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Natural Language Processing (NLP)

The Human Brain.-. The Neurocortex.- The Brain and Methods of ML.- Spare Distributed Representation (SDRs).- A New Brain-Inspired Sequence Learning Memory.- Spelling Check Problem.- ThaiWord Segmentation.- Conclusion and Future Work.

This book brings together key ideas from neuroscience and artificial intelligence to show how they can work together. It helps readers understand how studying the brain can lead to more adaptable and efficient AI systems. Instead of treating the two fields as separate, it highlights how brain-inspired models can help overcome current challenges in AI, improve existing techniques, and spark new and creative solutions. The journey begins with the biological foundations of intelligence, focusing on the brain’s structure, evolution, and functions, particularly the neocortex, which plays a central role in learning and prediction. Building on this foundation, the book surveys both traditional and modern AI methods in an accessible way and offers a critical analysis of their strengths and shortcomings. The discussion then moves from theory to practice, showing how brain-inspired ideas can be applied to real-world Natural Language Processing (NLP) tasks such as spelling correction and Thai word segmentation, where conventional models often struggle with nuance and complexity. In its final sections, the book reflects on the broader significance of integrating neuroscience and AI, encouraging continued exploration and innovation at the intersection of these disciplines. Key benefits of this book include: - Exploring biologically plausible models of intelligence to open new pathways - Gaining foundational insights into how neuroscience can inform AI design - Presenting practical examples to enhance NLP tasks in complex languages - Offering a testbed for experimentation with brain-inspired computational models - Serving as a valuable resource for advanced students, researchers, and professionals seeking to deepen their understanding of nature-inspired intelligent systems While refining existing AI models may lead to meaningful progress, it remains uncertain whether such approaches alone can achieve a deeper form of intelligence. By contrast, drawing inspiration from the structure and function of the human brain may offer a promising direction toward creating systems that are more flexible, adaptive, and capable of exhibiting human-like behavior.

Chapter 1. Understanding MLOps.- Chapter 2. Providing Practical Guidance.- Chapter 3. The Gold Standard MLOps.- Chapter 4. Conclusion.

Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.

Advanced Computational Methods, Numerical Tools and Novel Technologies for Sustainable Aeronautics and Transport

Computational Science and Engineering

Advanced Computational Physics Computational Mathematics Methods and Tools for Improving a Climate Neutral Digitalized Transport .- Multi-Disciplinary Design & Optimization of Novel HEX (Heat Exchangers) for Green Aviatio.- Cold Transportation.- Advanced CFD Applications for Complex Aircraft Configurations.- Contributions of EU Funded Projects Managed by CINEA towards Greener and Digital Transport.- The Leading-Edge Computational Methods for Green Aviation.- Prospects for Green Aircraft Critical Technologies and Operational Aspects Driving Overall Desig.- Disruptive Aircraft’s Wing Configurations towards Greening of Aviation.

This book offers a comprehensive exploration of advanced methods and technologies for sustainable aviation and surface transport, featuring contributions from leading experts presented at the Special Technology Sessions (STS) of the ECCOMAS Congress 2024 in Lisbon. It covers a wide range of topics, including cutting-edge computational methods, multidisciplinary design and optimization, drag reduction, and advanced CFD applications, all aimed at supporting climate-neutral transport systems. This book also highlights EU-funded research, disruptive technologies, and the latest advancements in industrial simulations, providing a valuable overview of current trends and future directions in greening aeronautics and surface transport.

Optimization.- Metaheuristic Algorithms.- Population initialization for metaheuristic algorithm based on the Gibbs sampling methodology.- Metaheuristic optimization with dynamic strategy adaptation.- Harnessing Locust Swarm Dynamics for Optimization Algorithms.- Diversity-Opposition hybridization of the Cheetah Optimizer for global optimization.

This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.

Control, Robotics, Automation

Electro Optical Technologies Optical Systems for Remote Sensing Satellite Imaging.- Calculation of RMS Radiuses of the Point Spread Function Using Zernike Fringe Coefficients.- Approach to Solving the Problem of Registration of Images of Objects with a Large Brightness Interval.- Biomedical Technologies Photo dissociation of Hemoglobin Molecules In Vivo under External Irradiation.- Design of Multi-Wavelength Photoplethysmography System for Non-Invasive Measurement of Oxyhemoglobin and Carboxyhemoglobin.- Automated Complex for Reproducing Alternating Magnetic Fields.- EEG Signal Classification Using Linear Process Model Based Feature Extraction and Supervised Learning.- Technological Support Recognition of the Technical Condition of Elements of Rotating Systems by Neural Networks During Vibro acoustic Monitoring.- Protection of Buildings and Engineering Structures from the Effects of Wind and Snow Loads.- Diagnostics of Stress-Strain State of Shaped Profiles of Metal Structures.- Walking Small Unmanned Vehicles.- Method and Automated System of Technological Preparation of Small Scale Assembly Production in Instrumentation.

This book is the second in the series Advanced System Development Technologies: Studies in Systems, Decision and Control. As in the previous volume, the sections of the book are grouped into three directions: optoelectronic technologies, medical technologies, and technological support. The prerequisites for solving the problem of increasing the efficiency of mechanical assembly production are presented, based on the creation of a new method and system for technological preparation of small-scale production in instrument engineering, which takes into account the dominant influence of assembly processes on the time and cost of instrument manufacturing. A new structure and sequence for the technological preparation of instrument production with a formalized relationship between machining and assembly production are proposed. A new model of an assembled product in instrument engineering has been developed, taking into account the labor intensity and specifics of assembly, adjustment, and control-measurement operations, as well as methods for automated design of technological assembly processes. Additionally, a functional model for forming the product structure based on the labor intensity of manufacturing assembly units is proposed, allowing for the formulation of a substantiated plan for the production of parts. A mathematical model of the assembly sequence has also been developed, enabling the construction of an assembly sequence considering organizational and technical factors.

Advancements in Optimization and Nature-Inspired Computing for Solutions in Contemporary Engineering Challenges

Introduction chapter.- Data mining to build the OD matrix in developing countries: preliminary analysis in an Argentinean case study.- Spatial – Temporal Trajectory Mining for sustainable Mobility and Transport.- Multiobjective evolutionary approach for flight planning of an autonomous fleet of unmanned aerial vehicles in exploration and surveillance missions.- Genetic algorithm for generating alternative corridor locations.- An optimization model for the investment portfolio allocation problem: case study.- Continuous User Authentication System using Different Biometrics.- Automation of Student marking system.- Mini review of models and algorithms for the Cell Switch-Off Optimization problem in 5G/6G telecommunications ultradense networks.- Enhancing Human-Computer Interaction through Vision-Based Hand Gesture Recognition: An OpenCV and Keras Approach.- Sustainable Creations: A Holistic Guide to Optimize Digital Footprints and Foster Climate-Conscious Innovation.- Biometric Insights - Harnessing Nature’s Blueprints for Advanced Computing Solutions.- Kalman Filtering Based on Interval Type-2 Evolving Takagi-Sugeno Fuzzy Model Via Multivariable Spectral Analysis of Experimental Data.- A Comparative Analysis of Machine Learning Classifiers in Demand Forecasting.- Optimizing Sorting Algorithms for Small Arrays: A Comprehensive Study of the Chunked Insertion Sort Approach.- Use of Neural Networks to model an inventory system as a family of industrial belts within the supply chain.- A system for making decisions in the subcontracting of transport providers.

This book brings together cutting-edge research, methodologies, and applications in the field of optimization and nature-inspired computing, providing a comprehensive overview of the latest advancements and their applications in addressing contemporary challenges in engineering. The book demonstrates diverse applications of mathematical modeling in various aspects of production, logistic, design, energy, materials, and other engineering areas. The book includes topics in optimization algorithms nature-inspired computing multi-objective optimization hybrid optimization techniques evolutionary algorithms swarm intelligence machine learning for optimization applications of optimization in engineering sustainable engineering solutions big data analytics for optimization metaheuristic approaches cloud computing in optimization cyber-physical systems decision support systems emerging trends in optimization.

Advances in Artificial Intelligence: Efficiency, Reliability, and Innovations in Machine Learning to Healthcare, and Blockchain

1. EM Algorithm for Tensor Network Logistic Regression based on Polya-Gamma Augmentation.- 2. Reproducibility Analysis for Results of Coupled Tensor Decompositions Based on Federated Learning.- 3. Image-based Skin Disease Classification Using Transfer Learning Model and Fusion Strategy.- 4. A High Precision Symptom Prediction and Diagnosis of Atrial Fibrillation Using CNN and LSTM with Multimodal Feature Fusion Technique.- 5. Personalised Profiling in Mental Health: A CAT-based Approach for Maternal Well-being and Mood Disorders.

Artificial intelligence is transforming the way we live, work, and heal. But true progress depends on more than raw power—it requires systems that are efficient, reliable, and trustworthy. Advances in Artificial Intelligence: Efficiency, Reliability, and Innovations in Machine Learning, Healthcare, and Blockchain explores cutting-edge breakthroughs across machine learning, healthcare, and blockchain. From interpretable tensor models and life-changing medical applications to secure decentralized learning and safer large language models, this book highlights how innovation can meet responsibility. Written by leading researchers, this book is an essential resource for anyone looking to understand and shape the next generation of AI.

1.Studying Encoder–Decoder Relation between Cellular Automata to Uncover their Computational Structure.- 2.Growing Point Automata.- 3.Convolutional neural networks for automated cellular automaton classification.- 4.Quantum Cellular Automata.- 5.Waves, Crystals and Chaos -- A Survey of State Symmetric Cellular Automata.- 6.Modeling virus dynamics by colored Petri nets: Ebola and COVID case study.- 7.Cellular Automata Models of Pedestrian Dynamics.- 8.Cellular Automata in Urban Modelling.- 9.A safety-based multilane cellular traffic model for heterogeneous vehicles.- 10.Cellular Automaton Approach for Modeling Traffic System.- 11. Cellular Automata and Cryptography.- 12.Strengthening Lightweight Authenticated Ciphers using Cellular Automata.- 13.Design and Implementation of Cellular Automata Computing Architectures.- 14.Cellular Automata in Crowd Modeling: A Comprehensive Review of Perspectives and Applications.- 15.Interactive Graph Visualization in DDLab.- 16.On Circular Growth of Life-like Automata.

This book embarks on a journey through the captivating universe of cellular automata and discover its distinct realm, characterised by discrete time, space, and states, where cells form regular patterns and offer insights into complex systems across various domains. From theoretical foundations to practical applications, each chapter unveils innovative ideas and implementations, from solving initial value problems to exploring reversible computing and real-time pattern generation. Explore hyperbolic spaces, analyse elementary automata, and delve into network growth dynamics. Shedding light on synchronisation, control, and computational structures, this book is an invaluable resource for computer scientists, modellers, mathematicians, engineers, physicists, and professionals across disciplines. Immerse yourself in the intricate complexities and boundless potential of cellular automata, whether unravelling pedestrian dynamics or exploring cryptographic applications.

1.Solving the initial value problem for cellular automata by pattern decomposition.- 2.Composing a rotary element in simple reversible cellular automata to make reversible computers.- 3.Evolution of a model.- 4.Extensions on the analysis of elementary cellular automata with process graphs.- 5.Real-Time Pattern Generation by One-Dimensional Cellular Automata.- 6.Versatile Topology for Two–Dimensional Cellular Automata.- 7.A Class of the Smallest 4-state FSSP Partial Solutions for Rings– A Survey.- 8.Loop Patterns Formed by Cellular Automata.- 9.Experimental Classification of Elementary Cellular Automata using Markov Chains.- 10.Developmental Construction of Face-centered Cubic Cellular Automata Inspired by Excitable Media.- 11.Synchronization and Control of Cellular Automata.- 12.A study on the composition of elementary cellular automata.- 13.Symbolic Dynamics of Cellular Automata.- 14.Recent insights into number-conserving cellular automata.- 15.Entropy of linear CA over the ring Zm.- 16.An Invitation to Number-Conserving Cellular Automata.

This book embarks on a journey through the captivating universe of cellular automata and discover its distinct realm, characterised by discrete time, space, and states, where cells form regular patterns and offer insights into complex systems across various domains. From theoretical foundations to practical applications, each chapter unveils innovative ideas and implementations, from solving initial value problems to exploring reversible computing and real-time pattern generation. Explore hyperbolic spaces, analyse elementary automata, and delve into network growth dynamics. Shedding light on synchronisation, control, and computational structures, this book is an invaluable resource for computer scientists, modellers, mathematicians, engineers, physicists, and professionals across disciplines. Immerse yourself in the intricate complexities and boundless potential of cellular automata, whether unravelling pedestrian dynamics or exploring cryptographic applications.

Introduction to Deep Learning Architectures.- Evolutionary Algorithm-based Neural Architecture SearchEvolutionary Algorithm-based Neural Architecture Search.- Gradient-based Neural Architecture Search.

This book describes novel ways of using deep learning to solve real-world problems. It covers advanced deep learning topics like neural architecture search, ensemble deep learning, transfer learning techniques, lightweight architectures, hybrid deep learning approaches, and generative adversarial networks. The book discusses the use of these advanced topics in selected applications like image classification, object detection, image steganography, protein secondary structure prediction, and gene expression data classification. Various challenges and future research directions falling under the scope of these topics are discussed.

Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions

Metaheuristics theory and applications.- Machine learning.- Engineering applications.

This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches. The chapters are meticulously selected and organized in three sections—metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights. Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.

1.Quantum-Enhanced Bioinspired Algorithms: An Overview of Optimization and Learning.- 2.Decoding Market Dynamics: Variational Quantum Circuit in Stock Prediction.- 3.Quantum Dot Cellular Automata: Breaking Barriers in Electronics Circuitry for Tomorrow's Technologies.- 4.Quantum Natural Language Processing: Revolutionizing Language Processing.- 5.Quantum Machine Learning in Climate Science and Climate Change Solutions.- 6.Quantum Cloud Computing: Key Technologies, Challenges, and Opportunities.- 7.Security, Privacy, and Trust in Quantum Edge-driven Intelligence: Challenges and Solutions.- 8.Quantum-Inspired Aspect-Based Sentiment Analysis Using Natural Language Processing.- 9.The Applications of Quantum Machine Learning in Today's World.- 10.Unveiling the Role of Internet of Things (IoT) in the Landscape of Quantum Healthcare Monitoring.- 11.Quantum Neural Networks: Exploring Quantum Enhancements in Deep Learning.

This book advances the artificial intelligence techniques by strongly inspired from quantum computing. Quantum computing (QC), rooted in the mysterious principles of quantum mechanics, has fascinated scientists, researchers, and visionaries for a long time. Its potential lies in changing how we compute, moving from classical bits to quantum bits (qubits), and unlocking incredible processing capabilities. At the same time, artificial intelligence (AI) has been on a trajectory of rapid advancement, with deep learning becoming proficient at tasks that were once deemed impossible. At this juncture, integrating these two fields through various means can play a pivotal role to bring a technological revolution.  

Exploring the Data Balance Effect: Artificial Neural Network Classification on Rodent Tuber’s Liquid Chromatography Mass Spectrometry Data.- Explainable Edge AI for Transparent and Accessible Telemedicine Diagnostics .- Insights and Recommendations for Earthquake  Logistics in Indonesia: Analyzing Twitter (X) Data.- Machine Learning Models for Early Prediction of Student Success and Dropout in Higher Education.- Predicting Stock Price Movements Using the RNN-MGU Model.- etc..

This book explores the evolution of computing beyond current AI paradigms, with a particular emphasis on the potential of knowledge to be used to develop more intelligent, adaptive, and potentially conscious systems. This book has three elements: First, moving beyond "Narrow AI" and data-centric paradigms were investigated.The book likely discusses the inherent limitations of current "narrow AI", despite impressive performance in specific tasks, interpretability, reasoning capabilities, and true understanding. Second, the modelling and uses cases were explored. The book would explore advanced methods for representing knowledge and performing logical or probabilistic reasoning over that knowledge. Third, provide the applications and impact of post-AI. The book provides with details of smart knowledge computing which can lead to more informed, explainable, and robust decision-making in complex real-world scenarios. In essence, this presents itself as a forward-looking investigation into how intelligence might be constructed by combining the capacity of data processing with explicit knowledge representation and advanced reasoning.

.- Preface. .- Part I: Risk analyses, decision making and decision support in value and supply chains in agriculture and horticulture. .- 1: Decision Making in Vegetables Production: Araucania’s Plan Predial (Chile) as a Model. .- 2: Frozen and Quick Frozen Food´s Risk in the Agri-Food Value Chain. .- 3: Current State of Chemical Food Safety and Risk Assessment in Fresh Vegetables Produced in Chile. .- Part II: Development and innovation strategies and programs in agriculture, horticulture and livestock and cattle breeding. .- 4: Agroknowledge: co-creation of a repository ofagricultural best practices, to improve knowledgedisemination and interoperability. .- 5: Regional Programs to Encourage and Develop Vegetables Production for Fresh and Processing Purposes. .- 6: Breeding Challenges for Species Adaptation in the South of Chile. .- 7: Adopting and Adapting Design Thinking's Strategies in Distributed Research and Innovation Projects: A Case Study. .- 8: Integration of Supply Chain of Vegetables in Distrito Federal, Brazil: The Structuration of the Problem with Strategic Options and Development Analysis (SODA) Methodology. .- Part III: Knowledge management, information and communication in agriculture. .- 9: The Role of Information and Communication in Marketing Channels in the Tea Industry. .- 10: Dealing with Uncertainties in IT Solutions for Agriculture. .- 11: Data Mining in Agriculture with Durum Wheat Price Predictions. .- 12: Optical Spectroscopy for Sustainable Agriculture: Crop and Soil Management. .- 13: Analysis of N and K elements in root-removed rice samples using laseri nduced breakdown spectroscopy: evaluating different modes of tillage and Fertilization. .- 14: Challenges and Opportunities for Nanotechnology to Enhance Food Safety of Fresh Vegetables in Chile. .- Part IV: Sustainable agricultural developmenet and Industrial Revolution 4.0. .- 15: Transition of the Agricultural Sector Towards a Circular Economy. .- 16: Sustainable Intensification of Gourd Vegetables in Chile: Challenges and Opportunities. .- 17: A fuzzy multistage control model for sustainable regional agricultural development: Effectiveness and stability. .- 18: GAP-A-Farm: A Tool To Support GAP Compliance and Information Based Decision Making in Horticulture.

