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This book gathers a selection of peer-reviewed papers presented at the 5th Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2023) conference, held in Fuyang, China, on December 28–29. The contributions, prepared by an international team of scientists and engineers, cover the latest advances and challenges made in the field of big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.

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.

This book of Springer Nature is another proof of Springer's outstanding and greatness on the lively interface of Smart Computational Optimization, Green Infrastructure, Innovative Modeling and Deep Learning Architectures! It is a Master Piece of what our community of Academics and Experts can provide when an Interconnected Approach of Joint, Mutual and Meta Learning is supported by Holistic Operational Research and Experience of the World-Leader Springer Nature! The 7th edition of International Conference on Intelligent Computing and Optimization took place at Baitong Hotel & Resort on October 26–27, 2023, with tremendous support from the global research scholars across the planet. Objective was to celebrate “Global Research Quality with Compassion and Wisdom” with researchers, scholars, experts and investigators in Intelligent Computing and Optimization across the globe,to share knowledge, experience and innovation—a marvelous opportunity for discourse and mutuality by novel research, invention and creativity. This proceedings book of the 7th ICO'2023 is published by Springer Nature—Creativity Label of Inspiration.

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.

This open access book presents interdisciplinary and cross-sectoral research results fostering event analytics across languages and communities. It is based on the CLEOPATRA International Training Network, which explored how we analyze and understand the major events that influence and shape our lives and societies, and how they unfold online. This analysis was achieved through various case studies, the development of novel methodologies in fields such as data mining and natural language processing, and the creation of new event-centric datasets aggregated in the Open Event Knowledge Graph (OEKG), a multilingual event-centric knowledge graph that contains more than 1 million events in 15 languages. The book is divided into three parts, focusing on different aspects of event analytics across languages and communities: Part I Event-centric Multilingual and Multimodal NLP Technologies presents five chapters reporting on recent developments in NLP technologies required to process multilingual information. Next, the four chapters of Part II: Event-centric Multilingual Knowledge Technologies discuss technologies integrating multilingual event-centric information in knowledge graphs and providing user access to such information. Finally, Part III: Event Analytics covers three selected aspects of multilingual event analytics, namely an analysis of event-centric news spreading barriers, claim detection in social media, and the narrativization of events as a means of presenting event data. This book is mainly written for researchers in academia and industry, who work on topics like natural language processing, large language models, multilingual information retrieval or event analytics.

'Artificial Intelligence (AI) has rapidly emerged as a revolutionary force across various sectors, with a profound influence permeating the domain of higher education. AI in higher education encompasses a wide range of applications designed to enhance teaching methodologies, streamline administrative processes, and personalize learning experiences. The transformative potential of AI lies in its ability to process vast amounts of data, identify patterns, and make intelligent decisions, which can significantly improve educational outcomes. AI-Driven Learning and Engagement in Higher Education provides a comprehensive exploration of these themes and offers insights into the theoretical foundations, practical applications, and ethical implications of AI in education. Each chapter delves into specific aspects of AI integration, from personalized learning and intelligent tutoring systems to administrative automation and ethical considerations. Covering topics such as applied artificial intelligence, online learning, and student success, this book is an excellent resource for educators, administrators, policymakers, researchers, academicians, and more.'--

The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper- proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity.

With the exponential growth of science and technology, the delivery of dental care has shifted from conventional methods to intelligent techniques. In addition to adapting intelligent techniques, sustainable dental practice is of the utmost importance. Eco-friendly dentistry, sustainable dentistry, or green dentistry are approaches that reduce the environmental impact of dental practice and help safeguard planetary and community well-being. This handbook provides the latest and most comprehensive evidence-based guidance on intelligent and sustainable approaches in dentistry.Handbook of Intelligent and Sustainable Smart Dentistry: Nature and Bio-Inspired Approaches, Processes, Materials, and Manufacturing highlights how Dentistry 4.0 has come to the rescue after COVID-19 and how it has helped in providing needed e-healthcare. This handbook bridges the gap between research and development in the field of smart dentistry for professionals and clinicians. Intelligent materials, equipment, instrumentation, and the latest behavior management techniques and how these techniques provide superior care and treatment to society are explored in detail. The scope of nature-inspired techniques and procession, along with green solutions, are also discussed in this one-of-a-kind handbook.This valuable handbook is a single-stop solution for practitioners, researchers, scholars, students, academicians, and clinicians interested in updating their knowledge on intelligent and sustainable dentistry. The handbook will bestow the readers with not only theoretical knowledge but will equip them with clinical skills as well.

