This book is a comprehensive guide that explores the latest developments in anomaly detection techniques across a range of fields, including cybersecurity, finance, image processing, sensor networks, social network analysis, health systems, and IoT systems. With 15 chapters covering various topics such as deep learning-based anomaly detection, feature selection and extraction techniques, ensemble methods, and evaluation metrics, this book offers a comprehensive understanding of advanced anomaly detection techniques and their applications in different fields. This book will be an excellent resource for researchers, practitioners, and students interested in anomaly detection and its applications in various domains.
The book will also summarize various protocols that work based on learning strategies and applied intelligence in the context of IoT environments.
Chapter 1 : Anomaly Detection in Carbon Sequestration: Advancements, Challenges, and Future Directions.
Chapter 2: Intelligent Anomaly Detection in Social Media.
Chapter 3: Anomaly Detection in Health-Care.
Chapter 4: Safeguarding Health in the Cloud: A Cutting-Edge Machine Learning Intrusion Detection System.
Chapter 5: Deep Learning for Anomaly Detection in IoT Time Series.
Chapter 6: Pen Ink Analysis in Handwritten Document Forensics: Classification and Current Trends
Abdul Wahid, PhD, Department of Computer Science and Engineering, Indian Institute of Information Technology, Dharwad, India.
Praveen Kumar Donta, PhD, Department of Computer and Systems Sciences, Stockholm, Sweden.
Rohkem infot Taylor & Francis e-raamatute kohta