This book stands as a significant milestone in the realm of agricultural research and innovation, representing the comprehensive efforts of the RUC-APS Project. The RUC-APS Project, officially known as "Enhancing and Implementing Knowledge-Based ICT Solutions Within High-Risk and Uncertain Conditions for Agriculture Production Systems," was a collaborative initiative under the H2020 European Framework, running from October 3, 2016, to April 2, 2022. Spearheaded by the esteemed Prof. Jorge Hernández, formerly of the University of Liverpool, UK, and currently affiliated with Universidad Adolfo Ibañez, Santiago de Chile, the project brought together a consortium of 16 participants from 5 EU countries and 3 partners from 2 third countries. This book marks the culmination of rigorous research and insights garnered through a meticulous peer-review process involving contributions from various RUC-APS international conferences held between 2021 and 2023. It delves into the pressing need to address the challenges faced by modern agricultural systems, particularly agricultural value chains, amidst volatile and uncertain environmental conditions. The ever-changing landscape of resources, coupled with factors like unpredictable weather patterns, pest infestations, market fluctuations, and fluctuating commodity prices, necessitates a holistic and adaptive approach to ensure resilience and sustainability across the agricultural value chain. Navigating these complexities requires informed decision-making processes that account for risks throughout the entire agricultural lifecycle. The complexity of these decision-making paradigms underscores the critical importance of this book and the insights gleaned from the RUC-APS Project. By shedding light on multifaceted challenges and proposing innovative solutions, this collaborative effort aims to pave the way for a more resilient and sustainable agricultural landscape.

A Multidisciplinary Analysis from Early to Higher Education and Specialized Sectors

Use of gamification for the inclusion of students with SEN.- Gamification in physical education teaching.- Higher education: Technological applications for gamification and ludification.- Disruption in Teaching and Learning Through Gamification.- Cultural Clicks: Maximizing Arts Engagement Based on the Unveiled Drives Behind Cultural Consumption.- Psychological Self-Assessment through Digital Platforms: A Novel Approach for Early Detection of Emotional Disorders in Educational Settings.- Review: Artificial intelligence applied to educational logistics in higher education institutions.- AI-Based browser extension for inclusive education: enhancing accessibility for students with dyslexia.- AI Technology Resources for Online Education.- Conversational AI for Metahumans Projected from Holographic Fans.

This book bridges Industry 4.0 principles with educational innovation, offering groundbreaking approaches that transform learning environments across diverse contexts. Readers discover practical applications ranging from AI-powered diagnostics to gamification strategies, all designed to make education more accessible, engaging, and effective. The unique collaboration between Hispanic American and European researchers brings fresh perspectives to educational technology, organizing cutting-edge research into six comprehensive sections. What distinguishes this work is its human-centered approach—each technological advancement connects directly to pedagogical improvement. Educators, researchers, and technology specialists find value in the book’s exploration of solutions like holographic displays, 3D-printed prosthetics for STEM education, and psychological self-assessment platforms. Beyond serving as a reference, this book establishes a foundation for continued cross-cultural dialogue and future innovation, helping institutions integrate emerging technologies while keeping human development at the core of educational transformation.

.- AI for Next Generation Computing: Emerging Trends and Future Directions.- Emerging Trends and Challenges in AI and Edge Computing.- AI-Enhanced Edge Systems for Immediate Decision Making, etc.

"AI at Edge: Transforming Edge Networks with Computational Intelligence" explores how artificial intelligence enhances the efficiency and capabilities of edge computing. It focuses on key topics like task offloading, resource management, communication optimization, Explainable AI, Security, privacy, and real-time decision-making at the edge. Readers will gain insights into practical methods and tools used to solve real-world challenges in low-latency and high-efficiency networks. The book includes clear explanations, practical case studies, and illustrations to help readers understand complex concepts. It is designed for researchers, professionals, and students, offering a mix of foundational knowledge and advanced techniques. By reading this book, readers will learn how to design smarter, faster, and more secure edge networks using AI. " AI at Edge: Transforming Edge Networks with Computational Intelligence " is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of computational intelligence and edge computing.

Degree of Approximation by Parametrized logistic activated convolution operators.- Approximation by Parametrized logistic activated Multivariate convolution operators.-  Degree of Approximation by symmetrized and perturbed hyperbolic tangent activated convolution operators.- Approximation by Symmetrized and Perturbed Hyperbolic Tangent activated Multivariate convolution operators.- Symmetrized and perturbed hyperbolic tangent neural network multivariate approximation over infinite domains.

This book presents the new idea of going from the neural networks main tools, the activation functions, to convolution integrals and singular integrals approximations. That is the rare case of employing applied mathematics to treat theoretical ones. Authors introduce and use also the symmetrized neural network operators able to achieve supersonic speeds of convergence. Authors use a great variety of activation functions. Thus, in this book all presented is original work by the author given at a very general level to cover a maximum number of different kinds of Neural Networks: giving ordinary, fractional, and stochastic approximations. It is presented here univariate, fractional, and multivariate approximations. Iterated-sequential multi-layer approximations are also studied.

Graduate/advanced undergraduate textbook

Deep learning basics.- Convolutional Neural Networks.- Recurrent Neural Networks.- Attention Mechanisms, Transformers and LLMs.

This comprehensive textbook offers a unique approach to learning machine learning and deep learning by seamlessly integrating theoretical foundations with hands-on laboratory exercises. The book bridges the gap between theory and practice, providing students and practitioners with both the conceptual understanding and practical implementation skills necessary in today's AI landscape. Each chapter follows a carefully structured format that combines theoretical explanations with immediate practical applications. This innovative approach allows readers to reinforce their understanding through direct implementation, creating a more engaging and effective learning experience.

The Evolution of AI in Journalism.- The Transformative Impact of Artificial Intelligence on Global Media and Journalism: Opportunities, Challenges and Future Prospects.- AI Tools Improve Data Collection, Analysis, and Verification Processes.- Automated Content Generation and Distribution.- Emotional Intelligence in Digital Human-AI Interaction: A Meta-Analysis of Empathy, Trust, and Engagement.- AI-Powered Journalism: Redefining News Creation, Ethics, and Audience Engagement.- The Use of AI in Newsrooms - A Systematic Literature Review.- Navigating Ethical Boundaries: The Use of AI in Journalistic Practice.- Building AI-ready newsrooms.- The Future of Journalism in an AI world.

As artificial intelligence reshapes the global media landscape, AI-assisted journalism and media: opportunities and challenges provides a timely and comprehensive analysis of this technological revolution. Edited by Dr. Pham Chien Thang and Dr. A. Prasanth, this volume brings together cutting-edge research to explore how AI tools from natural language generation to predictive analytics are redefining journalistic practices. Going beyond technical theory, this book focuses on the practical relationship between human journalists and machines, demonstrating how automation can enhance rather than replace traditional reporting. It addresses critical gaps in the current literature by offering a global perspective and featuring case studies from diverse regions and countries worldwide. Key Features: Actionable Strategies: Offers practical guidelines for newsrooms to implement AI solutions, train staff, and manage technological transitions while maintaining high professional standards. Ethical & Legal Frameworks: Provides in-depth coverage of the ethical implications of AI, including transparency, accountability, and copyright considerations necessary for preserving democratic values. Balanced Analysis: Examines both the promising opportunities and the critical challenges of AI adoption, helping readers navigate issues like data quality and the changing definition of "newsworthy". Future-Proofing Media: Explores emerging trends and prepares professionals for the future of human-machine collaboration in the newsroom. Target Audience: This book is an indispensable resource for journalism professionals, newsroom managers, and media organizations seeking to navigate the AI transition. It is equally vital for journalism students, academics, and technology developers working on solutions for the news industry.

Transportation Technology and Traffic Engineering

1. Troglodytic architecture in southern Tunisia in the context of culture, space and society; vertical troglodytes in Matmata as a case study.- 2. Screening Cities: Cultural Heritage and Contemporary Urban Experience in East Asia.- 3. Exploring Tradition of the New on Dubai’s Self-Image.- 4. Weaving Nature into the Urban Tapestry.- 5. Living on Stilts: An Architectural Analysis of Traditional Badjao Houses in Surigao City.

In today’s rapidly advancing technological landscape, the integration of Artificial Intelligence (AI) and Intelligent Transportation Systems (ITS) is revolutionizing transportation. This integration is reshaping ITS by enhancing accuracy and reliability, enabling efficient navigation, and optimizing both traffic and public transport management. AI-Driven Transportation Systems: Real-Time Applications & Related Technologies explores the powerful synergy between AI and modern transportation infrastructures, highlighting their transformative impact on traffic management, autonomous vehicles, and real-time decision-making. This book delves into the next generation of transportation systems, where AI-driven solutions enhance efficiency, safety, and sustainability. From intelligent traffic monitoring and predictive analytics to autonomous navigation and smart city applications, AI is redefining how transportation networks operate, ensuring seamless connectivity and optimized resource utilization. The authors provide in-depth analyses of emerging trends, addressing the challenges of AI integration in ITS, along with ethical considerations and anticipated future advancements. With a focus on cutting-edge research and real-world applications, this book serves as an essential resource for researchers, engineers, policymakers, and professionals interested in the future of AI-powered transportation. Whether you are exploring the potential of AI in transportation or seeking to understand the future of ITS, this book is your gateway to the next era of smart and connected transportation networks.

MindBody Wellbeing.- AI for WorkLife Harmony.- AI Technologies Driving Wellness Transformation.- AI in Workplace Wellness Programs.- AI Applications for General WellBeing.- AI and Organizational Wellness Culture.- Ethical considerations and challenges.- Future Trends in AI for Holistic Wellness.- Case Studies and Global Best Practices.- Conclusions.

The book examines how artificial intelligence (AI) is changing methods used by individuals and organizations to address wellbeing. The book examines the convergence of AI, psychology, workplace culture, and personal wellness, providing practical advice on utilizing technology to foster healthier, happier, and more efficient settings. The book emphasizes that AI technologies such as sentiment analysis, customized health applications, and intelligent wearables can track and enhance both physical and mental wellbeing. It explores algorithms that identify burnout, stress, and disengagement, enabling prompt interventions in personal and work settings. Real world case studies demonstrate how AI enhances communication, streamlines workloads, and creates work environments that improve employee contentment. The text explores the ways in which AI-powered platforms can tailor employee experiences, covering aspects such as mental health assistance and career growth. The book confronts ethical challenges head-on, discussing privacy issues, data security, and the need for transparency in implementing AI solutions connected to wellbeing. It anticipates upcoming trends such as emotional AI, predictive analytics for preventative health actions, and the merging of AI with human-centred leadership.

Social Media Analytics.- The Role of Social Media Networks in Enhancing School Libraries.- Machine Learning Approaches for Sentiment Analysis on Social Media.- Harnessing Supervised Machine Learning for Sentiment Analysis in Urdu Text.- Early Depression Detection from Social Media State of the Art Approaches.- Classification of student stress levels using a hybrid machine learning model.- From Disease Detection to Health Campaigns The Role of Social Media Analytics in Public Health.- Effect Of Social Media Networks on People’ Bahaviour In The Light Of The Fourth Technological Revolution A Perspective From Their Families.- An Efficient High Speed Feature Extraction Analysis Methods of Digital Images in Social Media Platforms.- Cybersecurity and Digital Safety.- Quantum Computing Transforming Cybersecurity and Social Media in the Digital Age.- Factors influencing on the cybersecurity in an emerging economy.- Innovative Security Measures A Comprehensive Framework for Safeguarding the Internet of Things.- Adaptive Social Network Quality ASNQ Model A Conceptual Quality Model.- Advanced Technologies and Their Broader Impacts.- Authorship Attribution Performance Model Evaluation.- Digital Storytelling Developing 21st Century Skills in Arabic Language Education.- Effective Techniques in Lexicon Creation Moroccan Arabic Focus.- The Role of Future Technology in the Preparation of Prospective Teachers.- A Website Development of UCSI e Market Hub Transforming Unwanted Possessions.- Investigating LLMs Potential in Software Requirements Evaluation.- Advancements and Applications of Multimodal Large Language Models Integration, Challenges, and Future Directions.

This book presents state-of-the-art research, conceptual frameworks, and practical solutions, focusing on the intersection of these vital fields. The ever-evolving digital landscape has fostered a close relationship between social media and cybersecurity. Both social media analytics and cybersecurity are prominent research areas that shape the lives of individuals, organizations, and communities. It covers three key categories: First, social media analytics, which explores how data from platforms like Twitter and Facebook is harnessed for insights, sentiment analysis, and trend predictions. Second, cybersecurity and digital safety, which addresses emerging threats and explores tools and strategies to secure digital spaces. Third, advanced technologies and their broader impacts, which examines the technologies shaping social media platforms. This book is an invaluable resource for researchers, professionals, and students, providing comprehensive insights into the application of advanced technologies and analytical techniques for safeguarding digital environments. It is essential reading for anyone interested in social media analytics, digital safety, and the future of technology.

Electronic Circuits and Systems

Primer on AI in Electronic Circuit and System Design.- AI in Semiconductor Design and Production.- AI-assisted lithography, device modeling, and transistor level circuit design.- Harnessing AI for ASIC Design.- AI Application in Analog and RFIC Design.- The Role of AI in Place and Route.- Timing Analysis through AI.- AI's Contribution to Clock Tree Synthesis.- Securing Hardware with AI.- AI in 3D Integration.- Co-Development of Hardware-Software via AI.- AI in FPGA Design.- AI in PCB Level Design.- AI in Adaptive and Resilient Digital Circuits and Systems.- Cognitive-Inspired Computing Systems via AI.- Safe and Secure Machine Learning with AI.- Hardware Accelerators for AI.- Cognitive-Inspired Computing with Big Data using AI.

As our world becomes increasingly digital, electronics underpin nearly every industry. Understanding how AI enhances this foundational technology can unlock innovations, from smarter homes to more powerful gadgets, offering vast opportunities for businesses and consumers alike. This book demystifies how AI streamlines the creation of electronic systems, making them smarter and more efficient. With AI’s transformative impact on various engineering fields, this resource provides an up-to-date exploration of these advancements, authored by experts actively engaged in this dynamic field. Stay ahead in the rapidly evolving landscape of AI in engineering with “AI-Enabled Electronic Circuit and System Design: From Ideation to Utilization,” your essential guide to the future of electronic systems.  A transformative guide describing how revolutionizes electronic design through AI integration. Highlighting trends, challenges and opportunities; Demystifies complex AI applications in electronic design for practical use; Leading insights, authored by top experts actively engaged in the field; Offers a current, relevant exploration of significant topics in AI’s role in electronic circuit and system design. Editor’s bios. Dr. Ali A. Iranmanesh is the founder and CEO of Silicon Valley Polytechnic Institute. He has received his Bachelor of Science in Electrical Engineering from Sharif University of Technology (SUT), Tehran, Iran, and both his master’s and Ph.D. degrees in Electrical Engineering and Physics from Stanford University in Stanford, CA. He additionally holds a master’s degree in business administration (MBA) from San Jose State University in San Jose, CA. Dr. Iranmanesh is the founder and chairman of the International Society for Quality Electronic Design (ISQED). Currently, he serves as the CEO of Innovotek. Dr. Iranmanesh has been instrumental in advancing semiconductor technologies, innovative design methodologies, and engineering education. He holds nearly 100 US and international patents, reflecting his signifi cant contributions to the field. Dr. Iranmanesh is the Senior life members of EEE, senior member of the American Society for Quality, co-founder and Chair Emeritus of the IEEE Education Society of Silicon Valley, Vice Chair Emeritus of the IEEE PV chapter, and recipient of IEEE Outstanding Educator Award.  Dr. Hossein Sayadi is a Tenure-Track Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He earned his Ph.D. in Electrical and Computer Engineering from George Mason University in Fairfax, Virginia, and an M.Sc. in Computer Engineering from Sharif University of Technology in Tehran, Iran. As a recognized researcher with over 14 years of research experience, Dr. Sayadi is the founder and director of the Intelligent, Secure, and Energy-Efficient Computing (iSEC) Lab at CSULB. His research focuses on advancing hardware security and trust, AI and machine learning, cybersecurity, and energy-efficient computing, addressing critical challenges in modern computing and cyber-physical systems. He has authored over 75 peer-reviewed publications in leading conferences and journals. Dr. Sayadi is the CSU STEM-NET Faculty Fellow, with his research supported by multiple National Science Foundation (NSF) grants and awards from CSULB and the CSU Chancellor’s Office. He has contributed to various international conferences as an organizer and program committee member, including as the TPC Chair for the 2024 and 2025 IEEE ISQED.

Biomedical Engineering and Bioengineering

Ethical Considerations and Challenges in AI Dermatology.- The Role of Artificial Intelligence in Atopic Dermatitis Research and Management.- Efficient Diagnosis of Psoriasis and Lichen Planus by Deep Learning.- Recent Breakthroughs in Artificial Intelligence for Dermatology.- AI Assisted Theoretical Modeling and Computational Design of Drug Delivery Systems for Enhanced Treatment of Skin Diseases A Review.- Artificial Intelligence for Dermatological Diagnosis and Care A Comprehensive Overview.

This book unveils the cutting-edge research, ethical insights, and practical applications that are shaping the future of skin health, from faster and more accurate diagnoses to innovative treatment strategies.  Artificial Intelligence is transforming dermatology like never before. A must-read for clinicians, researchers, and AI enthusiasts ready to explore the next frontier in dermatology.

The Black Box of Computational Imaging.- Algorithmic Mediascapes.- Algorithms and the Arts.- Cultural Critique of Algorithmic Media.- Posthuman/Non-Human Imagery.

Contemporary media ecosystems are deeply entangled with algorithms, which play an increasingly pervasive role in shaping our environment and mediating our perception of reality. This book aims to provide a critical map of a rapidly evolving mediascape in which humans, machines, and data negotiate forms of agency, cultural imaginaries, and scopic regimes. In particular, the volume addresses the multiple challenges posed by algorithmic media within the domain of visual culture, and identifies a paradigmatic shift: in the age of artificial intelligence, images are no longer merely captured or represented—they are synthesized, inferred, and predicted by probabilistic models. As active agents shaping creativity and meaning-making, AI systems carry far-reaching epistemological, aesthetic, and political implications. They open new possibilities for content accessibility and participatory engagement, yet they also encode existing asymmetries. Trained on massive datasets, machine learning models often reproduce structural biases, amplify cultural and social exclusions, and perpetuate inequalities. At the same time, algorithmic systems are being subverted and reappropriated by artists and curators, transforming them into sites of resistance and experimentation for imagining alternative futures. Bringing together perspectives from computer science, semiotics, philosophy, critical theory, aesthetics, art theory, visual culture, and film and media studies, this book sets out an interdisciplinary exploration to make key concepts—such as algorithm, latent space, neural networks, generative models, and NFTs—accessible to a humanities audience, while providing a set of critical tools to interrogate both the ruptures and continuities that define this ongoing transformation. The contributions span a wide array of topics: from machine vision and facial recognition to social robots; from computational art analysis to generative AI, distributed authorship, and curatorial practices; from biometric surveillance and predictive modeling to the politics of datasets and decolonial critique; from virtual and augmented reality to posthuman imagery, digital afterlives, and AI-mediated memory. Compelling us to revisit foundational categories in media studies, this book offers an analytical framework to open a debate that is not merely about “technology’s impact” but about how algorithms have become the media themselves: veritable algomedia.

