Advances in Image Processing, Reliability, and Artificial Intelligence

Data Centred-Techniques and Applications in Edge Computing

Mario J. Divan, Prashant Johri, Francesc Guim, Dmitry Shchemelinin, Marcos Carranza

• Publisher: Elsevier
• Year: 2025
• Topic: Computer Science
• eBook ISBN:
  9780443342677
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Advances in Image Processing, Reliability, and Artificial Intelligence: Data Centred-Techniques and Applications in Edge Computing provides a clear outlook of the mechanisms, risks, challenges, and opportunities in system reliability for image processing and AI applications running on edge devices. It provides Best Known Configuration (BKC) and Methods (BKM) while discussing trends and future works based on current research. The content serves as a reference for practitioners and provides a state-of-the-art for researchers in the area. It provides foundations to analyse and replicate different applications through use cases. It tackles concerns for how reliability aspects (i.e., fault tolerance, availability, maturity, and recoverability) are addressed for applications running in an environment that is not fully controlled and exposed to environmental variations. - Provides an analysis of current challenges and trends in systems reliability, AI, and image processing in edge computing for supporting different data-driven decision-making strategies - Considers the challenges and opportunities regarding data sovereignty, sustainability, model lifecycle and AI ethics in edge computing - Explains strategies and trends for monitoring and meta-monitoring AI deployments and system reliability in edge computing - Addresses the top concerns in the reliability, AI, and image processing in edge computing for supporting distributed decision-making - Describes an industry perspective for different verticals, outlining trends and future research directions

Table of contents

• Title of Book
• Chapter 1. Resilient AI for healthcare IoT: Addressing failure modes and safety challenges
• Chapter 2. Unsupervised strategies for image processing workload profiling on the edge: A systematic mapping study
• Chapter 3. Enhancing system reliability through artificial intelligence–powered software vulnerability discovery prediction
• Chapter 4. Enhancing software reliability models through neural network driven weight adaptation
• Chapter 5. Intelligent orchestration mechanisms for image workload placement
• Chapter 6. Managing reliability of global networks and AI platforms with world-class key performance indicators
• Chapter 7. Mitigating software aging in virtualized environments: A semi-markov approach to system rejuvenation
• Chapter 8. Reliability and risk prevention in image processing
• Chapter 9. Unveiling illusions: Advanced techniques in fake image detection
• Chapter 10. AI-driven image enhancement techniques for edge devices balancing quality and performance
• Chapter 11. Introduction to quantum imaging, quantum image processing, and quantum control
• Chapter 12. Leveraging edge computing and machine learning for proactive intrusion detection in IoT
• Chapter 13. Image processing strategy and target device
• Chapter 14. Sustainable optimization of medical imaging workflows
• Chapter 15. Detection of breast cancer using generative adversarial network and convolutional neural network
• Chapter 16. Relationship between object detection and image classification stages in Pap test
• Index