<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>633594a3-addf-48d4-a76a-b93cdfb558c1</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijrte.A8030.13010524</doi>
<citation_list><citation key="ref0"><doi>10.1109/INMIC.2012.6511498</doi><unstructured_citation>A. Idris, &quot;Customer Churn Prediction for Telecommunication : Employing various various features selection techniques and tree based ensemble classifiers,&quot; no. September, 2015, doi: 10.1109/INMIC.2012.6511498. https://doi.org/10.1109/INMIC.2012.6511498</unstructured_citation></citation><citation key="ref1"><doi>10.1016/S1874-8651(09)60003-X</doi><unstructured_citation>X. I. A. Guo-en and J. I. N. Wei-dong, &quot;Model of Customer Churn Prediction on Support Vector Machine,&quot; Syst. Eng. - Theory Pract., vol. 28, no. 1, pp. 71-77, 2008, doi: 10.1016/S1874-8651(09)60003-X. https://doi.org/10.1016/S1874-8651(09)60003-X</unstructured_citation></citation><citation key="ref2"><doi>10.4236/ijcns.2015.85012</doi><unstructured_citation>A. Hudaib, R. Dannoun, O. Harfoushi, R. Obiedat, and H. Faris, &quot;Hybrid Data Mining Models for Predicting Customer Churn,&quot; no. May, pp. 91-96, 2015. https://doi.org/10.4236/ijcns.2015.85012</unstructured_citation></citation><citation key="ref3"><unstructured_citation>M. C. Mozer, R. Wolniewicz, and D. B. Grimes, &quot;Churn Reduction in the Wireless Industry,&quot; no. January 1999, 2015.</unstructured_citation></citation><citation key="ref4"><doi>10.35940/ijrte.A9170.078219</doi><unstructured_citation>J. Pamina, T. D. Rajkumar, S. Kiruthika, T. Suganya, F. Femila, and I. Introduction, &quot;Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector,&quot; vol. 3878, no. 2, pp. 299-308, 2019, doi: 10.35940/ijrte.A9170.078219. https://doi.org/10.35940/ijrte.A9170.078219</unstructured_citation></citation><citation key="ref5"><doi>10.4108/eetmca.v6i21.2181</doi><unstructured_citation>S. O. Abdulsalam, J. F. Ajao, B. F. Balogun, and M. Olaolu, &quot;EAI Endorsed Transactions A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms,&quot; vol. 7, no. 21, pp. 1-8, 2022. https://doi.org/10.4108/eetmca.v6i21.2181</unstructured_citation></citation><citation key="ref6"><unstructured_citation>G. Thakre, P. Wankhede, S. Patle, S. Joshi, and P. A. Chauhan, &quot;Implementation of Machine Learning Model for Employee Retention Prediction,&quot; pp. 503-508.</unstructured_citation></citation><citation key="ref7"><doi>10.1109/ACCESS.2019.2914999</doi><unstructured_citation>I. Ullah, B. Raza, A. K. Malik, S. U. L. Islam, S. W. O. N. Kim, and M. Imran, &quot;A Churn Prediction Model Using Random Forest : Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector,&quot; IEEE Access, vol. 7, pp. 60134-60149, 2019, doi: 10.1109/ACCESS.2019.2914999. https://doi.org/10.1109/ACCESS.2019.2914999</unstructured_citation></citation><citation key="ref8"><doi>10.1088/1757-899X/879/1/012090</doi><unstructured_citation>I. O. P. C. Series and M. Science, &quot;Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes,&quot; 2020, doi: 10.1088/1757-899X/879/1/012090. https://doi.org/10.1088/1757-899X/879/1/012090</unstructured_citation></citation><citation key="ref9"><doi>10.30534/ijatcse/2021/1561032021</doi><unstructured_citation>I. Pathan, N. A. Kanasro, F. B. Shaikh, M. U. R. Maree, and A. A. Chandio, &quot;An