Vision, Sensing and Analytics: Integrative ApproachesКНИГИ » ПРОГРАММИНГ
Название: Vision, Sensing and Analytics: Integrative Approaches Автор: Md Atiqur Rahman Ahad, Atsushi Inoue Издательство: Springer Год: 2021 Страниц: 416 Язык: английский Формат: pdf (true), epub Размер: 49.0 MB
This book serves as the first guideline of the integrative approach, optimal for our new and young generations. Recent technology advancements in computer vision, IoT sensors, and analytics open the door to highly impactful innovations and applications as a result of effective and efficient integration of those. Such integration has brought to scientists and engineers a new approach - the integrative approach. This offers far more rapid development and scalable architecting when comparing to the traditional hardcore developmental approach.
Featuring biomedical and healthcare challenges, we present a collection of carefully selective cases with significant added- values as a result of integrations, e.g., sensing with AI, analytics with different data sources, and comprehensive monitoring with many different sensors, while sustaining its readability.
Computer vision has advanced so far that machines now can think and see as we humans do. Especially Deep Learning (DL) has raised the bar of excellence in computer vision. However, the recent emergence of deep reinforcement learning is threatening to soar even greater heights as it combines deep neural networks with reinforcement learning along with numerous added advantages over both. This, being a relatively recent technique, has not yet seen many works, and so its true potential is yet to be unveiled. Thus, the Chapter 2 focuses on shedding light on the fundamentals of deep reinforcement learning, starting with the preliminaries followed by the theory and basic algorithms and some of its variations, namely, attention aware deep reinforcement learning, deep progressive reinforcement learning, and multi-agent deep reinforcement learning. The chapter also discusses some existing deep reinforcement learning works regarding computer vision such as image processing and understanding, video captioning and summarization, visual search and tracking, action detection, recognition and prediction, and robotics.
1. Deep Architectures in Visual Transfer Learning 2. Deep Reinforcement Learning: A New Frontier in Computer Vision Research 3. Deep Learning for Data-Driven Predictive Maintenance 4. Multi-criteria Fuzzy Goal Programming Under Multi Uncertainty 5. Skeleton-Based Human Action Recognition on Large-Scale Datasets 6. Sensor-Based Human Activity and Behavior Computing 7. Radar-Based Non-Contact Physiological Sensing 8. Biomedical Radar and Antenna Systems for Contactless Human Activity Analysis 9. Contactless Monitoring for Healthcare Applications 10. Personalized Patient Safety Management: Sensors and Real-Time Data Analysis 11. Electrical Impedance Tomography Based Lung Disease Monitoring 12. Image Analysis with Machine Learning Algorithms to Assist Breast Cancer Treatment 13. Role-Framework of Artificial Intelligence 14. Time Series Analysis 15. Challenges Ahead in Healthcare Applications for Vision and Sensors
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Vision, Sensing and Analytics: Integrative Approaches 
Название: Vision, Sensing and Analytics: Integrative Approaches