Introduction.- Literature survey on caries detection methods.- Caries lesion detection from colored images.- Caries lesion detection from x-ray images.- Quality improvement of x-ray medical images.- Conclusions and future scope.

This book provides an insight on different types of hardware and software-based caries detection methods and their limitations from treatment perspective. The different chapters are motivated to address some of the important gaps in the prevailing approaches and suggest suitable solution to them. Overall, it helps the reader to understand the caries detection methods to identify interproximal and occlusal caries. This also assists the practitioners. The book identifies multiple research issues on dental caries detection that open the practitioners’ horizons to an array of domains from which they can conclude practical insights about their area of interest.

Automated Pattern Recognition

Graduate/advanced undergraduate textbook

Introduction.- Data Preprocessing.- Linear Parametric Models.- Learning Score Functions.- Analytic Learning.- Penalized Learning.- Network Learning.- Ensemble Learning.- Performance Evaluation.- Applications.

This textbook is a consolidation of learning methods which comes in an analytic form. The covered learning methods include classical and advanced solutions to problems of regression, minimum classification error, maximum receiver operating characteristics, bridge regression, ensemble learning and network learning. Both the primal and dual solution forms are discussed for over-and under-determined systems. Such coverage provides an important perspective for handling systems with overwhelming samples or systems with overwhelming parameters. For goal driven classification, the solutions to minimum classification-error, maximum receiver operating characteristics, bridge regression, and ensemble learning represent recent advancements in the literature. In this book, the exercises offer instructors and students practical experience with real-world applications.

Business and Management

1. An Introduction to Fuzzy Logic in Real Time Application Paradigm.- 2. Application of Bayesian Algorithm for Impacting Social Media Marketing on Smart Electric Motorcycle Purchase Intention.- 3. Optimizing Traffic Signal Control using Fuzzy Logic: A Solution for Urban Congestion Management.- 4. Optimizing Energy Efficiency in Smart Grids Using Deep Fuzzy Nets: A Comprehensive Approach to Power Regulation and Control.- 5. Making Sense of the Messy: How Fuzzy Logic Can Help Us Solve Real-Life Problems.- 6. FUZZY BASED MACHINE LEARNING MODEL FOR SMART INFORMATION ACCUMULATION DEVELOPMENT.- 7. Synthesis of Fuzzy Sets and Their Practical Applications.- 8. Optimizing Inventory Levels in Retail: Fuzzy Logic-Based Decision Support Systems for Adaptive Inventory Management.- 9. Neutrosophic Numbers in Identifying Best Teacher Awardee using SWOT Analysis.- 10. Fuzzy Logics in Multi-Criteria Decision Making Problems.- 11. A Case Study on Pharmaceutical supplier Selection by using the Integrated Fuzzy AHP MABAC Method.- 12. A Novel Approach for Multi-Cluster-Based River Flood Early Warning System Using Fuzzy-Logic-Based Learning and Rule Optimization.- 13. Evaluating Gamification Tools for Operating Management in Industrial Engineering: A Dual-Model Approach Using Fuzzy AHP and MACBETH.- 14. Exploring the Efficacy of Machine Learning Algorithms across Diverse Feature Selection Strategies in Rice Classification Tasks.- 15. Industrial Intelligence: A Fuzzy Logic Approach.- 16. Sensors and Data driven approaches in Precision Agriculture: A Comprehensive Review.- 17. Fuzzy Logic Control for Adaptive Fan Speed Regulation Based on Temperature Variations in Cold, Warm, and Hot Seasons.- 18. Improving Diabetic Patient Care through Fuzzy Logic: A Comprehensive Approach.- 19. Causal Analysis of Heart Failure Mortality among Females: A Fuzzy Logic Approach.- 20. Design and Realization of Interval Type-2 Fuzzy Logic Controller for a TITO Aero-Dynamic System.- 21. Modeling a Fuzzy Transportation Problem for Securing  Mission Critical Servers using Attack Graph.- 22. The ranking function-based defuzzication technique and application of type-2 fuzzy logic to study a triple goal-based 4D-transportation problem with carbon emission effect.- 23. Exploring Machine Learning Techniques for Enhanced Chronic Kidney Disease Diagnosis: A Comprehensive Survey.- 24. Enhancing Fuzzy Multi Criteria Decision Making Technique in Engineering Design Problem.- 25. The Assessment of Customer Satisfaction of a Product in a Manufacturing System using Adaptive Neuro Fuzzy Interference System.- 26. Application of Neutrosophic Pythagorean Supra Topological Spaces in Attribute Decision-Making.- 27. Application of Fuzzy Logic to Model and Control Rotavirus Spread Among Vaccinated and Unvaccinated Children.

The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model. The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well. The development of the  book and  its  content  layout will be done so meticulously proving the  enough  insights  of  the   subjects to the readers so that  the  readers  can easily pursue their research concept from the  book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations.

Chapter 1. Theory and Application of a Novel Online Fractional-Order Learning Algorithm and an Interval Type-3 Fuzzy System.- Chapter 2. A Pragmatic Matlab Simulink and M-file for Robotic, Control, and Modelling Uses for an Applied Type-3 Fuzzy Logic System.- Chapter 3. Advancements and Applications In Modern Technology.

This book provides the fundamental approaches to designing and using type-3 fuzzy systems in real-world applications. Basic Matlab codes are provided to use type-3 fuzzy systems in a straightforward scheme. The main differences between type-3 fuzzy systems and other types are analyzed and compared. The effectiveness of type-3 fuzzy systems is analyzed and studied in various applications, such as robotics, intelligent control systems, and data science.

Artificial General-Internet of Things (AG-IoT) for Robotics: Advanced Computer Vision Applications and Future Trends

AG-IoT and Robotics Solutions for Early Intervention in Developmental Delays Among Mentally Retarded Children.- Exploring the Impact of AR, IoT, and Robotics on Cognitive and Motor Functioning in Children with Neurodevelopmental Disorders.- Evolution of the Internet of Things (IoT) in Robotics.- Human -Robotic Interaction in AG-IoT Environment.- Future Trends for Artificial General- Internet of Things for Robotics.- Future AIoT-Based Green Smart Cities Essential Role in Enhancement of Healthcare Services.

This book delves into the next generation of robotics, where the fusion of AGI with IoT networks brings unprecedented capabilities to machines, allowing them to think, adapt, and collaborate in ways previously confined to science fiction. In the rapidly evolving world of technology, the convergence of Artificial General Intelligence (AGI) and the Internet of Things (IoT) is unlocking new frontiers for robotics, transforming how we interact with machines and the environment. This book explores the groundbreaking synergy between AGI and IoT, focusing on their transformative impact on robotics and automation systems. It presents advanced applications in computer vision, showing how robots are becoming more intelligent, autonomous, and capable of interpreting and interacting with the world around them with human-like precision. The author offers insightful analyses into the future of robotics, discussing upcoming trends, the challenges of developing AG-IoT ecosystems, and the ethical implications of these technological advancements. With a focus on cutting-edge research and real-world examples, this book serves as an essential resource for researchers, engineers, and enthusiasts interested in the future of robotics, artificial intelligence, and the IoT. Whether you are exploring the potential of AG-IoT integration or seeking to understand the future of intelligent robotics, this book is your gateway to the future of automation and intelligent systems.

SDG 3 Good Health and Well-being.- Hybrid Quantum Convolutional Network for Spine Fracture Classification.- RSNA 2024 Multi view Quantum Deep Learning Model for Degenerative Lumbar Spine Classification.- Enhancing Nocturnal Blood Pressure Detection through Interval Type 2 Fuzzy Classification Systems.- Glioma Segmentation in T1 Weighted MRI Images Using Deep Learning.- Vision Transformers for the Segmentation of Patterns Associated to Pathology in Diabetic Retinopathy.- Segmentation of High Quality Histopathological Micrographs Using a Genetic Algorithm and Otsu's Method.- SDG 9 Industry, Innovation, Technology and Infrastructure.- AI based Algorithm for the Automatic Detection of Missing Solder Balls in Encapsulated Semiconductors.- Membrane Pseudo Bacterial Potential Field with GPU Acceleration for Mobile Robot Path Planning.- Error Correction in Quantum Computing.- Supervised Quantum Machine Learning.- Hybrid Quantum Genetic Algorithm with a Strongly Entanglement Recombination Process for Maximizing the Coverage Area of a Set of Unmanned Aerial Vehicles in Natural Disaster Locations.- Review on Quantum Optimization of Type 1, Type 2, and Type 3 Fuzzy Systems.- Review on Quantum Optimization of Neural Networks and Fuzzy Systems.- Finance in the Quantum Computing Era A Survey from 2019 to 2024.- From Classical Challenges to Quantum Solutions Grover’s Algorithm for the N Queens Problem.

This book delves into how these transformative technologies are being integrated to revolutionize fields such as health care, infrastructure, and data security. In an era dominated by rapid advancements in technology, this book stands at the forefront of the intersection between AI and quantum computing. Key Highlights: · Quantum Architectures: Explore revolutionary quantum architecture search methods that leverage quantum evolutionary algorithms, setting the stage for next-generation computing power and efficiency. This section delves into the mechanisms that could significantly enhance quantum device functionality and scalability. · Quantum-Enhanced Machine Learning: Discover how supervised quantum machine learning is redefining problem-solving in various industries by enabling faster, more efficient data processing and opening new frontiers in artificial intelligence. · Agricultural Innovations: Delve into the application of AI in agriculture with a focus on improving crop yield predictions and optimizing resource management, showcasing how technology is transforming traditional farming practices. · Health Care: Examine the transformative impact of AI and quantum computing in health care—from improving diagnostic accuracy with advanced imaging techniques to optimizing treatment protocols and patient management systems. · Software Reliability: Explore the vital role of machine learning algorithms in enhancing software reliability, focusing on predictive techniques that identify potential faults and improve the quality of software development processes. This book is an essential resource for researchers, engineers, and policymakers, providing both theoretical foundations and practical applications of AI and quantum computing. It is designed to inspire innovation and encourage the adoption of these technologies in solving some of the most pressing challenges of our time. This book is more than just a technical guide—it is a blueprint for the future, demonstrating the profound impact of these technologies on global health, sustainable infrastructure, and beyond. Dive into this pivotal work to understand how artificial intelligence and quantum computing are not only shaping our world but also paving the way for a brighter, more sustainable future.

1.The Climate Crisis and the Four Pillars of Energy Transition: Decarbonization, Digitization, Decentralization, and Democratization.- 2.The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review.- 3.Scalable Framework for Intelligent System Architecture to Address Challenges in the Energy Sector.- 4.Deep Learning Models for Short-Term Forecasting of Photovoltaic Energy Production.- 5.Machine Learning-Driven Energy Consumption Forecasting for Building Profiling.- 6.Meta-Learning Approaches for Assessing Energy Efficiency Investments in Buildings.- 7.Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings.- 8.Optimization Model for Scheduling Flexible Loads to Mitigate Energy Peaks.- 9.Optimization Model for Electric Vehicle Integration and Energy Storage to Achieve Energy Autonomy.- 10.Future Directions of Intelligent Energy Management and the Role of Generative AI.

This book focuses on creating an integrated library of learning models and optimization techniques to assist decision-making on issues in the energy and building sector. It provides modern solutions to energy management and efficiency while addressing a scientific gap in the development of advanced algorithmic methods to solve these problems. More specifically, the focus is on the development of models and algorithms for problems falling into three broader categories, namely: (a) Distributed Energy Generation, (b) Microgrid Flexibility, and (c) Building Energy Efficiency. Artificial Intelligence models and mathematical optimization techniques are developed and presented for applications related to each of these categories, through a thorough analysis of the fundamental parameters of each application as well as the interactions among them. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.

Control and Systems Theory

AI for sustainable innovation management.- AI for risk managemt.- AI strategies and innovation change management Digital risk managementInnovation capabilitiesAI for financial risks and operational risks.- Data mining Machine learning.- Artificial neural network.- Natural language processing.- Automated identification.- Automated risk detection.- Parallel computing environment.- AI applications for public safety.- AI and social media /PR crisis management.- Fuzzy Logic in finance and banking risks.- Fuzzy decision support system for risk management.- Disruptive technological innovation.- Data breaches and security risk responses.- AI risk management-related governance, policies, and strategies Social risksEconomic risks.

In a rapidly evolving technological landscape, integrating artificial intelligence (AI) into sustainable innovation and risk management practices is increasingly vital. This edited volume titled "Artificial Intelligence for Sustainable Innovation Management and Risk Management: A Systems (and Network) Perspective" offers a comprehensive exploration of how AI and other technologies can be utilized in today’s challenging world of dynamic environments and industries. This book targets a diverse audience, including academics and researchers in management, sustainability studies, and technology, who will find valuable insights to advance their research on AI, sustainability, and management. Graduate and postgraduate students pursuing studies in management, AI, and sustainability will also find this book to be a comprehensive resource, enriching their academic curriculum with contemporary issues and cases. Overall, this book is positioned to discuss the critical role of AI and technology in fostering sustainable practices and addressing multifaceted challenges in the modern world.

Recent Advences of Artificial Intelligence in Civil Engineering.- Metaheuristic-based optimization and application of metaheuristics on reinforced concrete structure.- Artificial neural networks model for natural frequency prediction in reinforced concrete slabs.- Human-inspired optimization algorithms for cost minimization in reinforced concrete column design.-Multioutput Regression for Reinforced Retaining Wall Optimum Design with Machine Learning.- Advanced Regression Strategies for Concrete Strength Estimation: A Comparative Ensemble Approach.- Optimization of Triangular Reinforced Concrete Beam with Various Metaheuristic Algorithms.- Metaheuristic-based Detailed Optimization of Reinforced Concrete T-beams and Evaluation of the Effect of Concrete Class.- Artificial Intelligence for Structural Vibration Control: A Review of Previous Studies and Methodologies.- Isolator Damping Capacity with Catboost Algorithm Hyperparameter Optimization in Classification.- Optimum Multiple Tuned Mass Dampers for Soft Story Structure.- Artificial Intelligence for Structural Health Monitoring: Techniques, Applications, and Future Directions.- Surface Crack Detection Using Deep Learning and Stacking Ensemble.- Optimization of Time-Cost-Environmental Impact Problems in Construction Projects with Grey Wolf Algorithm.- Automated Machine Learning for the Prediction of Compression Index of Soils.- Shear Wave Velocity Prediction Using Machine Learning.- Geospatial LULC and LST Change Analysis with Future Growth Prediction Using Random-Forest and MLP-MCA Algorithms.- Precipitation Forecasting in the Konya Closed Basin Using LSTM: A Comparative Analysis of Optimization Algorithms.- Machine Learning for Water Quality Monitoring: Comparative Analysis of AI Models in River Assessment.- A deep learning application to predict reservoir operations.- Global Impact of Artificial Intelligence on the Sustainability of Civil Engineering Infrastructure.- An Analysis of Rolling Horizon based Route Optimization for the Pickup-and-Delivery Problem with Time Windows in the Context of Paratransit Applications.- Development of Feature Selection Based Preventive Railway Track Maintenance Decision System.- Development of Machine Learning Algorithms Based Prediction of Power Efficient Railway Track Maintenance Model.- Impact Of Artificial Intelligence in Civil Engineering Education.

Artificial Intelligence in Civil Engineering: Recent Advances provides a comprehensive overview of the latest developments in applying artificial intelligence (AI) techniques to solve complex civil engineering problems. Civil engineers today face challenges that demand not only safety and cost-effectiveness, but also sustainability and resilience against natural hazards. This volume demonstrates how AI offers powerful tools to meet these demands by transforming traditional engineering methodologies. The chapters present a broad spectrum of applications, including predicting shear wave velocity, slope stability, estimating uniaxial compressive strength of soils, optimization-based design of reinforced concrete retaining walls, structural control strategies for seismic resilience, and the integration of Building Information Modeling (BIM) with AI-driven solutions. Machine learning, deep learning, and metaheuristic algorithms are explored in detail, supported by real-world case studies and experimental data. By bridging theory with practice, the book highlights how AI enhances predictive accuracy, optimizes design processes, and reduces computational effort in engineering tasks. Researchers, graduate students, and practicing engineers will find this volume a timely reference, offering both methodological foundations and practical insights into AI-powered approaches that are reshaping the future of civil engineering.

In honour of Professor Dr. George A. Tsihrintzis for his Invaluable Contributions

Biomedical Engineering and Bioengineering

Self-supervised and disease management system for diabetic retinopathy detection.- GAN-enhanced Abdominal MR-CT Image Fusion in Transform Domain.- U-Net with Attention for the Automatic Segmentation of the Major Temporal Arcade in Retinal Fundus Images.- Harnessing Few-Shot Learning Segmentation for Histopathology: A Comprehensive Practical Study.- Healthcare Applications of Object Detection for Tumor Detection Across Diverse Medical Imaging.- Automatic Multilabel Classification of Coronary Stenosis using Lightweight Deep  Learning Techniques.

This book brings together cutting-edge research and innovative applications of artificial intelligence, machine learning, wearable technologies and decision support systems in modern healthcare. The chapters demonstrate how these artificial intelligence innovations are driving next-generation healthcare solutions. The chapters present diverse clinical applications through advanced artificial intelligence approaches in diagnostics, treatment, rehabilitation and healthcare management, with a strong emphasis on patient-centred solutions. This book is directed to students, professors, researchers, practitioners in the field of healthcare, engineering, computer science and related areas.

Engineering and Technology Education

SECTION 1: CONCEPTUAL AND POLICY FOUNDATIONS OF AI IN EDUCATION AND RESEARCH.- 1. Generative Artificial Intelligence (GenAI) in Language Education: Current Trends and Perspectives.- 2. AI Integration into Higher Education Curricula: A Multiple-Case Study of  Key Universities in Vietnam.- SECTION 2: AI APPLICATIONS IN TEACHING AND LEARNING CONTEXTS.- 3. Turkish Students’ Use of NotebookLM in an Online Digital Literacy Course.- 4. Impacts of Chatbot Training on EFL Teachers’ Confidence and Acceptance of AI Technologies.- 5. Building Audiobooks with AI Support as Digital Resources for ESL Learning.- SECTION 3: AI-MEDIATED LEARNING AND INTERACTION.- 6. Chatbot-Human Interaction In Vietnamese EFL Speaking Classes: Exploring The Unchartered Territory.- 7. EFL Learners’ Engagement in ChatGPT–Generated Speaking Tasks from a Text-Driven Approach: A Case in Vietnam.- 8. Artificial Intelligence-Supported Willingness to Communicate and Intercultural Communication.- 9. Artificial Intelligence-Enhanced Scaffolding and Peer Collaboration in Culturally and Linguistically Diverse Classrooms: Evidence from Asia.- SECTION 4: AI FEEDBACK AND LEARNING ASSESSMENT.- 10. AI-Mediated L2 Written Feedback: Characteristics of Prompts, AI Tools, and Learners’ Feedback Use.- 11. Ethical, Pedagogical, and Research Implications of AI in Education and Academic Research.

