D. Jude Hemanth – författare

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.

Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection Contains an analysis of medical databases to provide diagnostic expert systems Addresses the integration of intelligent data analysis techniques within biomedical information systems

Telemedicine Technologies: Big Data, Deep Learning, Robotics, Mobile and Remote Applications for Global Healthcare illustrates the innovative concepts, methodologies and frameworks that will increase the feasibility of the existing telemedicine system. The book also focuses on showcasing prototypes of remote healthcare systems, thus emphasizing the data processing side that is often recognized as the backbone of any telemedicine system.

Illustrates the innovative concepts, methodologies and frameworks that will increase the feasibility of the existing telemedicine system Focuses on showcasing prototypes of remote healthcare systems

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer's disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders

Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development focuses on recent advances and benefits of wearable telemedicine techniques for remote health monitoring and prevention of chronic conditions, providing real time feedback and help with rehabilitation and biomedical applications. Readers will learn about various techniques used by software engineers, computer scientists and biomedical engineers to apply intelligent systems, artificial intelligence, machine learning, virtual reality and augmented reality to gather, transmit, analyze and deliver real-time clinical and biological data to clinicians, patients and researchers.

Wearable telemedicine technology is currently establishing its place with large-scale impact in many healthcare sectors because information about patient health conditions can be gathered anytime and anywhere outside of traditional clinical settings, hence saving time, money and even lives.

Provides readers with methods and applications for wearable devices for ubiquitous health and activity monitoring, wearable biosensors, wearable app development and management using machine learning techniques, and more Integrates coverage of a number of key wearable technologies, such as ubiquitous textile systems for movement disorders, remote surgery using telemedicine, intelligent computing algorithms for smart wearable healthcare devices, blockchain, and more Provides readers with in-depth coverage of wearable product design and development

Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye.� Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders.

This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration.

Presents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye Conveys the role of a variety of computational methods and algorithms for efficient and effective diagnosis of ophthalmologic disorders, including Diabetic Retinopathy, Glaucoma, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders Explains how to develop and apply a variety of computational diagnosis systems and technologies, including medical image processing algorithms, bioinspired optimization, Deep Learning, computational intelligence systems, fuzzy-based segmentation methods, transfer learning approaches, and hybrid Artificial Neural Networks

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues.

Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Introduces and provides reviews on cyber physical and edge computing systems, with different architectures and models needed to address sustainable solutions to social, environmental and economic applications Presents the different architectures of edge computing for building cyber physical systems with dynamic resource provisioning and security solutions Provides AI based perspectives to edge-based cyber physical systems with different algorithms and AI based security solutions Covers different case studies and applications in detail, with real-life examples and possible challenges that can be encountered Offers perspectives for the design, development and commissioning of intelligent edge-based cyber physical systems

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics.The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions.The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. The book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries.Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control.Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this processProvides in-depth case studies and state-of-the-art methods, along with the experimental studyPresents a block diagram based complete set-up in each chapter to help in real-time implementation

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images.The book also discusses hybrid intelligence approaches for early-stage detection and the use of machine learning classifiers for cancer detection, staging and density assessment in order to develop a proper treatment plan. This book will not only aid computer scientists and medical practitioners in developing a real-time AI based mammogram analysis system, but also addresses the issues and challenges with the current processing methods which are not conducive for real-time applications.

Presents novel ideas for AI based mammogram data analysisDiscusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancerFeatures dozens of real-world case studies from contributors across the globe

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

Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.Includes basic concepts, technical explanations, and case studies for in-depth explanation of the Sentiment AnalysisAids computer scientists in developing practical/real-world AI-based Sentiment Analysis systemsProvides readers with real-world development applications of AI-based Sentiment Analysis, including transfer learning for opinion mining from pandemic medical data, sarcasm detection using neural networks in human-computer interaction, and emotion detection using the random-forest algorithm

Leveraging Metaverse and Analytics of Things (AoT) in Medical Systems explores the potential benefits and applications of emerging technologies such as metaverse and AoT in the field of healthcare. The book provides insights into how these technologies can be leveraged to improve the efficiency, effectiveness, and quality of medical systems. It explores the concept of metaverse and its potential applications in healthcare, including the use of virtual and augmented reality technologies for medical education, training, and simulation, as well as the development of immersive environments for patient care and therapy.The book also delves into the field of AoT, such as the use of wearable devices, smart sensors, and other connected technologies. Applications to monitor patient health, track medical outcomes, and inform clinical decision-making are covered. Integrating both technologies can help improve medical training, diagnosis, treatment, and patient outcomes through the use of virtual reality and real-time data analytics.

