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.

• Part I: Human-robotChapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectorsChapter 2: Artificial intelligence for affective computing: an emotion recognition case studyChapter 3: Machine learning-based affect detection within the context of human-horse interactionChapter 4: Robot intelligence for real-world applicationsChapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller Part II: NetworkChapter 6: Predictive mobility management in cellular networksChapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networksChapter 8: Deep Q-network-based coverage hole detection for future wireless networksChapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodesChapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection Part III: SensingChapter 11: EEG-based biometrics: effects of template ageingChapter 12: A machine-learning-driven solution to the problem of perceptual video quality metricsChapter 13: Multitask learning for autonomous drivingChapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemiaChapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected healthChapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reductionChapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learningChapter 18: Conclusions

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.

• Part I: Human-robotChapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectorsChapter 2: Artificial intelligence for affective computing: an emotion recognition case studyChapter 3: Machine learning-based affect detection within the context of human-horse interactionChapter 4: Robot intelligence for real-world applicationsChapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller Part II: NetworkChapter 6: Predictive mobility management in cellular networksChapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networksChapter 8: Deep Q-network-based coverage hole detection for future wireless networksChapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodesChapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection Part III: SensingChapter 11: EEG-based biometrics: effects of template ageingChapter 12: A machine-learning-driven solution to the problem of perceptual video quality metricsChapter 13: Multitask learning for autonomous drivingChapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemiaChapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected healthChapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reductionChapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learningChapter 18: Conclusions

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.

• Part I: Human-robotChapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectorsChapter 2: Artificial intelligence for affective computing: an emotion recognition case studyChapter 3: Machine learning-based affect detection within the context of human-horse interactionChapter 4: Robot intelligence for real-world applicationsChapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller Part II: NetworkChapter 6: Predictive mobility management in cellular networksChapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networksChapter 8: Deep Q-network-based coverage hole detection for future wireless networksChapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodesChapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection Part III: SensingChapter 11: EEG-based biometrics: effects of template ageingChapter 12: A machine-learning-driven solution to the problem of perceptual video quality metricsChapter 13: Multitask learning for autonomous drivingChapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemiaChapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected healthChapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reductionChapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learningChapter 18: Conclusions