This book is a machine-generated literature overview of the legal and ethical debates over privacy and data protection measures in the last three decades, showcasing the expectations vis-à-vis realities of their presence and application in different sectors. The book identifies the role and application of consent in different situations. Over time, consent in its various forms and types, informed, explicit and otherwise, ensured data subjects have a measured understanding of the purpose of data processing. The idea of consent with time has been challenging to implement with the rapid advancement of research in different areas. It remains the most critical fulcrum, yet there are instances when the implementation continues to challenge. Owing to the nature of this sub-discipline, it remains a work in progress yet portrays a comprehensive range of issues. The entire narrative is being explored through two such machine-generated overview volumes and this is the firstof the two. These volumes have consciously tried to remain both jurisdictional and technology neutral while considering a range of data protection and privacy issues. Towards that end, this book has chapters that capture overarching issues about data protection and privacy; conceptualizes data protection from different perspectives and its existing debates with other rights and developments in a democratic society; provides a snapshot of developments happening in various jurisdictions and how data protection framework engages with other laws. It also broaches the critical issue of consent and how consent as a requirement has evolved and integrated with health research and other allied areas. The subsequent volume, titled ‘Operationalizing Expectations and Mapping Challenges of Information Privacy and Data Protection Measures in the Last Three Decades', would focus on different sectors and how these sectors have been tackling different expectations concerning data protection and privacy. It will also showcase how technology plays a catalyst in implementing data protection requirements. The book highlights the future research areas in the context of data protection and privacy. The volumes are an invaluable resource for not only researchers, but also policy makers, practitioners, corporate sector, across disciplines, and anyone looking to get an idea about the evolution privacy, data protection issues and the application of consent over the last three decades since 1990.

This LNCS conference 4-volume set constitutes the proceedings of the 16th International Conference on Social Networks Analysis and Mining, ASONAM 2024, in Rende, Italy, during September 2–5, 2024. The 33 full papers together with 36 short papers included in this volume were carefully reviewed and selected from 167 submissions. The conference covers a wide spectrum of research contributions to the foundations and applications of social networks.

This book features high-quality research papers presented at the International Conference on Computing and Machine Learning (CML 2024), organized by Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India, during April 29 – 30, 2024. The volume book presents diverse range of topics, including machine learning algorithms and models, deep learning and neural networks, computer vision and image processing, natural language processing, robotics and automation, reinforcement learning, big data analytics, cloud computing, internet of things, human-–robot interaction, ethical and social implications of AI, applications in health care, finance, and industry, computer modeling, quantum computing, high-performance computing, cognitive and parallel computing, cloud computing, distributed computing, embedded computing, human-centered computing, and mobile computing.

This book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.

This book provides a platform for scholars and researchers to present the latest insights and findings on the role of artificial intelligence and smart digital solutions in fostering the circular economy. Additionally, the book shades the lights on the vital role of circular economy in achieving sustainability and social responsibility. It covers a variety of research topics including circular economy, sustainability, AI applications, legislation and regulation, smart cities, social entrepreneurship and AI, digital transformation, knowledge management, and cybersecurity. This edited book serves as a reference and guide to graduate students, researchers, and academics conducting research and taking courses in artificial intelligence, sustainability, and the circular economy.

This book gathers selected papers presented at 6th International Conference on Communication and Computational Technologies (ICCCT 2024), jointly organized by Soft Computing Research Society (SCRS) and Rajasthan Institute of Engineering & Technology (RIET), Jaipur, during January 8–9, 2024. The book is a collection of state-of-the art research work in the cutting-edge technologies related to the communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.

