Evolving LSTM Networks for Time-Series Classification in EdgeIoT

We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Mathematical problems in engineering 2023-01, Vol.2023 (1)
Hauptverfasser: Cui, Pei, Li, San, Jiang, Kaina, Liu, Zhendong, Sun, Xingkai
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title Mathematical problems in engineering
container_volume 2023
creator Cui, Pei
Li, San
Jiang, Kaina
Liu, Zhendong
Sun, Xingkai
description We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.
doi_str_mv 10.1155/2023/6469030
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2804963628</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2804963628</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1390-6f66c9d6575454009cd0f376a829f056dd0135c87cf815a4feeaf5a07652a1f93</originalsourceid><addsrcrecordid>eNp90E1LAzEQgOEgCtbqzR8Q8KhrJ5-7uSmlaqHqoSt4CyGb1NR2U5N-4L93S3v2NHN4mIEXoWsC94QIMaBA2UByqYDBCeoRIVkhCC9Pux0oLwhln-foIuc5ACWCVD30MNrGxTa0MzyZ1q_4za13MX1n7GPCdVi6YupScBkPFybn4IM16xBbHFo8amZuHOtLdObNIrur4-yjj6dRPXwpJu_P4-HjpLCEKSikl9KqRopScMEBlG3As1KaiioPQjYNECZsVVpfEWG4d854YaCUghriFeujm8PdVYo_G5fXeh43qe1ealoBV5JJWnXq7qBsijkn5_UqhaVJv5qA3jfS-0b62Kjjtwf-FdrG7ML_-g-6EGOu</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2804963628</pqid></control><display><type>article</type><title>Evolving LSTM Networks for Time-Series Classification in EdgeIoT</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley-Blackwell Open Access Titles</source><source>Alma/SFX Local Collection</source><creator>Cui, Pei ; Li, San ; Jiang, Kaina ; Liu, Zhendong ; Sun, Xingkai</creator><contributor>Gao, Hao ; Hao Gao</contributor><creatorcontrib>Cui, Pei ; Li, San ; Jiang, Kaina ; Liu, Zhendong ; Sun, Xingkai ; Gao, Hao ; Hao Gao</creatorcontrib><description>We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2023/6469030</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Civilization ; Classification ; Data mining ; Deep learning ; Genetic algorithms ; Intelligence ; Neural networks ; Optimization ; Optimization algorithms ; Simulation ; Society ; Target recognition ; Time series ; Unmanned aerial vehicles</subject><ispartof>Mathematical problems in engineering, 2023-01, Vol.2023 (1)</ispartof><rights>Copyright © 2023 Pei Cui et al.</rights><rights>Copyright © 2023 Pei Cui et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1390-6f66c9d6575454009cd0f376a829f056dd0135c87cf815a4feeaf5a07652a1f93</cites><orcidid>0000-0002-7728-9696 ; 0000-0002-5427-9102 ; 0009-0008-4519-6779 ; 0000-0002-7560-105X ; 0009-0007-4058-3000</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><contributor>Gao, Hao</contributor><contributor>Hao Gao</contributor><creatorcontrib>Cui, Pei</creatorcontrib><creatorcontrib>Li, San</creatorcontrib><creatorcontrib>Jiang, Kaina</creatorcontrib><creatorcontrib>Liu, Zhendong</creatorcontrib><creatorcontrib>Sun, Xingkai</creatorcontrib><title>Evolving LSTM Networks for Time-Series Classification in EdgeIoT</title><title>Mathematical problems in engineering</title><description>We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Civilization</subject><subject>Classification</subject><subject>Data mining</subject><subject>Deep learning</subject><subject>Genetic algorithms</subject><subject>Intelligence</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Simulation</subject><subject>Society</subject><subject>Target recognition</subject><subject>Time series</subject><subject>Unmanned aerial