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.