This book provides theory on the use of artificial intelligence techniques and relevant insight on application development by organizing intelligent systems too be applied in diverse ways for the benefit of humanity. The book appeals to a range of audiences from academicians, practitioners, researchers, and students to stakeholders. It can support graduate students and interns to develop a deep understanding of the latest paradigms in artificial intelligence techniques.

The 10-volume set LNAI 15201-15210 constitutes the proceedings of the 17th International Conference on Intelligent Robotics and Applications, ICIRA 2024, which took place in Xi'an, China, during July 31–August 2, 2024. The 321 full papers included in these proceedings were carefully reviewed and selected from 489 submissions. They were organized in topical sections as follows: Part I: Innovative Design and Performance Evaluation of Robot Mechanisms. Part II: Robot Perception and Machine Learning; Cognitive Intelligence and Security Control for Multi-domain Unmanned Vehicle Systems. Part III: Emerging Techniques for Intelligent Robots in Unstructured Environment; Soft Actuators and Sensors; and Advanced Intelligent and Flexible Sensor Technologies for Robotics. Part IV: Optimization and Intelligent Control of Underactuated Robotic Systems; and Technology and application of modular robots. Part V: Advanced actuation and intelligent control in medical robotics: Advancements in Machine Vision for Enhancing Human-Robot Interaction; and Hybrid Decision-making and Control for Intelligent Robots. Part VI: Advances in Marine Robotics; Visual, Linguistic, Affective Agents: Hybrid-augmented Agents for Robotics; and Wearable Robots for Assistance, Augmentation and Rehabilitation of human movements. Part VII: Integrating World Models for Enhanced Robotic Autonomy; Advanced Sensing and Control Technologies for Intelligent Human-Robot Interaction; and Mini-Invasive Robotics for In-Situ Manipulation. Part VIII: Robot Skill Learning and Transfer; Human-Robot Dynamic System: Learning, Modelling and Control; AI-Driven Smart Industrial Systems; and Natural Interaction and Coordinated Collaboration of Robots in Dynamic Unstructured Environments. Part IX: Robotics in Cooperative Manipulation, MultiSensor Fusion, and Multi-Robot Systems; Human-machine Co-adaptive Interface; Brain inspired intelligence for robotics; Planning, control and application of bionic novel concept robots; and Robust Perception for Safe Driving. Part X: AI Robot Technology for Healthcare as a Service; Computational Neuroscience and Cognitive Models for Adaptive Human-Robot Interactions; Dynamics and Perception of Human-Robot Hybrid Systems; and Robotics for Rehabilitation: Innovations, Challenges, and Future Directions.

This book delves into how these technologies, including artificial intelligence, machine learning, data analytics, and the Internet of Things, are revolutionizing business operations. Through real-world case studies and expert analysis, the book showcases practical applications of these technologies in sectors like manufacturing, health care, finance, and logistics. It highlights the benefits and challenges of adopting these innovations, offering valuable insights for organizations seeking improved efficiency. The book also addresses ethical considerations and societal implications, including data privacy, security, and the future of work in an increasingly digitized world. It emphasizes the responsible implementation of digital technologies for a sustainable and inclusive future.

The book explores the intersection of AI, telehealth, and viral infections, discussing the role of AI in pandemics, telehealth services, viral diagnosis, and antiviral drug development. It explores the integration of AI into telehealth, predicting outbreaks and monitoring mutations. The book emphasizes patient-centric healthcare, highlighting AI's transformative potential in infectious diseases. It covers medical virology, bioinformatics, machine learning, and neural networks. AI enables streamlined workflows, improved patient care, and new research methodologies. It addresses challenges and tailored solutions for AI integration in telehealth. It serves as a valuable resource for clinicians, physicians, virologists, researchers, and healthcare professionals seeking to navigate the evolving landscape of telehealth and harness the full potential of AI in transforming healthcare delivery.