This book presents empirical and conceptual manuscripts across contexts and disciplines, offering insights into how AI has been applied in teaching, learning, and research practices. This book, with eleven chapters spanning different aspects of AI use in educational settings, contributes to the knowledge of how AI can be responsibly and effectively incorporated into educational and research practices that foster innovation, inclusivity, and lifelong learning. As the landscape of AI integration in education and research is evolving rapidly, the future use of AI should be guided by humanistic and ethical principles

A Comparative Analysis of the Integral Individuality of the Professional Psychologist and Artificial Intelligence as a Simulated Model of the Psychologists Individuality.- Distinctive Characteristics of Personal Coping Strategies in the Face of AI Driven Fraud.- Prospects for Using Artificial Intelligence in Socio Psychological Research.- Ethical Strategizing of the Intelligent Maturity of Industrial Ecosystems in the Transition to the AI Economy.- Social Interaction in Youth Virtual Communities Identification of Social Cohesion Factors by AI Algorithms.- Using IT and AI for Multilingual Data Analysis in Scientific Research in Kyrgyzstan.- The Digital Culture of a Medical Organization in the Context of the Introduction of Telemedicine Technologies.- Interaction Dynamics of Agents in the Social Sector within the Digital Space.- Artificial Intelligence in Socio Economic Systems New Solutions Oriented Toward Sustainable Development.- A Multivariate Risk Model for WACC Estimation Application of Earnings Margins Volatility Coefficients.

This book aims to create a cohesive and logically structured scientific concept, a systemic methodology, and a set of practical recommendations for the human-centered use of artificial intelligence (AI) in socio-economic systems. In its five parts, the book explains the role and place of the individual in smart socio-economic systems, presenting advanced human-centered solutions for the use of AI in the economy, which spans various industries, regions, and countries. The book is notable for its multidisciplinary approach, offering a systemic perspective on human-centered AI applications from the standpoint of various social sciences, including economics and management, law, sociology, philosophy, cultural studies, pedagogy, and psychology. The novelty of this book lies in its consideration of the empirical experience of humanizing cyber-social systems in developed and emerging smart economies, including the dynamically developing EAEU and Central Asian countries. The primary audience for the book consists of scholars studying AI. In this book, they will find a socio-humanitarian view of the AI economy and a comprehensive scientific methodology for its humanization. Additionally, the book is of interest to representatives of smart enterprises, leaders of smart regions, and government officials in countries implementing artificial intelligence who are concerned with issues related to its humanization.

Artificial Intelligence Techniques for Analysing Sensitive Data in Medical Cyber-Physical Systems

Cyber-Physical Systems

Chapter 1: Artificial Intelligence and Machine Learning for Medical Applications and Digital Health.- Chapter 2: Healthcare & Cyberspace: Medical Cyber-Physical Systems.- Chapter 3: Privacy Risk Analysis in Medical Cyber–Physical Systems.- Chapter 4: Healthcare & Cyberspace: From Cyber-Physical Systems to Medical Digital Twins.- Chapter 5: Federated Learning in Agents Based Cyber-Physical Systems.- Chapter 6: Application of Solid Platform and Federated Learning for Decentralized Health Data Management.- Chapter 7: TinyML and Federated Learning in Resource-Constrained Medical Devices.- Chapter 8: The Role of Blockchain in AI-Driven Medical Cyber-Physical Systems.- Chapter 9: Artificial Intelligence Applications in Healthcare Security.

This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data.  Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed.  In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.

Artificial Intelligence, Sustainable Technologies, and Business Innovation: Opportunities and Challenges of Digital Transformation

Impact of Open Innovation on Sustainability of Islamic Banks in Jordan.- Digital Finance and Fintech Innovations to-wards Disrupting Finance Innovation.- A Glimpse into Audiovisual Translation: Revoicing as a Case Study.- The Role of Sense Relations in Marketing Campaigns.- Navigating Linguistic and Cultural Aspects in Translating Economic Policy Announcements for Exchange Market Communication.- Business Discourse Analysis: Uncovering Linguistic Patterns and Power Dynamics in Corporate Communications.

This book offers a comprehensive exploration of the symbiotic relationship between artificial intelligence, sustainable technologies, and business innovation. Innovation has always been the main engine of an improved standard of living throughout history. However, the process of innovation can be highly disruptive as it makes more conventional technologies obsolete This book presents trendy and important topics such as open innovation and sustainability of Islamic Banks, Fintech, financial inclusion, IOT, business intelligence capabilities, innovation through AI, circular economy practices, and trends in cybersecurity. The reader-base from diverse backgrounds, including scholars, industry experts, policymakers, and students, engage with the perspectives and topics discussed in this book. By understanding the opportunities and challenges of this dynamic landscape, the authors can collectively work together to shape a future where technology and sustainability co-exist to drive positive change.

Artificial Intelligence—Based Games as Novel Holistic Educational Environments to Teach 21st Century Skills

21st Century Skills via Prosocial Educational Games: An Overview.- Computer games for Entertainment and Education: A Literature Review.- User-player and Student Modelling in Personalized Educational Games: A Literature Review.- Fuzzy Logic and Applications in Education and Games.- EPATHLO Educational Game Suite and Fuzzy Reasoning.- Playing Educational Games in Web - Reusability - Interoperability.- Authoring Intelligent Educational Games with Fuzzy Reasoning.- Practicing Soft Skills for Programming through an Intelligent Adventure Game.- General Data Protection Regulation and Adaptive Educational Games.- Contributions and Discussions on Advancing 21st Century Skills Through EPATHLO.

This book offers a visionary look at how AI can promote learning for modern skillsets by examining the fusion of AI, prosocial gaming, personalisation, ethics, and education. The book introduces the EPATHLO Suite, a novel AI platform that personalises both educational content and gameplay, creating tailor-made learning experiences and entertainment for each student. By blending personalised games with education, these AI-driven environments make learning more dynamic and enjoyable, while focusing on prosocial behaviour development, encouraging cooperation, empathy, and ethical understanding. It also provides roles for human teachers, as content creators of the EPATHLO Suite authoring tool. The book highlights the importance of twenty-first-century skills—such as critical thinking, collaboration, communication, and creativity—while also addressing ethical issues like data privacy (including GDPR compliance). It provides practical AI-driven solutions and reviews relevant literature, offering a comprehensive understanding of these interconnected fields. This book is an indispensable resource for those looking to explore these state-of-the-art topics. It is ideal for academics, researchers, students, educators, game designers, programmers, and professionals in the educational gaming industry who want to understand AI's role in shaping the future of education with games. Whether readers aim to enhance their classroom, develop new learning technologies, or better grasp the evolving technology of educational games with AI, this book offers valuable knowledge and practical tools for success.

Integrating AI and Behavioral Insights to Drive Economic Efficiency and Sustainability

Business and Management

Price-Earnings And Market Sentiment In IPO Market: During Changes In Malaysia’s Capital Market Structure.- Bridging Leadership and Innovation: The Role of Servant, Charismatic, and Ethical Leadership in Enhancing Professors’ Innovative Work Behavior.- The Likelihood of Financial Distress for Sponsor-Driven Market Listed Companies in Bursa Malaysia.- Improving the Teacher’s Quality of Work Life Balance by Analyzing the Relationship between Distributed Leadership and Social Capital.- Examining the Key Factors Influencing the Adoption of Innovations in Agriculture Sector: A Feasibility Study.- A Proposal of Cash Waqf-Mudarabah Model Through Fintech Application for Islamic Microfinancing in Malaysia etc...

This book explores the transformative potential of integrating behavioural economics and computational intelligence to address today’s complex sustainability challenges. Featuring contributions from global experts across disciplines—including economics, cognitive science, data analytics, and environmental studies—it presents innovative frameworks and AI-driven strategies for informed, ethical, and sustainable decision-making. Topics include behavioural modelling, neuroeconomics, policy simulation, and intelligent governance. Bridging theory and practice, the book is ideal for researchers, policymakers, and practitioners seeking data-informed solutions that centre both human behaviour and technological advancement. It is a timely resource for building equitable, efficient, and future-ready systems in a rapidly evolving world.

Intelligent Support for Personnel Decision Making and Socially Responsible HRM Using Big Data and AI.- Algorithm for Managerial Decision making Using Big Data and AI.- Big Data and AI as Human Machine Interaction Technologies in Managerial Decision Making within Digital Business.- The Mechanism of Automatization of Smart Business through Decision making and Management of Production Based on Big Data and AI.- Methodological Approach to Analyzing the Effectiveness of Intelligent Decision Support Using Big Data and AI.

This book focuses on the systemic scientific-methodological and practical exploration of organizational-technical and socio-economic issues related to the automation of decision-making in the smart economy under Industry 4.0 using big data and artificial intelligence (AI). The scientific novelty of the results presented in the book lies in uncovering the “black box” of decision-making automation in the smart economy through these technologies. The book clarifies the role and significance of big data and AI in decision-making within the smart economy, highlighting its fundamental importance. Additionally, the book thoroughly discusses international experiences in decision-making automation in the smart economy, with examples from Armenia, Kyrgyzstan, and other Central Asian countries, demonstrating its empirical value. The book reveals advanced organizational-economic models for decision-making based on big data and AI. It also presents the latest trends in the development of the smart economy using big data and AI. Moreover, the book explains the socio-ecological and legal aspects of the ethics in applying big data and AI technologies. Additionally, the book proposes prospective applied solutions for decision-making in the smart economy based on big data and AI. The target audience of the book includes scientists studying big data and AI.

Computational Linguistics

Basic Concepts.- Bipolar Complex Fuzzy Sets and Their Fundamental Properties.- Algebraic Structures Associated with Bipolar Complex Fuzzy Sets.- Distance Measures for Bipolar Complex Fuzzy Sets and Their Application in Automated Trading Systems, etc.

This book offers a comprehensive review of bipolar complex fuzzy sets theory. It investigates the algebraic structures such as lattices, semigroup, groups, semirings and rings. In turn, it discusses distance measures, similarity measures, correlation coefficient and entropy measures, together with the associated properties for bipolar complex fuzzy sets. Further, it describes the aggregation operators such as Hamy mean operators and power generalized Heronian mean operators in the environment of bipolar complex fuzzy set. Last but not least, it shows an application in supply chain management. The book addresses mathematicians and computer science working with imprecise, unclear information, and set the basis for new applications of fuzzy sets in the field of Intelligent Data-Driven Systems and Artificial Intelligence.

Cyber-Physical Systems

Blockchain Mechanisms for Metaverse Applications.- Emerging and Advanced Blockchain Mechanisms for Enhancing Interoperability, Security, and Decentralized Functionality in Metaverse Applications.- Quantum Computing and Blockchain: Advanced Security in Metaverse.- Emerging Paradigms and Advanced Architectures in the Blockchain Metaverse.- Quantum Computing and Blockchain: Advanced Security in the Metaverse.- Digital Twin Models in the Blockchain Metaverse Framework: Convergence of Virtual Replicas and Decentralized Ecosystems.- Artificial Intelligence and Blockchain in the Metaverse.- Emerging Trends in Blockchain Metaverse for Intelligent Sensor Network Applications.- Integrating Blockchain and Metaverse for Next Generation Smart Healthcare Networks.- Blockchain-Based Authentication in 6G Metaverse Using Hyperledger Indy for Self-Sovereign Identity (SSI).- Sustainable Blockchain and WSN architecture.- IoT in Blockchain-Enabled Metaverse Infrastructures.- Sustainable Blockchain and WSN Architecture for the Metaverse.- MetaBlockSphere Self Evolving Framework (MBSF) for Scalable and Interoperable Blockchain Metaverse Systems.- Integrating Explainable AI and Blockchain for Secure and Transparent Skin Disease Diagnosis in Virtual Healthcare.

The future of digital interaction lies at the crossroads of blockchain, metaverse, and smart wireless sensor networks. This book explores how these transformative technologies are reshaping communication, security, and trust in next-generation virtual ecosystems. With contributions from leading researchers and practitioners, the chapters provide insights into blockchain mechanisms, decentralized data management, AI-driven applications, and immersive smart environments. By blending theory with practical perspectives, the book offers a roadmap for building scalable, secure, and intelligent systems within the metaverse. Designed for researchers, engineers, professionals, and students, it highlights applications across healthcare, education, agriculture, smart cities, and industry 4.0, while addressing pressing challenges such as privacy, interoperability, and sustainability.

1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence.- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning.- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model.- 4.Generative Coding: Unlocking Ontological AI.- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation.- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation.- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques.- 8.Machine Learning Techniques for the Measurement of Software Attributes.- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison.- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention.- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model.- 12.An Overview of AI Workload Optimization Techniques.- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence.- 14.Applications of Machine Learning Algorithms in Open Innovation.

This book explores the transformative effects of AI and ML on software engineering. It emphasizes the potential of cutting-edge software development technologies such as Generative AI and ML applications. This book incorporates data-driven strategies across the entire software development life cycle, from requirements elicitation and design to coding, testing, and deployment. It illustrates the evolution from traditional frameworks to agile and DevOps methodologies. The potential of Generative AI for automating repetitive tasks and enhancing code quality is highlighted, along with ML applications in optimizing testing, effort estimation, design pattern recognition, fault prediction, debugging, and security through anomaly detection. These techniques have significantly improved software development efficiency, predictability, and project management effectiveness. While remarkable progress has been made, much remains to be done in this evolving area. This edited book is a timely effort toward advancing the field and promoting interdisciplinary collaboration in addressing ethical, security, and technical challenges.

Information and Control Theory Models of Cognitive Dysfunction

Control and Systems Theory

AI as Cultural Artifact.- Stress, Uncertainty, and Cognitive Failure in Siloed Enterprise.- In Praise of Mission Command and Workplace Autonomy.- Moving On.- Psychopathology isn’t a bug It’s a feature.- Hypervigilance, Psychic Numbing and Bounded Rationality.

This book views cognitive processes of bounded rationality at different scales and levels of organization as cultural artifacts, seen through the lens of the asymptotic limit theorems of information and control theories. The resulting probability models are novel, deep, and provide a route for the development of new statistical tools aimed at the address of real-world, real-time phenomena ranging across individual minds, institutions, newly-developed machine cognition, and their various composites. A central finding is that, for any and all cognitive systems, culture-bound syndromes of ‘psychopathology’ aren’t bugs, but arise as inherent features of real-world challenge. This book is intended both as a tutorial and a toolbox for scientifically literate students, researchers, and practitioners. Potential applications range across the study and remediation of individual mental disorders, failures of machine cognition, and institutional dysfunction. This book is of particular use to those tasked with the maintenance of public health, public order, and collective security.

User Interfaces and Human Computer Interaction

Introduction.- Interacting Subjects in the Age of Artificial Intelligence.- The Rise of the Large Language Models.- What are LLMs anyway? (Not overly) technical introduction to LLMs.- Modeling (everything) in LLMs.- Psychology hidden in the (Language) Model.- Traits Models of Personality in LLMs.- Simulating Personalities.- Simulating Personalities in Healthcare.- Simulating Personalities in Business.- Simulating Personalities in Training & Education.- Simulating Personalities in Games.- Simulating Personalities in Art.- Simulating Personalities in Scientific Research.- Conclusion.

This book provides an innovative exploration into the realm of artificial intelligence (AI) by developing personalities for large language models (LLMs) using psychological principles. Aimed at making AI interactions feel more human-like, the book guides you through the process of applying psychological assessments to AIs, enabling them to exhibit traits such as extraversion, openness, and emotional stability. Perfect for developers, researchers, and entrepreneurs, this work merges psychology, philosophy, business, and cutting-edge computing to enhance how AIs understand and engage with humans across various industries like gaming and healthcare. The book not only unpacks the theoretical aspects of these advancements but also equips you with practical coding exercises and Python code examples, helping you create AI systems that are both innovative and relatable. Whether you’re looking to deepen your understanding of AI personalities or integrate them into commercial applications, this book offers the tools and insights needed to pioneer this exciting frontier.

.- Part I The Cognitive Memory. .- 1 Overview. .- 2 Cognitive Memory. .- PART II Autoassociative Neural Networks and Cognitive Memory Design. .- 3 The LMS Algorithm. .- 4 ADALINE. .- 5 Sigmodal ADALINE. .- 6 Backpropagation for Multi-layer Neural Networks. .- 7 Autoassociative neural networks. .- 8 The design of a cognitive memory, etc.

How does human memory work?  How does human pattern recognition work?  The book’s motivation is twofold, to add to knowledge in the field of neuroscience, and to design a highly simplified cognitive memory constructed using software and existing electronic components.  Readers are taken on an inspiring journey through the fundamentals of human memory, how it is constructed, and how it works in everyday life.  The book goes more in-depth into the human side of cognitive memory — how seeing, hearing, walking and speaking works.  Impairments in cognitive memory are also discussed.  Lastly, the book sheds light on how meaning is extracted from sensory inputs and from stored data. This book is not without controversy.  Neuroscientists accept the engrams (or memory traces) model that long-term memory is stored in the brain’s neural networks. The authors believe that long-term human memory is stored digitally, in the DNA of brain cells, and not in analog neural networks.  Further, the authors believe that innate knowledge of humans and animals is inherited, transmitted from parents to offspring at the moment of conception.  The single cell contains the innate knowledge in the DNA of its nucleus.   Memory is stored in DNA.  The brain’s neural networks are for access and retrieval of memory and not for actual storage.  This book offers a unique, inspiring reading to researchers and other readers interested in the science of memory.

Computational Analysis of Human and Organisational Decision Processes for the Control of Risk Management and Cybersecurity

Control and Systems Theory

A Guide to the Computational Analysis of Human and Organisational Decision Processes in Cyber Risk Management.- Rules and Realities in Human and Organisational Decision Processes for Cyber Risk Management.- Integration of Nonlinear Computational Methods and Risk Assessment Methods Applied for Cybersecurity.- Computational Analysis of Employees Learning from Phishing Simulations.- Adaptive Network Modeling for Computational Risk Management of Phishing Attacks in Cybersecurity.- Human-Centric Cybersecurity: Adaptive Modeling of Spear-Phishing Responses.

This book aims to present in much detail how complex adaptive processes in cybersecurity can be analysed based on multiple levels of adaptation and control in a multilevel adaptive dynamical system architecture. Furthermore, it is shown how such complex adaptive systems can be modelled by self-modelling temporal-causal network models. Here, the temporal dimension is an important factor for different timescales from responding to attacks in milliseconds or seconds to developing more secure infrastructure and well-educated employees in an organisation over months or years. From the applicational perspective, different well-recognised cybersecurity themes are addressed here such as phishing, using complex authentication procedures, cybersecurity in health care, threats far away (third-party contracting), and threats close by (insider threats). Moreover, for the organisational level joint decision-making for cybersecurity is addressed involving, for example, groupthink and organisational alignment.

