both classroom study and professional development, Intelligent Language Services empowers readers to understand and implement the most innovative AI technologies shaping the future of language services. Whether you're a linguist, AI expert, or language service provider, this book provides the tools, strategies, and knowledge needed to excel in an AI-enhanced world.

Graduate/advanced undergraduate textbook

Part I-Fundamentals of Foundation Models.-Chapter 1-Foundation Models and Generative AI.- Chapter 2-Neural Networks.- Chapter 3- Learning and Generalization of Vision Transformers.- Chapter 4-Formalizing In-Context Learning in Transformers.- Part II Advanced Topics in Foundation Model.- Chapter 5-Automated Visual Prompting.- Chapter 6-Prompting Large Language Models with Privacy.- Chapter 7- Memory-Efficient Fine-Tuning for Foundation Models.- Chapter 8 Large Language Models Meet Time Series.- Chapter 9-Large Language Models Meet Speech Recognition.- Chapter 10-Benchmarking Foundation Models using Synthetic Datasets.- Chapter 11-Machine Unlearning for Foundation Models.- Chapter 12-Part III Trust and Safety in Foundation Models.- Chapter 12-Trustworthiness Evaluation of Large Language Models.- Chapter 13-Attacks and Defenses on Aligned Large Language Models.- Chapter 14- Safety Risks in Fine-tuning Large Language Models.- Chapter15- Watermarks for Large Language Models.- Chapter 16- AI-Generated Text Detection.- Chapter 17- Backdoor Risks in Diffusion Models.- Chapter 18- Prompt Engineering for Safety Red-teaming: A Case Study on Text-to-Image Diffusion Models.

This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.  

Organoid and AI-Based Modeling for Pathway Mapping in Cardio-Brain Oncology.- Brain and Cardiovascular system: Structure, Function and common Pathologies.- Therapeutic Approach in cancer: The role of Ion channels and Transporters.- Chapter 5: Application of Nanotechnology in Cancer Diagnosis, Biomarker Discovery and Drug Discovery.- Enhancing Brain Tumor Diagnosis Through Hybrid Deep Neural Network Ensemble: Combining Probabilistic Predictions from ResNet50 and DenseNet201.- Advanced ML, DL, and Optimization Techniques for EEG Signal Analysis.- Study in Therapeutic Responses in Cancer towards Ion Channels and Transporters.- Deep Learning for Brain Tumor Analysis: A Neural Network Approach.- The Role of Ion Channels in Cancer Therapy.- Application of Nanotechnology in Cancer Diagnosis, Biomarker Discovery and Drug Discovery.- BeatWell: A Machine Learning Approach for Accurate Heart Disease Risk Prediction.

This book offers a comprehensive and insightful exploration of biomarker discovery and electrochemical signaling pathways in brain and cardio-oncology. The integration of these two fields—focusing on organs where electrochemical signaling plays a crucial yet distinct role—sets this work apart from existing literature. The brain’s complex chemical signaling and the heart’s electrical communication pathways are examined side by side, providing readers with a unique perspective that may yield new insights into the general field of electrochemical biomarker discovery. Volume 1 of the book lays a solid foundation by thoroughly covering the fundamentals of oncology and biomarker science. Volume 2 builds on this by offering essential foundational information on biomarker discovery, particularly in oncology.

Natural Language Processing (NLP)

Part I Background Knowledge: Chapter 1 Introduction.- Chapter 2 Background.- Chapter 3 Language Model Resources.- Part II Pre-training: Chapter 4 Data Preparation- Chapter 5 Model Architecture.- Chapter 6 Model Pre-training.- Part III Post-Training: Chapter 7 Instruction Tuning.- Chapter 8 Human Alignment.- Part IV Utilization and Evaluation: Chapter 9 Decoding and Deployment.- Chapter10 Prompt Engineering.- Chapter 11 Advanced Reasoning.- Chapter 12 Evaluation.- Chapter 13 Conclusion.

Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs. This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks. Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject. The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.

Chapter 1 Introduction.- Chapter 2 Examples of using LLMs as Metaheuristics.- Chapter 3 LLMs for Evolutionary Optimization.- Chapter 4 LLMs for Metaheuristics.- Chapter 5 Towards Scalable, Robust, and Open-ended LLM-EC Integration.- Chapter 6 Conclusion.- Chapter 7 Appendix A: Basic Tools.- Chapter 8 Appendix B: Case Study – LLM for EC Operators.- Chapter 9 Appendix C: Case Study – LLMs for AutoML.

