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All issues Volume 56 (2023) ITM Web Conf., 56 (2023) 05012 References
Open Access
Issue
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
Article Number 05012
Number of page(s) 11
Section Machine Learning & Neural Networks
DOI https://doi.org/10.1051/itmconf/20235605012
Published online 09 August 2023
  1. Chellam G.H. A. Mahalekshmi, “Analysis of customer churn prediction using machine learning and deep learning algorithms”, Int. J. Health Sci., vol 11684-11693, 2022, doi: https://doi.org/10.53730/ijhs.v6nS1.7861. [Google Scholar]
  2. Cui Ye, Xia, Duan & Bohan, Yunhuai, “Analysis and Prediction of Telecom Customer Churn based on Machine Learning”, Highlights in Science, Engineering and Technology, vol 16, pp 131-145, DOI: 10.54097/hset.v16i.2495. (2022) [CrossRef] [Google Scholar]
  3. A. Almazroi, Syed Fakhar Bilal, Khan F.H., Bashir, S., “An ensemble based approach using combination of clustering and classification algorithm to enhance customer churn prediction in telecom industry”, Peerj Computer Science, vol 8, DOI: 10.7717/peerj-cs.854, (2022). [Google Scholar]
  4. B. Raza, I. Ullah, A. K. Malik, S.U. Islam, M. Imran and S.W. Kim, “A Churn Prediction Model Using Random Forest Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector”, IEEE Access, vol 7, pp 60134-60149, DOI: 10.1/ACCESS.2019.2194999. [Google Scholar]
  5. Sumesh, Sandeep & Sood, Ranjan, “Sentimental Analysis Based Telecom Churn Prediction, Journal of Web Engineering and Technology”, vol 7 Issue 1, pp 6-12, (2020). [Google Scholar]
  6. Sook Ling L., Mustafa, M.M., & Abdul Razak, S.F., “Telecom churn prediction for telecommunication industry: A Malaysian Case Study”, F1000 Research, vol 10, DOI: 10.12688/f1000research.73597.1, (2021). [Google Scholar]
  7. Aman Shakya, Sagar Maan Shrestha, “A Customer Churn Prediction Model Using XGBoost for the Telecom Industry in Nepal”, Procedía Computer Science, vol 215, pp. 652-661, doi: https://doi.org/10.1016/j.procs.2022.12.067, (2022) [CrossRef] [Google Scholar]
  8. Jain, Ajay & Shrivastava, Hemalatha & Khunteta, Sumit, “Churn Prediction in Telecommunication using Logistic Regression and Logit Boost”, ICCDS (2019), vol 167 pp. 101-112, (2020) [Google Scholar]
  9. Dwivedi, A., “Telecom Industry: Customer Churn Prediction”, (2019) [Google Scholar]
  10. Jafar, A., Ahmad, A.K. & Aljoumaa, K., “Customer churn prediction in telecom using machine learning in big data platform”, J big data vol 6 pp. 28, doi: https://doi.org/10.1186/s40537-019-0191-6, (2019) [Google Scholar]
  11. Muhammad Jagurnauth, Jooflo, Rameshwar & Jooflo, Khalid, “Customer Churn Prediction in Telecom Using Machine Learning in Big Data Platform”, Journal of Critical Reviews vol. 7 pp. 1991, (2020). [Google Scholar]
  12. D. Maheswari, Ammar A.Q., “Churn prediction on huge telecom data using hybrid firefly based classification”, Egyptian Informatics Journal (EIJ) vol. 18 Issue 3 pp. 215-220, (2017). [CrossRef] [Google Scholar]
  13. R.A. Jugurnauth, M.B.A. Joofloo, and K.M.B.A. Joofloo, “A Systematic Review of Algorithms applied for Telecom Churn Prediction” 3rd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), Balaclava, Mauritius pp. 136-140, DOI: 10.1109/ELECOM.2020.9296999. (2020). [Google Scholar]
  14. Mishra, M.K., Lalwani, P., Chadha, J.S. et al., “Customer churn prediction system: a machine learning approach” Computing vol. 104 pp. 271-294, (2022). [CrossRef] [MathSciNet] [Google Scholar]
  15. Aditya Bhoite, Kulkarni, Sachin, “Customer Churn Analysis and Prediction”, International Journal of Computer Applications Technology and Research (IJCATR) vol 8, DOI: 10.7753/IJCATR0809.1005, (2019). [Google Scholar]
  16. Babar Shah, Feras Al-Obeidat, Adnan Amin, Awais Adnan, Sajid Anwar, Jonathan Loo, “Customer churn prediction in telecommunication industry using data certainity”, Journal of Business Research (JBR) vol. 94 pp. 290-301, (2019). [CrossRef] [Google Scholar]
  17. B. Sriman, P. Baby Shamini, Annie Silviya S.H., N.V. Keerthana, A. Elangovan, “Deep Learning based Plant Leaf Disease Detection and Classification”, ICIRCA pp. 702-710, DOI: 10.1109/ICIRCA54612.2022.9985548, (2022) [Google Scholar]
  18. J. Pathmanaba, Annie Silviya, S.H., B. Sriman, S. Kingsley, “Prediction and Prevention Analysis Using Machine Learning Algorithms for detecting the Crime Data”, ICCST pp. 986-991, (2022) [Google Scholar]

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