A note on aggregation of T-fuzzy subgroups.- Independent subcontexts in fuzzy formal concept analysis .- Two sufficient conditions for having a maximal solution to fuzzy relational equations.- Towards compatible subcontext by means of classical attribute reduction.

This book focuses on collecting some of the best results with the goal, dealing with the important and efficient combination of mathematics and computational intelligence, with a stress of fuzzy systems, and fuzzy logic. Artificial intelligence appears nowadays very much in the media, and also in the practice. In addition, every day, thousands of automatic intelligent systems for decision making are designed and applied around the world. In their design, many advanced techniques based on mathematics and computational intelligence are considered and used. Therefore, it is fundamental to continuously work on novel approaches in these areas and research models and algorithms with foundation in the mutual relationship of these two fields.  Naturally, an important part of this issue focuses on the applications of these both areas, taking a special interest in the development and application of mathematics, artificial intelligence, and automatic reasoning tools applicable to Digital Forensics, also following the main objective of the COST Action DigForASP-CA17124.

Decision making assisted by generative artificial intelligence.- Characterization of SLM conversational systems models, overview.- Classification with Low Level Fuzzy Cognitive Maps.- Method for Revenue Assurance and Financial Fraud Alerting Supported by LDS Techniques.- Ecosystem IADESCom for conversational system construction.- Computational intelligence for decision making in project management environments.- A Efficient Model for Startups Creation with Low Risk and Uncertainty, Study Cases in IADES.- Decision Making in Artificial Intelligence Training Programs.- Platform for Project Management IADESPro, Supported by Artificial Intelligence.- Model for the Creation and Decision Making in Project Management Offices (PMO).- Assisted decision making in environments with uncertainty associated with sport and health.- Sport Customized Training Plan Assisted by Linguistic Data Summarization.- Differentiation of stroke type by supervised classification.- Sports Talent by Combining Computing With Word and Neutrosophic Theory.- Managing uncertainty in business decision-making.- Measurement of perceived quality in conversational systems (chatbots).- Extension of the similarity quality measure for Multi Target Prediction problems.- Framework for strategic planning and assisted by Artificial Intelligence.

This book is dedicated to all those interested in the application of computational intelligence techniques for decision-making in uncertain environments. The book is organized into four parts. The first part groups together four works related to conversational systems and decision-making using generative artificial intelligence. The second part includes four articles associated with decision-making in project-oriented environments. The third part includes three works related to decision-making in human health environments and decision-making in sports training. The fourth part of the book contains three articles associated with business decision-making. This book combines different artificial intelligence techniques for solving decision-making problems, among which the following stand out: generative artificial intelligence, linguistic data summarization techniques, neutrosophic theory, computing with words, among other techniques. The techniques proposed in the book aim to simulate human tolerance in decision-making processes in environments with uncertainty and imprecision. The authors of the book stand out for their extensive experience in the development of basic and applied applications of computational intelligence. The authors Pedro Y. Piñero Pérez, Iliana Pérez Pupo, Janusz Kacprzyk, and Rafael E. Bello Pérez have published several books associated with artificial intelligence and applied computational intelligence. They continue to work on fundamental and applied research on different artificial intelligence techniques to assist decision-making in different areas of knowledge. The authors thank all the engineers, professors, and researchers without whose efforts this book could not have been written.

Digital and Analog Signal Processing

Unveiling Hyperspectral and Multispectral Image Processing: A Contemporary Exploration of Band Selection, Dimensionality Reduction, and Compressive Sensing Techniques.- Hyperspectral Image Registration: State-of-the-Art and Future Challenges.- Hardware Accelerators for Hyperspectral Image Classification in Space: A Survey on Deep Learning based Architectures for Hardware Accelerators.

Hyperspectral imaging has revolutionized fields ranging from environmental monitoring to medical diagnostics by providing detailed spectral information across vast datasets. However, the complexity and high dimensionality of hyperspectral data pose significant challenges in analysis and interpretation. “Computational Intelligence based Hyperspectral Image Processing" is a comprehensive guide to overcoming these challenges by leveraging the latest computational intelligence techniques. This Volume comprises of 9 chapters that illustrate how computational intelligence-based solutions has been successful to improve efficiency and productivity, quality control, and in enhancing innovation and competitiveness with cost reduction. This book is an essential resource for researchers, professionals, and students working in remote sensing, image processing, and computational intelligence. Whether you're new to the field or looking to deepen your expertise, this book provides the tools and knowledge you need to succeed in the fast-evolving world of hyperspectral imaging.

Computational Intelligence for a Greener Future: Innovations in Renewable Energy Systems

Integrating AI and Behavioral Insights to Drive Economic Efficiency and Sustainability

Energy Policy, Economics and Management

A Review of the Genetic Algorithm Approach in Predictive Maintenance and Energy Forecasting.- Deep Reinforcement Learning in Energy Management System for Fuel Cell Hybrid Vehicles: A Review on Reward Design and Testing Framework.- Forecasting Renewable Energy and Electricity Consumption using Evolutionary Computation.- Enhancing EV Battery Safety: SOH Estimation with Machine Learning.- Short-Term Renewable Energy Forecasting Methods Using Artificial Neural Networks: A Comprehensive Review.- etc...

This book aims to explore the intersection of computational intelligence techniques and renewable energy technologies, serving as a valuable resource for researchers, engineers, and policymakers. By compiling cutting-edge research and innovative applications, it seeks to demonstrate how CI can contribute to a more sustainable future, highlighting both theoretical advancements, practical implementations, and future directions. Nowadays, as the world faces the pressing challenges of climate change and the depletion of fossil fuel resources, the shift toward decarbonization and renewable energy systems has become a vital priority. Innovations in computational intelligence (CI) offer promising opportunities to optimize and manage energy production, distribution, and consumption. By leveraging techniques like nature-inspired optimization algorithms, fuzzy methods, machine learning, and clustering, researchers and practitioners can enhance the efficiency and management of renewable energy systems.

Impact of interactive digital environments: literacy development in middle school.- Real Time Carbon Footprint Indexing for Sustainable FinTech Reporting.- The Impact of Digital Transformation on Sharia Compliance in Jordanian Islamic Banks.- Islamic Fintech and Digital Banking: Shariah-Compliant Innovation in Malaysia’s Financial Sector.- Using UTAUT2 to study the impact of Chatbot on the development of M-commerce in Jordan.

This book offers a complete and forward-looking discussion of how artificial intelligence and advanced analytics are changing modern business decision-making. This book combines foundational theory with leading-edge application in computational intelligence to offer a structured approach to intelligent, data-driven enterprise systems. This book brings together contributions from leading thinkers in the field and covers foundational methodologies including machine learning, deep learning, neural networks, fuzzy logic, soft computing, evolutionary and genetic algorithms, and hybrid intelligent systems. It also explores other important areas including predictive and prescriptive analytics, optimization algorithms, big data analytics, and data mining techniques to support intelligent business systems. This book also explores how these methodologies are applied in financial analytics, risk management, portfolio optimization, customer analytics, market segmentation, demand forecasting, supply chain analytics, and operations optimization. It also places special emphasis on explainable artificial intelligence, intelligent decision support systems, and enterprise performance analytics to ensure that advanced computational models are transparent, reliable, and strategically aligned to enterprise goals. This book is intended for researchers, postgraduate students, data scientists, financial analysts, operations managers, digital transformation leaders, and other professionals who are interested in computational intelligence and its application in intelligent business systems. It offers a structured approach to intelligent enterprise systems, which can increase competitiveness, improve forecasting, optimize enterprise resources, and support digital transformation in a sustainable manner. With its blend of rigorous analytical techniques and practical business insights, this book is an essential reference to how intelligent algorithms and decision systems are changing the future of business analytics.

Natural Language Processing (NLP)

Natural Language Generation from Wikidata - Architecture, Scalability and Challenges.- A Survey on Language Completeness of the Lambek Calculus and Its Extensions.- On Algebras and Phase Spaces for Linear Logics.- Applying Quantales to Lambek Calculi with Cyclic Negation.

This book covers theoretical work, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language and reasoning methods are proliferating. Adequate coverage of related works and advanced applications encounter difficult problems related to the phenomena of partiality, underspecification, perspectives of agents, and context dependency. These phenomena are signature features of information in nature, natural languages, and reasoning. The goal of this collection of works is to promote advanced, intelligent approaches of Mathematical Logic and its applications to systems of proofs and verification, Mathematical and Computational Linguistics, and Natural Language Processing (NLP). The intended readers of this book are researchers in Mathematical Logic and Computer Science, as well as Mathematical and Computational Linguistics, to advance work on theories and applications to Artificial Intelligence (AI), Natural Language Processing (NLP), and related subjects.

Computational Science and Engineering

Advanced Study on the Evaluation of Acoustic Characteristics for Sound Absorbing Foams.- Analysis of Changes of Elastic Constants in Multiphase Polycrystalline Materials.- Analysis of the Physical and Mechanical Properties of Steel Used for Cold Forming.- Application of Artificial Intelligence in Construction Equipment  Management with the Support of Autonomous Drones.- Numerical Modeling and Performance Evaluation of PEMFCs: A Multiphysics Approach.- Assessment of Cylindrical Surfaces Accuracy in PLA Parts Produced by Material Extrusion.- Complementary Methods for Assessing Integrity of Layered Mesostructured.- Concepts, Methods, and Solutions to Produce Sound absorbing and Sound-Insulating Panels for Highways.- Design and Structural Optimization of a Four Axis Robotic Manipulator.- Evaluation of the Performance of Sound absorbing Materials in the Reverberation Chamber at ICECON.- Influence of Material Selection on Fatigue Life Prediction of Automotive Exhaust Manifolds Using the Smith at on popper Parameter.- Models for the Study of Elastic Multibody Systems with Voids.- Surface Defects Identified on Parts Obtained by the Injection Molding Process.- Tribological Analysis of Joints in Construction Exoskeletons Manufactured by 3D Printing.- Validation of an In-House Developed Topology Optimization Algorithm.- Comparative Analysis of the Thermal and Hygrometric Behavior of a Residential Building Before and After Thermal Insulation.- Stresses in the Sealing Elements of a High performance Spherical Valve.- Study of the Mechanical Properties of the Structure of a 3D Printed Materials Made of PLA and PETG.

This book provides a state-of-the-art overview of computational methods and virtual engineering tools, featuring contributions from leading international researchers presented at the 11th International Conference on Computational Mechanics (COMEC-2025). It explores emerging trends, modeling strategies, and practical implementations in fields such as structural analysis, vibration control, optimization, additive manufacturing, and energy systems. By addressing both fundamental principles and applied research, the volume serves as a valuable guide for academics, engineers, and industry professionals engaged in simulation, design, and performance assessment of complex mechanical systems.

The Enhancement of K-Nearest Neighbourhood in Optimizing the Selection of Distribution Centre for Disaster Relief Operation.- Evaluating Advanced Dimensionality Reduction Techniques for Effective Clustering of High-Dimensional Adolescent Development Data.- A Decade-Long Bibliometric Analysis of Electronic Health Record Research Trends (2014-2024).- Performance Analysis of Deep Learning Models in Detection and Counting of Bees for Hive Monitoring.- Smarter Learning Environment: Exploring the Need for an Intelligent Tutoring System in Iraqi Universities.- Automated Mandible Segmentation from Computed Tomography Scans Using U-Net and U-Net Transformer.- AI-Enhanced Cybersecurity: A Comprehensive Review of Techniques and Challenges.- A Bibliometric Review of Trends and Insights of Internet of Things on Cybersecurity Issues.- Artificial Intelligence Implementation in the Tourism Industry: A Literature Review.- Artificial intelligence (AI)-enabled mobile fitness apps and goal attainment: Systematic Literature Review.- Artificial Intelligence in Energy Informatics: An In-depth Review.- AI Strategies for Handling Disciplinary and Cyber Bullying in Schools.- Artificial Intelligence and Industry 4.0: Catalysts for Sustainable Organizational Development.- Text Classification: A Comprehensive Survey from Traditional Approaches to Deep Learning Methods.- Development of Smart Contract Blockchain Integrated Model (Lightblock) For IOT Applications.- Hybrid Deep Learning for Detecting Hate Speech Across Social Media Platforms.- AI-DRIVEN SMART CITY SECURITY AND SURVEILLANCE SYSTEM: A BIBLIOMETRIC ANALYSIS.- Adoption and Implementation Trends of Vehicle-to-Everything (V2X) Technologies: A Comprehensive Bibliometric Analysis.- Artificial Intelligence Methods and Image Recognition Techniques in Ophthalmic Robotic Surgery: A Review.- Examining Factors Influencing Behavioral Intentions to Use Artificial Intelligence in the Recruitment Processes of United Nations Agencies.- A Study on the use of Enterprise Resource Planning to Improve the Sustainability of Malaysian SMEs.- Determinants of Biometric as a Service (BaaS) Adoption in the Telecommunication Companies: The Mediating Effect of Perceived Trust.- The State of the Art in AI-Based Phishing Detection: A Systematic Literature Review.

This book offers a deep dive into cutting-edge advancements and emerging trends that are reshaping industries and society. From healthcare and cybersecurity to disaster management and energy informatics, this book brings together expert insights and real-world applications, showcasing AI’s profound impact and boundless potential. Perfect for researchers, practitioners, and students alike, this comprehensive guide not only highlights current innovations but also provides a forward-looking perspective on how AI will drive the next wave of technological breakthroughs.

Crime Control and Security

Why is Cyber Espionage one of the top threats to Australia’s National Security?.- The Digital Face of Espionage: Analyzing Cyber Threats to National Security.- Cyber-Surveillance Technologies, National Security, and Human Rights in Africa.- The Underbelly of Cyber Espionage.- Intelligence Conspiracy & Collaborations through Cyberspace: How New Professional & transformative Cybercrimes Affect Moroccan National Security?.- AI and Espionage: AI for Spyware Development.- Historical Unconventional Warfare Employed in Cyber Warfare.- “Cambridge Five” Redux: Legal and Policy Implications of Cyber Espionage for Universities.

This book is a vital resource for policymakers, legal professionals, and government leaders navigating the evolving landscape of cyber threats and their impact on national security. Cyber espionage poses a direct and growing threat to both national and international security. In the era of information warfare and cognitive warfare, traditional cybersecurity measures—such as penetration testing and Red Team vs. Blue Team exercises—are increasingly inadequate for addressing complex, large-scale attacks. New approaches are needed to counter these threats to cyber security. Cyber security does require legal clarity. The legal frameworks for prosecuting cybercrime also require greater clarity and accessibility. This book bridges the gap between cybersecurity practice and legal policy, offering insights that are essential for professionals working at the intersection of technology, law, and governance.

Social Engineering in the Era of AI Unmasking the Changing Face of Cyber Threats.- Unlocking the Human Element in Cybercrime Exploring the Realities of Victims.- Unveiling the Human Factor in Cybercrime and Cybersecurity Motivations, Behaviors, Vulnerabilities, Mitigation Strategies, and Research Methods.- Cyberspace Reimagining Cybersecurity and Its Impact on State Sovereignty.- Global Cybersecurity Governance The Role of International Norms in Cyberspace.- Cybercrime from Controllability to Subjectivity A Serious Call for Legislative Review.- Privacy Paradigm Shift Zero Knowledge Proofs in Criminal E evidence Collection.- The Implications of AI Algorithms on Cybercrime and Digital Privacy A Study of ChatGPT and DarkBERT.- Unveiling the Traits of X/Twitter's Terrorism Propaganda Accounts.- Cryptocurrencies and the Dark Web A Gateway to Money Laundering.-Cybercrimes and Drone Forensic Tool Development.- Analyzing the Procedure for Investigation in Cybercrime and Admissibility of Electronic Evidence.- United Nations and Beyond Legal Strategies for Defending Critical Energy Infrastructure against Cyber Attacks.- Cybercrime during COVID 19 Pandemic in Urban Kano, Nigeria.- Real Time Anomaly Detection in Low Light Environments for Enhanced Cybercrime Mitigation.

The book offers a comprehensive examination of the ever-evolving landscape of cybercrime. Bringing together experts from various legal and technical backgrounds, this book presents an integrated approach to understanding the complexities of cyber threats. It explores various topics, from social engineering and AI-enhanced cybercrime to international cybersecurity governance and the Dark Web’s role in money laundering. By offering theoretical insights and practical case studies, the book is a vital resource for policymakers, cybersecurity professionals, legal experts, and academics seeking to grasp the intricacies of cybercrime. This book includes 15 rigorously selected chapters from 31 submissions, chosen through a double-blind peer review by an international panel of referees. Each chapter delves into a unique aspect of cybercrime, from the role of AI in modern cyber threats to the emerging legal challenges posed by global cybersecurity norms. Contributors from around the world provide diverse perspectives, making this book a global reference on the topic of cybercrime and digital security. As cybercrime continues to grow in both complexity and impact, this book highlights the critical importance of collaboration between legal and technical experts. By addressing the key challenges posed by cyber threats, whether through AI, cryptocurrency, or state sovereignty—this book provides readers with actionable insights and strategies to tackle the most pressing issues in the digital age.

Mathematical Models of Cognitive Processes and Neural Networks

1. Introduction to Cybernetics, Cognition, and Machine Learning.- 2. Foundations of Cybernetics.- 3. Cognition and Cognitive Systems.- 4. Introduction to Machine Learning.- 5. Cybernetics and Machine Learning Integration.

This book presents the fascinating intersection of human cognition and artificial intelligence. Written by leading experts in the fields of cybernetics, cognitive science, and machine learning, this book seeks to bridge the gap between these disciplines and explores the synergies that emerge when humans and machines work together. The book examines the challenges posed by biased data, lack of transparency, and the "black box" nature of some machine learning algorithms. It proposes novel ways to address these issues and foster greater trust and accountability in AI systems. Drawing on cutting-edge research and real-world case studies, it presents a comprehensive and forward-looking perspective on the future of AI and its impact on society. In conclusion, this book offers a compelling exploration of the synergy between human cognition and machine learning, providing insights that are relevant to scholars, researchers, policymakers, and anyone interested in the transformative potential of artificial intelligence.