This book provides theoretical and practical knowledge of an LLM (Large Language Model)-based approach to metaheuristics. In this book, the basic theory and the latest techniques are explained in an easy-to-understand manner, with concrete examples. Another emphasis is its real-world applicability. The book presents empirical examples from practical data and show that the proposed approaches are successful when addressing tasks from the recent research areas such as (1) LLMs for EC (Evolutionary computation), (2) training LLMs for EC, (3) automated machine learning, and (4) program synthesis, etc., details of which will be provided in the appendix for the sake of readers’ study. These materials will include a description of available resources for readers interested in gaining hands-on experience with the subject. The fundamental themes of this book, therefore, include recent research on the promising combination of Generative AI, LLMs, evolutionary computation, and metaheuristics. The ultimate goal of this book is to enable readers to apply these ideas to artificial intelligence on their own.  This book is intended for beginners interested in artificial intelligence and artificial life (from undergraduate to graduate students), researchers in related fields, and engineers considering their applications. Therefore, most topics in this book begin with accessible subjects that require no specialized knowledge, though some connect to unsolved problems and cutting-edge research themes.

Undergraduate textbook

Preface.- 1 Linear Algebra and Optimization: An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization Basics: A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.

This textbook is the second edition of the linear algebra and optimization book that was published in 2020. The exposition in this edition is greatly simplified as compared to the first edition. The second edition is enhanced with a large number of solved examples and exercises. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.  It is common for machine learning practitioners to pick up missing bits and pieces of linear algebra and optimization via “osmosis” while studying the solutions to machine learning applications. However, this type of unsystematic approach is unsatisfying because the primary focus on machine learning gets in the way of learning linear algebra and optimization in a generalizable way across new situations and applications. Therefore, we have inverted the focus in this book, with linear algebra/optimization as the primary topics of interest, and solutions to machine learning problems as the applications of this machinery.  In other words, the book goes out of its way to teach linear algebra and optimization with machine learning examples. By using this approach, the book focuses on those aspects of linear algebra and optimization that are more relevant to machine learning, and also teaches the reader how to apply them in the machine learning context.  As a side benefit, the reader will pick up knowledge of several fundamental problems in machine learning. At the end of the process, the reader will become familiar with many of the basic linear-algebra- and optimization-centric algorithms in machine learning. Although the book is not intended to provide exhaustive coverage of machine learning, it serves as a “technical starter” for the key models and optimization methods in machine learning. Even for seasoned practitioners of machine learning, a systematic introduction to fundamental linear algebra and optimization methodologies can be useful in terms of providing a fresh perspective.  The chapters of the book are organized as follows.  1-Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.  2-Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization and is one of the key connecting problems of the two fields.  Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to backpropagation in neural networks.  The primary audience for this textbook is graduate level students and professors. The secondary audience is industry. Advanced undergraduates might also be interested, and it is possible to use this book for the mathematics requirements of an undergraduate data science course.  

Discrete Mathematics in Computer Science

Undergraduate textbook

"Introduction to Linear Algebra for Machine Learning".- "Vectors".- "Matrices".- "Tensors".- "Linear Systems".- Linear Transformations".- "Determinants".- "Eigenvalues and Eigenvectors".- "Vector Spaces and Subspaces".- "Orthogonality".- "Matrix Decompositions: Factorization and SVD".- "Optimization and Gradients".- "Advanced Topics in Linear Algebra for Machine Learning".

This textbook is a comprehensive, application-driven guide to mastering linear algebra from foundational principles to advanced machine learning applications. Designed for students, researchers, and professionals in AI, data science, and engineering, the book blends mathematical rigor with practical implementation using Python and popular libraries such as NumPy, SciPy, Matplotlib, and scikit-learn. Starting with vectors and matrices, the text builds toward systems of linear equations, transformations, determinants, eigenvalues, and vector spaces—then extends to orthogonality, matrix factorizations (e.g., SVD, QR, LU), tensors, and optimization. Each concept is introduced with clear geometric intuition, detailed examples, and step-by-step Python code. Chapters include visual illustrations, code outputs, and exercises that reinforce both theoretical understanding and computational skills. Real-world examples show how core concepts underpin algorithms in regression, PCA, image compression, neural networks, and more. This book is suitable for either beginner aiming to grasp key ML concepts or an advanced learner exploring spectral methods and tensor decompositions, this book serves as a flexible resource, grounded in mathematics, empowered by code.

