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Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.
Cognitive impairment, through Alzheimer’s disease or other related forms of dementia, is a serious concern for afflicted individuals and their caregivers. Understanding patients’ mental states and combatting social stigmas are important considerations in caring for cognitively impaired individuals. Technology is playing an increasing role in the lives of the elderly. One of the most prevalent developments for the aging population is the use of technological innovations for intervention and treatment of individuals with mental impairments. Research Anthology on Diagnosing and Treating Neurocognitive Disorders examines the treatment, diagnosis, prevention, and therapeutic and technological...
Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
November 13-14, 2017 Athens, Greece Key Topics : Food Safety, Quality & Policy, Food, Nutrition & Health, Food Spoilage & Preservation, Characterization of Food Hazard, Food Poisoning & its Control, Biotechnological Exploitation in Food Safety, Food Safety Regulatory Affairs, Foodborne Pathogen, Challenges of Food Safety & Hygiene, Environmental Protection Co-Management with Food Safety,
Medical imaging provides medical professionals the unique ability to investigate and diagnose injuries and illnesses without being intrusive. With the surge of technological advancement in recent years, the practice of medical imaging has only been improved through these technologies and procedures. It is essential to examine these innovations in medical imaging to implement and improve the practice around the world. The Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention investigates and presents the recent innovations, procedures, and technologies implemented in medical imaging. Covering topics such as automatic detection, simulation in medical education, and neural networks, this major reference work is an excellent resource for radiologists, medical professionals, hospital administrators, medical educators and students, librarians, researchers, and academicians.
The exploding science of human milk, lactation, breastfeeding and human milk feeding has brought us to our current understanding that we are studying a complex system operating at multiple levels: molecular, cellular, physiological, immunological, nutritional, ontological and socio-behavioral and techno-political. Modern research in this field is responding to new scientific questions and emerging policy needs that can leverage technological innovations and sophisticated multidisciplinary approaches. The International Society for the Study of Human Milk and Lactation 2022 international conference brings together professionals from research disciplines (such as anthropology, biochemistry, immunology, molecular biology, maternal and child health and nutrition, physiology, toxicology), health care professionals (such as medical doctors, nurses, midwifes, dieticians, breastfeeding consultants), policy makers and commercial entities that promote, protect and support breastfeeding.
The outbreak of global health issues due to rapid urbanization, industrialization, and changing climatic conditions are severely impacting health and lifestyle. Yet, healthcare and medical services continue to increase in cost in developed nations. This can result in medical tourism, wherein patients travel across countries in order to benefit from medical treatment that might not be accessible in the traveler’s nation of origin. Developing countries are prepared to capitalize on this growing industry by offering multi-specialty healthcare hospitals, cost-effective treatments, and the promotion of online medical consultancy. Global Developments in Healthcare and Medical Tourism provides in...
In the post-COVID-19 healthcare landscape, the demand for smart healthcare solutions and precision medicine systems has grown significantly. To address these challenges, the book AI and IoT-Based Technologies for Precision Medicine provides a comprehensive resource for doctors, researchers, engineers, and students. By leveraging AI and IoT technologies, the book equips healthcare professionals with advanced tools and methodologies for predictive disease analysis, informed decision-making, and other aspects of precision medicine. This resource bridges the gap between theory and practice, exploring concepts like machine learning, deep learning, computer vision, AI-integrated applications, IoT-...
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately?
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