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This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more. The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for management systems using machine learning techniques, and for fusion applications in medical image processing. The chapter authors discuss using data mining and machine learning techniques for COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare. Key features: Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcare Details specific applications for the use of AI, big data, and IoT in healthcare Discusses how AI technology can help in formulating protective measures for COVID-19 and other diseases Includes case studies Intelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications.

In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised.The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation.This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book's discussions on both the technical and ethical aspects of this rapidly growing field.

This book provides a comprehensive analysis of the tools and techniques used today for designing and modeling of efficient and robust swarm-intelligence based systems: highly (or fully) decentralized, semi-autonomous, highly-scalable infrastructures in various real-life scenarios. Among others, the book reviews the use of the swarm intelligence paradigm in financial investment, blockchain protocols design, shared transportation systems, communication networks, bioinformatics, and military applications. Theoretical and practical limitations of such systems, as well as trade-offs between the various economic and operational parameters of the systems, are discussed. The book is intended for researchers and engineers in the fields of swarm systems, economics, agriculture, nutrition, and operation research.

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.

We stand at the precipice of a technological revolution; the entrepreneurial landscape is undergoing a metamorphosis. In the academic corridors of today, a pressing challenge emerges - the need to comprehend and dissect the profound transformations underway in the world of entrepreneurship. The fusion of emerging technologies with the age-old spirit of entrepreneurship is creating seismic shifts, ushering in new possibilities that beg exploration. Improving Entrepreneurial Processes Through Advanced AI emerges as a beacon of insight and innovation in this new entrepreneurial realm of possibility. This book embarks on a captivating journey, tailored to the discerning minds of PhD students, university educators, independent researchers, and scholars in related fields, guiding them through the intricacies of technology integration and the transformation of entrepreneurial processes. As technology continues to advance at an unprecedented pace, traditional paradigms are being upended, leaving researchers grappling with complex questions. The emergence of Artificial Intelligence (AI) as a game-changer in entrepreneurship introduces a host of intricate issues and uncertainties. Amidst this sea of change, the fundamental challenge lies in understanding how these advancing AI systems can address core entrepreneurial challenges and open new horizons of opportunity in the era of digital transformation. Improving Entrepreneurial Processes Through Advanced AI presents an invaluable solution to the academic quest for understanding and adapting to digital transformation. Its pages unravel the transformative potential of AI systems, providing fresh insights into the evolving traits of digital entrepreneurs, the role of data, machine learning, and the dynamics of open innovation. It explores the nuances of technology adoption and the resilience of family business in the face of ai-driven entrepreneurship. As scholars seek to navigate this dynamic landscape, the book serves as a compelling resource, offering a roadmap to explore the complex intersection of innovation, technology, and entrepreneurship, and to seize the abundant future possibilities this fusion holds for entrepreneurial ventures.

'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.'--

Today's research scholars face the problem of how to effectively navigate the transformative impact of Artificial Intelligence (AI) while maintaining ethical integrity and scholarly rigor. AI technologies have permeated every aspect of scholarly inquiry, from information retrieval to research methodologies. As such, scholars grapple with the ethical implications, challenges, and opportunities presented by this technological revolution. Plagiarism, bias, and copyright issues in AI-assisted research threaten to undermine the integrity of academic scholarship. Navigating AI in Academic Libraries: Implications for Academic Research is presented as a groundbreaking solution to the complex challenges posed by AI integration in academia. This comprehensive volume serves as a guide for scholars seeking to navigate the intricacies of AI while upholding ethical standards and scholarly integrity. By addressing critical issues such as plagiarism detection, bias mitigation, and copyright concerns, the book equips scholars with the tools and strategies needed to harness the full potential of AI for academic inquiry. Through interdisciplinary perspectives and evidence-based practices, Navigating AI in Academic Libraries: Implications for Academic Research fosters collaboration and empowers scholars to leverage AI technologies responsibly and effectively. By offering insights into effective instructional strategies and the impact of AI on traditional research methods, the book provides a roadmap for scholars to thrive in an increasingly AI-driven academic landscape. With its holistic approach and practical guidance, this volume emerges as an indispensable resource for scholars navigating the complexities of AI integration and shaping the future of scholarly research.