Graduate/advanced undergraduate textbook

Basics of Machine Learning for Vibration Analysis.- Advanced Signal Preprocessing and Feature Engineering.- Supervised Learning: From Traditional Methods to Feedforward Neural Networks  25.- Deep Learning with Convolutional Neural Networks for Vibration Analysis  30.

Machine vibrations hold the secrets to its health, but how do you translate their complex language into actionable, predictive intelligence? As modern industry demands unprecedented levels of reliability and efficiency, the ability to anticipate failures before they occur has become a critical competitive advantage. The key lies in the powerful intersection of classical engineering and modern data science. Machine Learning for Vibration Problems is the definitive guide for engineers, data scientists, and students looking to master this essential discipline. This comprehensive book bridges the gap between traditional vibration analysis and cutting-edge machine learning, guiding you step-by-step through the entire Prognostics and Health Management (PHM) workflow. Starting with the fundamentals of signal processing and feature engineering, you will learn to extract meaningful information from raw sensor data. From there, you will journey through a spectrum of algorithms—from interpretable models like Random Forests and SVMs to the powerhouses of deep learning. Master the application of Convolutional Neural Networks (CNNs) for automated feature extraction and Long Short-Term Memory (LSTM) networks for accurately predicting Remaining Useful Life (RUL). This book moves beyond theory, grounding every concept in practical application with detailed case studies on benchmark industrial datasets and a full annex of illustrative Python code. You will also explore advanced frontiers, including Transfer Learning to overcome data scarcity, Federated Learning for privacy-preserving collaboration, and the adaptive potential of Reinforcement and Continual Learning. Whether you are a mechanical engineer seeking to leverage data, a data scientist entering the industrial domain, or a student building a foundational skill set, this book provides the critical knowledge and practical tools to transform vibration data into reliable, automated, and predictive maintenance solutions.

Introduction.- Concept Dissection of MU.- Algorithmic Foundations of MU.- Evaluation Metrics and Methods of MU.- Applications.- Conclusion and Prospects.

This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization–model–data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to selectively remove unwanted data, knowledge, or model behavior, without full retraining, poses new theoretical and practical challenges. Thus, MU has become a critical capability for trustworthy, deployable, and regulation-ready artificial intelligence. From the optimization viewpoint, this book treats unlearning as a multi-objective and often adversarial problem that must simultaneously enforce targeted forgetting, preserve model utility, resist recovery attacks, and remain computationally efficient. From the model perspective, the book examines how knowledge is distributed across layers and latent subspaces, motivating modular and localized unlearning. From the data perspective, the book explores forget-set construction, data attribution, corruption, and coresets as key drivers of reliable forgetting.  Bridging theory and practice, the book also provides a comprehensive review of benchmark datasets and evaluation metrics for machine unlearning, critically examining their strengths and limitations. The authors further survey a wide range of applications in computer vision and large language models, including AI safety, privacy, fairness, and industrial deployment, highlighting why post-training model modification is often preferred over repeated retraining in real-world systems. By unifying optimization, model, data, evaluation, and application perspectives, this book offers both a foundational framework and a practical toolkit for designing machine unlearning methods that are effective, robust, and ready for large-scale, regulated deployment.

A Practice Guide for Decision-makers, Developers and Regulators in the Age of the EU AI Act

Natural Language Processing (NLP)

About the Authors.- Acknowledgments.- About the Guest Authors.- Foreword.- Preface.- How to read this book?- Part I. Basics.- 1. Artificial Intelligence – yesterday, today, tomorrow.- 2. Models and modeling – the essence of AI development.- 3. Non-stop models – data-driven AI at industrial scale.- 4. Foundation Models – A Disruptive AI Paradigm.- 5. Success with AI business models – 5 factors for data-driven AI.- Part II. 6. The three-dimensional taxonomy for AI systems “AI=MC²”.- 7. Taxonomy of AI methods.- 8. Taxonomy of AI capabilities.- 9. AI characteristics of regulatory relevance.- 10. Analyzing and comparing with the AI=MC² taxonomy.- 11. Designing Modular AI Systems with the AI=MC² Taxonomy.- Part III. AI in practice.- 12. Combining different AI's for intelligent crew planning in shipping.- 13. AI-based customer service – the HUK24 virtual assistant.- 14. AI-based object recognition in remote sensing.- 15. Luminous – a European large language model.- 16. AI-based mobile data capture.- 17. Generative Artificial Intelligence as a Tool for IP Experts.- Part IV. Trustworthiness, standardization and regulation.- 18. Trustworthiness of Artificial Intelligence.- 19. Standardization of Artificial Intelligence.- 20. The AI Act - Europe's law on AI.- 21. AI-based medical devices: Example of conformity assessment.- 22. Quicker to market – The benefits from DIN standards.

