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DOI: 10.2174/97816810894301210101 eISBN: 978-1-68108-943-0, 2021 ISBN: 978-1-68108-944-7 ISSN: 2810-9457 (Print) ISSN: 2810-9465 (Online)
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For Books Deepak Gupta, Suresh Chavhan , " Computational Intelligence For Data Analysis ", Bentham Science Publishers (2021). https://doi.org/10.2174/97816810894301210101
Print ISBN978-1-68108-944-7
Online ISBN978-1-68108-943-0
Page: i-iii (3) Author: Deepak Gupta and Suresh Chavhan DOI: 10.2174/9781681089430121010001
Page: iv-iv (1) Author: DOI: 10.2174/9781681089430121010002
Page: 1-15 (15) Author: Hima Bindu Gogineni, E. Laxmi Lydia* and N. Supriya DOI: 10.2174/9781681089430121010003 PDF Price: $15
Page: 16-31 (16) Author: B.M.S. Rani, E. Laxmi Lydia* and G. Jose Moses DOI: 10.2174/9781681089430121010004 PDF Price: $15
Page: 32-48 (17) Author: M. Vasumathi Devi, E. Laxmi Lydia* and Hima Bindu Gogineni DOI: 10.2174/9781681089430121010005 PDF Price: $15
Page: 49-62 (14) Author: Irina V. Pustokhina, Denis A. Pustokhin, M. Ilayaraja and K. Shankar* DOI: 10.2174/9781681089430121010006 PDF Price: $15
Page: 63-75 (13) Author: B. Sai Viswanath*, P. Sandeep and Suresh Chavhan DOI: 10.2174/9781681089430121010007 PDF Price: $15
Page: 76-88 (13) Author: Dhananjay Kumar K.S., Prakash Reddy O., Sanath Gowtham G., Shailaja A. Chougule and Suresh Chavhan* DOI: 10.2174/9781681089430121010008 PDF Price: $15
Page: 89-112 (24) Author: Bibaswan Bose*, Vijay Kumar Tayal and Bedatri Moulik DOI: 10.2174/9781681089430121010009 PDF Price: $15
Page: 113-125 (13) Author: Pranjal Kapur* and Suresh Chavhan DOI: 10.2174/9781681089430121010010 PDF Price: $15
Page: 126-131 (6) Author: DOI: 10.2174/9781681089430121010011
New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas. <p> This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends. <p> Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. <p> Key Features: <p> - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas <p> - Covers classification of traffic behavior <p> - Demonstrates the application of artificial immune system algorithms for traffic prediction <p> - Covers traffic density estimation using deep learning models <p> - Covers Fog and edge computing for intelligent transportation systems <p> - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers <p> - Presents a current perspective on an urban hyperloop system for India <p> This volume is essential reading for scholars and professionals involved in courses and training programs in the field of transportation, computer science, data science and applied machine learning.
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