Evolutionary Approach of Machine Learning for Monitoring Churn Prediction of Broadband Customer,&quot; vol. 10, no. 3, pp. 2623-2629, 2021. https://doi.org/10.30534/ijatcse/2021/1561032021</unstructured_citation></citation><citation key="ref10"><doi>10.1007/s10479-008-0400-8</doi><unstructured_citation>J. Qi et al., &quot;ADTreesLogit model for customer churn prediction,&quot; pp. 247-265, 2009, doi: 10.1007/s10479-008-0400-8. https://doi.org/10.1007/s10479-008-0400-8</unstructured_citation></citation><citation key="ref11"><unstructured_citation>N. Hashmi, N. A. Butt, and M. Iqbal, &quot;Customer Churn Prediction in Telecommunication A Decade Review and Classification,&quot; no. May 2014, 2013.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>E. Radmehr and M. Bazmara, &quot;A Survey on Business Intelligence Solutions in Banking Industry and Big Data Applications,&quot; vol. 7, no. 23, pp. 3280-3298, 2017.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>H. Cho, Y. Lee, H. Lee, H. Lee, and Y. Lee, &quot;Toward Optimal Churn Management : A Partial Least Square ( PLS ) Model Toward Optimal Churn Management : A Partial Least Square ( PLS ) Model,&quot; 2010.</unstructured_citation></citation><citation key="ref14"><doi>10.1016/j.ejor.2012.06.040</doi><unstructured_citation>Z. Chen, Z. Chen, Z. Fan, and M. Sun, &quot;A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data,&quot; Eur. J. Oper. Res., vol. 223, no. 2, pp. 461-472, 2012, doi: 10.1016/j.ejor.2012.06.040. https://doi.org/10.1016/j.ejor.2012.06.040</unstructured_citation></citation><citation key="ref15"><unstructured_citation>V. Jinde and P. A. Savyanavar, &quot;Customer Churn Prediction System using Machine Learning,&quot; vol. 29, no. 5, pp. 7957-7964, 2020.</unstructured_citation></citation><citation key="ref16"><doi>10.1016/j.asoc.2014.08.041</doi><unstructured_citation>A. Keramati, R. Jafari-marandi, M. Aliannejadi, I. Ahmadian, and M. Mozaffari, &quot;Improved churn prediction in telecommunication industry using data mining techniques,&quot; Appl. Soft Comput. J., vol. 24, pp. 994-1012, 2014, doi: 10.1016/j.asoc.2014.08.041. https://doi.org/10.1016/j.asoc.2014.08.041</unstructured_citation></citation><citation key="ref17"><unstructured_citation>M. Ewieda, E. M. Shaaban, and M. Roushdy, &quot;Review of Data Mining Techniques for Detecting Churners in the Telecommunication Industry&quot;.</unstructured_citation></citation><citation key="ref18"><doi>10.1007/s11235-017-0310-7</doi><unstructured_citation>M. Azeem, M. Usman, and A. C. M. Fong, &quot;A churn prediction model for prepaid customers in telecom using fuzzy classifiers,&quot; Telecommun. Syst., vol. 66, no. 4, pp. 603-614, 2017, doi: 10.1007/s11235-017-0310-7. https://doi.org/10.1007/s11235-017-0310-7</unstructured_citation></citation><citation key="ref19"><doi>10.5539/mas.v11n9p151</doi><unstructured_citation>U. G. Inyang, O. O. Obot, M. E. Ekpenyong, and A. M. Bolanle, &quot;Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification,&quot; vol. 11, no. 9, pp. 151-164, 2017, doi: 10.5539/mas.v11n9p151. https://doi.org/10.5539/mas.v11n9p151</unstructured_citation></citation><citation key="ref20"><doi>10.1016/j.ijinfomgt.2018.08.015</doi><unstructured_citation>A. Amin, B. Shah, A. Masood, F. Joaquim, and L. Moreira, &quot;Just-in-time Customer Churn