Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries

Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Electronic research archive Ročník 31; číslo 8; s. 4443 - 4458
Hlavní autori: Abdullaev, Ilyоs, Prodanova, Natalia, Altaf Ahmed, Mohammed, Laxmi Lydia, E., Shrestha, Bhanu, Prasad Joshi, Gyanendra, Cho, Woong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: AIMS Press 01.01.2023
Predmet:
artificial intelligence
data exploration
deep learning
jaya optimization algorithm
ISSN:2688-1594, 2688-1594
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.
AbstractList Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.
Author Cho, Woong
Shrestha, Bhanu
Altaf Ahmed, Mohammed
Laxmi Lydia, E.
Abdullaev, Ilyоs
Prodanova, Natalia
Prasad Joshi, Gyanendra
Author_xml – sequence: 1
  givenname: Ilyоs
  surname: Abdullaev
  fullname: Abdullaev, Ilyоs
  organization: Department of Management and Marketing, Urgench State University, Urgench 220100, Uzbekistan
– sequence: 2
  givenname: Natalia
  surname: Prodanova
  fullname: Prodanova, Natalia
  organization: Basic Department Financial Control, Analysis and Audit of Moscow Main Control Department, Plekhanov Russian University of Economics, Moscow 117997, Russia
– sequence: 3
  givenname: Mohammed
  surname: Altaf Ahmed
  fullname: Altaf Ahmed, Mohammed
  organization: Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
– sequence: 4
  givenname: E.
  surname: Laxmi Lydia
  fullname: Laxmi Lydia, E.
  organization: Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam 530049, India
– sequence: 5
  givenname: Bhanu
  surname: Shrestha
  fullname: Shrestha, Bhanu
  organization: Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea
– sequence: 6
  givenname: Gyanendra
  surname: Prasad Joshi
  fullname: Prasad Joshi, Gyanendra
  organization: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
– sequence: 7
  givenname: Woong
  surname: Cho
  fullname: Cho, Woong
  organization: Department of Electronics, Information and Communication Engineering, Kangwon National University, Gangwon-do, Samcheok-si 25913, Korea
BookMark eNptUE1LAzEUDFLBWnvyD-xdtuZjN9kcRfwoFLzoOYS3b7cp201JUsV_b2oriHh6w3szw5u5JJPRj0jINaMLoUV1i8EuOOWCc3VGplw2TclqXU1-4Qsyj3FDKeUNo7SSU7JZ4XsW9m7siy0mu8Z9cDE5iMWHS-vChuQ6B84OhRsTDoPrcQQsOh8K2Mfkt5jBeh_GYhewdZCcHzO1yFwEv82wzbTgMF6R884OEeenOSNvjw-v98_l6uVpeX-3Kq2QOpVKQieBSZCSKVtTmdcdAGuA15ZJxLrpuJCsBc11VVctp1qqRmFdaS0oiBlZHn1bbzdmF9zWhk_jrTPfCx96c0gFAxrBFXBdK9nWompBaCtUB0px3WA-YfZiRy8IPsaAnQGX7CFjCtYNhlFz6N7kCs2p-6y5-aP5-eE_9hfzj4jc
CitedBy_id crossref_primary_10_1016_j_eswa_2024_125993
crossref_primary_10_1051_e3sconf_202344904001
crossref_primary_10_1051_bioconf_20248206015
crossref_primary_10_3390_electronics14101916
crossref_primary_10_3389_frai_2025_1600357
crossref_primary_10_3390_biomimetics9010001
crossref_primary_10_1051_e3sconf_202344907001
crossref_primary_10_1371_journal_pone_0303881
crossref_primary_10_1051_e3sconf_202344904005
crossref_primary_10_1051_e3sconf_202344902001
crossref_primary_10_1051_e3sconf_202344907005
Cites_doi 10.1016/j.engappai.2016.07.006
10.3390/math10071031
10.1080/15623599.2019.1683692
10.1016/j.suscom.2022.100705
10.1007/s13198-022-01759-2
10.1109/ICISS49785.2020.9315951
10.1016/j.ijforecast.2019.03.029
10.1016/j.cie.2018.12.017
10.1080/15472450.2021.1890070
