Kapil Joshi – författare

The book provides a comprehensive overview of the fundamentals, technologies, and recent advances in surgical robotics and computer-assisted surgery and provides a thorough understanding of the fundamentals and advances in surgical robotics and computer-assisted surgery. It covers core concepts, system architectures, control mechanisms, imaging integration, AI-assisted decision support, and emerging robotic platforms. Through theoretical insights, practical case studies and clinical applications, the book bridges engineering principles with medical practice, highlighting current challenges, ethical considerations, and future directions in robotic-assisted surgical systems. The book is intended for researchers, graduate students, surgeons, biomedical engineers, and industry professionals working in surgical robotics, medical imaging, healthcare technology, and computer-assisted intervention systems.Key Features:Covers fundamental principles, technological advancements, clinical applications, and future directions.Includes contributions from leading experts in surgical robotics and computer-assisted surgery.Provides a comprehensive overview of robotic systems, sensors, instrumentation, and advanced materials.Explores clinical applications in urology, cardiothoracic surgery, neurosurgery, orthopedics, and more.Discusses artificial intelligence, machine learning, virtual reality, and augmented reality in robotic surgery.Offers case studies, best practices, and lessons learned from robotic surgery programs.

This book discusses the key concepts of AI in imaging and healthcare. It includes a critical analysis of its algorithms and applications, along with emerging trends in medical diagnostics, device optimization, and personalized medicine. The book uses metadata heuristic algorithms, deep learning for disease detection, medical image segmentation, explainable AI, and IoT and hybrid algorithms integrating all this into the healthcare system. Besides, the book touches on specific issues such as CNNs, multi-class and multi-label disease classification, and digital transformation of health services. Special features of the book include:Multidisciplinary approach covering topics such as AI, deep learning, IoT, and metaheuristics with a focus on healthcare.Real examples with detailed information for analyzing current technologies.Discusses several topics on interpretability and ethical considerations in AI, making it rich material for technical experts and healthcare professionals.Identifies efficient models, with which the automated medical diagnosis and prognosis is possible.The book is specifically oriented towards researchers, practitioners, and students in the areas of artificial intelligence, medical imaging, healthcare technology, and bioinformatics.

Human Cancer Diagnosis and Detection Using Exascale Computing The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology. Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer. The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer. Audience This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.

Human Cancer Diagnosis and Detection Using Exascale Computing

The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology.

Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer.

The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer.

Audience

This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.

Human Cancer Diagnosis and Detection Using Exascale Computing

The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology.

Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer.

The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer.

Audience

This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;methods capable of retrieving photometric and geometric transformed images;concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;a comprehensive study of content-based image-retrieval techniques for feature extraction;machine learning approaches to understanding angiogenesis;handwritten image enhancement based on neutroscopic-fuzzy.Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.

Applications highlighted in the book include:

diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy.

Audience

The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.

Applications highlighted in the book include:

diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy.

Audience

The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Empower your response to global climate extremes by mastering the transformative power of synthetic aperture radar technology with this essential guide to its role in disaster management. As humanity navigates the escalating challenges of a rapidly changing world marked by intensifying climate extremes, environmental degradation, and urbanization, the demand to mitigate disasters and protect vulnerable communities has never been more urgent. This book is a critical resource at this pivotal juncture, uniting the frontiers of synthetic aperture radar technology with the pressing demands of disaster resilience. It illustrates how science, engineering, and policy can converge to build a safer, more sustainable future, offering a transformative lens through which to address the complex interplay of global vulnerabilities and technological innovation. Using a holistic exploration of synthetic aperture radar’s transformative role in disaster management, the book equips readers to harness this emerging technology for rapid disaster response, infrastructure recovery, and climate resilience. Readers will find the volume: Explores the role of machine learning, deep learning, and multi-sensor data fusion in interpreting SAR data for real-time decision-making;Includes real-world case studies that demonstrate the effectiveness of SAR in disaster scenarios, bridging theory with actionable solutions;Combines radar technology, computational advancements, and policy frameworks to address the interdisciplinary challenges of disaster management;Covers the end-to-end workflow of SAR in disaster contexts, from data acquisition and processing to interpretation and decision-making.Audience Designed for professionals, researchers, and students in remote sensing, geoinformatics, earth observation, disaster management, environmental agencies, and geospatial intelligence.

Empower your response to global climate extremes by mastering the transformative power of synthetic aperture radar technology with this essential guide to its role in disaster management. As humanity navigates the escalating challenges of a rapidly changing world marked by intensifying climate extremes, environmental degradation, and urbanization, the demand to mitigate disasters and protect vulnerable communities has never been more urgent. This book is a critical resource at this pivotal juncture, uniting the frontiers of synthetic aperture radar technology with the pressing demands of disaster resilience. It illustrates how science, engineering, and policy can converge to build a safer, more sustainable future, offering a transformative lens through which to address the complex interplay of global vulnerabilities and technological innovation. Using a holistic exploration of synthetic aperture radar s transformative role in disaster management, the book equips readers to harness this emerging technology for rapid disaster response, infrastructure recovery, and climate resilience. Readers will find the volume: Explores the role of machine learning, deep learning, and multi-sensor data fusion in interpreting SAR data for real-time decision-making; Includes real-world case studies that demonstrate the effectiveness of SAR in disaster scenarios, bridging theory with actionable solutions; Combines radar technology, computational advancements, and policy frameworks to address the interdisciplinary challenges of disaster management; Covers the end-to-end workflow of SAR in disaster contexts, from data acquisition and processing to interpretation and decision-making. Audience Designed for professionals, researchers, and students in remote sensing, geoinformatics, earth observation, disaster management, environmental agencies, and geospatial intelligence.

Empower your response to global climate extremes by mastering the transformative power of synthetic aperture radar technology with this essential guide to its role in disaster management. As humanity navigates the escalating challenges of a rapidly changing world marked by intensifying climate extremes, environmental degradation, and urbanization, the demand to mitigate disasters and protect vulnerable communities has never been more urgent. This book is a critical resource at this pivotal juncture, uniting the frontiers of synthetic aperture radar technology with the pressing demands of disaster resilience. It illustrates how science, engineering, and policy can converge to build a safer, more sustainable future, offering a transformative lens through which to address the complex interplay of global vulnerabilities and technological innovation. Using a holistic exploration of synthetic aperture radar s transformative role in disaster management, the book equips readers to harness this emerging technology for rapid disaster response, infrastructure recovery, and climate resilience. Readers will find the volume: Explores the role of machine learning, deep learning, and multi-sensor data fusion in interpreting SAR data for real-time decision-making; Includes real-world case studies that demonstrate the effectiveness of SAR in disaster scenarios, bridging theory with actionable solutions; Combines radar technology, computational advancements, and policy frameworks to address the interdisciplinary challenges of disaster management; Covers the end-to-end workflow of SAR in disaster contexts, from data acquisition and processing to interpretation and decision-making. Audience Designed for professionals, researchers, and students in remote sensing, geoinformatics, earth observation, disaster management, environmental agencies, and geospatial intelligence.