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.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications 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 phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform 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 to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

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

Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications. The book will help readers acquire the basic notions of AI trustworthiness and understand its concrete application in medical imaging, identify pain points and solutions to enhance trustworthiness in medical imaging applications, understand current limitations and perspectives of trustworthy AI in medical imaging, and identify novel research directions. Although the problem of trustworthiness in AI is actively researched in different disciplines, the adoption and implementation of trustworthy AI principles in real-world scenarios is still at its infancy. This is particularly true in medical imaging where guidelines and standards for trustworthiness are critical for the successful deployment in clinical practice. After setting out the technical and clinical challenges of AI trustworthiness, the book gives a concise overview of the basic concepts before presenting state-of-the-art methods for solving these challenges. - Introduces the key concepts of trustworthiness in AI. - Presents state-of-the-art methodologies for trustworthy AI in medical imaging. - Outlines major initiatives focusing on real-world deployment of trustworthy principles in medical imaging applications. - Presents outstanding questions still to be solved and discusses future research directions.

Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medical Things, intelligent healthcare systems, and extended health intelligence (EHI). This volume contains four sections. The focus of the first section is health data analytics and applications. The second section covers research on information exchange and knowledge sharing. The third section is on the Internet of Things (IoT) and the Internet of Everything (IoE)-based solutions. The final section focuses on the implementation, assessment, adoption, and management of healthcare informatics solutions. This new volume in the Advances in Ubiquitous Sensing Applications for Healthcare series focuses on innovative methods in the healthcare industry and will be useful for biomedical engineers, researchers, and students working in interdisciplinary fields of research. This volume bridges these newly developing technologies and the medical community in the rapidly developing healthcare world, introducing them to modern healthcare advances such as EHI and Smart Healthcare Systems. - Provides a comprehensive technological review of cutting-edge information in the wide domain of Healthcare 5.0 - Introduces concepts that combine computational methods, network standards, and healthcare systems to provide a much improved, more affordable experience delivered by healthcare services to its customers - Presents innovative solutions utilizing informatics to deal with various healthcare technology issues

This two-volume set LNAI 15442-15443 constitutes the refereed proceedings of the 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, held in Melbourne, VIC, Australia, during November 25-29, 2024. The 59 full papers presented together with 3 short papers were carefully reviewed and selected from 108 submissions. Part 1: Knowledge Representation and NLP; Trustworthy and Explainable AI; Machine Learning and Data Mining. Part 2: Reinforcement Learning and Robotics; Learning Algorithms; Computer Vision; AI for Healthcare.

Computational Intelligence in Sustainable Computing and Optimization: Trends and Applications focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in applications, such as agriculture, biomedical systems, bioinformatics, business intelligence, economics, disaster management, e-learning, education management, financial management, and environmental policies. The book presents research in sustainable computing and optimization, combining methods from engineering, mathematics, artificial intelligence, and computer science to optimize environmental resourcesComputational intelligence in the field of sustainable computing combines computer science and engineering in applications ranging from Internet of Things (IoT), information security systems, smart storage, cloud computing, intelligent transport management, cognitive and bio-inspired computing, and management science. In addition, data intelligence techniques play a critical role in sustainable computing. Recent advances in data management, data modeling, data analysis, and artificial intelligence are finding applications in energy networks and thus making our environment more sustainable. - Presents computational, intelligence–based data analysis for sustainable computing applications such as pattern recognition, biomedical imaging, sustainable cities, sustainable transport, sustainable agriculture, and sustainable financial management - Develops research in sustainable computing and optimization, combining methods from engineering, mathematics, and computer science to optimize environmental resources - Includes three foundational chapters dedicated to providing an overview of computational intelligence and optimization techniques and their applications for sustainable computing