10.3390/jtaer17040077
10.1007/s40747-021-00353-6
10.1109/ICONAT53423.2022.9725957
10.1007/s00607-021-00908-y
10.1155/2022/4720539
10.2174/1872212113666190211130117
10.1007/s10489-014-0590-5
10.1109/ICDABI53623.2021.9655792
10.1080/09540091.2022.2083584
10.5267/j.ijiec.2015.8.004
10.1108/JM2-01-2021-0032
10.3390/computation9030034
10.1007/s11227-021-03737-0
10.3390/jtaer17020024
10.1007/s00521-022-07067-x
10.1109/ICComm.2016.7528311
ContentType Journal Article
DBID AAYXX
CITATION
DOA
DOI 10.3934/era.2023227
DatabaseName CrossRef
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
DeliveryMethod fulltext_linktorsrc
Discipline Mathematics
EISSN 2688-1594
EndPage 4458
ExternalDocumentID oai_doaj_org_article_327c29576d534dc39a37fc77298e27ce
10_3934_era_2023227
GroupedDBID AAYXX
ABDBF
ALMA_UNASSIGNED_HOLDINGS
AMVHM
CITATION
GROUPED_DOAJ
IAO
ICD
ITC
RAN
TUS
M~E
ID FETCH-LOGICAL-a369t-76cf6c16c6617a506a36fcc18c25a16ee58f2361dc929454d2096787e549930c3
IEDL.DBID DOA
ISICitedReferencesCount 11
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001071130500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2688-1594
IngestDate Tue Oct 14 19:05:49 EDT 2025
Sat Nov 29 01:41:24 EST 2025
Tue Nov 18 21:55:20 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 8
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a369t-76cf6c16c6617a506a36fcc18c25a16ee58f2361dc929454d2096787e549930c3
OpenAccessLink https://doaj.org/article/327c29576d534dc39a37fc77298e27ce
PageCount 16
ParticipantIDs doaj_primary_oai_doaj_org_article_327c29576d534dc39a37fc77298e27ce
crossref_citationtrail_10_3934_era_2023227
crossref_primary_10_3934_era_2023227
PublicationCentury 2000
PublicationDate 2023-01-01
PublicationDateYYYYMMDD 2023-01-01
PublicationDate_xml – month: 01
  year: 2023
  text: 2023-01-01
  day: 01
PublicationDecade 2020
PublicationTitle Electronic research archive
PublicationYear 2023
Publisher AIMS Press
Publisher_xml – name: AIMS Press
References key-10.3934/era.2023227-20
key-10.3934/era.2023227-21
key-10.3934/era.2023227-22
key-10.3934/era.2023227-23
key-10.3934/era.2023227-28
key-10.3934/era.2023227-29
key-10.3934/era.2023227-24
key-10.3934/era.2023227-25
key-10.3934/era.2023227-26
key-10.3934/era.2023227-27
key-10.3934/era.2023227-3
key-10.3934/era.2023227-2
key-10.3934/era.2023227-5
key-10.3934/era.2023227-4
key-10.3934/era.2023227-1
key-10.3934/era.2023227-10
key-10.3934/era.2023227-11
key-10.3934/era.2023227-12
key-10.3934/era.2023227-17
key-10.3934/era.2023227-18
key-10.3934/era.2023227-19
key-10.3934/era.2023227-7
key-10.3934/era.2023227-13
key-10.3934/era.2023227-6
key-10.3934/era.2023227-14
key-10.3934/era.2023227-9
key-10.3934/era.2023227-15
key-10.3934/era.2023227-8
key-10.3934/era.2023227-16
References_xml – ident: key-10.3934/era.2023227-9
  doi: 10.1016/j.engappai.2016.07.006
– ident: key-10.3934/era.2023227-16
  doi: 10.3390/math10071031
– ident: key-10.3934/era.2023227-24
  doi: 10.1080/15623599.2019.1683692
– ident: key-10.3934/era.2023227-21
– ident: key-10.3934/era.2023227-4
– ident: key-10.3934/era.2023227-3
  doi: 10.1016/j.suscom.2022.100705
– ident: key-10.3934/era.2023227-12
  doi: 10.1007/s13198-022-01759-2
– ident: key-10.3934/era.2023227-18
  doi: 10.1109/ICISS49785.2020.9315951
– ident: key-10.3934/era.2023227-15
  doi: 10.1016/j.ijforecast.2019.03.029
