The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features:Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanicsExplores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniquesProvides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical modelsExplains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological systemPresents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscleMechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features:Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanicsExplores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniquesProvides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical modelsExplains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological systemPresents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscleMechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

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.

The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare dataK. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare dataK. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images.The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images.The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

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.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

This comprehensive book delves into the key features introduced in Java 22, providing insights and practical examples to help readers understand and implement these enhancements within the context of medical applications with the goal of enhancing medical practices, streamlining processes, and improving patient care.Java 22 for Healthcare and Medical Applications explores the latest Java 22 and features the newest advancements in Java programming, including unnamed variables, launch multi-file source-code programs, and foreign function and memory APIs. From implementing advanced algorithms for medical image analysis to developing efficient healthcare management systems, this book shows how this tool can revolutionize the medical field. With its focus on accessibility, interoperability, and data security, this book empowers healthcare professionals to leverage technology in innovative ways, ultimately leading to better patient outcomes and improved healthcare delivery. Whether it's optimizing electronic health record systems, developing telemedicine platforms, or advancing medical research through data analysis, the author equips medical professionals with the knowledge and skills needed to harness the full potential of Java programming in the healthcare industry. With clear explanations and 300+ hands-on examples, readers will learn how to leverage Java 22's cutting-edge features to build powerful and efficient applications.The book caters to a diverse audience ranging from students to professionals and provides valuable insights into the latest advancements in Java programming and its applications within healthcare and medical technology.

This comprehensive book delves into the key features introduced in Java 22, providing insights and practical examples to help readers understand and implement these enhancements within the context of medical applications with the goal of enhancing medical practices, streamlining processes, and improving patient care.Java 22 for Healthcare and Medical Applications explores the latest Java 22 and features the newest advancements in Java programming, including unnamed variables, launch multi-file source-code programs, and foreign function and memory APIs. From implementing advanced algorithms for medical image analysis to developing efficient healthcare management systems, this book shows how this tool can revolutionize the medical field. With its focus on accessibility, interoperability, and data security, this book empowers healthcare professionals to leverage technology in innovative ways, ultimately leading to better patient outcomes and improved healthcare delivery. Whether it's optimizing electronic health record systems, developing telemedicine platforms, or advancing medical research through data analysis, the author equips medical professionals with the knowledge and skills needed to harness the full potential of Java programming in the healthcare industry. With clear explanations and 300+ hands-on examples, readers will learn how to leverage Java 22's cutting-edge features to build powerful and efficient applications.The book caters to a diverse audience ranging from students to professionals and provides valuable insights into the latest advancements in Java programming and its applications within healthcare and medical technology.

The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features:Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanicsExplores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniquesProvides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical modelsExplains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological systemPresents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscleMechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features:Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanicsExplores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniquesProvides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical modelsExplains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological systemPresents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscleMechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

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.

The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare dataK. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare dataK. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images.The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.

This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images.The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

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.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

This comprehensive book delves into the key features introduced in Java 22, providing insights and practical examples to help readers understand and implement these enhancements within the context of medical applications with the goal of enhancing medical practices, streamlining processes, and improving patient care.Java 22 for Healthcare and Medical Applications explores the latest Java 22 and features the newest advancements in Java programming, including unnamed variables, launch multi-file source-code programs, and foreign function and memory APIs. From implementing advanced algorithms for medical image analysis to developing efficient healthcare management systems, this book shows how this tool can revolutionize the medical field. With its focus on accessibility, interoperability, and data security, this book empowers healthcare professionals to leverage technology in innovative ways, ultimately leading to better patient outcomes and improved healthcare delivery. Whether it's optimizing electronic health record systems, developing telemedicine platforms, or advancing medical research through data analysis, the author equips medical professionals with the knowledge and skills needed to harness the full potential of Java programming in the healthcare industry. With clear explanations and 300+ hands-on examples, readers will learn how to leverage Java 22's cutting-edge features to build powerful and efficient applications.The book caters to a diverse audience ranging from students to professionals and provides valuable insights into the latest advancements in Java programming and its applications within healthcare and medical technology.

This comprehensive book delves into the key features introduced in Java 22, providing insights and practical examples to help readers understand and implement these enhancements within the context of medical applications with the goal of enhancing medical practices, streamlining processes, and improving patient care.Java 22 for Healthcare and Medical Applications explores the latest Java 22 and features the newest advancements in Java programming, including unnamed variables, launch multi-file source-code programs, and foreign function and memory APIs. From implementing advanced algorithms for medical image analysis to developing efficient healthcare management systems, this book shows how this tool can revolutionize the medical field. With its focus on accessibility, interoperability, and data security, this book empowers healthcare professionals to leverage technology in innovative ways, ultimately leading to better patient outcomes and improved healthcare delivery. Whether it's optimizing electronic health record systems, developing telemedicine platforms, or advancing medical research through data analysis, the author equips medical professionals with the knowledge and skills needed to harness the full potential of Java programming in the healthcare industry. With clear explanations and 300+ hands-on examples, readers will learn how to leverage Java 22's cutting-edge features to build powerful and efficient applications.The book caters to a diverse audience ranging from students to professionals and provides valuable insights into the latest advancements in Java programming and its applications within healthcare and medical technology.