Prediction : With and Without Data Transformation International Journal of Information Management Cross-company customer churn prediction in telecommunication : A comparison of data transformation methods,&quot; Int. J. Inf. Manage., no. August 2020, pp. 0-1, 2018, doi: 10.1016/j.ijinfomgt.2018.08.015. https://doi.org/10.1016/j.ijinfomgt.2018.08.015</unstructured_citation></citation><citation key="ref21"><doi>10.1007/s00521-018-3548-4</doi><unstructured_citation>E. S. J. Vijaya, &quot;Hybrid PPFCM-ANN model : an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network,&quot; Neural Comput. Appl., vol. 31, no. 11, pp. 7181-7200, 2019, doi: 10.1007/s00521-018-3548-4. https://doi.org/10.1007/s00521-018-3548-4</unstructured_citation></citation><citation key="ref22"><unstructured_citation>C. G. Mena, A. De Caigny, K. Coussement, K. W. De Bock, and S. Lessmann, &quot;Churn Prediction with Sequential Data and Deep Neural Networks A Comparative Analysis ∗,&quot; pp. 1-12, 2019.</unstructured_citation></citation><citation key="ref23"><doi>10.1016/j.ejor.2018.11.072</doi><unstructured_citation>E. Stripling and B. Baesens, &quot;Profit Driven Decision Trees for Churn Prediction,&quot; no. December 2017, 2018, doi: 10.1016/j.ejor.2018.11.072. https://doi.org/10.1016/j.ejor.2018.11.072</unstructured_citation></citation><citation key="ref24"><doi>10.1007/978-981-10-5520-1</doi><unstructured_citation>S. Babu and N. R. Ananthanarayanan, &quot;Enhanced Prediction Model for Customer Churn in Telecommunication Using Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE,&quot; no. February, 2018, doi: 10.1007/978-981-10-5520-1. https://doi.org/10.1007/978-981-10-5520-1</unstructured_citation></citation><citation key="ref25"><unstructured_citation>K. U. Leuven, &quot;B AGGING AND B OOSTING C LASSIFICATION T REES TO Aurélie Lemmens and Christophe Croux&quot;.</unstructured_citation></citation><citation key="ref26"><doi>10.1016/j.eswa.2012.01.014</doi><unstructured_citation>K. W. De Bock and D. Van Den Poel, &quot;Expert Systems with Applications Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models,&quot; vol. 39, pp. 6816-6826, 2012, doi: 10.1016/j.eswa.2012.01.014. https://doi.org/10.1016/j.eswa.2012.01.014</unstructured_citation></citation><citation key="ref27"><doi>10.1109/ICSMC.2012.6377917</doi><unstructured_citation>A. Idris and A. Khan, &quot;Genetic Programming and Adaboosting based churn prediction for Telecom,&quot; no. October, 2012, doi: 10.1109/ICSMC.2012.6377917. https://doi.org/10.1109/ICSMC.2012.6377917</unstructured_citation></citation><citation key="ref28"><doi>10.1007/s10586-017-1154-3</doi><unstructured_citation>A. Idris and A. Iftikhar, &quot;Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling,&quot; Cluster Comput., vol. 22, no. s3, pp. 7241-7255, 2019, doi: 10.1007/s10586-017-1154-3. https://doi.org/10.1007/s10586-017-1154-3</unstructured_citation></citation><citation key="ref29"><unstructured_citation>C. Science and Z. Zhang, &quot;Using Combined Model Approach for Churn Prediction in Telecommunication Fa-Gui LIU, Zhi-Jie ZHANG*, Xin YANG,&quot; vol. 131, no. Eeeis, pp. 269-276, 2017.