Vision, sensing and analytics : integrative approaches / Md Atiqur Rahman Ahad, Atsushi Inoue, editors.
2021
TA1634
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Details
Title
Vision, sensing and analytics : integrative approaches / Md Atiqur Rahman Ahad, Atsushi Inoue, editors.
ISBN
9783030754907 (electronic bk.)
3030754901 (electronic bk.)
3030754898
9783030754891
3030754901 (electronic bk.)
3030754898
9783030754891
Publication Details
Cham : Springer, [2021]
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-75490-7 doi
Call Number
TA1634
Dewey Decimal Classification
006.3/7
Summary
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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Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 16, 2021).
Series
Intelligent systems reference library ; v. 207. 1868-4394
Available in Other Form
Vision, sensing and analytics.
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Table of 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.
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.