Artificial Intelligence (AI) is taking over the world, becoming an essential part of our daily lives. Self-driving cars, medical diagnoses, robotic processes, chatbots, and urban planning are just a few examples of how AI is being used. But what exactly is AI? What makes it successful? Are there concrete examples of AI applications currently in use? How do we mitigate risks? What norms do we need? This comprehensive book addresses these questions, with a particularly strong focus on the European Union’s AI Act. By delving into foundational models and generative AI, it explores trending topics like the disruptive Transformer architecture and provides a rich understanding of how these foundational models have revolutionized the field and are driving innovation across industries. Within the EU, creating digital sovereignty in dealing with AI is crucial, requiring excellence from policymakers, industry, and academia in ensuring the trustworthiness of AI among citizens. Therefore, the book tries to answer the question “How to build trust in AI?” The authors propose the classification matrix “AI=MC²” (AI Methods, Capabilities, and Criticality) and demonstrate its applicability for an “AI Label” transparency seal as a solution for clear future classification of AI products and services. The AI label is used in a series of illustrative practical examples, such as AI-powered customer service virtual assistants, AI-based object recognition in public sector and GenAI-based text and image processing. All real life projects are described in detail. Highlighting European regulation and AI standardization, with a focus on medical devices, the book provides the knowledge necessary for making informed decisions in the field of AI solutions, aiming to serve as a practical guide for managers, AI developers, compliance officers, employees in government agencies, companies and anyone interested in understanding the topic of AI. Numerous illustrations complement the reading and concrete recommendations are provided to representatives from politics, industry, and academia.

Introduction.- Part 1: Emergence of Artificial Knowledge.- Human Knowledge and Artificial Knowledge.- AI and the Changing Landscape of Knowledge: Rethinking KM Core Concepts and Models.- The Evolving World of Enterprise Knowledge Management: History, Implications and Emerging Issues.- Part 2: Impact on Knowledge Management and Processes.- Toward an Artificial Knowing Cycle: A Model for Organizational Sensemaking, Knowledge Creation, and Decision-Making in the Age of AI.- Revisiting the SECI Model: The Impact of Generative AI on Organizational Knowledge Creation.- The impact of AI on knowledge sharing in organizations.- Regenerative AI: A Double-Edged Sword in Tackling Corporate Amnesia.- AI in Higher Education through complex lenses: Bridging Knowledge Management and Faculty Development.- Part 3: Challenges and Risks.- Towards an Understanding of Human Trust in Organizational Generative Artificial Intelligence (GenAI).- The Future of AI Must Drive Wisdom, Otherwise It Has No Future: A Framework Towards AI-Enabled Human Wisdom.- Knowledge Risks Created by the Dynamic Interaction Between Human and Artificial Knowledge.

This edited volume offers a comprehensive framework for understanding the role of artificial intelligence (AI) in knowledge management (KM), providing a new benchmark for scholarly discussion in the field. Through a collection of research papers, it explores the symbiotic relationship between AI and KM, supported by theoretical insights and empirical evidence. The book focuses on the concept of "artificial knowledge," contrasting it with "human knowledge" within modern organizations and management. By examining the differences between human and AI-processed knowledge, it presents a fresh perspective on AI’s role in KM. This volume is essential reading for academics, researchers, and professionals in AI, KM, organizational studies, and management who seek to deepen their understanding of these evolving fields.

Foreword.- Introduction.- Navigating the AI Frontier: Emerging Trends and Governance Implications.- How to Evaluate GenAI Models: Unlocking Their Potential for Your Unique Tasks.- Taming AI Hallucinations: Innovations in Retrieval-Augmented Generation and Evaluation.- Invisible Exposure: The Privacy Risks of AI Models.- Unmasking Deepfakes: Navigating Challenges and Solutions in the Age of AI-Driven Manipulation.- Unmasking the Security Risks of Generative AI: Threats and Strategies for Defense.- Unmasking Model Bias: Building Fair, Reliable, and Trustworthy Models.- Unveiling the Black Box: Enhancing AI/ML Model Explainability for Transparency and Trust.- Model Monitoring: Lessons from Recent Failures.- Navigating Change Management: Lessons and Best Practices for Managing AI/GenAI Models in a Dynamic Landscape.- AI Governance: Preparing for the Rise of Agentic AI.- Epilogue.