The two-volume set LNAI 15243 + 15244 constitutes the proceedings of the 27th International Conference on Discovery Science, DS 2024, which took place in Pisa, Italy, during October 14-16, 2024. The 53 full papers presented in the proceedings were carefully reviewed and selected from 121 submissions. They were organized in topical sections as follows: Part I: LLM, Text Analytics, and Ethical Aspects of AI; Natural Language Processing, Sequential Data and Science Discovery; Data-Driven Science Discovery Methodologies; Graph Neural Network, Graph Theory, Unsupervised Learning and Regression; Part II: Tree-Based Models and Causal Discovery; Security and Anomaly Detection; Computer Vision and Explainable AI; Classification Models; SoBigData++: City for Citizens and Explainable AI; SoBigData++: Societal Debates and Misinformation Analysis.

This book bridges the gap between theory and practice, offering a rich exploration of project management practices across Europe, Asia, and Africa. This book presents a collection of cutting-edge research and case studies that illustrate the application of project management principles in various industries and regions. Through contributions from global scholars and practitioners, this book covers a wide range of topics, from the rise of hybrid project management methodologies to the challenges of managing public projects, to the role of artificial intelligence in multicultural leadership. Key themes include decision-making, critical thinking, resilience in collaborative R&D, and project management in emerging markets such as Africa and Asia's tiger economies. This book is designed for project managers, researchers, and professionals who want to understand the unique challenges and opportunities of managing projects in diverse cultural and economic contexts. By examining case studies and research from around the globe, readers will gain valuable insights into how to apply project management techniques in real-world scenarios.

Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields.Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions.This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.

As the travel landscape evolves rapidly, propelled by technological innovations, AI emerges as a powerful force reshaping tourism practices worldwide. This book aims to explore the dynamic intersection of artificial intelligence (AI) and the travel industry, offering a comprehensive guide to harnessing AI for enriched, tailored experiences and sustainable development. This groundbreaking book illuminates how AI is revolutionizing personalized recommendations, optimizing operational efficiencies, and promoting eco-conscious practices, fostering a harmonious balance between exploration and environmental stewardship. At its core, AI Technologies for Personalized and Sustainable Tourism seeks to provide a holistic understanding of AI's transformative potential in the hospitality sector. By delving into the latest advancements and real-world applications of AI, readers are guided through practical insights into leveraging AI to enhance traveler experiences while advancing sustainability goals. From AI-powered recommendation systems to predictive analytics and virtual assistants, each chapter offers invaluable insights into how AI can be deployed to create personalized, sustainable tourism experiences. Tailored for a diverse audience spanning industry professionals, researchers, policymakers, and students, this book serves as a beacon of knowledge, bridging the gap between theory and practice in AI-driven tourism. Professionals in the tourism sector will discover valuable strategies for enhancing customer experiences and promoting sustainable practices. Meanwhile, researchers and academics will find comprehensive coverage of cutting-edge developments and case studies. Government officials and policymakers will benefit from practical guidance on harnessing AI to drive economic growth while ensuring environmental and social sustainability, and students will gain a deep understanding of the opportunities and challenges presented by AI in shaping the future of tourism. AI Technologies for Personalized and Sustainable Tourism stands as an indispensable resource, inspiring meaningful progress and fostering responsible innovation in the ever-evolving world of travel and tourism.

Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry's next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book's insights and coverage.

Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.

This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 8th International Conference on ICT for Sustainable Development (ICT4SD 2024), held in Goa, India, on 8–9 August 2024. The book covers the topics such as big data and data mining, data fusion, IoT programming toolkits and frameworks, green communication systems and network, use of ICT in smart cities, sensor networks and embedded system, network and information security, wireless and optical networks, security, trust, and privacy, routing and control protocols, cognitive radio and networks, and natural language processing. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.

'Big Data and Internet of Things'is the latest volume in the renowned Lecture Notes in Networks and Systems series. This book compiles the latest research presented at the Seventh International Conference on Big Data and Internet of Things (BDIoT'24), showcasing innovative solutions, emerging trends, and practical applications in the fields of big data and IoT. An essential read for researchers, professionals, and students looking to stay ahead in the rapidly evolving world of technology. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems. Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems Automation, Manufacturing, Smart Grids. Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.

