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

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.

Preface

Part I : Natural Language Processing Using ML
Chapter 1 : Application of Classification and Regression Techniques in Bank Fraud Detection
Chapter 2 : A Survey on Water Quality Monitoring and Controlling Using Different Modality of Machine Learning Model
Chapter 3 : Design of Tea Ontology for Precision-based Tea Crop Yield Prediction Using Machine Learning
Chapter 4 : Analyzing the Social Media Activities of Users Using Machine Learning and Graph Data Science

Part II : AI and ML in Healthcare
Chapter 5 : Deep Learning-Based Multiple Myeloma Identification Using Micro-imaging Technique
Chapter 6 : Artificial Intelligence (AI) on a Rise in Healthcare
Chapter 7 : Applications of Multitarget Regression Models in Healthcare
Chapter 8 : XAI-based Autoimmune Disorders Detection Using Transfer Learning
Chapter 9 : Wearable Smart Technologies : Changing the Future of Healthcare
Chapter 10 : Different Security Breaches in Patients’ Data and Prevailing Ways to Counter Them

Part III : Security Aspects of ML
Chapter 11 : The Intersection of Biometrics Technology and Machine Learning : A Scientometrics Analysis
Chapter 12 : Can ML Be Used in Cybersecurity?
Chapter 13 : GAN Cryptography
Chapter 14 : Security Aspects of Patient’s Data in a Medical Diagnostic System

Index