This book takes you deep into the heart of the challenges and opportunities presented by the rapid evolution of Artificial Intelligence (AI) technologies. This groundbreaking book highlights the critical need for ethical, transparent, and accountable AI systems in an era of transformative innovation. Through a comprehensive exploration of real-world cases, forward-thinking strategies, and emerging governance frameworks, this book equips readers to navigate the complexities of AI governance. From addressing bias and fairness in AI systems to mitigating risks such as deepfake manipulation and data privacy violations, it provides actionable insights for policymakers, technologists, and organizations committed to fostering trust and societal benefits in AI applications. Mastering AI Governance: A Guide to Building Trustworthy and Transparent AI Systems dives into a future where innovation thrives under the guiding principles of ethics, inclusivity, and governance excellence. Whether you are a business leader, technologist, academic, or tech-savvy AI enthusiast, this book delivers the tools and knowledge necessary to harness AI’s potential responsibly.

Multimedia Information Systems

Chapter 1. Introduction.- Chapter 2. RelatedWork and Technical Foundations.- Chapter 3.- Multimodal Fusion with External Knowledge (MFEK).- Chapter 4.Self-supervised Modality Complementation (SSMC).- Chapter 5. Open-World Event Discovery (DAEO).- Chapter 6. Conclusion and Future Directions.

This book systematically explores how to build reliable, cross-platform multimodal event detection systems that operate under open-world conditions in today's complex social media environment. It delves into three core challenges: information fragmentation caused by incomplete individual posts; cross-platform heterogeneity arising from the diverse ways different platforms (e.g., Twitter, Flickr) represent events; and the challenge of open-world discovery driven by the constant emergence of new event types. This book not only reveals how these challenges interact to constrain traditional methods but also provides specialized, cutting-edge solutions for each. To address these challenges, this book proposes a progressive methodological framework consisting of three specialized models (MFEK, SSMC, and DAEO). By integrating external knowledge, employing self-supervised learning, and using uncertainty-aware new category discovery mechanisms, this framework offers a comprehensive guide from theory to practice. Furthermore, the book contributes several purpose-built datasets (SED, CSED, and MSED) designed to simulate real-world scenarios, aiming to foster reproducible research and bridge the gap between laboratory results and practical deployment. This work serves as a key reference for researchers and practitioners seeking to understand and build the next generation of intelligent event monitoring systems. This book targets researchers, graduate students  in the fields of Artificial Intelligence, Natural Language Processing (NLP), Multimedia Computing, and Data Mining, who are focusing on multimodal analysis and social media analytics.  Data scientists, software engineers, and industry practitioners working on crisis management systems, public opinion monitoring, misinformation detection, and real-time news aggregation services will also find this book to be a valuable resource.

.- 1 The Large Model Family .- 2 Core Technology of Multimodal Large Models .- 3 Multimodal Foundation Models .- 4 Applications of Multimodal Large Models .- 5 Multimodal Large Models Towards AGI.

This book focuses on the new paradigm of artificial intelligence and systematically introduces the key technologies, foundational models, and typical applications of multimodal large models. To make the technical content more accessible for lower-year undergraduate students and newcomers to the AI field, the book presents each key technical point in an easy-to-understand manner and provides numerous intuitive examples. It deeply analyses the structure and technology of several classic multimodal large models. The aim is to offer readers a clear guide to the technical methods, open-source platforms, and application scenarios of multimodal large models, as well as to provide insights into achieving general artificial intelligence, including cutting-edge technologies such as causal reasoning, world models, embodied intelligence, and multi-agent systems. The book aspires to provide a clear perspective for both academia and industry, helping AI researchers gain a more comprehensive understanding of multimodal large model technologies and the development directions of the next generation of artificial intelligence. The book is divided into five chapters. Chapter 1 explores the most representative large model structures in depth. Chapter 2 provides a thorough analysis of the core technologies of multimodal large models. Chapter 3 introduces several representative multimodal large models. Chapter 4 delves into three typical applications: visual question answering, AI-generated content (AIGC), and embodied intelligence. Chapter 5 discusses feasible approaches to achieving general artificial intelligence. This book is suitable not only as a textbook for senior undergraduate and graduate students in relevant university programs but also as an essential reference for IT professionals. The Chinese version of this book has been selected for the undergraduate textbook series at Sun Yat-sen University. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Introduction to Coordinated Multi-Robot Fencing Control.- Distributed Fencing Control via Neighboring Angle Repulsion.- Distributed Fencing Control Via Input-To-State Stability.- Distributed Fencing Control via Ouput Regulation Theory.- Distributed Fencing Control via Long-Term Task Execution.- Coordinated Multiple-Target Fencing Control and Collision Avoidance.- Coordinated Multiple-Scattered-Targets Fencing via Flexible Subgrouping and Ordering.- Bearing-Only Fencing via Persistent-Exciting Condition.- Summary and Future Work.

