Within the context of global health challenges posed by intractable neurodegenerative diseases like Alzheimer's and Parkinson's, the significance of early diagnosis is critical for effective intervention, and scientists continue to discover new methods of detection. However, actual diagnosis goes beyond detection to include a significant analysis of combined data for many cases, which presents a challenge of several complicated calculations. Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases stands as a groundbreaking work at the intersection of artificial intelligence and neuroscience. The book orchestrates a symphony of cutting-edge techniques and progressions in early detection by assembling eminent experts from the domains of deep learning and neurology. Through a harmonious blend of research areas and pragmatic applications, this monumental work charts the transformative course to revolutionize the landscape of early diagnosis and management of neurodegenerative disorders. Within the pages, readers will embark through the intricate landscape of neurodegenerative diseases, the fundamental underpinnings of deep learning, the nuances of neuroimaging data acquisition and preprocessing, the alchemy of feature extraction and representation learning, and the symphony of deep learning models tailored for neurodegenerative disease diagnosis. The book also delves into integrating multimodal data to augment diagnosis, the imperative of rigorously evaluating and validating deep learning models, and the ethical considerations and challenges entwined with deep learning for neurodegenerative diseases.

In the domain of women's health, the perimenopause to menopause transition stands out as a critical yet often misunderstood experience. Spanning several years before menopause, the perimenopause phase introduces a myriad of symptoms that impact a significant percentage of women globally. The distressing nature of these symptoms is underscored by the fact that while most subside within five years, a substantial minority endure them for up to 12 years. Comprehending and mitigating the impact of perimenopausal symptoms on women's health is an imperative challenge demanding a solution. Utilizing AI Techniques for the Perimenopause to Menopause Transition confronts the challenges faced by women during this pivotal period of change, employing cutting-edge deep learning approaches to identify, analyze, and address the associated symptoms. The book commences by elucidating the fundamental principles of perimenopause, providing readers with a robust foundation to comprehend the biological intricacies at play. Advanced machine learning techniques are then explored beyond conventional diagnostic methods, enabling a more nuanced identification and analysis of key menopausal symptoms. Statistical tools offer insights into global patterns of women's health. As methodologies are explored, the ethical landscape surrounding the collection of sensitive female health data is navigated. Addressing security and privacy concerns becomes paramount in the quest to harness AI for the betterment of women's health. Medical practitioners, healthcare providers, researchers, data scientists, and individuals experiencing perimenopause or menopause will find invaluable insights. Moreover, it holds significance for professionals in public health policies, educational institutions, and the pharmaceutical and health-tech industries.

Traditional patient care and treatment approaches often lack the personalized and interactive elements necessary for effective healthcare delivery. This means that the healthcare industry must find innovative solutions to improve patient outcomes, enhance rehabilitation processes, and optimize resource utilization. There is a gap between the traditional approach and the need for innovation that highlights the importance of a comprehensive understanding of emerging technologies, including Kinect Sensor technology, and the potential to transform healthcare practices with this tech. Revolutionizing Healthcare Treatment With Sensor Technology addresses this critical need by thoroughly exploring how Kinect Sensor technology can revolutionize patient care and treatment methodologies. By repurposing and customizing Kinect Sensor for healthcare applications, this book showcases how depth-sensing cameras, infrared sensors, and advanced motion tracking can capture and interpret real-time patient movements and interactions. This book is ideal for healthcare professionals, hospital administrators, researchers, patients, caregivers, and healthcare technology developers seeking to leverage Kinect Sensor technology for enhanced healthcare delivery. Through detailed case studies and practical examples, experts can learn how to integrate Kinect Sensor into various medical settings to gain valuable insights into patients' physical capabilities, monitor their progress, and create personalized treatment plans.

In the field of healthcare technology, the Internet of Medical Things (IoMT) stands at the forefront of progress, revolutionizing patient care through advanced monitoring and treatment modalities. However, this digital transformation brings forth a new challenge— the vulnerability of sensitive medical data to cyber threats. Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) examines ways to fortify IoMT against potential breaches through the exploration of these trust architectures. Delving deep into data privacy technologies, the book examines the implications of regulatory frameworks such as GDPR, HIPAA, and cybersecurity law. It assesses traditional security methods and considers innovative approaches, offering insights into certificate generation, digital identification, and the optimization of network protocols for secure data transmission.Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) illuminates the path forward for IoMT security. Its objectives are multi-faceted: from unraveling the intricacies of the health chain to dissecting the role of lightweight cryptographic key agreement mechanisms in safeguarding medical data. The book grapples with the challenges and advantages of implementing compact cryptographic techniques in healthcare, particularly within the decentralized framework of IoMT. By exploring the potential of Federated Learning (FL) in bolstering privacy and improving healthcare outcomes, the book aims to equip researchers, healthcare professionals, and IT experts with valuable knowledge. Through real-world case studies, it endeavors to pave the way for a future where security and efficiency seamlessly integrate in IoMT.

