Artificial Intelligence and Machine Learning for Open-world Novelty

1st Edition, Volume 134 - February 19, 2024
Imprint: Academic Press
Editors: Ganesh Chandra Deka, Shiho Kim
Language: English
Hardback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 9 2 8 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 9 2 9 - 8

Artificial Intelligence and Machine Learning for Open-world Novelty, Volume 134 in the Advances in Computers series presents innovations in computer hardware, software, theory… Read more

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Artificial Intelligence and Machine Learning for Open-world Novelty, Volume 134 in the Advances in Computers series presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on AI and Machine Learning for Real-world problems, Graph Neural Network for learning complex problems, Adaptive Software platform architecture for Aerial Vehicle Safety Levels in real-world applications, OODA Loop for Learning Open-world Novelty Problems, Privacy-Aware Crowd Counting Methods for Real-World Environment, AI and Machine Learning for 3D Computer Vision Applications in Open-world, and PIM Hardware accelerators for real-world problems.

Other sections cover Irregular Situations in Real-World Intelligent Systems, Offline Reinforcement Learning Methods for Real-world Problems, Addressing Uncertainty Challenges for Autonomous Driving in Real-World Environments, and more.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter One AI and machine learning for real-world problems
Abstract
1 Introduction
2 Artificial intelligence of things (AIoT)
3 Machines learning-deep learning
4 Smartness of supply chain with IoT, machine learning and AI
5 Conclusion
References
Chapter Two Survey of graph neural network for learning complex problems
Abstract
1 Introduction
2 Basics of graphs
3 Node embedding methods
4 Architecture of graphs neural networks
5 Types of graph neural networks
6 Machine learning model for training graph neural network
7 Applications of graph neural network
8 Graph Neural Network for learning complex problems [4]
9 Cons and pros of graph neural network
References
Chapter Three Adaptive software platform architecture for aerial vehicle safety levels in real-world applications
Abstract
1 Introduction
2 Background
3 AAV safety level (ASL) concept
4 Adjusting ASL with TCL
5 E/E architecture for aerial vehicles
6 E/E architecture based on TCL and ASL levels
7 Adaptive software platform architecture
8 Issues and challenges
9 Conclusion
Acknowledgment
References
Further reading
Chapter Four OODA loop for learning open-world novelty problems
Abstract
1 Introduction to open-world learning
2 Open-world novelty
3 Open-world Learning Application Areas
4 Reinforcement learning based open-world learning
5 Open-world novelty adaptation models
6 Summary
Acknowledgments
References
Further readings
Chapter Five Privacy-aware crowd counting methods for real-world environment
Abstract
1 Introduction
2 Problem statement
3 Crowd counting methods for privacy-aware CCTV images
4 Configurations for performance evaluation
5 Evaluation results
6 Conclusion
Acknowledgments
Appendix 1 Results of direct method models for crowd counting in EXCO CCTV images as input
References
Chapter Six AI and machine learning for computer vision applications in open world
Abstract
1 Introduction
2 Background
3 Conventional methods
4 CNN-based methods
5 Multimodal Processing
6 Deep learning for ADAS system
7 Meta-learning for open-world problems
8 Conclusion
References
Chapter Seven PIM hardware accelerators for real-world problems
Abstract
1 Introduction
2 Evolution of process-centric architectures
3 PIM hardware accelerator for neural network computation
4 Conclusion and future research directions
Acknowledgments
References
Chapter Eight Irregular situations in real-world intelligent systems
Abstract
1 Introduction
2 Few illustrations of IS in the real world: Case studies
3 IS in AV
4 Summary
Acknowledgments
References
Further reading
Chapter Nine Offline reinforcement learning methods for real-world problems
Abstract
1 Introduction
2 Problem statement
3 Considerations while offline learning
4 Offline RL methods
5 Beyond the offline dataset
6 Future work
7 Conclusion
Acknowledgments
References
Chapter Ten Addressing uncertainty challenges for autonomous driving in real-world environments
Abstract
1 Introduction
2 Uncertainties in autonomous driving
3 Issues of uncertainty in real-world driving environments
4 Example implementation and experiments
5 Conclusion
Acknowledgments
References

Ganesh Chandra Deka

Ganesh Chandra Deka is currently Deputy Director (Training) at Directorate General of Training, Ministry of Skill Development and Entrepreneurship, Government of India, New Delhi-110001, India. His research interests include e-Governance, Big Data Analytics, NoSQL Databases and Vocational Education and Training. He has 2 books on Cloud Computing published by LAP Lambert, Germany. He is the Co-author for 4 text books on Fundamentals of Computer Science (3 books published by Moni Manik Prakashan, Guwahati, Assam, India and 1 IGI Global, USA). As of now he has edited 14 books (6 IGI Global, USA, 5 CRC Press, USA, 2 Elsevier & 1 Springer) on Big data, NoSQL and Cloud Computing and authored 10 Book Chapters. He has published around 47 research papers in various IEEE conferences. He has organized 08 IEEE International Conferences as Technical Chair in India. He is the Member of the editorial board and reviewer for various Journals and International conferences. Member of IEEE, the Institution of Electronics and Telecommunication Engineers, India and Associate Member, the Institution of Engineers, India

Affiliations and expertise

Ministry of Skill Development and Entrepreneurship, New Delhi, India

Shiho Kim

Shiho Kim is a professor in the school of integrated technology at Yonsei University, Seoul, Korea. His previous assignment includes, System on chip design engineer, at LG Semicon Ltd. (currently SK Hynix), Korea, Seoul [1995-1996], Director of RAVERS (Research center for Advanced Hybrid Electric Vehicle Energy Recovery System, a government-supported IT research center. Associate Director of the ICT consilience program, which is a Korea National program for cultivating talented engineers in the field of information and communication Technology, Korea [2011-2012], Director of Seamless Transportation Lab, at Yonsei university, Korea [since 2011-]. His main research interest includes Development of software and hardware technologies for intelligent vehicles, Blockchain technology for intelligent transportation systems, and reinforcement learning for autonomous vehicles. He is the member of the editorial board and reviewer for various Journals and International conferences. So far he has organized 2 International Conference as Technical Chair/General Chair. He is a member of IEIE (Institute of Electronics and Information Engineers of Korea), KSAE (Korean Society of Automotive Engineers), vice president of KINGC (Korean Institute of Next Generation Computing), and a senior member of IEEE. He is the co-author for over 100 papers and holding more than 50 patents in the area of information and communication technology.

Affiliations and expertise

School of Integrated Technology, Yonsei University, Seoul, Korea

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