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Full Description
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.
Contents
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