– ident: key-10.3934/era.2023227-14
  doi: 10.1016/j.cie.2018.12.017
– ident: key-10.3934/era.2023227-13
– ident: key-10.3934/era.2023227-11
– ident: key-10.3934/era.2023227-25
  doi: 10.1080/15472450.2021.1890070
– ident: key-10.3934/era.2023227-7
  doi: 10.3390/jtaer17040077
– ident: key-10.3934/era.2023227-28
  doi: 10.1007/s40747-021-00353-6
– ident: key-10.3934/era.2023227-19
  doi: 10.1109/ICONAT53423.2022.9725957
– ident: key-10.3934/era.2023227-27
  doi: 10.1007/s00607-021-00908-y
– ident: key-10.3934/era.2023227-29
  doi: 10.1155/2022/4720539
– ident: key-10.3934/era.2023227-17
  doi: 10.2174/1872212113666190211130117
– ident: key-10.3934/era.2023227-8
  doi: 10.1007/s10489-014-0590-5
– ident: key-10.3934/era.2023227-2
  doi: 10.1109/ICDABI53623.2021.9655792
– ident: key-10.3934/era.2023227-6
  doi: 10.1080/09540091.2022.2083584
– ident: key-10.3934/era.2023227-22
  doi: 10.5267/j.ijiec.2015.8.004
– ident: key-10.3934/era.2023227-20
  doi: 10.1108/JM2-01-2021-0032
– ident: key-10.3934/era.2023227-5
  doi: 10.3390/computation9030034
– ident: key-10.3934/era.2023227-23
  doi: 10.1007/s11227-021-03737-0
– ident: key-10.3934/era.2023227-10
  doi: 10.3390/jtaer17020024
– ident: key-10.3934/era.2023227-1
  doi: 10.1007/s00521-022-07067-x
– ident: key-10.3934/era.2023227-26
  doi: 10.1109/ICComm.2016.7528311
SSID ssj0002810046
Score 2.3597767
Snippet Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML)...
SourceID doaj
crossref
SourceType Open Website
Enrichment Source
Index Database
StartPage 4443
SubjectTerms artificial intelligence
data exploration
deep learning
jaya optimization algorithm
Title Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries
URI https://doaj.org/article/327c29576d534dc39a37fc77298e27ce
Volume 31
WOSCitedRecordID wos001071130500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2688-1594
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002810046
  issn: 2688-1594
  databaseCode: DOA
  dateStart: 20220101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELZQxQAD4inKSx46IUVNYseOR0AgBqgYQOoWORdbbUUfSlN-P3dJqDIgsbBZzsmx7pz4u0v8fYwNYgys1EIGOlcykHHhgzQXJoh8rhDOGqMd1GITejRKx2Pz1pH6on_CGnrgxnFDEWuIDaLiAgctQBgrtAfChKnDS47evoh6OsnUrC4ZEROaag7kCSPk0JXEMoT4gfRjOltQh6m_3lKeDtlBiwX5XTOHI7bjFsds_3VLpLo-YbMXh2utVhLic1fZidu01MqcKqic5t9wQPBph1yTIxTlsEFgN3fYmOCN-KqkbzIUBzTlFenfLOfYbKQ73PqUfTw9vj88B608QmCFMlWgFXgFkQLcYrVNQoXdHiBKIU5spJxLUk_UKgUgBJKJLGJMV_D5dJQSihDEGestlgt3zrjM08KGYa5zD9KKxNLRjtxrb70Rkdd9dvvjsQxa7nCSsPjMMIcg92boiqx1b58NtsarhjLjd7N7cv3WhHiu6w6MftZGP_sr-hf_Mcgl26M5NYWVK9aryo27ZrvwVU3X5U29sL4Bg-zUNQ
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Leveraging+metaheuristics+with+artificial+intelligence+for+customer+churn+prediction+in+telecom+industries&rft.jtitle=Electronic+research+archive&rft.au=Ily%D0%BEs+Abdullaev&rft.au=Natalia+Prodanova&rft.au=Mohammed+Altaf+Ahmed&rft.au=E.+Laxmi+Lydia&rft.date=2023-01-01&rft.pub=AIMS+Press&rft.eissn=2688-1594&rft.volume=31&rft.issue=8&rft.spage=4443&rft.epage=4458&rft_id=info:doi/10.3934%2Fera.2023227&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_327c29576d534dc39a37fc77298e27ce
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2688-1594&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2688-1594&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2688-1594&client=summon