Advanced Techniques for Anomaly Detection

Beyond the Basics
1. Auflage 2025
ISBN: 978-1-032-72930-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 194 Seiten, Format (B × H): 156 mm x 234 mm

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 6 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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Wahid, Abdul

Kumar Donta, Praveen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge

Inhaltsverzeichnis

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. Chapter 5: Deep Learning for Anomaly Detection in IoT Time Series. Chapter 6: Pen Ink Analysis in Handwritten Document Forensics: Classification and Current Trends

Über Autor(innen)

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.