Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions is a book that discusses the use of machine learning in healthcare and security, including applications in medical diagnostic systems, security prevention, and natural language processing. It is a valuable resource for researchers and postgraduate students in these fields.

Format: Hardback
Length: 212 pages
Publication date: 19 January 2024
Publisher: Taylor & Francis Ltd

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions is a comprehensive book that brings together a diverse range of topics in machine learning and recent advancements in the field. From its application in healthcare to security, this book covers a wide spectrum of ML-related areas while also emphasizing the significance of traditional ML algorithms.

The book delves into the predictive analysis and forecasting techniques employed in various emerging and classical domains using the approaches of ML and AI. It explores the use of ML and AI in medical diagnostic systems, addressing the challenges and opportunities they present. Additionally, it sheds light on the security aspects of ML, discussing how it can be leveraged to address emerging security issues.

Furthermore, the book emphasizes the importance of Natural Language Processing (NLP) and provides insights into the techniques, challenges, and potential solutions associated with it. This is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

The book is organized into five chapters, each covering a specific aspect of machine learning in healthcare and security. The first chapter provides an introduction to the field, highlighting the importance of ML and its applications in healthcare and security. The subsequent chapters delve into specific topics, such as medical image analysis, text mining, and fraud detection.

Each chapter is well-structured, with an overview of the relevant concepts, techniques, and applications. The authors provide detailed explanations, examples, and case studies to illustrate their points, making the book accessible to a wide audience. The book also includes a comprehensive bibliography and an index, making it easier for readers to locate specific information.

In conclusion, Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions is a comprehensive and timely book that provides valuable insights into the latest developments in machine learning and their applications in healthcare and security. It is a valuable resource for researchers, practitioners, and students interested in this field.

Weight: 570g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032478418