Joel J. P. C. Rodrigues – författare

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology.

Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields.

This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology.

Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields.

This reference text addresses the importance of smart crop management for increasing yield and presents a framework for smart monitoring and regulation of crop observation. Further, it comprehensively covers important topics such as spatial decision support systems for precision farming, swarm intelligence in the optimal management of aquaculture farms, and intelligent harvesting algorithms for improving productivity.

This book:

• Presents meta-heuristic algorithms for optimization, economic crop planning, and use of effective water resource management.

• Discusses spatial decision support systems for crop productivity management, watershed management, and precision farming.

• Illustrates swarm intelligence-based optimization techniques, data mining, and machine learning methods for aquaculture operations.

• Highlights artificial intelligence and machine learning-based harvesting algorithms for improving productivity.

• Explains the use of green Internet of Things security solutions for agriculture, plant condition management, and greenhouse simulation.

It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

This reference text addresses the importance of smart crop management for increasing yield and presents a framework for smart monitoring and regulation of crop observation. Further, it comprehensively covers important topics such as spatial decision support systems for precision farming, swarm intelligence in the optimal management of aquaculture farms, and intelligent harvesting algorithms for improving productivity.

This book:

• Presents meta-heuristic algorithms for optimization, economic crop planning, and use of effective water resource management.

• Discusses spatial decision support systems for crop productivity management, watershed management, and precision farming.

• Illustrates swarm intelligence-based optimization techniques, data mining, and machine learning methods for aquaculture operations.

• Highlights artificial intelligence and machine learning-based harvesting algorithms for improving productivity.

• Explains the use of green Internet of Things security solutions for agriculture, plant condition management, and greenhouse simulation.

It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

Artificial Intelligence in Healthcare for the Elderly provides valuable insights into how artificial intelligence can transform healthcare through personalized monitoring, ethical considerations, and real-world applications.

Artificial intelligence has the potential to revolutionize healthcare for the elderly by providing efficient and personalized monitoring and care. Though this technology has the potential to revolutionize care, there is currently little information on the potential of this technology in elderly healthcare. Artificial Intelligence in Healthcare for the Elderly explores AI algorithms that can transform health monitoring for older adults by analyzing data from wearable devices, electronic health records, and other sources that provide real-time data analysis, detect early warning signs of diseases, and offer personalized treatment. This book addresses the critical ethical, societal, and practical aspects of elderly care that are often overlooked with insights from various disciplines, including healthcare, technology, ethics, and sociology, to offer a holistic perspective on AI’s impact on aging. Artificial Intelligence in Healthcare for the Elderly offers an all-encompassing perspective on AI technologies employed in elderly healthcare by examining the specific types of technology used and delineating its role in elderly healthcare, drawing insights from existing research and case studies.

Artificial Intelligence in Healthcare for the Elderly provides valuable insights into how artificial intelligence can transform healthcare through personalized monitoring, ethical considerations, and real-world applications.

Artificial intelligence has the potential to revolutionize healthcare for the elderly by providing efficient and personalized monitoring and care. Though this technology has the potential to revolutionize care, there is currently little information on the potential of this technology in elderly healthcare. Artificial Intelligence in Healthcare for the Elderly explores AI algorithms that can transform health monitoring for older adults by analyzing data from wearable devices, electronic health records, and other sources that provide real-time data analysis, detect early warning signs of diseases, and offer personalized treatment. This book addresses the critical ethical, societal, and practical aspects of elderly care that are often overlooked with insights from various disciplines, including healthcare, technology, ethics, and sociology, to offer a holistic perspective on AI’s impact on aging. Artificial Intelligence in Healthcare for the Elderly offers an all-encompassing perspective on AI technologies employed in elderly healthcare by examining the specific types of technology used and delineating its role in elderly healthcare, drawing insights from existing research and case studies.

This book presents research on how interpretable cognitive IoT can work to help with the massive amount of data in the healthcare industry. The authors give importance to IoT systems with intense machine learning features; this ensures the scope corresponds to use of cognitive IoT for understanding, reasoning, and learning from medical data. The authors discuss the interpretability of an intelligent system and its trustworthiness as a smart tool in the context of massive healthcare applications. As a whole, book combines three important topics: massive data, cognitive IoT, and interpretability. Topics include health data analytics for cognitive IoT, usability evaluation of cognitive IoT for healthcare, interpretable cognitive IoT for health robotics, and wearables in the context of IoT for healthcare. The book acts as a useful reference work for a wide audience including academicians, scientists, students, and professionals.