Optimizing The Selection Of Circular Economy Indicators Through A Large-Scale Comprehensive Minimum Cost Consensus Model.- Decision Making For Uncertainty In Spatial Data Using Intuitionistic Fuzzy Sets.- Q-Rung Orthopair Fuzzy Electre Based Multicriteria Decision Model For Green Supplier Selection.- An Integrated Intuitionistic Fuzzy Critic-Aras Method And Its Application in Health-Care Waste Treatment Method Selection.- Threat Evaluation For Dynamic Targets Using Q-Rung Orthopair Soft Rough Fuzzy Multi-Criteria Decision-Making.- Renewable Energy Policy Selection Based On Fermatean Fuzzy Promethee Method.- Multiple Criteria Decision-Making Method Based On Spherical Fuzzy Aggregation Operators With Application In Covid-19 Testing Facility.- Archimedean Norms-Based Choquet Integral Operators For Single-Valued Neutrosophic Linguistic Sets And Their Application To Decision Making.- Sustainable Oil And Gas Supplier Selection Based On Pythagorean Fuzzy Topsis and Vikor Method: A Comparative Analysis.- Failure Mode and Effect Analysis (Fmea) Of Different Types Of Cancer Treatments.- A Reliability Allocation Strategy For Gear Transmission System Of High-Speed Train Based On Prospect Theory.- Cubic Analytic Hierarchy Process With Application In Decision Making.- Determination Of Best Renewable Energy Sources In India Using Swara[1]Aras In Confidence Level Based Interval-Valued Intuitionistic Fuzzy Environment.- Modeling And Analyzing Innovation Diffusion Process Through Multi Stage Approach.- Decision Support System For The Assessment Of Emergency Logistics Supplier By Using An Integrated Q-Rung Orthopair Fuzzy Marcos Method.- Uncertainty In Mathematics Educations: How To Make A Decision With Sustainable And Gamified Mcda/F-Dematel Method.- Feature-Based Inventory Management With Demand Substitution: A Grg-Based Approach Incorporating Fuzzy Constraints.- An Eoq Model Of A Fresh Product Having Variable Demand Under Trade Credit Policy Considering Default Risk.- Decision Making Process Under Uncertain Domain Of Pythagorean Fuzzy Sets Based On An Enhanced Similarity Operators Heuristic Solutions For Interval Valued Games.- On The Study Of Multi-Objective Adjacent Only Quadratic Minimum Spanning Tree Problem Under The Paradigm Of Fuzzy Environment.- Root Hair Algorithm: A Swarm Intelligence Algorithm.

This book focuses on cutting-edge developments in optimal decision-making incorporating modeling and optimization for determining renewable energy sources, supply chain management, and environmental planning under uncertainty. It addresses mathematical models of cost-effective management policies. This book presents the best decision-making practices for solving real-world challenges. This book provides access to an invaluable collection of various decision-making issues that scholars and industry practitioners use as a reference. The readers are able to understand how decision-making problems are formulated under uncertainty and how to use right optimization strategies to fix problems.

Mathematical and Computational Engineering Applications

Using Shapley Additive Explanations for supporting individual and group recommendation: survey and new perspectives.- A P-based multiple criteria ordinal classification method where classes are described by subsets of “non-central” profiles.- A Framework of Targeted Marketing Based on the Outranking Approach.- SINDy meets Schelling. Transforming Agent-Based model spatial outputs into Dynamical Systems.- Optimizing Electric Vehicle Routes: A Hybrid Approach of Genetic Algorithm with Q-Learning.- Enhancing Clinical Decision-Making: An Integrated Approach Using ISPS and KMoS-SSA Framework.- New Rainfall Missing Values Estimation Proposal: Normal Ratio with Additive Correlation.

This book delivers actionable insights through 21 peer-reviewed chapters featuring new methods, models, and applications based on computational intelligence. Discover cutting-edge tools to support smart, efficient decision-making in complex, real-world scenarios. Organized into three parts—prescriptive analytics, soft computing models, and practical case studies—it spans domains such as healthcare, energy, mobility, finance, and public services. Readers will find innovative approaches using fuzzy logic, machine learning, optimization, and hybrid systems. Ideal for researchers, practitioners, and graduate students, this book offers practical solutions for modern decision challenges across industries.

Computer-Aided Diagnosis System for Liver Fibrosis Using Data Mining Techniques.- Deep learning for sequence alignment.- Protein Structure Prediction: A Computational Approach to Unravelling Molecular Mysteries.- Management of cancer‑associated thrombosis and related complications.- Integrating Machine Vision for Enhanced Biomedical Signal and Image Processing.- Diagnostic strategies using AI and ML in cardiovascular diseases: Challenges and Future Perspectives.- Analytics of medical data using Cognos.- Analytics of Medical Data.- Integrating Deep Learning into Electronic Health Records: Opportunities and Challenges.- Heart disease prediction using machine learning algorithms and quantum variational classifier.- Deep Learning Based Approaches for Early Detection of Parkinson’s  Disease.- Exploring Recent Developments in Radiographic Chest Disease Detection through Deep Learning Models.- Nano encapsulation for the targeted drug delivery to enhance the efficacy of drugs.- Early Detection of Brain Tumor Automation System using Hybrid SMOTE ENN And Deep Convolution Neural Network Technique.- Advancements in Medical Device Integration Technology and its Impact on Healthcare.- Image Classification To Detect Breast Cancer Using Transfer Learning.- Medical Computer Vision.- Machine Vision & Biomedical Signal and Image Processing.- Metaheuristic Algorithms for Solving Various Optimization Problems: Comprehensive Review.- Maximizing Renewable Energy: Harnessing the Power of Metaheuristic Optimization Techniques.- Review on occurrence of skin lesions due to increased ultra-violet rays for diagnosis of skin cancer to sustain life using deep learning model.- Early Detection of  PCOD Automation System using Deep Convolution Neural Network Technique.- Design of Nano Magnetorheological fluid damper - based Leg prosthesis for Amputees.- Learning Analytics in Higher Education: Promises and Challenges.- Neural Network Fusion for Forgery Detection in Digital Images.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision.  Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

Graduate/advanced undergraduate textbook

Computational Mechanics Meets Artificial Intelligence.- Neural Networks.- Machine Learning in Computational Mechanics.- Methodological Overview of Deep Learning in Computational Mechanics.- Index.

This book provides a first course without requiring prerequisite knowledge. Fundamental concepts of machine learning are introduced before explaining neural networks. With this knowledge, prominent topics in deep learning for simulation are explored. These include surrogate modeling, physics-informed neural networks, generative artificial intelligence, Hamiltonian/Lagrangian neural networks, input convex neural networks, and more general machine learning techniques. The idea of the book is to provide basic concepts as simple as possible but in a mathematically sound manner. Starting point are one-dimensional examples including elasticity, plasticity, heat evolution, or wave propagation. The concepts are then expanded to state-of-the-art applications in material modeling, generative artificial intelligence, topology optimization, defect detection, and inverse problems.

Mechanical Power Engineering

Microgrid Structure and Control Methods A Review.- Microgrid System Operation.- Energy Management of Residential Microgrid under Step-rate and Time-of-Use Tariffs.- Microgrid Energy Flow Management Considering Cost, Reliability and 𝑪𝑶2 emissions.- Model Predictive Control Based Strategy for Hybrid Microgrid.- A Survey on Home Energy Management.- DSM Strategy Using Optimal Day-Ahead Load Shifting.- Peak Load Minimization Through Electricity Pricing and Cooperative Load Scheduling.- Collective Energy Consumption Scheduling for Aggregated Smart Buildings.- Coordinated Blockchain Technique for Load Scheduling within Smart Buildings.- Efficient Charging Scheduling of Aggregated Electric Vehicles.- Summary and Scope.

This book covers the optimization of the energy flow management of a grid-tied photovoltaic-wind-battery energy storage system and the development of demand response algorithms for a single customer at the lowest level and extended it to a large community of participants at the highest level. Hence, this book consists of two parts. The first part is devoted to the design of energy management strategies for a residential PV-WT system, while considering time of use and feed in tariff schemes. Further, the developed management algorithms mitigate the greenhouse gas emissions. Additionally, a closed-loop control based-model predictive control scheme, to forecast the system behavior and deal with renewable energy resources and load disturbances, is elaborated. The second part is dedicated to developing effective demand side management techniques and cooperative energy scheduling in the framework of collective smart-buildings. Cooperative energy scheduling programs are designed while considering three objectives: minimizing peak load, minimizing electricity cost, and avoiding the occurrence of rebound peaks. Optimal consumption decisions are examined in residential, commercial, and industrial areas. Besides, the impact of electric vehicle charging methods is investigated for different EV penetration levels. Also, a game theory approach for energy consumption scheduling of a community of smart microgrids is built. In summary, this book successfully establishes the important synergy between microgrid energy management and demand response at the level of a smart home and an aggregated building community. The above aspects are illustrated in this book in 12 chapters. The scope is broad. This book chapters are very effective in theoretical analysis and design of optimization algorithms in the realm of smart grids. This book is intended to provide advanced undergraduate, graduate students, engineers, researchers on energy management within smart homes and clustered buildings, with a comprehensive understanding of various methods and designed algorithms. These strategies and algorithms aim to deal with defies ranging from augmented adoption of microgrids to the introduction of demand response approaches, to enhance the grid viability and resilience.

Developments in Information and Knowledge Management Systems for Business Applications

Business and Management

.- Research on the Service Sector in the Modern Economy.- Management of marketing communication in state administration.- Aspects of strategic management and online marketing.- Intelligent information system for controlling international innovation activities of an enterprise.- Optimization modeling of urgent investment tools of crisis management at enterprises.- Assessment of the Impact of Biofuel Production on the Sustainable Development of Enterprises in the Agrarian Sector of Ukraine.

In this series, we delve into the challenges and opportunities of managing business information efficiently. We explore various aspects of information processing for effective enterprise management, including methods, techniques, and strategies. Our goal is to provide solutions for competent information management that increases business usage, while also analyzing strategies to reduce data loss and improve customer satisfaction and maintenance levels. Our book offers practical knowledge on different facets of information and knowledge management in businesses, such as information processing theory and models, the benefits and implementation challenges of information/knowledge business, and information management methods for creating a global information society. We also cover topics such as collecting and analyzing data for enterprise management, modern business intelligence solutions and data management, information marketing, and innovative development of the enterprise information system. Our focus is on applications, benefits, and encounters within the field of efficient business information processing, and we offer solutions to increase performance using the latest IT technologies.

Developments in Information and Knowledge Management Systems for Business Applications

Business and Management

.- Research on the Service Sector in the Modern Economy. .- Management of marketing communication in state administration. .- Aspects of strategic management and online marketing. .- Intelligent information system for controlling international innovation activities of an enterprise. .- Optimization modeling of urgent investment tools of crisis management at enterprises. .- Assessment of the Impact of Biofuel Production on the Sustainable Development of Enterprises in the Agrarian Sector of Ukraine.

In this series, we delve into the challenges and opportunities of managing business information efficiently. We explore various aspects of information processing for effective enterprise management, including methods, techniques, and strategies. Our goal is to provide solutions for competent information management that increases business usage, while also analyzing strategies to reduce data loss and improve customer satisfaction and maintenance levels. Our book offers practical knowledge on different facets of information and knowledge management in businesses, such as information processing theory and models, the benefits and implementation challenges of information/knowledge business, and information management methods for creating a global information society. We also cover topics such as collecting and analyzing data for enterprise management, modern business intelligence solutions and data management, information marketing, and innovative development of the enterprise information system. Our focus is on applications, benefits, and encounters within the field of efficient business information processing, and we offer solutions to increase performance using the latest IT technologies.

Fundamentals and Architecture OF Digital Twins in the Context of Geospatial Data.- Emergent Technologies Driving Digital Twins for Geo Spatial Science.- Advancements in 3D Modeling and Visualization for GIS Digital Twins.- A Geo-Artificial Intelligence (AI) based Digital Twins: A Synergistic Approach to Spatial Data Science.- A digital simulation tool for Geospatial research metric evaluation.- Digital Twins in Geospatial Science for Urban planning and development.- GIS-based digital twins for Environmental Monitoring and Climate Change.- Infrastructure Management and Asset Tracking using Digital Twins for Geo-spatia science.- A Framework for Object Detection in Digital Twin-Driven Geospatial Systems.- Hypergraph Convolutional Refinement in Latent Space for Underwater Digital Twins.- An Artificial Intelligence based Early Crop Disease Detection for GIS Digital Twins.- The Role of Artificial Intelligence (AI) and Geographic Information Systems (GIS) in Powering Digital Twins for Digital Plant Pathology.- Digital Twins Revolutions-Are we ready for it?.

This book explores utilization of digital twin technology and geospatial science for the digital transformation of urban planning, environmental monitoring, infrastructure, and disaster response using a series of present tools of data collection and applications. It covers transformational impact of digital twins, from their origin to their real-time applications in various domains. It presents real-time case studies focusing on real-world outcomes while addressing the potential risks. It also covers challenging issues related to geospatial twin technology and its solutions to handle relevant challenges. This book comprises several chapters that provide an overview of basic concepts from historical background and architecture twin technology with geospatial data, highlighting its capabilities for modeling, simulation, and optimizing complex systems. It also investigates the technical and ethical barriers to the widespread adoption of digital twin technology in geospatial science. It is a valuable source for students, engineers, professionals, researchers as well as organizations.

Introduction to Industry 4.0: Practical issues and challenges.- Explainable AI Principles for Industry 4.0.- Explainable AI principles of building industry 4.0.- Transformative Healthcare: Industry 4.0 Integration of Distributed Deep Learning and XAI.

This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.

Data and Information Security

Introduction.- Introduction to Distributed Ledger Technology (DLT).- Exploring the Evolution of DLT in Communication.- Foundations of Blockchain Technology.- Unraveling the Core Concepts of Blockchain.- Exploring Different Types of Blockchains.- Blockchain and Communication Infrastructure.- Role of Blockchain in Modern Communication Networks.- Integrating Blockchain into Existing Communication Frameworks.- Decentralization and Security.- Securing Communication Networks with Blockchain.- Smart Contracts in Communication.- Exploring Smart Contracts: Definitions and Features.- Smart Contract Use Cases in Communication Systems.- Privacy and Data Protection.- Implementing Privacy-Preserving Techniques in Communication.- Interoperability Challenges in Blockchain-Enabled Communication.- Standardization Efforts for Seamless Integration.- IoT and Blockchain Integration.- Case Studies of Blockchain-Enabled IoT Communication.- Token Economy in Communication Networks.- Enabling Micropayments and New Payment Models.- Case Studies: Real-World Applications.- Blockchain Solutions for Secure Messaging and VoIP.- Supply Chain Tracking and Management with Blockchain.- Challenges and Future Directions.- Scalability and Performance Issues in Communication Blockchain.- Emerging Trends and Future Prospects in DLT and Communication.- Implementing Blockchain in Communication Systems.- Practical Steps for Integrating Blockchain: A Guide for Practitioners.- Overcoming Implementation Hurdles and Best Practices.- Conclusion.

This book offers a comprehensive exploration of distributed ledger technology (DLT) in communication. The book first delves into the foundational principles of blockchain technology, unravelling its intricacies and explaining its relevance in the communication landscape. The book goes on to examine the evolution of DLT and addresses its practical implications, enabling readers to grasp the profound impact it has on communication infrastructure. Key topics covered include the integration of blockchain into communication networks, leveraging its decentralized nature to enhance security and privacy. The book explores the deployment of smart contracts to automate processes, while spotlighting critical issues such as privacy preservation and data protection in the blockchain era. The book presents a definitive guide that bridges the gap between theoretical understanding and real-world application, catering to professionals, researchers, and enthusiasts seeking to harness the transformative potential of blockchain technology within the realm of communication. Offers a comprehensive exploration of distributed ledger technology (DLT) in communication; Unveils blockchain's impact on communication, merging theory with hands-on implementation; Demonstrates the integration of blockchain into communication networks and the deployment of smart contracts.

Emotion and Facial Recognition in Artificial Intelligence: Sustainable Multidisciplinary Perspectives and Applications

Introduction to Emotion Expression and AI.- Theories of Emotions in Psychology.- Classical Face Recognition: Geometric Models, Subspace Techniques and Local Descriptors.- Integrating Acoustic Feature Extraction And LSTM Models For Emotion Classification In Speech.- Emotion Detection in Human-Machine Interaction Using ML Techniques.- Real Time: 3D Facial Expression Recognition Using Improved Alex-Net Convolutional Network via Deep-Emotion.- Feature Aggregation for Efficient Continual Learning of Complex Facial Expressions.- Advanced Techniques In Facial Landmark Detection And Feature Extraction For Emotion-Aware AI Systems.- Detection of Micro-expressions and Subtle Emotions.- Navigating the Future of Emotion AI: Technical Barriers, Ethical Concerns, and Sustainable Advancements.- Emotion AI in Mental Health.- A Conceptual Framework for Adaptive Student Assessment Using AI-Driven Recommendations and Facial Expression Recognition.- Integrating AI-Driven Facial Emotion Recognition into E-Learning Systems: Sustainable Educational Markets through Interdisciplinary Innovations.- Integrating Positive Emotions to Support Self-Directed Learning.- Emotion AI in Business and Customer Services.- Deep Learning Approaches and Technical Challenges in Facial Emotion Recognition (FER).- Emotion Recognition and the Law: Bridging Technology and Human Rights.- Emotion AI: Challenges and Future Directions.

This book explores one of the most transformative frontiers of modern science the intersection of emotion, facial recognition, and artificial intelligence, in an age where machines are evolving from analytical processors to emotionally perceptive entities. Bringing together leading voices from AI research, psychology, neuroscience, human–computer interaction, and policy, this book delivers an in-depth examination of how intelligent systems are learning to understand, interpret, and simulate human emotions. From theoretical foundations to advanced deep learning models, readers gain a panoramic view of how emotional data can reshape education, health care, mental health diagnostics, business intelligence, governance, and law enforcement. Spanning eighteen comprehensive chapters, the book explores: •    Computational models of emotion and facial recognition using cutting-edge AI architectures. •    The role of affective computing in sustainable and ethical technology ecosystems. •    Applications of emotion AI in mental health, adaptive learning, and personalized education. •    Forensic and legal implications of emotion recognition, privacy, and data governance. •    The future of emotionally intelligent and human-centric AI systems. Each contribution in this edited book offers a multidisciplinary and globally relevant perspective, blending empirical research with ethical reflection and sustainable design principles. A must-read for academicians, researchers, industry professionals, policy experts, and AI practitioners, this book serves as both a scholarly reference and a visionary roadmap toward emotionally aware and ethically responsible AI. As technology begins to feel, not just think, this book invites readers to explore how the fusion of emotion and intelligence can redefine the future of human–machine coexistence

Biomedical Engineering and Bioengineering

Integrating Person Centric Healthcare with Hospital Management Systems A Comprehensive Analysis for Enhanced Emergency Case Management.- Exploring Machine Learning Techniques for Predicting Brain Stroke from Heart Disease Insights from ANN, NCF, and XG Boost.- Optimizing Lung Cancer Prediction Models A Hybrid Methodology Using GWO and Random Forest.- Person Centric Healthcare A Deep Survey on the Integration of Machine Intelligence Technology.- Blockchain Driven Security for Protected Health Information in Person Centric Smart Healthcare The BHKEM Framework with Ambient Assistive Technology.- Privacy Preserving Federated Learning with Homomorphic Encryption Alzheimer’s Detection Use Case.- Enhancing Person Centric Health Care for Diabetes Prediction A Comparative Study of LightGBM, XGBoost, and Hybrid LIGB Model.- Unveiling Multi Disease Prediction Using Machine Learning A Comprehensive Study.- Performance Evaluation of Different Deep Learning Models for Automatic Yoga Pose Classification.- Recognizing Human Activities in Ambient Assisted Environment from Wearable Sensor Data Using Gramian Angular Field and Deep CNN.- Detection of Hepatocellular Carcinoma Using Machine Learning and Small Set of Clinical Features.- Enhancing Patient Data Clustering in Smart Healthcare A Semi Supervised Approach for Person Centric HealthCare Treatment and Resource Optimization.- Case study to role of Large language models in prediction of the future illness.- Person Centric Care for Alzheimer's Patients A Multi Criteria Decision Support Framework for Activity Recognition.

