Moolchand Sharma – författare

In the era of the Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such a humongous amount of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such a computing framework for a large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade.Applications of Cloud Computing: Approaches and Practices lays a good foundation for the core concepts and principles of cloud computing applications, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into the applications of it.It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.

New prospects for biomedical and healthcare engineering are being created by the rapid development of Robotic and Artificial Intelligence techniques. Innovative technologies such as Artificial Intelligence, Deep Learning, Robotics, and IoT are currently under huge influence in today’s modern world. For instance, a micro-nano robot allows us to study the fundamental problems at a cellular scale owing to its precise positioning and manipulation ability; the medical robot paves a new way for the low-invasive and high-efficient clinical operation, and rehabilitation robotics is able to improve the rehabilitative efficacy of patients. This book aims at exhibiting the latest research achievements, findings, and ideas in the field of robotics in biomedical and healthcare engineering, primarily focusing on the walking assistive robot, telerobotic surgery, upper/lower limb rehabilitation, and radiosurgery. As a result, a wide range of robots are being developed to serve a variety of roles within the medical environment. Robots specializing in human treatment include surgical robots and rehabilitation robots. The field of assistive and therapeutic robotic devices is also expanding rapidly. These include robots that help patients rehabilitate from severe conditions like strokes, empathic robots that assist in the care of older or physically/mentally challenged individuals, and industrial robots that take on a variety of routine tasks, such as sterilizing rooms and delivering medical supplies and equipment, including medications. The objectives of the book are in terms of advancing the state-of-the-art of robotic techniques and addressing the challenging problems in biomedical and healthcare engineering. This book Lays a good foundation for the core concepts and principles of robotics in biomedical and healthcare engineering, walking the reader through the fundamental ideas with expert ease. Progresses on the topics in a step-by-step manner and reinforces theory with a full-fledged pedagogy designed to enhance students’ understanding and offer them a practical insight into the applications of it. Features chapters that introduce and cover novel ideas in healthcare engineering like Applications of Robots in Surgery, Microrobots and Nanorobots in Healthcare Practices, Intelligent Walker for Posture Monitoring, AI-Powered Robots in Biomedical and Hybrid Intelligent Systems for Medical Diagnosis, and so on.Deepak Gupta is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.Moolchand Sharma is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.Vikas Chaudhary is a Professor at the JIMS Engineering Management Technical Campus, GGSIPU, Greater Noida, India.Ashish Khanna currently works at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.

New prospects for biomedical and healthcare engineering are being created by the rapid development of Robotic and Artificial Intelligence techniques. Innovative technologies such as Artificial Intelligence, Deep Learning, Robotics, and IoT are currently under huge influence in today’s modern world. For instance, a micro-nano robot allows us to study the fundamental problems at a cellular scale owing to its precise positioning and manipulation ability; the medical robot paves a new way for the low-invasive and high-efficient clinical operation, and rehabilitation robotics is able to improve the rehabilitative efficacy of patients. This book aims at exhibiting the latest research achievements, findings, and ideas in the field of robotics in biomedical and healthcare engineering, primarily focusing on the walking assistive robot, telerobotic surgery, upper/lower limb rehabilitation, and radiosurgery. As a result, a wide range of robots are being developed to serve a variety of roles within the medical environment. Robots specializing in human treatment include surgical robots and rehabilitation robots. The field of assistive and therapeutic robotic devices is also expanding rapidly. These include robots that help patients rehabilitate from severe conditions like strokes, empathic robots that assist in the care of older or physically/mentally challenged individuals, and industrial robots that take on a variety of routine tasks, such as sterilizing rooms and delivering medical supplies and equipment, including medications. The objectives of the book are in terms of advancing the state-of-the-art of robotic techniques and addressing the challenging problems in biomedical and healthcare engineering. This book Lays a good foundation for the core concepts and principles of robotics in biomedical and healthcare engineering, walking the reader through the fundamental ideas with expert ease. Progresses on the topics in a step-by-step manner and reinforces theory with a full-fledged pedagogy designed to enhance students’ understanding and offer them a practical insight into the applications of it. Features chapters that introduce and cover novel ideas in healthcare engineering like Applications of Robots in Surgery, Microrobots and Nanorobots in Healthcare Practices, Intelligent Walker for Posture Monitoring, AI-Powered Robots in Biomedical and Hybrid Intelligent Systems for Medical Diagnosis, and so on.Deepak Gupta is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.Moolchand Sharma is an Assistant Professor at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.Vikas Chaudhary is a Professor at the JIMS Engineering Management Technical Campus, GGSIPU, Greater Noida, India.Ashish Khanna currently works at the Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India.

In the era of the Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such a humongous amount of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such a computing framework for a large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade.Applications of Cloud Computing: Approaches and Practices lays a good foundation for the core concepts and principles of cloud computing applications, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into the applications of it.It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.

Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges.This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation.It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.

Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges.This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation.It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics. It presents practical scenarios from the industry for the application of the internet of things in various domains. Next Generation Communication Networks for Industrial Internet of Things Systems presents concepts and research challenges in communication networking for Industrial internet of things systems. Features: Discusses process monitoring, environmental monitoring, control, and maintenance monitoring. Covers data collection and communication protocols in a comprehensive manner. Highlights the internet of things industrial applications, and industrial revolution 4.0. Presents 5G-enabled internet of things technology and architecture. Showcases artificial intelligence techniques in the IoT networks. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas of electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics. It presents practical scenarios from the industry for the application of the internet of things in various domains. Next Generation Communication Networks for Industrial Internet of Things Systems presents concepts and research challenges in communication networking for Industrial internet of things systems. Features: Discusses process monitoring, environmental monitoring, control, and maintenance monitoring. Covers data collection and communication protocols in a comprehensive manner. Highlights the internet of things industrial applications, and industrial revolution 4.0. Presents 5G-enabled internet of things technology and architecture. Showcases artificial intelligence techniques in the IoT networks. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas of electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.