Soft computing and Industry 5.0 are two distinct concepts that, when combined, can have a significant impact on sustainability initiatives within various industries. Soft computing is a subfield of artificial intelligence (AI) that aims to address problems characterized by uncertainty, imprecision, and partial truth. It encompasses various computational techniques, such as fuzzy logic, neural networks, genetic algorithms, and machine learning, which enable machines to deal with complex and uncertain data in a more human-like manner. Soft computing techniques are particularly valuable in sustainability efforts because they can handle non-linear relationships and uncertain data that often arise in environmental and social contexts. For example, they can be used to optimize energy consumption, waste management, and resource allocation in industries by considering various factors and trade-offs. The book highlights the latest innovations in intelligent systems in classical machine learning, deep learning, Internet of Things (IoT), Industrial Internet of Things (IIoT), blockchain, knowledge representation, knowledge management, big data, and natural language processing. (NLP). The book contains many contemporary articles from both scientists and practitioners working in many fields where soft computing, intelligent systems and the IIoT can break new ground. Intelligent systems and the Internet of Things are now essential technologies in almost every field. From agriculture to industry to healthcare, the scope of smart systems and IIoT is as wide as the horizon. Nowadays, these technologies are extensively used in developed countries, but they are still at an early stage in emerging countries. The primary market of this book is senior undergraduate students, post graduate students, practitioners, researchers, academicians, industrialists, and professionals working in areas of core computer science, electrical engineering, mechanical engineering, environmental engineering and agricultural engineering. The secondary audience of this book is individuals working in the areas of manufacturing, agriculture, remote sensing, environmental engineering, health care, smart cities, smart farming, remote sensing, supply chain management and hydrology.

This book constitutes the refereed proceedings of the First International Conference, Intelligent Systems in Computing and Communication, ISCComm 2023, held in Moodabidri, India, during December 8–9, 2023. The 36 full papers and 17 short papers were carefully reviewed and selected from 245 proceedings. They were categorized under the topical sections as follows: Part I: Artificial Intelligence and Machine Learning Part II: IoT&Blockchain Technology, Communication Technology and Data Processing, Image/Video Analysis and Processing

This book constitutes the refereed proceedings of the First International Conference on Machine Learning Algorithms, ICMLA 2024, held in Himachal Pradesh, India, during February 23–24, 2024. The 23 full papers and 17 short papers included in this book were carefully reviewed and selected from 400 submissions. They were organized in the following topical sections: machine learning; image processing; deep learning.

'أخلاقيات الذكاء الاصطناعي هي مجموعة من المبادئ والقيم التي تهدف إلى توجيه تصميم واستخدام التكنولوجيا الذكية بطريقة تحافظ على الكرامة البشرية وتحقق الفوائد الاجتماعية بشكل عادل ومسؤول. من بين القضايا الأخلاقية الرئيسية التي تتعامل معها أخلاقيات الذكاء الاصطناعي يمكن تلخيصها فيما يلي: 1. الخصوصية والأمان: ضمان حماية بيانات المستخدمين وخصوصيتهم أثناء جمع البيانات ومعالجتها وتخزينها. 2. العدالة والتمييز: ضمان عدم وجود تمييز غير عادل أو غير مبرر في استخدام التكنولوجيا الذكية، وضمان تكافؤ الفرص وعدم انحياز النظام ضد فئات معينة من الأشخاص. 3. الشفافية والشفافية: توفير مستوى من الشفافية في كيفية تصميم وتدريب واستخدام أنظمة الذكاء الاصطناعي، بما في ذلك فهم الخوارزميات والمعايير التي يتم استخدامها. 4. المساءلة والمسؤولية: تحميل الجهات المطورة والمست

'Dive deep into the evolving landscape of AI with'Towards Ethical and Socially Responsible Explainable AI'. This transformative book explores the profound impact of AI on society, emphasizing transparency, accountability, and fairness in decision-making processes. It offers invaluable insights into creating AI systems that not only perform effectively but also uphold ethical standards and foster trust. Essential reading for technologists, policymakers, and all stakeholders invested in shaping a responsible AI future.'

In a rapidly changing world, are we on the brink of creating technology that outpaces our ability to control it?Astrophysicist J. Craig Wheeler, former president of the American Astronomical Society, takes a critical look at the technological advances shaping our future. From artificial intelligence to genetic engineering, Wheeler explores how these innovations are interconnected and the potential they hold for humanity's evolution. He warns of a future where autonomous machines outsmart us and genetic modifications challenge our very essence. With thought-provoking insights into the ethical dilemmas we face, Wheeler stresses the importance of staying informed and proactive.Key Questions Raised by Wheeler:Will there be jobs for those willing to work in a future dominated by automation?How might social media companies manipulate our decisions, potentially stripping us of free will?Could AI influence or even dictate our voting behaviors?If widespread mental connectivity becomes a reality, could we see the emergence of a collective consciousness that erases individuality?Have we exhausted Earth's resources, and is population control necessary?What implications arise if we solve aging? How will society adapt to the challenges of perpetual youth?What are the realistic prospects of migrating to space as Earth becomes increasingly inhospitable?Our decisions today will determine if we control technology or if it controls us. Through an engaging narrative, Wheeler not only outlines the challenges but also offers practical advice on how we can retain control over our technological destiny.Includes a Foreword by Neil DeGrasse Tyson.

