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Vision, Sensing and Analytics: Integrative Approaches / edited by Md Atiqur Rahman Ahad, Atsushi Inoue.

Contributor(s): Ahad, Md. Atiqur Rahman, editor literario | Inoue, Atsushi, editor literario
Series: (Intelligent Systems Reference Library, 1868-4408; 207); (Intelligent Technologies and Robotics (SpringerNature-42732)); (Intelligent Technologies and Robotics (R0) (SpringerNature-43728)).Publisher: Cham : Springer International Pulishing, 2021Edition: First edition 2021.Description: 1 recurso en línea (X, 413 páginas) : 125 ilustraciones, 81 ilustraciones a color.ISBN: 9783030754907.Subject: Aprendizaje automáticoOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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.
Abstract: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TA1634 2021 EB (Browse shelf(Opens below)) Acceso electrónico eBook.23122257
Total holds: 0

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.

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.

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