Vision, Sensing and Analytics: Integrative Approaches

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 including COVID-19, 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.

Presents recent technology advancements and integrative approaches in Vision, Sensing, and Analytics Collects innovations and applications of vision and sensing that are potentially impactful in our societies in the near future such as automated vehicles, or population health Contains overviews, guidelines, and challenges of such integrative approaches for scholars and practitioners

Texte du rabat

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 including COVID-19, 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.

Contenu
Deep Architectures in Visual Transfer Learning.- Deep Reinforcement Learning: A New Frontier in Computer Vision Research.- Deep Learning for Data-driven Predictive Maintenance.- Multi-Criteria Fuzzy Goal Programming under Multi-Uncertainty.- Skeleton-based Human Action Recognition on Large-Scale Datasets.