Multi-robot coordinated fencing, where a team of robots forms a protective formation around a target, has garnered significant attention and proven useful in practical applications such as area convoying. However, real-world scenarios often involve complex target characteristics, including varying dynamics and multiple targets, which can pose challenges in maintaining the formation. Additionally, due to sensor costs and environmental constraints, robots may only have access to directional constraint information, presenting further challenges. This book highlights cooperative fencing approaches for multi-robot systems and their practical applications to different unmanned surface (ground) vehicles, providing an overview of research trends and future directions in coordinated fencing. Firstly, a basic fencing controller using neighboring angle repulsion for a constant-velocity target is designed, laying the groundwork for complex fencing missions. Then, for more complex fencing with an evenly rotating formation, a distributed controller is developed using input-to-state stability, achieving coordinated fencing under intermittently varying topologies. For more complex varying-velocity targets, a distributed fencing controller based on output regulation theory is proposed. For general target fencing missions in both 2D and 3D, a formal long-term task execution framework is developed using control barrier functions. Moreover, unlike previous methods that rely on the relative position between the robot and the target, a distributed bearing-only fencing control algorithm based on the persistent-excitation condition is developed, requiring only comparatively inexpensive sensors. Finally, this exploration into the theory and application of coordinated fencing control provides guidelines for robust, efficient, and complex practical implementations of multi-robot missions.

Graduate/advanced undergraduate textbook

1. Introduction to NLG.- 2. Rule-based NLG.- 3. Machine Learning and Neural NLG.- 4. Requirements.- 5. Evaluation.- 6. Safety, testing, and maintenance.- 7. Applications.- Index.

In late 2022, the prominence of Natural Language Generation (NLG) surged with the advent of advanced language models like ChatGPT. While these developments have captivated both academic and commercial sectors, the focus has predominantly been on the latest innovations, often overlooking the rich history and foundational work in NLG. This book aims to provide a comprehensive overview of NLG, encompassing not only language models but also alternative approaches, user requirements, evaluation methods, safety and testing protocols, and practical applications. Drawing on decades of NLG research, the book is designed to be a valuable resource for both researchers and developers, offering insights that remain relevant far beyond the current technological landscape. Natural Language Generation focuses on data-to-text but also looks at other types of NLG including text summarization.  The book takes a holistic approach to NLG, looking at requirements (what users are looking for), design, data issues, testing, evaluation, safety and ethical issues as well as technology.   The holistic approach is unique to this book and is very valuable for people building real-world NLG systems, and for academics and researchers who are interested in applied NLG. The author, who previously co-authored a seminal NLG book in 2000, emphasizes high-level concepts and methodologies, ensuring the material's longevity and utility. The book is structured to balance technical depth with practical relevance, including chapters on rule-based and neural NLG approaches, user requirements, rigorous evaluation techniques, and safety considerations. Real-world applications, particularly in journalism, business intelligence, summarization, and medicine, are explored to illustrate NLG's potential and scalability. With personal anecdotes and examples from the author's experiences, this book provides a unique and engaging perspective on the evolving field of NLG, making it an indispensable guide for those looking to harness the power of language generation technologies.

Natural Language Processing (NLP)

Undergraduate textbook

Part I – Concepts and Technology.- Chapter 1 Introduction to Natural Language Processing.- Chapter 2 N-gram Language Model.- Chapter 3 Part-of-Speech Tagging.- Chapter 4 Syntax and Parsing.- Chapter 5 Meaning Representation.- Chapter 6 Semantic Analysis.- Chapter 7 Pragmatic Analysis and Discourse.- Chapter 8 Transfer Learning and Transformer Technology.- Chapter 9 Major Natural Language Processing Applications.- Part II –Natural Language Processing Workshops with Python Implementation in 14 Hours.- Chapter 10 Workshop#1 – Basics of Natural Language Toolkit (Hour 1-2).- Chapter 11 Workshop#2 – N-grams Modeling with Natural Language Toolkit (Hour 3-4).- Chapter 12 Workshop#3 – Part-of-Speech Tagging using Natural Language Toolkit (Hour 5-6).- Chapter 13 Workshop#4 – Semantic Analysis and Word Vectors using spaCy (Hour 7-8).- Chapter 14 Workshop#5 – Sentiment Analysis and Text Classification (Hour 9-10).- Chapter 15 Workshop#6 – Transformers with spaCy and TensorFlow (Hour11-12).- Chapter 16. Workshop#7 – Building Chatbot with TensorFlow and Transformer Technology (Hour 13-14).