This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.

Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance.This book:Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance.This book:Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

This book is a comprehensive exploration of the dynamic interplay between bioinformatics and artificial intelligence (AI) within the healthcare landscape. This book introduces readers to the foundational principles of bioinformatics and AI, elucidating their integration and collaborative potential.Bioinformatics and Beyond: AI Applications in Healthcare explores the transformative impact of data-driven insights, showcasing the applications of machine learning in diagnostics, personalized medicine, and genomic advancements. The book unveils the pivotal role AI plays in accelerating pharmaceutical research. Moreover, it addresses the practical implementation of AI in clinical decision support systems, while also critically examining challenges and ethical considerations associated with these technologies. Finally, the book looks toward the future, envisioning emerging trends and technologies that promise to reshape the future of healthcare. Aimed at professionals, researchers, and students across diverse disciplines, this book serves as an invaluable guide to understanding and navigating the evolving landscape of AI applications in healthcare.This book is tailored to meet the needs of scientists, researchers, practitioners, professionals, and educators actively engaged in the realms of bioinformatics, artificial intelligence, and healthcare. It will be an indispensable resource for those seeking advanced strategies to address challenges and harness opportunities in the rapidly evolving fields of medical and biomedical research.

This book is a comprehensive exploration of the dynamic interplay between bioinformatics and artificial intelligence (AI) within the healthcare landscape. This book introduces readers to the foundational principles of bioinformatics and AI, elucidating their integration and collaborative potential.Bioinformatics and Beyond: AI Applications in Healthcare explores the transformative impact of data-driven insights, showcasing the applications of machine learning in diagnostics, personalized medicine, and genomic advancements. The book unveils the pivotal role AI plays in accelerating pharmaceutical research. Moreover, it addresses the practical implementation of AI in clinical decision support systems, while also critically examining challenges and ethical considerations associated with these technologies. Finally, the book looks toward the future, envisioning emerging trends and technologies that promise to reshape the future of healthcare. Aimed at professionals, researchers, and students across diverse disciplines, this book serves as an invaluable guide to understanding and navigating the evolving landscape of AI applications in healthcare.This book is tailored to meet the needs of scientists, researchers, practitioners, professionals, and educators actively engaged in the realms of bioinformatics, artificial intelligence, and healthcare. It will be an indispensable resource for those seeking advanced strategies to address challenges and harness opportunities in the rapidly evolving fields of medical and biomedical research.

This book explores the intersection of artificial intelligence and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches.Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of generative AI, generative AI models, and possible areas in healthcare where generative AI can work. It discusses how generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science.

This book explores the intersection of artificial intelligence and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches.Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of generative AI, generative AI models, and possible areas in healthcare where generative AI can work. It discusses how generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science.

The rapid development in AI and quantum computing has resulted in a new domain termed Quantum-Inspired Neural Networks (QINNs). These models utilize ideas from quantum mechanics, including superposition, entanglement, and quantum probability, to improve the efficiency and performance of classical neural networks. This book examines the theoretical underpinnings, frameworks, and practical implementations of QINNs, rendering it an essential resource for scholars, academics, and industry experts. It examines mathematical frameworks behind quantum-inspired models, their implementation methodologies, and their relevance in diverse fields, including healthcare, finance, cybersecurity, and natural language processing. It serves as a comprehensive guide for individuals seeking to comprehend and apply QINNs in practical situations, utilizing theoretical insights, algorithmic frameworks, and case examples. The book is distinct due to its emphasis on the present and future of quantum-inspired deep learning. It integrates discussions on hybrid quantum-classical architectures, optimization strategies, and scalability difficulties, addressing the gap between quantum computing and classical AI, which are often treated separately in previous literature. Furthermore, it examines the constraints and future potential of QINNs, providing a framework for the shift from traditional deep learning to quantumaugmented models. Readers will acquire a profound comprehension of how quantum-inspired methodologies might transform the AI domain and propel innovation in nascent technologies.Key Features:Investigates the integration of quantum computing concepts with neural networks, a dynamically advancing domain with transformational capabilitiesConnects quantum computing, artificial intelligence, and machine learning, making it applicable across several fieldsAppeals to both academic researchers and industry professionals by addressing theoretical advancements and practical applicationsExplores the security implications of quantum AI and ethical concerns, making it relevant for policymakers and tech leadersCaters to researchers, academics, AI practitioners, and students looking to explore next-gen AI technologies

This book explores the convergence of artificial intelligence (AI) and the Internet of Things (IoT) in transforming healthcare delivery. It provides a comprehensive overview of AIoT healthcare systems, covering concepts, architectures, applications, challenges, and future directions.Transforming Healthcare with AI and IoT: Intelligent Solutions for a Digital Future delves into core AI and IoT concepts, it explores their integration in healthcare settings, and showcases real-world examples. Readers will gain valuable insights into how AI and IoT are already revolutionizing healthcare delivery, inspiring them with tangible examples of success. Through real-world case studies and practical insights, readers will learn how AI and IoT can optimize resource allocation, improve patient outcomes, and enhance overall system sustainability. It covers topics including big data analytics, personalized medicine, smart hospitals, telehealth, and virtual care. The book also addresses ethical considerations, security concerns, and challenges in deploying AIoT healthcare systems.The book is primarily written for professionals and researchers involved in healthcare management, information technology, and AI. Additionally, undergraduate and graduate students studying healthcare technology, AI, or related fields would also find it invaluable.