About the Book
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.
Table of Contents:
1. Building Resilient AI Systems at the Edge: Addressing Failure Modes and Safety for Regulatory Compliance
2. Unsupervised Strategies for Image Processing Workload Profiling on the Edge: A Systematic Mapping Study
3. Enhancing System Reliability through AI-Powered Software Vulnerability Discovery Prediction
4. Enhancing Software Reliability Models through Neural Network-Driven Weight Adaptation
5. Intelligent Orchestration Mechanisms for Image Workload Placement
6. Managing Reliability of Global Networks and AI Platforms with World-class Key Performance Indicators
7. Mitigating Software Aging in Virtualized Environments: A Semi-Markov Approach to System Rejuvenation
8. Reliability and Risk Prevention in Image Processing
9. Unveiling Illusions: Advanced Techniques in Fake Image Detection
10. AI-Driven Image Enhancement Techniques for Edge Devices Balancing Quality and Performance
11. Introduction to Quantum Imaging, Quantum Image Processing and Quantum Control
12. Leveraging Edge Computing and Machine Learning for Proactive Intrusion Detection in IoT
13. Image Processing Strategy and Target Device
14. Sustainable Optimization of Medical Imaging Workflows
15. Breast Cancer Detection using GANs and CNN models
16. Relationship between Object Detection and Image Classification stages in Pap test
About the Author :
Dr. Mario J Divan is an ACM & IEEE Senior member and Sr. AI Software Development Engineer with Intel Corporation (Customer Client Group), where he leads multiple AI topics applied to Observability and Reliability Engineering. He received an Engineering degree in Information Systems from the National Technological University (Argentina) in 2003, holds a Data Mining and Knowledge Discovery Specialty from the University of Buenos Aires (Argentina) in 2007, and a High-Performance and Grid Computing Specialty from the National University of La Plata (Argentina) in 2012. He received a Ph.D. in Computer Science from the National University of La Plata in 2012.
Dr. Divan is a former VP with the IEEE CIS Argentina Chapter, a Former Full Professor with the National University of La Pampa (Argentina), an Honorary Professor with the Amity Institute of Information Technology (India), holding 25+ years expertise in multiple verticals as a AI-based Decision-Making Consultant, such as Retail, Pharmaceutical, Educational, Real State, and Government, among others.
Dr. Prashant Johri is a Professor in the School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He received his B.Sc.(H) and M.C.A. from Aligarh Muslim University, Aligarh, and a Ph.D. in Computer Science from Jiwaji University, Gwalior, India. He has also worked as a Professor and Director (M.C.A.), Galgotias Institute of Management and Technology (G.I.M.T.), and Noida Institute of Engineering and Technology (N.I.E.T.) Greater Noida. He has served as Chair in many conferences and affiliated as a member of the program committee in many conferences in India and abroad. He has supervised 10 PhD students and many PG and U G Students for their theses and projects. He has published over 200 scientific articles, including journal papers, book chapters, and conference papers. He has published many edited books with reputable publications. He has organized several conferences/Workshops/Seminars at the national and international levels. He voluntarily served as a reviewer for various International Journals and conferences. His research interests include Artificial Intelligence, Machine Learning, Data Science, Blockchain, Healthcare, Agriculture, Entrepreneurship, Sustainable Development, Image Processing, Software Reliability, and Cloud Computing. He is actively publishing in these areas.
Francesc Guim holds a PhD in Architecture and Computer Science. He spent 5 years at the Barcelona Supercomputing Center performing research on HPC. He holds more than 60 publications and has tutored multiple PhDs during his career. After that, he moved to Intel. First, Cesc was in the Intel Product Group for 7 years working as a Hardware Architect (including CPU architecture, design, and performance modeling). Afterwards, he moved into the Intel Data Center and AI division for 6 years. While continuing work on the product architecture, Cesc expanded his work areas into Hardware and Software System Architecture. For his last years at Intel, Cesc has been a Senior Principal Engineer and the Network and Edge Chief System Architect in the Network and Edge Intel CTO Office. Last, but not least, Cesc transitioned as a Chief Executive Officer at Openchip with the ambition to continue his work in creating and delivering system-focused products and designs to solve end-users' problems. In this new mission, Cesc is laser-focused on contributing to Europe’s technology sovereignty while providing solutions that aim to help and improve European citizens’ lives.
Dr. Dmitry Shchemelinin is Vice President of AI Cloud Reliability Engineering and Operations at Intel Corporation (Office of the CTO – Intel® Tiber™ AI Cloud), where he leads the Site Reliability Engineering function and oversees the building and deployment of AI at scale.
He earned a Master’s degree in Engineering, specializing in Telecommunications and Information Systems, from Saint Petersburg State University of Telecommunications (Russia) 2002. He received an MBA from Saint Petersburg Polytechnic University (Russia) in 2005 and a Candidate of Science (Ph.D.) degree in Information Systems Analysis from Saint Petersburg State University of Telecommunications in 2008. In 2022, he defended a Doctor of Science (Sc. D.) degree in Computer Systems Analysis at Saint Petersburg Polytechnic University.
Dr. Shchemelinin is the author of numerous scientific papers and books and has previously served as an associate professor at Saint Petersburg State University of Telecommunications. He has also developed reliability engineering frameworks for several global IT and UCaaS companies.
Marcos Carranza is a Principal Engineer with Intel Corporation (NEX group), where he leads device/server/accelerators manageability topics and the targeted application of AI in this context, focusing also on scalability and security. He received an engineering degree in Information Systems from the National Technological University (Argentina), with additional specializations in SW Engineering. He generated over 250 patents and applications in AI, autonomous systems, computer vision, and update management. He has been recognized as one of Intel's Top Inventors in 2021, 2022, 2023, and 2024.
