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.

• 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