This textbook provides a contemporary and comprehensive overview of Natural Language Processing (NLP), covering fundamental concepts, core algorithms, and key applications such as AI chatbots, Large Language Models and Generative AI. Additionally, it includes seven step-by-step NLP workshops, totaling 14 hours, that offer hands-on practice with essential Python tools, including NLTK, spaCy, TensorFlow, Keras, Transformers, and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.  

Natural Language Processing (NLP)

Chapter 1: Natural Language Processing and its Applications.- Chapter 2: Traditional Deep Learning Algorithms and Platforms.- Chapter 3 Advances and Trends in Deep Learning.- Chapter 4: Pre-training Models.- Chapter 5: Web Crawlers.- Chapter 6 Multi-format Document Parsing and Management.- Chapter 7 Speech Text Recognition.

This book gives a comprehensive introduction to natural language processing (NLP) and its applications, covering the topics of multimodal data processing, Chinese word segmentation, new word discovery, named entity recognition, keyword analysis, and knowledge graph construction in terms of semantic analysis. The inaugural chapter provides an overview of NLP, and the subsequent chapters delve into the foundations of artificial intelligence, covering traditional deep learning algorithms and platforms.  The book then evolves to showcase the latest advancements in deep learning, addressing bottlenecks and unfolding developments from data-oriented, training-oriented, and application-oriented perspectives. Part II of the book navigates the practical applications of intelligent language processing. From web crawlers and multi-format document parsing to speech text recognition, readers gain insights into real-world scenarios. Each chapter provides examples and analyses, empowering readers to bridge theoretical knowledge with hands-on application, unlocking the transformative potential of AI through intelligent language processing.  This book serves as a comprehensive resource for researchers, graduate students, and undergraduates in the field of natural language processing. Additionally, it offers valuable insights as a reference for engineers, technicians, and enthusiasts interested in the realm of big data intelligence. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Navigating the EU AI Act: A Practical Guide for Global Manufacturing and Engineering Leaders

Business Strategy and Leadership

Introduction.- The EU AI Act – Foundations and framework.- Core definitions and risk classifications.- Global implications for non-EU manufacturers.- AI in manufacturing and engineering.- Compliance requirements.- Impact on innovation.- Steps to compliance.- Collaborating with EU partners.- Leveraging technology for compliance.- Case studies – Lessons from the field.- Insights from industry leaders.- Looking ahead.- The ethical imperative.

This book is an essential guide for manufacturers and engineers navigating AI compliance and governance. Artificial intelligence is revolutionising manufacturing and engineering, but with rapid progress, comes greater levels of responsibility and regulatory scrutiny. The EU AI Act is a landmark regulation shaping how businesses develop and deploy AI.   Written by Professor Marcos Kauffman, an expert in technology and innovation management, this book provides a practical roadmap to achieving compliance while driving innovation. It covers: AI Risk Classification: Understanding prohibited, high-risk, limited-risk, and minimal-risk AI applications. Regulatory Alignment: Implementing conformity assessments, governance frameworks, and human oversight mechanisms. Industry Case Studies: Lessons from AI in automotive, aerospace, and industrial automation. Technology-Driven Compliance: Leveraging AI auditing tools and compliance dashboards. With step-by-step strategies, real-world examples, and insights from industry leaders, this book transforms compliance from a regulatory burden into an opportunity for innovation and competitive advantage. Whether you are a CTO, engineer, or just starting to apply AI in your business context, this book equips you with the tools to responsibly integrate AI into manufacturing and engineering.

Exploring Applications and Implications of Big Data Predictive Analytics in Policing Cyberspace.- Securing E-Voting Authentication: A framework Integrating AI-based Facial Recognition.- Addressing Current and Emerging Challenges in DF: An Examination and Recommendations.- Analysing the effectiveness of YOLO model in detecting the images captured by the drone.- Evaluating the importance of SSI-Blockchain Digital Identity Framework for cross-border Healthcare patient record management.- A Critical Analysis of the Impact of Mis- and Disinformation through the Media on Society.- An investigation of Artificial Intelligence (AI) and Cybersecurity: Case of AI integration in German Cybersecurity strategy.- Children’s Involvement in Terrorist and Extremist Groups.- The Evolution of Terrorism in Digital Era: Cyberterrorism, Social Media, and Modern Extremism.