</unstructured_citation></citation><citation key="ref30"><doi>10.1007/s10586-017-1172-1</doi><unstructured_citation>J. V. E. Sivasankar, &quot;An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing,&quot; Cluster Comput., vol. 22, no. s5, pp. 10757-10768, 2019, doi: 10.1007/s10586-017-1172-1. https://doi.org/10.1007/s10586-017-1172-1</unstructured_citation></citation><citation key="ref31"><doi>10.14569/IJACSA.2021.0121132</doi><unstructured_citation>M. K. Awang, M. Makhtar, N. Udin, and N. F. Mansor, &quot;Improving Customer Churn Classification with Ensemble Stacking Method,&quot; vol. 12, no. 11, 2021. https://doi.org/10.14569/IJACSA.2021.0121132</unstructured_citation></citation><citation key="ref32"><doi>10.28945/5086</doi><unstructured_citation>H. Tran, N. Le, and V. Nguyen, &quot;C USTOMER C HURN P REDICTION IN THE B ANKING S ECTOR U SING M ACHINE L EARNING -B ASED,&quot; vol. 18, pp. 87-105, 2023.</unstructured_citation></citation><citation key="ref33"><doi>10.1007/s00607-018-0633-6</doi><unstructured_citation>J. Vijaya and E. Sivasankar, &quot;Computing efficient features using rough set theory combined with ensemble classification techniques to,&quot; Computing, vol. 100, no. 8, pp. 839-860, 2018, doi: 10.1007/s00607-018-0633-6. https://doi.org/10.1007/s00607-018-0633-6</unstructured_citation></citation><citation key="ref34"><doi>10.33093/jetap.2023.5.2.12</doi><unstructured_citation>T. Y. Lin et al., &quot;Journal of Engineering Technology and Applied Physics Stacking Ensemble Approach for Churn Prediction : Integrating CNN and Machine Learning Models with CatBoost Meta-Learner,&quot; vol. 5, no. 2, pp. 99-107, 2023. https://doi.org/10.33093/jetap.2023.5.2.12</unstructured_citation></citation><citation key="ref35"><doi>10.14569/IJACSA.2018.090238</doi><unstructured_citation>S. F. Sabbeh, &quot;Machine-Learning Techniques for Customer Retention : A Comparative Study,&quot; vol. 9, no. 2, pp. 273-281, 2018. https://doi.org/10.14569/IJACSA.2018.090238</unstructured_citation></citation><citation key="ref36"><unstructured_citation>B. Zhu, B. Baesens, and K. L. M. Seppe, &quot;An empirical comparison of techniques for the class imbalance problem in churn prediction,&quot; vol. 32, no. 0.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>C. K. N, &quot;RESEARCH ON CHURN PREDICTION IN MOBILE COMMERCE USING SUPERVISED MODEL .,&quot; no. 05, pp. 29-43, 2022, doi: 10.17605/OSF.IO/ZRX7H.</unstructured_citation></citation><citation key="ref38"><doi>10.35940/ijsce.F3502.0510521</doi><unstructured_citation>Sharma, N., Raj, A., Kesireddy, V., &amp; Akunuri, P. (2021). Machine Learning Implementation in Electronic Commerce for Churn Prediction of End User. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 5, pp. 20-25). https://doi.org/10.35940/ijsce.f3502.0510521</unstructured_citation></citation><citation key="ref39"><doi>10.35940/ijisme.D1186.016420</doi><unstructured_citation>Thakur, T. B., &amp; Mittal, A. K. (2020). Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 4, pp. 1-4). https://doi.org/10.35940/ijisme.d1186.016420</unstructured_citation></citation><citation key="ref40"><doi>10.35940/ijese.C7895.09111023</doi><unstructured_citation>Tamilarasi, Dr. A., Karthick, T. J., R. Dharani, &amp; S. Jeevitha. (2023). Eye Disease Prediction Among Corporate Employees using Machine Learning Techniques. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 10, pp. 1-5). https://doi.org/10.35940/ijese.c7895.09111023</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
