{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:26:43Z","timestamp":1773376003908,"version":"3.50.1"},"reference-count":38,"publisher":"Emerald","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,14]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox\u2019s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and \u201cVisual Geometry Group 16 (VGG16)\u201d, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is \u201cTemporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)\u201d, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is \u201cDTCN, RNN, and LSTM\u201d, where the optimization is performed with the aid of the ME-DTBO model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN\u00a0+\u00a0DTCN\u00a0+\u00a0LSTM correspondingly when the learning percentage is 55.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/k-08-2023-1516","type":"journal-article","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T01:14:58Z","timestamp":1716081298000},"page":"4594-4625","source":"Crossref","is-referenced-by-count":27,"title":["Ensemble-based deep learning techniques for customer churn prediction model"],"prefix":"10.1108","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2482-9620","authenticated-orcid":true,"given":"R. Siva","family":"Subramanian","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, R.M.K College of Engineering and Technology , ,","place":["Puduvoyal, India"]}]},{"given":"B.","family":"Yamini","sequence":"additional","affiliation":[{"name":"Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology , ,","place":["Kattankulathur, India"]}]},{"given":"Kothandapani","family":"Sudha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Business Systems, R.M.D. Engineering College , ,","place":["Kavaraipettai, India"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1334-9389","authenticated-orcid":true,"given":"S.","family":"Sivakumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, S A Engineering College , ,","place":["Chennai, India"]}]}],"member":"140","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"2026030906390863800_ref001","doi-asserted-by":"publisher","first-page":"220816","DOI":"10.1109\/ACCESS.2020.3042657","article-title":"A survey on churn analysis in various business domains","volume":"8","author":"Ahn","year":"2020","journal-title":"IEEE Access"},{"key":"2026030906390863800_ref002","first-page":"7","article-title":"Negative correlation learning for customer churn prediction: a comparison study","volume":"2015","author":"Ali","year":"2015","journal-title":"The Scientific World Journal"},{"key":"2026030906390863800_ref003","doi-asserted-by":"publisher","first-page":"7940","DOI":"10.1109\/ACCESS.2016.2619719","article-title":"Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study","volume":"4","author":"Amin","year":"2016","journal-title":"IEEE Access"},{"issue":"6","key":"2026030906390863800_ref004","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/tevc.2003.819264","article-title":"A novel evolutionary data mining algorithm with applications to churn prediction","volume":"7","author":"Au","year":"2003","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"2026030906390863800_ref005","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1109\/tii.2016.2547584","article-title":"A big data clustering algorithm for mitigating the risk of customer churn","volume":"12","author":"Bi","year":"2016","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"2026030906390863800_ref006","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/tciaig.2015.2401979","article-title":"Churn prediction in online games using players\u2019 login records: a frequency analysis approach","volume":"7","author":"Castro","year":"2015","journal-title":"IEEE Transactions on Computational Intelligence and AI in Games"},{"key":"2026030906390863800_ref007","volume-title":"Dilated Recurrent Neural Networks","author":"Chang","year":"2017"},{"key":"2026030906390863800_ref008","doi-asserted-by":"publisher","first-page":"68017","DOI":"10.1109\/access.2022.3185227","article-title":"A sampling-based stack framework for imbalanced learning in churn prediction","volume":"10","author":"De","year":"2022","journal-title":"IEEE Access"},{"key":"2026030906390863800_ref009","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1109\/access.2022.3233768","article-title":"A representation-based query strategy to derive qualitative features for improved churn prediction","volume":"11","author":"De","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"2026030906390863800_ref010","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-14225-7","article-title":"A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process","volume":"12","author":"Dehghani","year":"2022","journal-title":"Scientific Reports"},{"key":"2026030906390863800_ref011","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110011","article-title":"Coati Optimization Algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems","volume":"259","author":"Dehghani","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"2026030906390863800_ref012","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2020.2968395","volume-title":"Brain MRI Super-resolution Using 3D Dilated Convolutional Encoder-Decoder Network","author":"Du","year":"2020"},{"issue":"7","key":"2026030906390863800_ref013","doi-asserted-by":"publisher","first-page":"2879","DOI":"10.1109\/tnnls.2020.3046629","article-title":"A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting","volume":"33","author":"Dudek","year":"2022","journal-title":"IEEE"},{"issue":"3","key":"2026030906390863800_ref014","doi-asserted-by":"publisher","first-page":"591","DOI":"10.3233\/aic-140652","article-title":"Election algorithm: a new socio-politically inspired strategy","volume":"28","author":"Emami","year":"2015","journal-title":"AI Communications"},{"key":"2026030906390863800_ref015","volume-title":"Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction","author":"Fi","year":"2016"},{"key":"2026030906390863800_ref016","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.indmarman.2022.09.023","article-title":"Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests","volume":"107","author":"Gattermann-Itschert","year":"2022","journal-title":"Industrial Marketing Management"},{"key":"2026030906390863800_ref017","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2021.100074","article-title":"Wild Geese Algorithm: a novel algorithm for large scale optimization based on the natural life and death of wild geese","volume":"11","author":"Ghasemi","year":"2021","journal-title":"Array"},{"issue":"3","key":"2026030906390863800_ref018","first-page":"410","article-title":"Churn