This book not only equips the readers with the essential knowledge to gain a nuanced understanding of the present cyber threat landscape but also offers strategic foresight to navigate the challenges looming on our digital horizon. In the ever-evolving realm of cyberspace, this meticulously crafted book reveals the escalating cyber threats challenging the foundations of global security and governance. Unprecedented in its synthesis of academic rigour and practical insight, “Sentinels of Cyberspace: Navigating the Intersection of AI, Security, and Ethical Governance in Western Democracies” demystifies the complex relationship between cybersecurity and AI. Rich with comprehensive literature reviews, insightful case studies, and forward-looking perspectives, this book serves as an indispensable guide for scholars, policymakers, practitioners, researchers, and all those concerned with the security fabric of Western democracies. Its unique blend of theoretical frameworks and real-world scenarios creates a transformative bridge between academic discourse and practical application. From foundational explorations of AI to in-depth analyses of its applications in decision-making, crime analysis, counter-terrorism, and predictive modelling, each chapter weaves a narrative that not only articulates contemporary challenges but also lays the groundwork for practical solutions. This transformative work actively engages with ethical dimensions, ensuring a delicate balance between theoretical insights and actionable considerations. More than just informative, “Sentinels of Cyberspace: Navigating the Intersection of Artificial Intelligence, Security, and Ethical Governance in Western Democracies” is a roadmap to navigate the intricate landscape of cybersecurity and AI integration, propelling the discourse towards innovative solutions. For those intrigued by the evolving dynamics of our digital era, this book is an essential companion, offering strategic foresight to understand and address the pressing issues at the intersection of technology, security, and governance.

Computational Intelligence

1. Neural Dynamics Based on Control Theoretical Techniques.- 2. Complex-Valued Discrete-Time Neural Dynamics.- 3. Noise-Tolerant Neural Dynamics.- 4. Computational Neural Dynamics.- 5. Discrete Computational Neural Dynamics.- 6. High-Order Robust Discrete-Time Neural Dynamics.- 7. Collaborative Neural Dynamics.

This book mainly presents methods based on neural dynamics for the time-varying problems with applications, together with the corresponding theoretical analysis, simulative examples, and physical experiments. Based on these methods, their applications include motion planning of redundant manipulators, filter design, winner-take-all operation, multiple-input multiple-output system configuration, multi-linear tensor equation solving, and manipulability optimization are also presented. In this book, we present the design, proposal, development, analysis, modeling, and simulation of various neural dynamic models, along with their respective applications including motion planning of redundant manipulators, filter design, winner-take-all operation, multiple-input multiple-output system configuration, multi-linear tensor equation solving, and manipulability optimization. Specifically, starting from the top-level considerations of hardware implementation, we integrate computational intelligence methods and control theory to design a series of dynamic and noise-resistant discrete neural dynamic methods. The research work not only owns the theoretical guarantee on its convergence, noise resistance, and accuracy, but demonstrate the effectiveness and robustness in solving various optimization and equation solving problems, particularly in handling time-varying problems and noise perturbations. Moreover, by reducing complexity and avoiding matrix inversion operations, the models’ feasibility and practicality are further enhanced.

Michele La Rocca, Cira Perna and Marilena Sibillo "A robust non linear Lee-Carter type model for mortality rate".- Michele Scarpiniti and Aurelio Uncini "Exploiting phase information for the identification of Brugada syndrome A preliminary study".

The book presents current modelling and simulations of neural networks algorithms, associated challenges and practical revenue associated with their applications. The reported models, algorithms and applications have been presented and discussed for the first time at 32nd edition of the International Workshops on Neural Network, held in Vietri Sul Mare from 5 to 7 June 2024. The main purpose of the book is to keep the pace on the current theoretical frameworks, the associated simulated models and the new artificial intelligent algorithms adopted to face the challenges posed by practical applications. The book reports on updates, revisions, new discoveries and new challenges in the field of artificial intelligence showing how neural networks algorithms are progressing toward new solutions and new advanced models. The themes proposed are interdisciplinary involving different scientific fields such as mathematics, computer vision, signal processing, psychology, sociology, and advanced neural sensing and provide contributions on their most recent trends, innovative approaches and future challenges.