Engineers have long been architects of progress, erecting massive buildings that touch the skies and designing technologies that redefine realms of known possibility. In this era of rapid change, keeping pace with innovation is the main challenge. Emerging Engineering Technologies and Industrial Applications is a comprehensive research book that delves deep into the heart of a relentless technological revolution within modern industry. From the limitless potential of the Internet of Things (IoT) to the cognitive wonders of Artificial Intelligence (AI), from the microscopic marvels of Nanotechnology to the precision and agility of Robotics, and from the revolutionary advancements in 3D Printing to the transformative capabilities of blockchain, this book explores emerging technologies. This book is an indispensable resource for professionals and researchers working in the field of emerging engineering technologies and their industrial applications. It caters to a diverse range of disciplines, including computer science and information technology, environmental, agricultural, and physical sciences, medical, healthcare, and life sciences, as well as science and engineering, and anyone seeking to navigate the current technological progress in the 21st century.

Experts in Medicine are under new pressures of advancing their studies while also reducing the impact they leave on the environment. Researchers within the fields of bio-neuro informatics, healthcare, engineering, and medical sciences require a dynamic platform that bridges the realms of academia, science, industry, and innovation. Green AI-Powered Intelligent Systems for Disease Prognosis facilitates a crossroads for a diverse audience interested in these two seldom coalesced concepts. Academicians, scientists, researchers, professionals, decision-makers, and even aspiring scholars all find a space to contribute, collaborate, and learn within the platform that this book provides. The book's thematic coverage is unequivocally compelling; by exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, it captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields. Two prominent tracks form the backbone of the book's content. The first covers the Bioinformatics and Data Mining of Biological Data (BiDMBD), and unravels the intricacies of biomedical computation, signal analysis, clinical decision support, and health data mining. This approach holds a treasure trove of insights into the mechanisms of health data acquisition, clinical informatics, and the representation of healthcare knowledge. The second covers Biomedical Informatics and is a symposium of computational modeling, genomics, and proteomics. Here, the fusion of data science with medical sciences takes center stage.

Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management. AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management.

The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.

Health professionals grapple with a critical challenge: the traditional Continuous Medical Education (CME) model falls short of fostering the unique skills and self-directed learning required for a dynamic career. As medical practitioners navigate a world of new epidemiological models, technologies, and strategies, the need for a transformative solution becomes evident. The Lifelong Learning Journey of Health Professionals: Continuing Education and Professional Development is a book that not only identifies the limitations of existing education models but also provides a comprehensive solution for ushering in a new era of lifelong learning. This compelling book advocates for a paradigm shift towards Continuous Professional Development (CPD), a contemporary concept that embraces non-traditional learning formats. It dismantles the inadequacies of credit-based training by emphasizing the importance of self-direction and self-assessment for adult learners. From core principles for designing a robust CPD system to exploring successful models, alternative credentials, and the role of learning communities, the book offers a holistic approach to reshaping medical education.

The COVID-19 pandemic clearly shows the vital role of accurate and reliable information in public health. Health literacy addresses not only patient needs but also the needs of the general population, who must not only comply with advice and instructions but also understand the severity of health crises and respond accordingly. A variety of crises imposed on healthcare systems constantly arise ranging from pandemics to natural catastrophes, terrorist attacks, and outbreaks of illnesses. In addition, there are crises within the healthcare systems, such as a lack of resources and an appropriate workforce. Crises in healthcare systems that are not efficiently dealt with may result in inefficiencies and inequalities in health provision. The Role of Health Literacy in Major Healthcare Crises examines the role of health literacy not only in informing the public but also in building a culture of cooperation between the healthcare systems and their users. The book also investigates the role of communication strategies and educational activities of multiple agencies at local, national, and global levels and explores ethical issues associated with healthcare crises and how they are negotiated in health campaigns. Covering key topics such as digital media, health information, and e-health, this premier reference source is ideal for healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.