This book includes high-quality research papers presented at the Seventh International Conference on Innovative Computing and Communication (ICICC 2024), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 16–17 February 2024. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.

The book presents selected papers from NIELIT's International Conference on Communication, Electronics and Digital Technology (NICEDT-2024) held during 16–17 February 2024 in Guwahati, India. The book is organized in two volumes and covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cybersecurity and forensic, network and mobile security, advance computing, cloud computing, quantum computing, VLSI and semiconductors, electronics system, Internet of Things, robotics and automations, blockchain and software technology, digital technologies for future, and assistive technology for Divyangjan (people with disabilities).

The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering approach.

This book constitutes the refereed proceedings of the 20th International Symposium on Bioinformatics Research and Applications, ISBRA 2024, held in Kunming, China, in July 19–21, 2024. The 93 full papers included in this book were carefully reviewed and selected from 236 submissions. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.

The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields.

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.Features: Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users'privacy Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area Analyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applications Comprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapter This book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

This volume addresses the major design challenges and research potential in electronic device applications in healthcare and biomedical systems, exploring the blending of innovative mobile communications, network technologies, and medical sensor and ubiquitous computing devices with medical and biological applications. The authors explore current and future trends in new communication and network technologies for healthcare delivery and new wireless telemedical and mobile health services. The chapters look at the application of machine learning, convolutional neural networks, smartphone-based devices, IoT sensors, and other smart technologies for health diagnosis and monitoring. The volume also looks at integrated circuit design for healthcare applications. The design of energy harvesting systems for a low power biomedical applications is considered, and another unique chapter illustrates the ability of mHealth technologies by using machine learning to predict which blood groups provide resistance against the COVID-19 Delta variant.The main driving forces for the transformation of current healthcare systems are the growing aging population, sharp rising healthcare costs, and frequent occurrences of chronic diseases, resulting in the need to deliver healthcare services in more cost-effective and responsive ways. The traditional hospital-centered healthcare systems, which mainly focus on diagnosis and treatment, are now ready to transform into individual-centered based healthcare system, which, in turn, focuses primarily on early detection, early diagnosis, and long-term monitoring. Electronic devices for biomedical and mHealth are facilitating this transformation in innovative ways.This volume, Advanced Research in Electronic Devices for Biomedical and mHealth, provides a selection of insightful chapters on topics that will be of interest to researchers, faculty, and industry professionals in the fields of biophysics, biomedical engineering, healthcare systems, medical informatics, bioinformatics, and digital electronics devise design.

The Volume 1 book on Accelerating Discoveries in Data Science and Artificial Intelligence (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, the International Association of Academicians (IAASSE), and the Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and data science. The book introduces key topics and algorithms and explains how these contribute to healthcare, manufacturing, law, finance, retail, real estate, accounting, digital marketing, and various other fields. The book is primarily meant for academics, researchers, and engineers who want to employ data science techniques and AI applications to address real-world issues. Besides that, businesses and technology creators will also find it appealing to use in industry.

The two-volume proceedings set CCIS 2121 and 2122 constitutes the refereed proceedings of the First International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023, which took place in Hyderabad, India, during August 25–26, 2023. The 46 papers included in these proceedings were carefully reviewed and selected from 138 submissions. They focus on digital healthcare, renewable energy, smart cities, digital farming, and autonomous systems.

FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources. Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user's experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service. Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing's help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage. This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered. Audience The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.

'Digital Transformation in Healthcare 5.0: IoT, AI, and Digital Twin'provides a comprehensive overview of the integration of cutting-edge technology with healthcare, from the Fourth Industrial Revolution (4IR) to the introduction of IoT, AI, and Digital Twin technologies. This in-depth discussion of the digital revolution expanding the healthcare industry covers a wide range of topics, including digital disruption in healthcare delivery, the impact of 4IR and Health 4.0, e-health services and applications, virtual reality's impact on accessible healthcare delivery, digital twins and dietary health technologies, big data analytics in healthcare systems, machine learning models for cost-effective healthcare delivery systems, affordable healthcare with machine learning, enhanced biomedical signal processing with machine learning, and data-driven AI for information retrieval of biomedical images.

