References
P. P. Singh, F. I. Anik, R. Senapati, A. Sinha, N. Sakib, and E. Hossain, ‘Investigating customer churn in banking: a machine learning approach and visualization app for data science and management’, Data Sci. Manag., vol. 7, no. 1, pp. 7–16, Mar. 2024, doi: 10.1016/j.dsm.2023.09.002. R. Bhuria and S. Aluvala, ‘Predicting Bank Customer Churn: An XGBoost Approach to Enhancing Customer Retention’, in 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India: IEEE, Apr. 2025, pp. 30–34. doi: 10.1109/InCACCT65424.2025.11011351. X. Li and Z. Chen, ‘Customer Churn Prediction in Bank Based on Different Machine Learning Models’, in 2022 2nd International Signal Processing, Communications and Engineering Management Conference , Montreal, ON, Canada: IEEE, Nov. 2022, pp. 274– 279. doi: 10.1109/ISPCEM57418.2022.00061. M. Singh, S. Singh, N. Seen, S. Kaushal, and H. Kumar, ‘Comparison of learning techniques for prediction of customer churn in telecommunication’, in 2018 28th International Telecommunication Networks and Applications Conference , Sydney, NSW: IEEE, Nov. 2018, pp. 1–5. doi: 10.1109/ATNAC.2018.8615326. X. Gong, L. Fang, Y. Liu, and S. Xu, ‘Research of Machine Learning Algorithm in Early Warning Analysis of Bank Customer Churn’, 2022. A. Raut and C. Puri, ‘Predicting Bank Customer Churn with Interpretable Machine Learning using SHAP’, in 2025 6th International Conference on Electronics and Sustainable Communication Systems , Coimbatore, India: IEEE, Sept. 2025, pp. 1611–1616. doi: 10.1109/ICESC65114.2025.11212534. H. A. Altairey and A. I. Al-Alawi, ‘Customer Churn Prediction in Telecommunication and Banking using Machine Learning: A Systematic Literature Review’, in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain: IEEE, Jan. 2024, pp. 483–490. doi: 10.1109/ICETSIS61505.2024.10459439. M. Galal, S. Rady, and M. Aref, ‘Enhancing Customer Churn Prediction in Digital Banking using Ensemble Modeling’, in 2022 4th Novel Intelligent and Leading Emerging Sciences Conference , Giza, Egypt: IEEE, Oct. 2022, pp. 21–25. doi: 10.1109/NILES56402.2022.9942408. A. Kaushik, R. Gupta, A. Singh, H. S. Ahmed, V. S. Rao, and B. A. Vijayalakshmi, ‘Hybrid CNN-LSTM Approach for Predicting and Analyzing Customer Churn in the Banking Sector’, in 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies , Bhilai, India: IEEE, Jan. 2025, pp. 1–6. doi: 10.1109/ICAECT63952.2025.10958962. G. U. Nneji, J. Cai, J. Deng, M. A. Hossin, S. Nahar, and J. Jackson, “Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans”, Diagnostics, vol. 12, no. 2, p. 540, Feb. 2022, https://doi.org/10.3390/diagnostics12020540. Li, X., Wang, Y., Monday, H.N. and Nneji, G.U., 2025. A novel residual learning of multi-scale feature extraction model for the classification of rice grain varieties. Computers and Electronics in Agriculture, 237, p.110491. Monday, H.N., Nneji, G.U., Hossin, M.A., Mark, K.D., Umana, E.S., Mgbejime, G.T. and Li, J., 2025. Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using P-ISSN 2695-186X Adaptive Focal Cross-Entropy Loss Function. IEEE Journal of Biomedical and Health Informatics. Nneji, G. U., Monday, H. N., Pathapati, V. S. R., Nahar, S., Mgbejime, G. T., Umana, E. S., & Hossin, M. A. (2025). FFS-IML: fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease. International Journal of Machine Learning and Cybernetics, 1-34. Nneji, G. U., Monday, H. N., Mgbejime, G. T., Pathapati, V. S. R., Nahar, S., & Ukwuoma, C. C. (2023). “Lightweight separable convolution network for breast cancer histopathological identification”. Diagnostics, 13(2), 299. https://doi.org/10.3390/diagnostics13020299 G. U. Nneji, J. Cai, J. Deng, H. N. Monday, E. C. James, and C. C. Ukwuoma, “Multi-Channel Based Image Processing Scheme for Pneumonia Identification”, Diagnostics, vol. 12, no. 2, p. 325, Jan. 2022, https://doi.org/10.3390/diagnostics12020325 Addo, D., Zhou, S., Jackson, J. K., Nneji, G. U., Monday, H. N., Sarpong, K., Patamia, R. A., Ekong, F., & Owusu-Agyei, C. A. (2022). “EVAE-Net: An ensemble variational autoencoder deep learning network for COVID-19 classification based on chest X-ray images”. Diagnostics, 12(11), 2569. https://doi.org/10.3390/diagnostics12112569 Mgbejime, G. T., Hossin, M. A., Nneji, G. U., Monday, H. N., & Ekong, F. (2022). “Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis”. Diagnostics, 12(10), 2484. https://doi.org/10.3390/diagnostics12102484 Agbley, B. L. Y., Li, J., Hossin, M. A., Nneji, G. U., Jackson, J., Monday, H. N., & James, E. C. (2022). “Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images”. Diagnostics, 12(7), 1669. https://doi.org/10.3390/diagnostics12071669 Z. Hu, F. Dong, J. Wu, and M. Misir, ‘Prediction of Banking Customer Churn Based on XGBoost with Feature Fusion’, in E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future, vol. 517, Y. P. Tu and M. Chi, Eds, in Lecture Notes in Business Information Processing, vol. 517. , Cham: Springer Nature Switzerland, 2024, pp. 159–167. doi: 10.1007/978-3-031-60324-2_13. A. Agnihotri and R. Saravanakumar, ‘Customer Retention in Banking: Utilizing AI and Machine Learning for Predictive Churn Analysis’, in 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication , Silchar, India: IEEE, Feb. 2025, pp. 140–144. doi: 10.1109/ISACC65211.2025.10969188.