This two-volume set CCIS 2309-2310 constitutes the refereed proceedings of the 11th International Conference on Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, FDSE 2024, held in Binh Duong, Vietnam, during November 27–29, 2024. The 44 full papers, 12 short papers and 1 keynote paper were carefully reviewed and selected from 189 submissions. They were organized in topical sections as follows: advances in machine learning for big data analytics; security and privacy engineering; data analytics and healthcare systems; smart city and industry 4.0 applications; big data query processing and optimization; and short papers; security and data engineering.

This book explores the transformative potential of artificial intelligence (AI) in addressing critical humanitarian challenges. It examines AI's role in enhancing emergency responses, poverty alleviation, and healthcare.Chapters authored by a diverse group of international contributors cover topics such as AI's application in disease prediction, ethical AI practices, and innovative resource distribution. This book uniquely blends theoretical insights with practical case studies, providing a road map for leveraging AI in humanitarian efforts. Readers will benefit from detailed explorations of AI's capabilities and challenges, gaining insights into how AI can drive social change and improve global humanitarian outcomes.Targeted at policymakers, researchers, practitioners, and anyone interested in the intersection of AI and humanitarianism, this book offers valuable perspectives on ensuring AI technologies are both advanced and ethically sound.

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

This book explores the intersection of artificial intelligence (AI) and sustainability in healthcare, focusing on how AI technologies are transforming medical practices while promoting environmentally responsible operations. It examines how AI-driven tools like machine learning and data analysis enhance diagnostic accuracy, streamline treatment planning, and personalize patient care by analysing large datasets, including genetic information. Additionally, the book addresses how AI can support sustainable healthcare practices by optimizing resource usage, such as energy consumption in hospitals, and improving supply chain management to reduce environmental impact. Practical case studies demonstrate how these technologies are being implemented to improve patient outcomes and achieve sustainability goals. The book considers the integration of AI into human resource management within healthcare, discussing AI's role in recruitment, performance management, and employee retention aligned with sustainability objectives. Ethical and regulatory issues surrounding AI adoption, such as data privacy and algorithmic transparency, are thoroughly examined, with an emphasis on creating responsible and equitable AI systems. Designed for healthcare professionals and administrators, this book provides practical strategies and real-world examples of AI implementation in sustainable healthcare, offering a balanced view of the opportunities and challenges ahead.

This book gathers a collection of high-quality peer-reviewed research papers presented at International Conference on Cyber Intelligence and Information Retrieval (CIIR 2023), held at Institute of Engineering & Management, Kolkata, India during 14 – 15 December 2023. The book covers research papers in the field of privacy and security in the cloud, data loss prevention and recovery, high performance networks, network security and cryptography, image and signal processing, artificial immune systems, information and network security, data science techniques and applications, data warehousing and data mining, data mining in dynamic environment, higher order neural computing, rough set and fuzzy set theory, and nature inspired computing techniques.

'Industrial quantum computing'(IQC) covers the applications of quantum computing innovations in general industry and industry 4.0. This book presents the application of quantum computations to the financial sector, medical services, the logistics industry, and the manufacturing industry.

This book delineates a comprehensive framework designed to ultimately assist organizations as they navigate the critical juncture—often termed the inflection point—in adopting generative Artificial Intelligence (AI). Addressing the exigent shift in organizational strategy mandated by Industry 4.0, and urging a transition from mere digital transformation to what is being progressively understood as'generative transformation,” it fills a conspicuous lacuna in both academic and practical discourses by presenting a timely synthesis of research, empirical studies, and case analyses. The authors utilize an interdisciplinary methodology, bridging the epistemological divide between the technological intricacies of generative AI and its effective implementation within institutional frameworks. Reorienting the focus of organizational leaders from a simplistic replacement paradigm to one of technological-human augmentation, it works toward a prescriptive blueprint for enabling organizations to adopt generative AI without compromising their most valuable asset: human intellectual and emotional capital. Fostering interdisciplinary dialog among academics, industry professionals, and policymakers, it fills a critical gap in current discourses and serves as a catalyst for future research and collaboration.

