Soft computing: theories and applications : proceedings of SoCTA 2020. Volume 2 /

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Bibliographic Details
Meeting name:	SoCTA (Conference) (5th : 2020 : Online).
Edition:	1st ed. 2021.
Imprint:	Singapore : Springer, [2021] ©2021
Description:	1 online resource (xx, 587 pages) : illustrations (some color).
Language:	English
Series:	Advances in intelligent systems and computing, 2194-5357 ; volume 1381 Advances in intelligent systems and computing ; 1381.
Subject:	Soft computing -- Congresses. Soft computing. Conference papers and proceedings. Conference papers and proceedings.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12613448

Hidden Bibliographic Details
Varying Form of Title:	SoCTA 2020
Other authors / contributors:	Sharma, Tarun K., editor. Ahn, Chang Wook, editor. Verma, Om Prakash, editor. Panigrahi, Bijaya Ketan, editor.
ISBN:	9789811616969 9811616965 9789811616952
Notes:	International conference proceedings. Includes author index. Online resource; title from PDF title page (SpringerLink, viewed July 1, 2021).
Summary:	This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2020), organized online. The book is divided into two volumes and offers valuable insights into soft computing for teachers and researchers alike; the book will inspire further research in this dynamic field.
Standard no.:	10.1007/978-981-16-1696-9

Table of Contents:

A study on the Effect of Optimal Control Strategies: An SIR Model with Delayed Logistic Growth
Emperor Penguin Optimized Clustering for Improved Multilevel Hierarchical Routing in Wireless Sensor Networks
A Collaborative Filtering based Recommendation System for Preliminary Detection of COVID-19
Frequencies of nonuniform triangular plate with two-dimensional parabolic temperature
Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs.

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