Machine Learning In Healthcare And Security
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Machine Learning in Healthcare and Security
This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions. This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.
Internet of Healthcare Things
INTERNET OF HEALTHCARE THINGS The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book. Audience Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.
Utilizing AI of Medical Things for Healthcare Security and Sustainability
The integration of AI and IoT in healthcare, particularly through the Internet of Medical Things (IoMT), is revolutionizing medical care by enhancing efficiency and personalization. These technologies enable more accurate patient monitoring, streamlined healthcare delivery, and customized treatment plans that address individual needs. With the ability to analyze vast amounts of patient data in real-time, AIoMT is improving diagnostics, outcomes, and the overall patient experience. This transformation holds significant potential to reduce healthcare costs, alleviate the burden on traditional systems, and improve overall public health. By fostering smarter healthcare practices, AIoMT is helping to shape a more responsive, efficient, and accessible medical landscape. Utilizing AI of Medical Things for Healthcare Security and Sustainability explores the transformative role of AI and IoMT in modern healthcare. It delves into how AI-driven technologies and smart medical devices are revolutionizing patient care through real-time monitoring, predictive analytics, and personalized treatment plans. Covering topics such as autonomous vehicles, disease prediction, and wearable health technology, this book is an excellent resource for researchers, healthcare professionals, academicians, technologists, and more.