vehicles</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90E1LAzEQgOEgCtbqzR8Q8KhrJ5-7uSmlaqHqoSt4CyGb1NR2U5N-4L93S3v2NHN4mIEXoWsC94QIMaBA2UByqYDBCeoRIVkhCC9Pux0oLwhln-foIuc5ACWCVD30MNrGxTa0MzyZ1q_4za13MX1n7GPCdVi6YupScBkPFybn4IM16xBbHFo8amZuHOtLdObNIrur4-yjj6dRPXwpJu_P4-HjpLCEKSikl9KqRopScMEBlG3As1KaiioPQjYNECZsVVpfEWG4d854YaCUghriFeujm8PdVYo_G5fXeh43qe1ealoBV5JJWnXq7qBsijkn5_UqhaVJv5qA3jfS-0b62Kjjtwf-FdrG7ML_-g-6EGOu</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Cui, Pei</creator><creator>Li, San</creator><creator>Jiang, Kaina</creator><creator>Liu, Zhendong</creator><creator>Sun, Xingkai</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-7728-9696</orcidid><orcidid>https://orcid.org/0000-0002-5427-9102</orcidid><orcidid>https://orcid.org/0009-0008-4519-6779</orcidid><orcidid>https://orcid.org/0000-0002-7560-105X</orcidid><orcidid>https://orcid.org/0009-0007-4058-3000</orcidid></search><sort><creationdate>20230101</creationdate><title>Evolving LSTM Networks for Time-Series Classification in EdgeIoT</title><author>Cui, Pei ; Li, San ; Jiang, Kaina ; Liu, Zhendong ; Sun, Xingkai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1390-6f66c9d6575454009cd0f376a829f056dd0135c87cf815a4feeaf5a07652a1f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Civilization</topic><topic>Classification</topic><topic>Data mining</topic><topic>Deep learning</topic><topic>Genetic algorithms</topic><topic>Intelligence</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Simulation</topic><topic>Society</topic><topic>Target recognition</topic><topic>Time series</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cui, Pei</creatorcontrib><creatorcontrib>Li, San</creatorcontrib><creatorcontrib>Jiang, Kaina</creatorcontrib><creatorcontrib>Liu, Zhendong</creatorcontrib><creatorcontrib>Sun, Xingkai</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cui, Pei</au><au>Li, San</au><au>Jiang, Kaina</au><au>Liu, Zhendong</au><au>Sun, Xingkai</au><au>Gao, Hao</au><au>Hao Gao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evolving LSTM Networks for Time-Series Classification in EdgeIoT</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2023-01-01</date><risdate>2023</risdate><volume>2023</volume><issue>1</issue><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2023/6469030</doi><orcidid>https://orcid.org/0000-0002-7728-9696</orcidid><orcidid>https://orcid.org/0000-0002-5427-9102</orcidid><orcidid>https://orcid.org/0009-0008-4519-6779</orcidid><orcidid>https://orcid.org/0000-0002-7560-105X</orcidid><orcidid>https://orcid.org/0009-0007-4058-3000</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1024-123X
ispartof Mathematical problems in engineering, 2023-01, Vol.2023 (1)
issn 1024-123X
1563-5147
language eng
recordid cdi_proquest_journals_2804963628
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley-Blackwell Open Access Titles; Alma/SFX Local Collection
subjects Accuracy
Algorithms
Civilization
Classification
Data mining
Deep learning
Genetic algorithms
Intelligence
Neural networks
Optimization
Optimization algorithms
Simulation
Society
Target recognition
Time series
Unmanned aerial vehicles
title Evolving LSTM Networks for Time-Series Classification in EdgeIoT
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T21%3A39%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evolving%20LSTM%20Networks%20for%20Time-Series%20Classification%20in%20EdgeIoT&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Cui,%20Pei&rft.date=2023-01-01&rft.volume=2023&rft.issue=1&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2023/6469030&rft_dat=%3Cproquest_cross%3E2804963628%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2804963628&rft_id=info:pmid/&rfr_iscdi=true