The book brings a blend of different areas of Machine Learning and the recent advances in the area. From the use of ML in healthcare to security, the book encompasses several areas related to ML while keeping a check on the 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 approach 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. The book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.

A valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.