AI for Emerging Verticals Human-robot computing, sensing and networking
Produktdetaljer
Om bidragsyterne
Muhammad Zeeshan Shakir is an associate professor at the School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, Scotland, United Kingdom. He is an expert in networks, Internet of Things and machine-learning/artificial intelligence. He has won over £1.5m research funding for the UK/EU and international projects and has published over 150 research articles. He is a senior member of IEEE Communications Society and IEEE, a fellow of Higher Education Academy, and a chair of the IEEE ComSoc emerging technologies initiative on backhaul/fronthaul communications. Naeem Ramzan is a full professor and director of the Affective and Human Computing for Smart Environment Research Centre at the University of the West of Scotland, Paisley, Scotland, United Kingdom. He has published nearly 200 highly cited publications and lead major national/EU/KTP projects worth over £10m. He is a senior member of the IEEE, a senior fellow of Higher Education Academy, a co-chair of MPEG HEVC verification (AHG5) group and a voting member of the British Standard Institution.By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes.
This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures.
The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.
This edited book explores novel concepts and cutting-edge research and developments towards designing fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking.
- Part I: Human-robot
- Chapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors
- Chapter 2: Artificial intelligence for affective computing: an emotion recognition case study
- Chapter 3: Machine learning-based affect detection within the context of human-horse interaction
- Chapter 4: Robot intelligence for real-world applications
- Chapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller
- Part II: Network
- Chapter 6: Predictive mobility management in cellular networks
- Chapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networks
- Chapter 8: Deep Q-network-based coverage hole detection for future wireless networks
- Chapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes
- Chapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection
- Part III: Sensing
- Chapter 11: EEG-based biometrics: effects of template ageing
- Chapter 12: A machine-learning-driven solution to the problem of perceptual video quality metrics
- Chapter 13: Multitask learning for autonomous driving
- Chapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia
- Chapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected health
- Chapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction
- Chapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learning
- Chapter 18: Conclusions