This book focuses more on the transformative impact of person-centric health care, where it explores cutting-edge advancements in integrating artificial intelligence and machine learning to deliver personalized and efficient care. Key topics include the application of predictive models for critical health conditions such as brain stroke, lung cancer, diabetes, and Alzheimer's, as well as the integration of secure frameworks to protect sensitive patient data. The book also covers advanced techniques for recognizing human activities in ambient environments, optimizing patient data clustering, and evaluating deep learning methods for unique use cases like yoga pose classification and resource optimization in smart healthcare. Designed for healthcare professionals, researchers, data scientists, and technologists, this book presents a harmonious blend of technical insights and practical applications, emphasizing person-centric approaches. By focusing on multi-disease prediction, assistive technologies, and enhanced emergency management, this book serves as a vital resource for innovating healthcare delivery in smart environments.

Environmental Monitoring with Integrated Earth Observation Data and Machine Learning.- Earth Observation Data Management and Analytical Tools.- Machine Learning for Earth Observation.- Case Study of the Narmada River Basin.- Challenges and Future Directions.

This book offers practical insights for researchers and policymakers to demonstrate how EO and ML can strengthen environmental monitoring to support informed decision-making, and advance sustainable development. The Earth faces rising challenges like climate instability, land degradation, water scarcity, rapid urban expansion, etc., making reliable environmental monitoring imperative. Traditional methods lack the precision and scale required for global environmental monitoring. However, advances in Earth Observation (EO) and Machine Learning (ML) enable accurate, large-scale environmental monitoring through accessible open-source datasets. Further ML integration with these datasets supports predictive analysis and detailed environmental assessments. Thus, this book outlines the principles of EO, data management, ML integration, and applies them through a case study of the Narmada River Basin, India to examine land use, pollution, and the overall environmental conditions.

Essential Uses of IoT and Machine Learning.- IoT Pro-Interventions: Transforming Industries and Enhancing Quality of Life.- A Comprehensive Review of Machine Learning Approaches in IoT and Cyber Security for Information Systems Analysis.- Application of Machine learning in the Internet of Things.- Empowering Industries with IoT and Machine Learning Innovations.- A Framework for Sustainable Smart Healthcare Systems in Smart Cities.- Cloud Computing Applications in Digital Health: Challenges related to Privacy and Safety.- An IoT-Based Blockchain-Enabled Secure Storage for Healthcare Systems.- Block-Chain Technology in Smart Telemedicine using IOT.- Securing the Future of IoT-Based Smart Healthcare: Challenges, Innovations, and Best Practice.- Smart City: Challenges & Opportunities Detection and Identification of Autonomous Vehicles Using Sensor Synthesis.- AN IOT BASED REAL TIME TRAFFIC MONITORING SYSTEM.- Internet of Things enabled Technological devices empowering expertise in improve Smart City operations.- Enhancing Smart City Retail: An Innovative IoT Driven Smart Billing-Enabled Shopping Cart.- Smart City: Challenges and Issues.- IoT based Real-Time Ecological Monitoring System Deploying an Arduino Board and Cloud Computing.- IoT Based Monitoring Of Waste Management And Air Pollutants.- IOT based smart dustbin design and implementation for monitoring under uncertain environments.- Smart garbage monitoring system using IOT for commercial purpose.- IoT Based Smart Home Systems.- A Survey on Various Secure Access Control and Authentication in a Block Chain -Enable Cloud IoT.- Uncovering the Truth: A Machine Learning Approach to Detect Fake Product Reviews and analyze Sentiment.- Real Time Fall Detection monitoring on elderly using IoT and Deep Learning.- CNN’s augmented with IoT for Traffic Optimization and Signal Regulation.- CVLSTMLW-CNN:A IoT-Enabled Hybrid CNN model for Heart Disease Prediction.- Advancements in Security Technologies for Smart Cities: A Comprehensive Overview.- A Deep Learning Framework based on Convolutional Neural Network for Automatic Detection of Cyberattacks in IoT Use Cases.- Digital Attack Identification for the Internet of Things Using Machine Learning.- IoT Applications and Cyber Threats: Mitigation Strategies for a Secure Future.- Internet of Things and OpenCV-Based Smart Posture Recognition Chair.- Security concerns in low power networks for Internet of Things (IoT).- Comprehensive Review of Security Challenges and issues in Wireless Sensor Networks Integrated with IoT.

Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.   Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable.

Exploring the Impact of Extended Reality (XR) Technologies on Promoting Environmental Sustainability

Harnessing XR Technologies and Deep Learning for Environmental Sustainability Innovations and Impacts.- Leveraging XR for Environmental Communication and Behavior Change A Media Perspective.- Quantum Computing Unlocking the Future of Sustainable Environmental Solutions.- AI and XR in Supply Chain Revolutionizing Sustainable Practices for a Better Tomorrow.- Data Engineering in Sustainability Building the Foundations for a Resilient Future.- Data Analytics for Environmental Conservation Harnessing Insights for Informed Decision Making.- Financial Innovation for Sustainability Leveraging AI, XR, and Data for Impactful Investments.- Natural Language Processing (NLP) for XR-based Environmental Communication.- Data Science for Environmental Impact Assessment using XR.- Data Analytics for XR-based Environmental Sustainability Metrics.

This book offers a groundbreaking exploration of how Extended Reality (XR) technologies can drive environmental sustainability. By integrating virtual, augmented, and mixed realities, it provides innovative solutions to enhance understanding and inspire action on environmental issues. The text is meticulously curated to cover the multifaceted applications of XR, from revolutionizing environmental practices with deep learning to leveraging quantum computing for sustainable solutions. It highlights the synergy between XR and artificial intelligence in optimizing supply chains and enhancing data analytics. Aimed at researchers, practitioners, and policymakers, this resource underscores the critical role of XR in environmental advocacy, emphasizing its potential in effective communication and behavior change. The book also delves into immersive education, fostering a culture of environmental stewardship through experiential learning. Ultimately, it calls for interdisciplinary collaboration to address pressing environmental challenges, envisioning a future where XR technologies are pivotal in safeguarding our planet.

Federated Learning in Health Care Technology Challenges, Solutions and Opportunities. Decentralized Tumor Classification with Federated Learning A Privacy-Preserving Approach.- Transforming Healthcare Analytics.- Privacy-Preserving Kidney Stone Detection from X-ray Image using Federated Learning. Federated Learning for Privacy-Preserving Healthcare Analytics: A Novel Framework for Fraud Detection in Healthcare.

This book offers an in-depth exploration of federated learning (FL), a groundbreaking approach that facilitates collaborative data analysis while ensuring patient privacy and data security. As healthcare systems worldwide generate vast amounts of data, the challenge lies in harnessing this information without compromising confidentiality. Federated learning addresses this by allowing multiple institutions to collaborate on machine learning models without sharing sensitive data. In this context, the authors delve into the foundational principles of FL, illustrating how it enables the aggregation of decentralized data to improve diagnostic accuracy, develop personalized treatment plans, and enhance overall healthcare outcomes. The authors present real-world applications across various medical fields, from predictive analytics in chronic disease management to precision medicine and beyond. Additionally, the authors discuss the ethical and regulatory landscapes, providing insights into the challenges and solutions associated with implementing FL in healthcare. This book is designed for a diverse audience, including researchers, healthcare practitioners, data scientists, and policymakers. It aims to bridge the gap between cutting-edge technology and practical medical applications, offering a comprehensive guide to leveraging FL for healthcare innovation.

Chapter 1.Empowering Federated Learning for Massive Models with NVIDIA FLARE.- Chapter 2.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications.- Chapter 3.Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations.- Chapter 4.A Review of Secure Gradient Compression Techniques for Federated Learning in the Internet of Medical Things.- Chapter 5.Federated Learning for Recommender Systems: Advances and perspectives.- Chapter 6.The Missing Subject in Health Federated Learning: Preventive and Personalized Care.- Chapter 7.Privacy-Enhancing Technologies for Federated Learning.- Chapter 8.Collaborative Defense: Federated Learning for Intrusion Detection Systems.

This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value. Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.

Computational and Systems Biology

Graduate/advanced undergraduate textbook

Introduction to Bioinformatics and Computational Biology.- Introduction to Molecular Biology and Genetics.- Processing Biological Sequences in MATLAB.

This book comprehensively covers all the core bioinformatics topics and includes practical examples completed using the MATLAB bioinformatics and machine learning toolboxes™. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics to enable physical science students to appreciate the challenges in biological data management, sequence analysis, and systems biology. The book is divided into five parts. The first one includes a survey of existing biological databases and tools that have become essential in today’s biotechnology research. The second part covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The third part of the book focuses on modeling biological sequences and patterns as Markov chains, covering core principles for analyzing and searching for sequences of significant motifs and biomarkers and developing stochastic ergodic hidden Markov models for biological sequence families. The fourth one is dedicated to systems biology and covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In turn, the last part of the book includes an introduction to machine-learning algorithms for bioinformatics and outlines strategies for developing intelligent diagnostic machine-learning applications, RNA sequence data, and deep learning systems for mass spectrometry data. All in all, this book offers a unique hands-on reference guide to bioinformatics and computational biology. This second edition has been updated to cover additional and most recent databases, and machine learning and deep learning applications in RNA sequence and mass-spectrometry data analysis. Moreover, it  presents significant enhancements to the chapter dedicated to microarray analysis, and more practical examples, with additional end-of-chapter problems.

Communications Engineering, Networks

Introduction to the Key Features of Qualitative Research.- Observational Data Collection for Qualitative Research.- Qualitative Research Measurements Classifications.- Qualitative Research Data Preparation and Cleaning Process.- Conclusion and Future Research.

This book is to provide readers with a clear and comprehensive understanding of research methodologies, with particular emphasis on the application of qualitative research. It aims to bridge the gap between theory and practice by offering conceptual explanations alongside practical illustrations. The scope extends to students, early-career researchers, and practitioners who require methodological knowledge to design, conduct, and interpret studies effectively. By addressing foundational concepts as well as contemporary applications, the book ensures that readers develop both technical skills and critical thinking abilities essential for research in education, health, and social sciences. Structure of the Book The book is organised in a logical sequence to guide readers from fundamental principles to advanced applications. It begins with an introduction to the philosophy of research, followed by chapters on qualitative, quantitative, and mixed-methods designs. Subsequent sections address data collection techniques, sampling strategies, ethical considerations, and statistical tools. Each chapter is designed to balance theoretical discussion with real-world case studies, ensuring accessibility for novices while still providing depth for more experienced readers. This structure allows for progressive learning while enabling selective reading based on individual research needs. Significance of Qualitative Research Qualitative research is significant because it offers systematic, objective, and replicable ways to investigate phenomena. Through numerical analysis, researchers can measure variables, test hypotheses, and establish relationships among constructs. This approach enhances the precision and generalisability of findings, making it indispensable in fields that demand evidence-based decision-making, such as healthcare, education, business, and public policy. Qualitative research not only supports predictive modelling but also enables comparisons across contexts and populations. Its significance lies in its ability to transform abstract concepts into measurable indicators, thereby advancing scientific knowledge and practical interventions.

High-Tech Agriculture using AI and IOT.- Futuristic development in health care using digital twin technology and AIoT.- Security, Privacy, and Interoperability in IoT Ecosystems.- Advancements in Smart Farming: Leveraging Artificial Intelligence and Internet of Things for Sustainable Agricultural Management.- Towards Sustainable Living: Eco-Friendly Design and Environmental Impacts of IoT Home Automation Systems.- EEG-based BCI Technology in IoT enabled Smart Home Environment: An in-depth Comparative Analysis on Human-Computer Interaction Techniques.- Smart Payment System-Using RFID Technology Sarthak Johri1, Perepi Rajarajeswari2*, Ishank Goel3, Aryan Mudgal4.- AI and IoT for Environmental Sustainability: A Comprehensive Analysis in Line with Sustainable Development Goals.- Machine Learning Approaches for Anomaly Detection in IoT Systems.- Tech-Enabled Sustainability: Leveraging Technology for a Greener Future through Smart Climate Agriculture.- Potato Disease Classification in an Uncontrolled Environment using Deep Learning Approaches.- Leveraging AI and IoT for Enhanced Resource Efficiency in Sustainable Smart Cities.- Synergizing MEMS Technology and Artificial Intelligence for Enhanced Sensor Applications.- Connected Realities: Exploring IoT's Technological Marvels and Varied Applications.- Sustainable Electric Vehicle Market Segmentation Using Deep Learning Techniques.- Energy Management for Household Integrated with Renewable and Electric Vehicle.- Implementation of Skin Disease Identification and Analysis through Deep Learning.

In an era where technological advancements are reshaping industries and societies, the convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Sustainability initiatives is driving unprecedented change. "Future of AI, IoT, and Sustainability" explores the transformative power of these technologies and their role in creating a more sustainable, efficient, and interconnected world. This insightful volume brings together leading experts and thought leaders to examine the latest innovations at the intersection of AI, IoT, and Sustainability. Through a series of compelling chapters, readers will discover how AI and IoT are revolutionizing industries such as energy, agriculture, transportation, and urban development, offering new solutions to the pressing challenges of climate change, resource management, and environmental preservation. Whether you are a technologist, policy maker, researcher, or sustainability advocate, this book provides a comprehensive guide to understanding the future landscape of AI, IoT, and sustainability. It offers practical insights and forward-thinking strategies that will inspire and equip you to contribute to a greener, smarter, and more sustainable future.

Business and Management

A real-life-based optimization model with reduction approach of type-2 uncertain variables under a natural disaster scenarios.- A novel integration of yolo and fuzzy logic for real-time, high-precision emotion detection.- Real-world applications of fuzzy logic: Bridging human reasoning and machine intelligence.- Fuzzy applications in game manufacturing.- A study on fuzzy strongly p-regular in NR.- Predictive modeling for dependence of catalytic conversion on acidic sites using artificial neural networks.- A study of American fuzzy call option buyer's model on future contracts involving general linear octagonal fuzzy numbers.- A generalized approach to derive a fuzzy optimal value of a constant sum game with payoffs as n-polygonal fuzzy numbers.- Innovative applications of fuzzy logic for adaptive and intelligent game manufacturing.- Fuzzy logic in sustainable urban development: Designing smart cities that adapt to uncertainty in energy, traffic and environmental systems.- Topological indices on the fuzzy graph for linear cyclobutane in real life.- Fuzzy logic-based predictive maintenance strategy for Optimizing industrial operations.- Route optimization in directed fuzzy graphs using distance-based topological indices.- Smart air quality control system.- Theories and applications of fuzzy optimization: A comprehensive approach.- Pythagorean distance measure based on cross-information difference and its application to student-oriented decision-making problems.- Intelligent cyber threat assessment using fuzzy logic.- Secure communication and management of IoT devices in smart healthcare using pentagonal fuzzy numbers and cryptographic network security systems.- Title of chapter: Fuzzy logic in the prediction of genetic diseases under the genotype-phenotype framework.- Fuzzy logic in smart traffic management system for cities: an overview.- Fuzzy logic-based assessment of production efficiency: A multi-level employee survey approach.- The effect of E-WOM on consumer buying behaviour towards electronic products.

This book bridges the gap between theoretical modelling and everyday problems by showing the application of fuzzy logic to address those problems that are complex, uncertain and human-centric. Its innovative approach stresses real applications in various kinds of domains, promoting interdisciplinary understanding and innovation. In general, it deals with engineering, management, social systems and decision-making scenarios, establishing itself as an extremely useful tool for researchers, practitioners and graduate students looking for workable methodologies. It finds application for problem-solving in academic, industrial and policymaking circles, thus making it useful for study as well as for implementation.

Spatiotemporal Data and Fuzzy Spatiotemporal Data Conceptual Model.- Fuzzy Spatiotemporal XML Data Model Supporting Dynamic Update.- Transformation of Fuzzy Spatiotemporal Data Between XML and Object-Oriented Databases.- Topological Relations of Fuzzy Spatiotemporal XML Data Over Time.- Algebraic Operations on Fuzzy Spatiotemporal XML Data and Querying Fuzzy Spatiotemporal Data Using XQuery.

Spatiotemporal data is not always precise, and fuzziness in spatiotemporal data is usually accepted because of the way the world is measured and represented. Although eXtensible Markup Language (XML) recommended by the World Wide Web Consortium (W3C) has become the de-facto standard for data representation and exchange on the Web, an edited collection of fuzzy spatiotemporal XML data management studies is still scarce. This book studies fuzzy XML approaches for spatiotemporal data management that can greatly enhance the utilization of this sort of data for spatiotemporal applications. The book is anticipated to serve as a valuable resource in this field and is expected to attract considerable interest from researchers and developers engaged in the applications within the domain of spatiotemporal data management.

Practices of Integrating GenAI in the K-12 Classroom, Volume 2

1. Comparison of Generative Ai Platforms: An Application Using the Task-technology Fit Model.- 2. AI-Generated Visualizations in Physics Education: A Framework for Enhancing Teaching Methodologies.- 3. Leveraging Generative AI to Transform STEM Education in Saudi Arabia: Enhancing Personalized Learning in Physics.- 4. The Use of Storytelling in Didactics of Mathematics in First Grade with the Help of Artificial Intelligence.- 5. AMPT: A Tool for Personalizing Math Learning with Generative AI.

This book approaches the use of Generative AI in primary and secondary education from both educator and student perspectives, showing how classroom strategies, professional development, and equitable design can create more personalized and empowering learning experiences. Its novelty lies in moving beyond higher-education debates to provide concrete pathways for primary and secondary teachers, combining evidence-based practice with cross-disciplinary case studies. The scope covers STEM, literacy, social studies and teacher training spanning conceptual, empirical and design-based peer-reviewed studies from around the globe, all rigorously peer-reviewed. Aimed at educators, school leaders, researchers, and policymakers, the book serves as both a hands-on guide and a reference for curriculum design, staff training, pilot projects, and systemic innovation.

Practices of Integrating GenAI in the K-12 Classroom, Volume 1

1. Comparison of Generative Ai Platforms: An Application Using the Task-technology Fit Model.- 2. AI-Generated Visualizations in Physics Education: A Framework for Enhancing Teaching Methodologies.- 3. Leveraging Generative AI to Transform STEM Education in Saudi Arabia: Enhancing Personalized Learning in Physics.- 4. The Use of Storytelling in Didactics of Mathematics in First Grade with the Help of Artificial Intelligence.- 5. AMPT: A Tool for Personalizing Math Learning with Generative AI.