Features research methods, data analysis techniques, and best practices related to the implementation of Metaverse and Analytics of Things (AoT) in medical systemsProvides practical guidance and recommendations on how healthcare organizations can adopt and implement Metaverse and Analytics of Things (AoT) in their operationsIncludes case studies and real-world examples of how healthcare organizations have successfully leveraged technology and data analytics to improve patient care and operational efficiency

Rehabilitation Engineering, Assistive Technologies and the Future of Healthcare Using Robotics??? presents the most recent developments in assistive technologies, rehabilitative engineering, and robotics, offering in-depth analysis of cutting-edge instruments and frameworks that have the power to transform rehabilitation and general well-being. The book provides insights into how the integration of assistive technology and robots can enhance healthcare delivery while also addressing global challenges related to healthcare accessibility, rehabilitation, the integration of advanced technologies, opportunities, and problems in assistive technologies. Sections are complemented by case studies from different countries.Provides insights into current trends and potential areas for further investigation using robotics in healthcareOffers ethical principles, strategic considerations, and prospective ramifications for incorporating new technologies into practicesExplores new developments in assistive technologies and rehabilitative engineering

Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.Explains key generative AI models (GANs, VAEs, Diffusion Models) tailored for neuroimaging dataPresents case studies that showcase successful applications of generative AI in disease diagnosis and personalized medicineDiscusses ethical considerations and best practices for responsible AI development in the context of neuroimaging

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics.The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions.The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

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.

Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs.The editors cover current and emerging frameworks, architectures, and solutions that address the requirements of secure big data analytics for the healthcare industry. The book also addresses the challenges of deploying security-based healthcare analytics for massive BDA (big data analytics) applications, through smart optimized network communication infrastructures, dense connectivity, and AI-driven models.Topics include big data analytics, trustworthy data sharing, security challenges and privacy preserving techniques, authentication and access control schemes, deep learning models, risk modelling, and blockchain integration. The book provides a great reference for researchers in academia, network professionals, healthcare industry professionals, and researchers working towards emerging secure BDA solutions in 5G and beyond networks.

The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.

The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence.

This book presents high-quality research papers that demonstrate how emerging technologies in the field of intelligent systems can be used to effectively meet global needs. The respective papers highlight a wealth of innovations and experimental results, while also addressing proven IT governance, standards and practices, and new designs and tools that facilitate rapid information flows to the user. The book is divided into five major sections, namely: “Advances in High Performance Computing”, “Advances in Machine and Deep Learning”, “Advances in Networking and Communication”, “Advances in Circuits and Systems in Computing” and “Advances in Control and Soft Computing”.

This book focuses on the emerging advances in distributed communication systems, big data, intelligent computing and Internet of Things, presenting state-of-the-art research in frameworks, algorithms, methodologies, techniques and applications associated with data engineering and wireless distributed communication technologies.

This book focuses on the human emotion analysis aspects of HCI, highlighting innovative methodologies for emotion analysis by machines/computers and their application areas. This multidisciplinary book is useful to researchers and academicians, as well as students wanting to pursue a career in computational intelligence.

This book focuses on the human emotion analysis aspects of HCI, highlighting innovative methodologies for emotion analysis by machines/computers and their application areas. This multidisciplinary book is useful to researchers and academicians, as well as students wanting to pursue a career in computational intelligence.

This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey).

This book emphasizes recent advances in the creation of biometric identification systems for various applications in the field of human activity. The book displays the problems that arise in modern systems of biometric identification, as well as the level of development and prospects for the introduction of biometric technologies.

This book emphasizes recent advances in the creation of biometric identification systems for various applications in the field of human activity. The book displays the problems that arise in modern systems of biometric identification, as well as the level of development and prospects for the introduction of biometric technologies.

This book looks at the growing segment of Internet of Things technology (IoT) known as Internet of Medical Things (IoMT), an automated system that aids in bridging the gap between isolated and rural communities and the critical healthcare services that are available in more populated and urban areas.

This book looks at the growing segment of Internet of Things technology (IoT) known as Internet of Medical Things (IoMT), an automated system that aids in bridging the gap between isolated and rural communities and the critical healthcare services that are available in more populated and urban areas.