prediction system for telecom using filter\u2013wrapper and ensemble classification","volume":"60","author":"Idris","year":"2017","journal-title":"The Computer Journal"},{"key":"2026030906390863800_ref020","article-title":"Churn prediction methods based on mutual customer interdependence","volume":"63","author":"Karmela Ljubi\u010di\u0107","year":"2023","journal-title":"Journal of Computational Science"},{"key":"2026030906390863800_ref021","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1016\/j.procs.2022.01.169","article-title":"Customer churn prediction in influencer commerce: an application of decision trees","volume":"199","author":"Kim","year":"2022","journal-title":"Procedia Computer Science"},{"issue":"1","key":"2026030906390863800_ref022","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TG.2018.2871215","article-title":"Profit optimizing churn prediction for long-term loyal customers in online games","volume":"12","author":"Lee","year":"2020","journal-title":"IEEE Transactions on Games"},{"issue":"2","key":"2026030906390863800_ref023","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1109\/tii.2012.2224355","article-title":"A customer churn prediction model in telecom industry using boosting","volume":"10","author":"Lu","year":"2014","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2026030906390863800_ref024","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-023-05259-9","article-title":"Exploiting time-varying RFM measures for customer churn prediction with deep neural networks","volume":"388","author":"Mena","year":"2023","journal-title":"Annals of Operations Research"},{"key":"2026030906390863800_ref025","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1155\/2013\/543940","article-title":"Hierarchical neural regression models for customer churn prediction","volume":"2013","author":"Mohammadi","year":"2013","journal-title":"Journal of Engineering"},{"issue":"2","key":"2026030906390863800_ref026","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/tg.2021.3067114","article-title":"RFM-LIR feature framework for churn prediction in the mobile games market","volume":"14","author":"Peri\u0161i\u0107","year":"2022","journal-title":"IEEE Transactions on Games"},{"issue":"4","key":"2026030906390863800_ref027","doi-asserted-by":"publisher","first-page":"3473","DOI":"10.1007\/s40747-021-00353-6","article-title":"Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector","volume":"9","author":"Pustokhina","year":"2023","journal-title":"Complex and Intelligent Systems"},{"issue":"1","key":"2026030906390863800_ref028","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/tg.2020.2992282","article-title":"Prediction of player churn and disengagement based on user activity data of a freemium online strategy game","volume":"13","author":"Rothmeier","year":"2021","journal-title":"IEEE Transactions on Games"},{"key":"2026030906390863800_ref029","article-title":"Recent advances in recurrent neural networks","author":"Salehinejad","year":"2018","journal-title":"arXiv"},{"key":"2026030906390863800_ref030","doi-asserted-by":"publisher","first-page":"7924","DOI":"10.1109\/access.2023.3239425","article-title":"Improving shopping mall revenue by real-time customized digital coupon issuance","volume":"11","author":"Seo","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"2026030906390863800_ref031","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1080\/09540091.2022.2083584","article-title":"A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy","volume":"24","author":"Sudharsan","year":"2022","journal-title":"Connection Science"},{"key":"2026030906390863800_ref032","doi-asserted-by":"crossref","DOI":"10.29322\/IJSRP.9.10.2019.p9420","volume-title":"Transfer Learning Using VGG-16 with Deep Convolutional Neural Network for Classifying Images","author":"Tammina","year":"2019"},{"issue":"3","key":"2026030906390863800_ref033","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1108\/k-04-2020-0214","article-title":"Hybrid ensemble learning approaches to customer churn prediction","volume":"51","author":"Tavassoli","year":"2022","journal-title":"Kybernetes"},{"key":"2026030906390863800_ref034","doi-asserted-by":"publisher","first-page":"60134","DOI":"10.1109\/access.2019.2914999","article-title":"A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector","volume":"7","author":"Ullah","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"2026030906390863800_ref035","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/tkde.2012.50","article-title":"A novel profit maximizing metric for measuring classification performance of customer churn prediction models","volume":"25","author":"Verbraken","year":"2013","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"2026030906390863800_ref036","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/s12559-018-9608-3","article-title":"Large-scale ensemble model for customer churn prediction in search ads","volume":"11","author":"Wang","year":"2022","journal-title":"Cognitive Computation"},{"key":"2026030906390863800_ref037","article-title":"Sequential prediction of social media popularity with deep temporal context networks","author":"Wu","year":"2017","journal-title":"arXiv"},{"key":"2026030906390863800_ref038","doi-asserted-by":"publisher","first-page":"62118","DOI":"10.1109\/access.2021.3073776","article-title":"Integrated churn prediction and customer segmentation framework for Telco business","volume":"9","author":"Wu","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"2026030906390863800_ref039","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00521-016-2477-3","article-title":"Particle classification optimization-based BP network for telecommunication customer churn prediction","volume":"29","author":"Yu","year":"2016","journal-title":"Neural Computing and Applications"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-08-2023-1516\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/k\/article-pdf\/54\/9\/4594\/10358507\/k-08-2023-1516en.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.emerald.com\/k\/article-pdf\/54\/9\/4594\/10358507\/k-08-2023-1516en.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:39:13Z","timestamp":1773052753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/k\/article\/54\/9\/4594\/1259512\/Ensemble-based-deep-learning-techniques-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":38,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,10,14]]}},"URL":"https:\/\/doi.org\/10.1108\/k-08-2023-1516","relation":{},"ISSN":["0368-492X","1758-7883"],"issn-type":[{"value":"0368-492X","type":"print"},{"value":"1758-7883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]}}}