This book approaches the use of Generative AI in primary and secondary education from both educator and student perspectives, showing how classroom strategies, professional development, and equitable design can create more personalized and empowering learning experiences. Its novelty lies in moving beyond higher-education debates to provide concrete pathways for primary and secondary teachers, combining evidence-based practice with cross-disciplinary case studies. The scope covers STEM, literacy, social studies and teacher training spanning conceptual, empirical and design-based peer-reviewed studies from around the globe, all rigorously peer-reviewed. Aimed at educators, school leaders, researchers, and policymakers, the book serves as both a hands-on guide and a reference for curriculum design, staff training, pilot projects, and systemic innovation.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE AND CHATBOT FOR THE ENHANCEMENT OF NEW AGE BANKING SYSTEMS: A SYSTEMATIC REVIEW.- A Generative AI based Work Assignment System.- NexaMedic: Revolutionizing Medication Access and Healthcare Management through Intelligent Assistance.- Responsible AI in Fintech: Addressing Challenges and Strategic Solutions.- The Transformative Impact of AI Technologies on Decision-Making Processes and Operational Efficiency Across Sectors, with a Focus on Finance.- Information Utilized in the Multimodal Method for Assessing Financial Risk and Fraud Detection in the Context of Digital Currency.- Generative AI in FinTech: Revolutionizing Fraud Detection, Personalized Advising, and Predictive Analytics.- ADVANCING FINANCIAL FORECASTING WITH HIERARCHICAL GAUSSIAN MIXTURES: THE ADAPTIVE GENERATIVE META-MODEL FOR FINANCIAL ENVIRONMENTS (AGM-FE).- Next-Generation Credit Scoring: Enhancing Model Performance through Synthetic Data Generation with Generative Adversarial Networks.- The Mediating Influence of Journalistic Narratives on the Adoption of Generative AI in Financial Services.- Balancing Innovation and Responsibility: Tackling Challenges in Generative AI for FinTech.- Adapting Generative Models with Meta Learning for Financial Applications.- Precedence of Generative AI in Fintech: A Road to Redefine the Sector’s Future.- Securing the Future: Addressing Risks and Vulnerabilities in Generative AI for Fintech.- The Role of Artificial Intelligence in a New Paradigm: Redefining the Banking Landscape.- Empowering Financial Access: A Generative AI Perspective.- Challenges for Responsible Implementation of Generative AI in Fintech.- Guardians of Accountability: The Role of Media in Oversight and Governance of Generative AI Applications in Fintech.

This book delves into the intersection of generative artificial intelligence (AI) and the financial Technology (FinTech) industry. This book provides a comprehensive exploration of how Generative AI, a cutting-edge subset of artificial intelligence, is fundamentally altering the landscape of finance. It meticulously unravels the intricate ways in which advanced algorithms, powered by generative AI, are transforming traditional financial processes, decision-making, risk assessment, portfolio management, fraud detection, and more. Through a detailed analysis of theoretical concepts and practical applications, we illustrate how generative AI techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are empowering FinTech applications to generate synthetic financial data, optimize trading strategies, and enhance customer experiences. Readers will gain a deep understanding of the potential of generative AI to create realistic financial scenarios, model market behaviour, and simulate various economic conditions for better planning and strategizing. Moreover, this book offers insights into ethical considerations and potential challenges associated with the use of generative AI in the FinTech domain, emphasizing the importance of responsible and accountable deployment. Additionally, Generative AI in FinTech serves as a practical guide for professionals, researchers, and enthusiasts seeking to implement generative AI solutions within the financial sector. It presents case studies and real-world examples that demonstrate the effectiveness and impact of generative AI in various FinTech applications.

Distributed Autoepistemic Reasoning Agent for Symbolic Control of Urban Traffic Congestion.- Comprehensive Analysis of Speech Emotion Recognition: Models, Methods, and Applications in Intelligent Interaction.- Integrating Drone Data Security and Route Optimization Algorithms: Addressing Security Challenges in Unmanned Aerial Systems.- SkipCNN based ECG Transformer Spike model for Accurate Cardiac Arrhythmia Detection.- Efficient Multimodal Breast Cancer Detection Using LightweightUNet and StyleGAN3.

This book provides a comprehensive overview how Generative AI could be efficiently used in healthcare sector. It will focus on its applications, challenges, and prospects in healthcare sector. This book contains a detailed analysis of real-world examples of how Generative AI is reshaping healthcare. The initial section of the book provides introduction of Generative AI in line with its significance in healthcare. The other sections cover applications of Generative AI in healthcare. The book also focuses on ethical and legal considerations that are surrounding the use of Generative AI in healthcare.

Foundations and Emerging Trends in Generative Artificial Intelligence (AI) for Industrial Applications.- Mathematical Foundations and Applications of Generative AI Models.- Mathematical Frameworks for Generative AI Applications.- Generative Adversarial Networks: A Comprehensive Review.- Generative Adversarial Networks: Security, Privacy, and Ethical Considerations.- Mitigating Hallucinations in LLMs using Sieve of Fallacies and Truths (SoFT): A Game Theoretic Perspective.- Implementing Generative AI in Identity Access Management.- Integrating Generative AI in Education: Themes, Challenges, and Future Directions.- The Impact of Generative AI on Healthcare.- Revolutionizing Healthcare with Generative Artificial Intelligence Technologies.- Identification of Face Age Progression and Rejuvenation using Generative Adversarial Networks.- Empowering Clinical Decision-making with Generative AI in Intelligent Decision Support Systems.- Leveraging Generative AI for Enhanced Predictive Maintenance and Anomaly Detection in Manufacturing.- The Transformative Role of Big Data Analytics and Generative AI in Redefining FinTech for New Business Models.- Enhancing Digital Security in Fintech Through Integration of Generative AI in Regulatory Practices.- Text Summarization: An Application of Generative AI.

"Generative Artificial Intelligence (AI) Approaches for Industrial Applications" explores the transformative potential of Generative AI technologies across various industries. With contributions from international scholars and experts, this book provides a comprehensive overview of the latest trends, mathematical foundations, and practical applications of Generative AI models. Key sections examine the fundamental concepts of Generative AI, including Generative Adversarial Networks (GANs) and their ethical and security considerations. Special attention is given to the revolutionary impact of Generative AI in healthcare technologies, clinical decision-making, and predictive maintenance within the manufacturing sector. Additionally, the role of Generative AI in FinTech, particularly in redefining business models and enhancing digital security, is thoroughly examined. This book features cutting-edge research on text summarization, age progression using GANs, and integrating AI with regulatory practices. This book is a vital resource for academics, professionals, and practitioners bridging the gap between theoretical AI frameworks and their real-world industrial applications, offering insights into how Generative AI is shaping the future of industries worldwide.

1. Introduction.- 2. A Regional Indicator to Describe Potential Artificial Intelligence in Agriculture.- 3. Ethics effects on the investment decision making process.- 4. The Intricate Balance between Artificial Intelligence, Ethics, and Algorithmic Standards.- 5. Smart Citizens and AI: Trusting the AI.- 6. Public Administration reengineered applying AI models towards Precision Governance.- 7. Artificial Intelligence: crucial area of geopolitical and power competition between the West and Emerging Countries.- 8. Artificial Intelligence enables Precision Accounting design and tools.- 9. Who’s afraid of “Big Bad” Generative AI? Brief notes on the IDPA provision against OpenAI ChatGPT.- 10. Predictability and calculability in tax proceedings: the state of the art.- 11.- The Applications of Artificial Intelligence in automated checks of Tax declarations. Critical reconstructive insights.- 12. Simulation of the Impact of Digital Innovation on Export Volumes and CO2 Emissions in Italy.- 13. Artificial Intelligence, territory, town, and anthropization processes.- 14. Cities facing climate change.

In this digital era, artificial intelligence (AI) is emerging as a catalyst for transformation across numerous fields, ranging from, economics, environment, finance to healthcare, AI's integration into daily operations and strategic planning presents a pivotal shift towards data-driven decision-making and automation. Each chapter in this volume addresses a unique aspect of AI, from theoretical frameworks and technological advancements to practical applications and ethical considerations. This volume not only highlights the advancements and applications of AI but also addresses the critical challenges of bias, privacy, and ethical implications associated with AI deployment. Through a multidisciplinary approach, it aims to provide readers with a nuanced understanding of AI's role in modern society and its potential to address some of the most pressing challenges of our times. As we stand on the brink of technological revolutions, this volume serves as a guide and a critical examination of the potential pathways AI might forge in the future. It is an essential read for academics, industry professionals, policymakers, and anyone interested in the profound changes AI is poised to bring. This is a multi-author book, but not a collection of essays. In fact, although signed by different authors, all the chapters of the book follow a line of development which is traced in the first part of the book and deepen the various aspects in logical order. The various parts of the book will explore the most important features of AI and analyse the implications of AI in Economics, Law, Policies, Smart Citizens and Territorial Aspects.

Generative Artificial Intelligence-Driven Innovation: Strategies and Ethical Frameworks for Responsible AI

1. Rethinking Lease Contracts: Understanding Digital Tacit Renewal and Legal Extensions.- 2. Exploring the Technological Protection of Geographical Indications: A Comparison of Local and Global Approaches in the Age of Technology.- 3. Digital Trade Secret of Digital trade secrets in the Modern Age: Challenges and Opportunities.- 4. Regulating the Protection of Digital Trade Secrets in the Modern Age.- 5. Safeguarding Digital Trade Secrets: A Review of Violations and Protective Strategies.

Artificial Intelligence stands as a pivotal force in reshaping industries and redefining possibilities. This book offers a comprehensive exploration of AI's transformative potential, examining its applications across marketing, creative arts, law, healthcare, business, and beyond. This book delves into critical areas such as AI-driven content generation for marketing, ensuring authenticity in AI-created content, optimizing customer journey mapping, and utilizing AI for enhanced diagnostic and monitoring capabilities. Beyond the theoretical, the book offers practical insights into integrating AI into various domains, balancing technological advancement with ethical considerations. Explore the role of digital transformation in supply chain resilience, uncover the latest AI applications in accounting information systems, and use business intelligence for decision-making through AI. Authored by experts in the field, this book serves as a vital resource for researchers, scholars, IT professionals, managers, business owners, policymakers, and anyone seeking to understand and navigate the AI revolution responsibly.

Sustainable Resource Management in Virtual Worlds.- Energy Efficient Computing in the Metaverse.- Mental Well-Being in the Metaverse: Minimizing Social Isolation by Incorporating Virtual Communities.- Integrating Fuzzy Logic-Based Optimization in Renewable Energy Solutions for Sustainable Virtual Environments.- CHEco-Twins: Pioneering Sustainable Digital Twins in the Metaverse.- Sustainable Inclusion Factorization in Green Metaverse and Virtual Worlds.- Sustainable Virtual Worlds: Implementing Renewable Energy Solutions.- Renewable Energy Solutions for Virtual Environments.- Governance and Policy for a Green Metaverse.- Consumer Behavior and Sustainability in Virtual Worlds.- Fuzzy Logic-Based Framework for Risk Management and Economic Optimization in Sustainable Virtual Economies.- Environmental Challenges and Opportunities in the Metaverse.- Smart Grids and Virtual Power Plants: Enabling the Future of Decentralized Energy Systems.- Foundation of Green Metaverse in synchronization with Fuzzy Logic.- Sustainable Practices in The Metaverse Ecometaverse: Virtual Carbon Footprint Tracker.- Technological innovations for sustainability in manufacturing Industrial technology education.- Impact of Metaverse and Financial Technologies on the Future of Digital Finance.

Fuzzy Logic Approaches to Sustainable Virtual Worlds explores how computational intelligence can shape digital societies through sustainability-driven design. By merging fuzzy logic models with ecological and ethical frameworks, the book presents strategies for resource management, governance, and resilience in virtual environments. Blending theory with applications, it serves as a reference for researchers, professionals, and policymakers aiming to align virtual ecosystems with real-world sustainability goals. Innovative Focus: First book dedicated to applying fuzzy logic in sustaining virtual worlds. Interdisciplinary Appeal: Connects AI, computer science, sustainability studies, digital environments, and social sciences. Applications: Demonstrates fuzzy logic’s role in decision-making, uncertainty management, environmental modelling, energy efficiency, and governance. Global Relevance: Aligns with UN SDGs, making it valuable for educators, policymakers, and technologists. Case Studies: Real-world implementations and simulations of green ICT, resource-efficient infrastructures, and sustainable digital societies. Audience: Researchers in AI, fuzzy systems, and sustainability Professionals in VR, metaverse, gaming, and eco-digital design Policymakers and educators in sustainable technology Graduate students exploring emerging tech + sustainability Publisher Value Add: Aligns with the publishing trend of AI for sustainability, boosting visibility and citation potential. Competitive Edge: Niche yet timely—bridging fuzzy logic, sustainability, and virtual world creation. Teaching Potential: Adaptable for workshops, MOOCs, and conference tracks on sustainable computing and fuzzy systems

.- The Comprehensive Impact of AI on the Psychological and Social Trajectories of Digital Natives.- AI-Driven Pedagogy: Transforming Higher Education with GenAI and Metaverse.- Early Childhood Education Through an Artificial Intelligence Lens - A Practical Guide to Enhance Learning Outcomes.- Navigating Parenting in The Age of Artificial Intelligence, etc.

Delivering a clear blueprint for harnessing Artificial Intelligence responsibly, this book proves that well-designed systems can reduce travel delays, tailor lessons, expedite diagnoses, enhance sports, and simplify daily living. Drawing on pilots such as Malta’s adaptive classrooms, emotion-aware pain coaches, data-driven training hubs and city-wide “digital traffic brains”, every chapter converts breakthroughs into plain language playbooks complete with metrics, checklists and mitigation hacks. Instead of treating sectors separately, the narrative weaves transport, education, health, sport, and home life into one cohesive story, revealing universal design patterns, governance guardrails, and lifecycle checks. Executives, policymakers, planners, curriculum leads, entrepreneurs, investors, and researchers receive fast-start tips for immediate rollouts, along with strategic roadmaps for regulation, funding, workforce upskilling, and evidence-based oversight.

‘Machine Intelligence’ is an oxymoron without real-time embodiment and regulation.- Dreams of reason: ‘AGI’, ‘Machine Consciousness’ and foundational embodiment.- The regulation and stabilization of embodied cognition.- A reformulation: culture itself as an information source.- Wicked problems.- High Frequency Trading and its discontents.- Subtleties.- The Scylla and Charybdis of intelligence: compression vs. complication.- Mission Command.- The psychopathologies of cultural artifacts.

This book applies the powerful asymptotic limit theorems of information and control theories to understanding the dynamics of dysfunction in cognitive cultural artifacts encompassing individual minds, small social groupings, institutions, machine systems, and their many critical composites. A particular focus is on attempts to build ‘intelligent machines’ that would supposedly rival or surpass human minds. All such efforts are blindsided by the reality that all such machines are cultural artifacts of those who build them, closely reflecting cultural priorities and blindness, and that intelligent entities lacking the feedback of high-speed embodiment must endure exaggerated levels of failure by fabulation and hallucination. A principal feature is the detailed working-out of many probability models that can serve as the foundation of statistical tools for the analysis of real-time, real-world data on cognitive failure across a broad range of modes, scales, and levels of organization.

Biomedical Engineering and Bioengineering

The Role of Artificial Intelligence in Smart Healthcare Systems.- Data Analytics for Smart Healthcare Systems: Bridging Patient and Provider Needs.- Integration of AI-based for Seamless Operations in Smart Healthcare Systems.- AI-Driven Imaging and Diagnostics in Smart Healthcare Systems.- Early Detection and Prevention Strategies in Smart Healthcare Systems.- Advanced Techniques for the Early Detection of Alzheimer's Disease Using Neuroimaging and Machine Learning.- Artificial Intelligence in Smart Healthcare Systems - Roles, Computer Vision and Imaging, Detection, and Strategies.- Definition of Metaverse in the Framework of Smart Healthcare System.- Integration of Metaverse Technologies in Smart Healthcare Systems.- A Human-Centered Design Framework for mHealth in Healthcare to Increase Healthy Aging.- Virtual Health Consultations and Telemedicine: Case Studies of Successful Virtual Healthcare Services.- Introduction to AI-Infused Metaverse Solutions in Healthcare.- Theoretical Foundations of AI-Infused Metaverse Solutions in Healthcare Systems.- Data Integration Challenges in AI-Infused Healthcare Metaverse.- Revolutionizing Remote Patient Monitoring with AI-infused Metaverse Technologies.- Applications of Fog, Edge, and Cloud-based Deployment of AI-infused Metaverse Integration in Smart Healthcare.- Digital Twins, and Extended Reality for AI-infused Metaverse Solutions for Smart Healthcare Systems.- Successful Case Studies in the Implementations of AI-infused Metaverse Solutions for Smart Healthcare Systems.- Edge and cloud-based deployment of AI-Driven Metaverse Integration in Smart Healthcare.- Blockchain technology, enabling security and privacy of AI-driven Metaverse Integration in Smart Healthcare.- Security and Privacy Concerns in AI-Infused Metaverse Solutions for Smart Healthcare Systems.- Navigating Legal and Ethical Guidelines of AI-Driven in Smart Healthcare Systems.- Data Privacy in Virtual Healthcare Environments and Ethical Guidelines for Metaverse Health Applications.

This book bridges cutting-edge technology with practical healthcare applications, offering readers a front-row seat to the transformation of patient care, diagnostics, training, and system management. These technologies enhance diagnostics, patient engagement, and care delivery by creating smarter, more connected, and patient-centric systems. This book is organized into four key sections. The first part focuses on AI’s role in diagnostics, predictive analytics, and early disease detection, showcasing how machine learning and data analysis improve accuracy and timeliness. The second part investigates metaverse applications, including virtual consultations, immersive therapies, and the design of mobile health (mHealth) solutions tailored for older adults, emphasizing accessibility and inclusivity. The third section presents integrated AI and metaverse solutions, such as digital twins, XR, edge computing, and real-time healthcare deployments, illustrating the potential for dynamic and intelligent care environments. The final part discusses critical issues surrounding data privacy, ethical governance, and security within virtual healthcare settings, highlighting blockchain-enabled approaches to safeguard sensitive information. Aimed at a broad audience of researchers, developers, healthcare providers, and policymakers, this book combines practical examples, theoretical approaches, and case studies to offer a comprehensive roadmap. The synthesis of these emerging technologies envisions a future healthcare paradigm that balances technological innovation with ethical considerations and patient-centered care principles. Whether you are a healthcare professional, technologist, policymaker, or innovator, this book provides invaluable insights into: AI in Smart Healthcare Systems Metaverse in Medicine AI-Infused Metaverse Solutions Security, Privacy, and Ethics Patient-Centric Healthcare Ecosystems Immersive Healthcare Experiences Inclusive mHealth Design Edge, Fog, and Cloud Integration Neuroimaging and Predictive Analytics