As the capabilities of Artificial Intelligence (AI) have increased over recent years, so have the challenges of how to govern its usage. Consequently, prominent stakeholders across academia, government, industry, and civil society have called for states to devise and deploy principles, innovative policies, and best practices to regulate and oversee these increasingly powerful AI tools. Developing a robust AI governance system requires extensive collective efforts throughout the world. It also raises old questions of politics, democracy, and administration, but with the new challenges posed by AI's growing influence on markets, governing structures, international relations, healthcare, science, and political activism. The Oxford Handbook of AI Governance delineates the scope of these issues and addresses the key questions of AI governance. Across forty-nine chapters, organized in nine major sections, the Handbook covers the theoretical and ethical foundations of AI governance, different frameworks for developing a governance structure for AI, practical perspectives on AI governance in different policy domains, economic analyses of AI governance, and concrete lessons about the impact of AI governance domestically and internationally. Chapter authors come from a wide set of disciplines, areas of study, and cultural backgrounds, providing a global perspective on AI governance.

This book is a collection of selected papers presented at the Fourth Congress on Intelligent Systems (CIS 2023), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 4–5, 2023. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.

This book features high-quality research papers presented at the Third International Conference on Data Science and Big Data Analytics (IDBA 2023), organized by Sri Aurobindo Institute of Technology, Indore, India, in association with ACM and IEEE Computer Society in hybrid mode during June 16–17, 2023. This book discusses the topics such as data science, artificial intelligence, machine learning, quantum computing, big data and cloud security, computation security, big data security, information security, forecasting, data analytics, mathematics for data science, graph theory and application in data science, data visualization, computer vision, and analytics for social networks.

The surge in digital transformation and the integration of innovative technologies into manufacturing processes have given rise to a pressing issue in supply chain management. Businesses are in dire need of solutions to navigate this complexity and harness the true potential of intelligent supply chains. Utilization of AI Technology in Supply Chain Management is a comprehensive guide tailored for academic scholars seeking to unravel the mysteries of artificial intelligence (AI) and machine learning (ML) in the context of supply chain management. Amid the hype surrounding AI and ML, there exists a critical need to bridge the gap between human expertise and technological advancements. Utilization of AI Technology in Supply Chain Management addresses this necessity by delving into real-world instances where teams have successfully employed these innovative technologies to enhance supply chain performance, reduce inventory, and optimize routes. The adoption of AI and ML is not just a trend; it is the cornerstone of digital acceleration initiatives, making it imperative for scholars to understand and leverage these technologies effectively. The topics within the book span the breadth of AI and ML applications in supply chain management, offering insights into automation's impact, supply chain network optimization, intelligent systems for industrial robots, and much more. Whether you are navigating the intricacies of computational biology or exploring the fuzzy Bayesian approach to Industry 6.0 supplier selection, this book provides a roadmap to unlock the full potential of AI and ML in solving the intricate problems faced by businesses in the supply chain domain. Join us in this intellectual journey as we present a compelling solution to the challenges of Industry 4.0, bridging the gap between theory and practical applications in the evolving landscape of supply chain management.

Academic scholars face the daunting challenge of keeping pace with the rapid evolution of innovative technologies. The emergence of AI-driven solutions, deep learning frameworks, and medical robotics introduces a complex terrain, demanding in-depth understanding and analysis. As scholars navigate the intricacies of patient hate speech detection, cardiovascular diseases AI-CDSS, and the revolution in medical diagnostics, a pressing need arises for comprehensive insights that bridge the gap between theoretical knowledge and practical applications. Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications serves as a solution in this era of transformative healthcare and addresses these challenges head-on. It unravels the complexities surrounding the implementation of AI in healthcare, offering in-depth discussions on the latest breakthroughs. From unraveling the mysteries of AI-driven cataract detection to exploring the implications of decentralized mammography classification, the book is a valuable resource that equips scholars with the knowledge to navigate this innovative landscape. As scholars grapple with the ongoing advancements in surgical robotics, healthcare smart sensors, and the fusion of IoT and deep learning, this book serves as an indispensable guide. It provides a comprehensive overview of the current challenges but also presents innovative solutions that pave the way for a smarter, sustainable healthcare future. In the realm of data management, big data analytics, and the intersection of cybersecurity and cloud computing, our book offers a holistic perspective, making it an essential companion for scholars seeking to deepen their understanding of the transformative forces shaping the healthcare industry.

