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Cited by 5
The Impact of Churn Labelling Rules on Churn Prediction in Telecommunications

2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
Luis Tejada-Vicente, Dana Rosado-Oliden, David Mauricio-Santos
https://doi.org/10.1109/INTERCON63140.2024.10833498
Conference  (2024), p. 1
A Deep Auto Imputation Integrated Bayes Optimized Transfer Learning Model with Hybrid Skill-Levy Search Algorithm (DAI-BOTS) for Call Drop Prediction in Mobile Networks
G. V. Ashok, P. Vasanthi Kumari
https://doi.org/10.12720/jait.15.8.941-955
Journal  Journal of Advances in Information Technology Volume 15, Issue 8 (2024), p. 941
A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners
Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo
https://doi.org/10.1109/ACCESS.2024.3402092
Journal  IEEE Access Volume 12 (2024), p. 70434
Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature
David Freire, David Santos Mauricio Sanchez, José Luis Castillo Sequera, Daniel Fiallo Moncayo
https://doi.org/10.1109/ACCESS.2024.3443318
Journal  IEEE Access Volume 12 (2024), p. 118968
Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention
Hira Farman, Samar Raza Talpur, Usman Amjad, Govari Shankar, Umm e Laila, Lubaba Naseem
https://doi.org/10.21015/vtse.v12i4.1928
Journal  VFAST Transactions on Software Engineering Volume 12, Issue 4 (2024), p. 70


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