The proceedings of the 2nd International Conference on Next-Generation Computing and Information Systems (ICNGCIS'23) includes research papers in diverse emerging domains such as AI, blockchain, web 3.0, metaverse and edge computing apart from traditional domains such as distributed computing and networks and cybersecurity. The proceedings include papers addressing currently relevant research issues such as smart contract security, interoperability in the metaverse, AI applications in healthcare, agriculture and related domains. The proceedings encompass findings with real-world implications for the growth and evolution of modern computing and information systems by addressing various challenges related to their design, deployment, operational aspects, performance and shortcomings. The intended audience for the proceedings of ICNGCIS'23 include researchers from industry and academia, practitioners, students, technology enthusiasts and even general audience looking to explore interesting applications, use-cases and fundamental issues in modern computing and information systems.

Recently, there has been a significant increase in the development and interest in applying generative AI across various domains, including education. The emergence of large language models (LLMs), such as the ChatGPT tool, fueled by advancements in generative AI, is profoundly reshaping education. The use of the ChatGPT tool offers personalized support, improves accessibility, and introduces innovative methods for students and educators to engage with information and learning materials. Furthermore, ChatGPT facilitates a wide range of language learning services, including language instruction, speech recognition, pronunciation feedback, and immersive virtual simulations for hands-on learning experiences.This book explores the transformative potential of the ChatGPT tool within education, shedding light on the opportunities that arise through the integration of the ChatGPT tool into various aspects of the learning process. It serves as a platform for the community to share cutting-edge research ideas concerning the use of the ChatGPT tool in digital education. Readers will discover how the ChatGPT tool can enhance student engagement, foster personalized learning experiences, facilitate intelligent tutoring systems, support virtual classroom interactions, and revolutionize assessment and feedback mechanisms.

'In mental health care, artificial intelligence (AI) tools can enhance diagnostic accuracy, personalize treatment plans, and provide support through virtual therapy and chatbots that offer real-time assistance. These technologies can help identify early signs of mental health issues by analyzing patterns in speech, behavior, and physiological data. However, the integration of AI also raises concerns about privacy, data security, and the potential for algorithmic bias, which could impact quality of care. As AI continues to evolve, its role in psychological well-being and healthcare will depend on addressing these ethical and practical considerations while harnessing its potential to improve mental health outcomes and streamline healthcare delivery. AI Technologies and Advancements for Psychological Well-Being and Healthcare discusses the latest innovations in AI that are transforming the landscape of mental health and healthcare services. This book explores how AI applications, such as machine learning algorithms and natural language processing, are enhancing diagnostic accuracy, personalizing treatment options, and improving patient outcomes. Covering topics such as behavioral artificial intelligence, medical diagnosis, and precision medicine, this book is an excellent resource for mental health professionals, healthcare providers and administrators, AI and data scientists, academicians, researchers, healthcare policymakers, and more.'--

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies. - Explores how IoT is driving the digital transformation wheel through existing and forthcoming case studies - Investigates architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities - Features supplemental data repository with IoT collected case studies as well as related analytics scripts in Python

Bei diesem Buch handelt es sich um eine Sammlung erweiterter Fassungen von Vorträgen, die auf dem internationalen KES-Gipfel Covid-19 Herausforderung gehalten wurden. Der Schwerpunkt des Buches liegt auf technologischen, wirtschaftlichen und sozialen Entwicklungen zur Bekämpfung der Auswirkungen globaler und lokaler Katastrophen sowie auf der Frage, wie die Erholung von Covid genutzt werden kann, um widerstandsfähigere und nachhaltigere Gemeinschaften und Industrien aufzubauen und die Umwelt zu verbessern. Auch die globalen Herausforderungen des vom Menschen beeinflussten Klimawandels werden erörtert. Die Kapitel befassen sich mit der Stärkung der Widerstandsfähigkeit von Städten und Gemeinden durch Energieautarkie, Lebensmittelproduktion, widerstandsfähige Wohnungen und Gebäude, menschliche Gesundheit und intelligente Systeme, z. B. für Vorhersagen und Prognosen.