Cognitive disorders are a growing concern, affecting individuals across the age spectrum and society. These disorders can profoundly disrupt daily life, and their timely diagnosis is crucial for effective intervention and care. As the prevalence of cognitive disorders continues to rise, the need for precise and early diagnosis has never been more pressing. Intelligent Solutions for Cognitive Disorders is a research-based book which delves into the intersection of medical science and technology, exploring the latest advancements in cognitive disorder diagnosis and treatment. This book assembles a multidisciplinary team of experts, including researchers, clinicians, and technologists, to address this challenge head-on. This book commences with an in-depth introduction to cognitive disorders, providing a solid foundation for readers of all backgrounds. It then navigates the role of intelligent systems in cognitive healthcare, unveiling the potential of artificial intelligence, machine learning, and deep learning techniques. The book highlights how these intelligent systems can enable the early and accurate detection of cognitive disorders, a pivotal factor in improving patients'quality of life. Furthermore, Intelligent Solutions for Cognitive Disorders dives into the world of intelligent assistive technologies. Caregivers and technology specialists will discover innovative tools and devices designed to provide optimal support to individuals with cognitive impairments, enhancing their overall well-being. The book addresses a diverse range of cognitive disorders, from Alzheimer's disease and attention deficit disorder (ADHD) to epilepsy-related cognitive dysfunction and early-onset dementia. Each chapter provides a deep dive into the latest techniques and technologies for detection and prediction, ensuring that readers stay at the forefront of cognitive healthcare research. Emerging technologies take center stage in the latter part of the book, with discussions on wearable devices, sensors, and the integration of deep learning in clinical settings. Intelligent Solutions for Cognitive Disorders is a pivotal contribution to cognitive healthcare. It bridges the gap between academia and clinical practice, fostering collaboration among experts to develop practical, intelligent solutions. This book is an invaluable resource for technologists, researchers, linguists, data scientists, healthcare practitioners, medical professionals, and students seeking a comprehensive understanding of cognitive disorders and the role of intelligent technologies in their diagnosis and care.

This book highlights how optimized big data applications can be used for patient monitoring and clinical diagnosis. In fact, IoT-based applications are data-driven and mostly employ modern optimization techniques. The book also explores challenges, opportunities, and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.

Artificial Intelligence and Machine Learning for Women's Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to women's health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on women's health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women's health issues. - Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women's health issues - Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities - Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women's healthcare

This book discusses a multidisciplinary and multi-stakeholder collaborative approach to small population clinical trials. It provides and illustrates a systematic framework that depends upon a collaborative infrastructure between the patient community, patient advocacy groups, researchers and investigators, pharmaceutical companies, global regulatory agencies, and customers. The book also highlights the considerations of natural history and patient registry, diagnostic criteria determination, identification of clinically meaningful endpoints, and global regulatory guidance. The authors detail the nuances of clinical trial design as specific to small population disease, drawing from real examples.

This open access book constitutes revised selected papers from the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023. The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning.

This edited volume explores the integration of artificial intelligence to improve crop production. It addresses the critical need for intelligent crop management in light of the world's escalating population. Encompassing a spectrum of technologies, including computer vision, image processing, soft computing, machine learning, and deep learning, the book explores advancements in decision-making systems. It integrates data science methodologies, Internet of Things, wireless communications, and a range of sensors and actuators to provide precise, timely, and cost-effective solutions to agricultural challenges, ultimately enhancing both the quality and quantity of crop yields. The book empowers its audience to direct their efforts towards designing models and prototypes that benefit society and the environment, making it an indispensable resource for those eager to shape the future of intelligent agriculture. It serves as a comprehensive guide for students, scholars, and academicians keen on delving into the transformative field of artificial intelligence in agriculture. Researchers, scientists, and field experts will find invaluable insights to guide their exploration and contribution to this domain.

The book is a collection of the high-quality research articles presented at International Conference on Computing and Communication Systems for Industrial Applications (ComSIA 2024), organized by Shaheed Rajguru College of Applied Sciences, University of Delhi, India during May 2024. The book covers state-of-the-art research articles from the researchers and practitioners working in the field of software engineering and automation, intelligent systems, data science and engineering, networks and data communication, internet of things, hardware architecture and communication technologies, cyber security and data privacy, and intelligent systems and automation.

