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Machine learning approaches for urba...

Bandyopadhyay, Mainak.

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Machine learning approaches for urban computing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning approaches for urban computing/ edited by Mainak Bandyopadhyay, Minakhi Rout, Suresh Chandra Satapathy.
other author:	Bandyopadhyay, Mainak.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xi, 208 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Urbanization: Pattern, Effects and Modelling -- Extraction of Information from Hyperspectral Imaging using Deep Learning -- Vehicle Detection and count in the captured Stream Video using Machine Learning -- Dimensionality Reduction and Classification in Hyperspectral Images using Deep Learning -- Machine learning and deep learning algorithms in the diagnosis of chronic diseases -- Security Enhancement of Contact less Tachometer Based Cyber Physical System -- Optimization of Loss Function on Human Faces Using Generative Adversarial Networks.
Contained By:	Springer Nature eBook
Subject:	Smart cities. -
Online resource:	https://doi.org/10.1007/978-981-16-0935-0
ISBN:	9789811609350

Machine learning approaches for urban computing
Machine learning approaches for urban computing[electronic resource] /edited by Mainak Bandyopadhyay, Minakhi Rout, Suresh Chandra Satapathy. - Singapore :Springer Singapore :2021. - xi, 208 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.9681860-949X ;. - Studies in computational intelligence ;v.968..

Urbanization: Pattern, Effects and Modelling -- Extraction of Information from Hyperspectral Imaging using Deep Learning -- Vehicle Detection and count in the captured Stream Video using Machine Learning -- Dimensionality Reduction and Classification in Hyperspectral Images using Deep Learning -- Machine learning and deep learning algorithms in the diagnosis of chronic diseases -- Security Enhancement of Contact less Tachometer Based Cyber Physical System -- Optimization of Loss Function on Human Faces Using Generative Adversarial Networks.

This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.

ISBN: 9789811609350

Standard No.: 10.1007/978-981-16-0935-0doiSubjects--Topical Terms:

3338351
Smart cities.

LC Class. No.: TD159.4 / .M334 2021

Dewey Class. No.: 628.0285

Machine learning approaches for urban computing
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245 0 0 $a Machine learning approaches for urban computing $h [electronic resource] / $c edited by Mainak Bandyopadhyay, Minakhi Rout, Suresh Chandra Satapathy.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xi, 208 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.968
505 0 $a Urbanization: Pattern, Effects and Modelling -- Extraction of Information from Hyperspectral Imaging using Deep Learning -- Vehicle Detection and count in the captured Stream Video using Machine Learning -- Dimensionality Reduction and Classification in Hyperspectral Images using Deep Learning -- Machine learning and deep learning algorithms in the diagnosis of chronic diseases -- Security Enhancement of Contact less Tachometer Based Cyber Physical System -- Optimization of Loss Function on Human Faces Using Generative Adversarial Networks.
520 $a This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.
650 0 $a Smart cities. $3 3338351
650 0 $a Machine learning. $3 533906
650 0 $a Municipal engineering $x Data processing. $3 3326369
650 0 $a Municipal services $x Data processing. $3 3395822
650 0 $a City planning $x Data processing. $3 545650
650 0 $a Big data. $3 2045508
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Bandyopadhyay, Mainak. $3 3494613
700 1 $a Rout, Minakhi. $3 3445484
700 1 $a Chandra Satapathy, Suresh. $3 3494614
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v.968. $3 3494615
856 4 0 $u https://doi.org/10.1007/978-981-16-0935-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)