This book aims to propose a suitable contingency plan that can minimize the negative effects of future health crises while strengthening the resilience and adaptability of SMEs. The starting point of this work is based on the work of the research project No. 221N283, jointly funded by TÜBİTAK and NARD. This project is part of an integrated approach from the laboratory to the market, with the aim of analyzing and promoting practical and strategic solutions for the development of SMEs in a crisis context. Following the results obtained, the scientific research team sought to broaden its scope of investigation by addressing the challenges that small and medium-sized enterprises (SMEs) face during global crises. This is a crucial issue in a context where economic sustainability and security are increasingly threatened by unforeseen events such as global epidemics.

This two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023. The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The papers were organized in the following topical sections: Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare. Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.

This LNCS conference 4-volume set constitutes the proceedings of the 16th International Conference on Social Networks Analysis and Mining, ASONAM 2024, in Rende, Italy, during September 2–5, 2024. The 33 full papers together with 36 short papers included in this volume were carefully reviewed and selected from 167 submissions. The conference covers a wide spectrum of research contributions to the foundations and applications of social networks.

This book includes peer-reviewed articles from the 1st International Conference on Intelligent Healthcare and Computational Neural Modelling (ICIHCNN—2022), held on November 30, 2022, at Dehradun, India. It covers the latest research trends and developments in areas of Data Science, Artificial Intelligence, Neural Networks, Cognitive Neuroscience and Machine Learning Applications, Cyber-Physical Systems, and Cybernetics.

This book constitutes revised selected papers from the 22nd Workshop on e-Business, WeB 2023, which took place in Hyderabad, India, on December 9, 2023. The purpose of WeB is to provide a forum for researchers and practitioners to discuss findings, novel ideas, and lessons learned to address major challenges and map out the future directions for e-Business. The WeB 2023 theme was “Generative AI in e-Business”. The 13 full papers included in this volume were carefully reviewed and selected from a total of 46 submissions. They focus on both the transformative potential and the challenges of integrating generative AI into e-business models, paving the way for a future where AI empowers businesses and enriches lives.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The Digital Doctor: How Digital Health Can Transform Healthcare discusses digital health and demonstrates the appropriateness of each technology using an evidence-based approach. It serves as a comprehensive summary on current, evidence-based digital health applications, future novel digital health technologies (e.g., mobile health, blockchain, web3.0), as well as some of the current challenges and future directions for digital health within the various medical subspecialties. This book is a comprehensive review of digital health for clinicians, researchers, bioinformatic students, biomedical engineers interested in this topic. - Provides a history and overview of the various modalities of digital health and their application within each field of medicine as narrated by leading experts - Discusses current digital health-based medical research, including landmark trials within each field of medicine - Addresses current knowledge gaps that clinicians commonly face that often prevent the application of digital health-based research to clinical practice - Provides examples of specific cases and discusses challenges and biases associated with digital health

This book highlights the technological advances that are transforming the future of digital marketing and covers important areas of research in this field. The book demonstrates advances in digital marketing as well as tools, techniques, methods and strategies based on artificial intelligence. It also identifies gaps in research into effective digital marketing tools, techniques and methods, and it bridges the interaction between digital marketing strategies and organisations'business plans, on the one hand, and customer relations, on the other, in the age of artificial intelligence.This book presents the concepts and applications of digital marketing in the age of artificial intelligence to readers in a comprehensive manner. The book highlights the major breakthroughs and technologies in digital marketing for effective interaction, analysis, prediction and design to formulate the best strategy for a business by using artificial intelligence solutions. This enables specialists to apply advances in digital marketing in the age of artificial intelligence in the right way to serve customers and solve business problems in this competitive age.Covering the intersection of marketing and artificial intelligence, this book is a first-rate reference and an indispensable resource for business leaders, academics, salespeople, marketing professionals, managers, business owners, researchers, practitioners, instructors, college and university libraries, students, consultants, businesspeople, computer scientists, and customer-focused institutions.