The book delves into innovations in AI and related computing paradigms for disease detection and diagnosis. The collected chapters elucidate the use of a variety of AI and related methodologies to address specific medical challenges. From detecting pancreatic cancer, classifying micro-emboli in stroke diagnosis, to segmenting brain tumours from MRI data, and more, the culmination of these studies underscores the transformative impact AI and digital technologies can have on healthcare, emphasising their potential to enhance medical treatment and improve patient care.

The book uniquely explores the fundamentals of blockchain and digital twin technologies and their uses in smart cities. In the previous decade, many governments explored artificial intelligence, digital twin, and blockchain, and their roles in smart cities. This book discusses the convergence of two transformative technologies, digital twin and blockchain, to address urban challenges and propel the development of smarter, more sustainable cities. This convergence empowers cities to create real-time replicas of urban environments (digital twins) and secure, transparent data management (blockchain) to improve city planning, management, and civic services. In this application, the concept of a digital twin involves creating a virtual, data-driven replica of a city or specific urban systems, such as transportation, energy, or infrastructure. This digital twin mirrors the real world, gathering data from various sensors, IoT devices, and other sources to provide a holistic view of the city's operations. Furthermore, blockchain technology offers a decentralized and tamper-resistant ledger for securely storing and managing data. In the context of smart cities, blockchain can ensure data integrity, privacy, and transparency, enabling trust and collaboration among various stakeholders. This book covers many important topics, including real-time city modeling; data security and the trustworthy storage of sensitive urban data; transparent governance to facilitate accountable governance and decision-making processes in smart cities; improved city services; disaster resilience (by providing insights into vulnerabilities and efficient resource allocation during crises); sustainable urban planning that optimizes resource allocation, reduces energy consumption, and minimizes environmental impact, which fosters sustainable development; citizen engagement; and much more. This book will not only provide information about more efficient, resilient, and sustainable urban environments, but it also empowers citizens to be active participants in shaping the future of their cities. By converging these technologies, cities can overcome existing challenges, encourage innovation, and create more livable, connected, and responsive urban spaces. Audience This book has a wide audience in computer science, artificial intelligence, and information technology as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and government/city policymakers working on smart cities, the circular economy, clean tech investors, urban decision-makers, and environmental professionals.

The book uniquely explores the fundamentals of blockchain and digital twin and their uses in smart hospitals. Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals provides fundamental information on blockchain and digital twin technology as effective solutions in smart hospitals. Digital twin technology enables the creation of real-time virtual replicas of hospital assets and patients, enhancing predictive maintenance, operational efficiency, and patient care. Blockchain technology provides a secure and transparent platform for managing and sharing sensitive data, such as medical records and pharmaceutical supply chains. By combining these technologies, smart hospitals can ensure data security, interoperability, and streamlined operations while providing patient-centered care. The book also explores the impact of collected medical data from real-time systems in smart hospitals, and by making it accessible to all doctors via a smartphone or mobile device for fast decisions. Inevitable challenges such as privacy concerns and integration costs must, of course, be addressed. However, the potential benefits in terms of improved healthcare quality, reduced costs, and global health initiatives makes the integration of these technologies a compelling avenue for the future of healthcare. Some of the topics that readers will find in this book include: Wireless Medical Sensor Networks in Smart Hospitals ● DNA Computing in Cryptography ● Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification ● Machine Learning-Enabled Digital Twins for Diagnostic And Therapeutic Purposes ● Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals ● Blockchain for Edge Association in Digital Twin Empowered 6G Networks ● Blockchain for Security and Privacy in Smart Healthcare ● Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Healthcare and Biomedical Sector ● Electronic Health Records in a Blockchain ● PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm ● AI and Transfer Learning Based Framework for Efficient Classification And Detection Of Lyme Disease ● Framework for Gender Detection Using Facial Countenances ● Smartphone-Based Sensors for Biomedical Applications ● Blockchain for Improving Security and Privacy in the Smart Sensor Network ● Sensors and Digital Twin Application in Healthcare Facilities Management ● Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy ● Machine Learning-Driven Digital Twins for Precise Brain Tumor and Breast Cancer Assessment ● Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare ● Digital Twin Application in Healthcare Facilities Management ● Cloud-based Digital Twinning for Structural Health Monitoring Using Deep Learning. Audience The book will be read by hospital and healthcare providers, administrators, policymakers, scientists and engineers in artificial intelligence, information technology, electronics engineering, and related disciplines.