Research Publications

Journal Articles

[1]         A. Amin et al., “Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study,” IEEE Access, vol. 4, pp. 7940–7957, 2016, doi: 10.1109/ACCESS.2016.2619719. [View] [PDF]

[2]         A. Amin, F. Al-Obeidat, B. Shah, A. Adnan, J. Loo, and S. Anwar, “Customer churn prediction in telecommunication industry using data certainty,” J. Bus. Res., vol. 1, no. 1, pp. 1–12, 2018, doi: 10.1016/j.jbusres.2018.03.003. [View] [PDF]

[3]         A. Amin et al., “Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods,” Int. J. Inf. Manage., vol. 46, 2019, doi: 10.1016/j.ijinfomgt.2018.08.015. [View] [PDF]

[4]         A. Amin, B. Shah, A. M. Khattak, T. Baker, H. ur R. Durani, and S. Anwar, “Just-in-time Customer Churn Prediction: With and Without Data Transformation,” J. Bus. Res., pp. 1–5, 2018. [View] [PDF]

[5]         A. Amin et al., “Just-in-time customer churn prediction in the telecommunication sector,” J. Supercomput., 2017, doi: 10.1007/s11227-017-2149-9. [View] [PDF}

[6]         A. Amin et al., “A prudent based approach for compromised user credentials detection,” Cluster Comput., vol. 93, no. June, pp. 435–443, 2017, doi: 10.1109/ENIC.2014.29. [View] [PDF]

7]          S. Ahmad, K. Li, A. Amin, M. S. Anwar, and W. Khan, “A Multilayer Prediction Approach for the Student Cognitive Skills Measurement,” IEEE Access, vol. 6, pp. 1–1, 2018, doi: 10.1109/ACCESS.2018.2873608. [View] [PDF]

[8]         C. Khan, S. Anwar, S. Bashir, A. Rauf, and A. Amin, “Site selection for food distribution using rough set approach and TOPSIS method,” J. Intell. Fuzzy Syst., vol. 29, no. 6, 2015, doi: 10.3233/IFS-151941. [View] [PDF]

[9]         S. Shah, A. Amin et al., “Compromised user credentials detection in a digital enterprise using behavioral analytics,” Futur. Gener. Comput. Syst., vol. 93, 2019, doi: 10.1016/j.future.2018.09.064. [View] [PDF]

[10]      S. Ahmad, K. A. N. Li, and A. Amin, “A Novel Technique for the Evaluation of Posterior Probabilities of Student Cognitive Skills,” IEEE Access, vol. 6, pp. 53153–53167, 2018. [View] [PDF]

[11]      H. Gul, A. Amin, A. Adnan, and K. Huang, “A Systematic Analysis of Link Prediction in Complex Network,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3053995. [View] [PDF]

[12]      S. Ahmad, A. Amin et al., “Deep Network for the Iterative Estimations of Students’ Cognitive Skills,” IEEE Access, vol. 8, pp. 103100–103113, 2020, doi: 10.1109/ACCESS.2020.2999064. [View] [PDF]

[13]      A. Rauf, A. Amin, S. Mahfooz, and S. Khusro, “The Performance of MapReduce Over the Varying Nature of Data,” Life Sci. J., vol. 41, no. 20, pp. 1–6, 2013. [PDF]

Conference Proceedings

[14]      A. Amin, S. Shehzad, C. Khan, I. Ali, and S. Anwar, “Churn Prediction in Telecommunication Industry Using Rough Set Approach,” in ICCCI 2014 – Language and Knowledge Processing – Multi-Agent Systems – Social, 2015, pp. 83–95. [View] [PDF]

[15]      A. Amin, C. Khan, I. Ali, and S. Anwar, “Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example,” in 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Springer., 2014, pp. 206–218. [View] [PDF]

[16]      A. Amin, S. Babar, K. Asad Masood, B. Thar, D. Hamood ur Rahman, and A. Sajid, “Just-in-time Customer Churn Prediction: With and Without Data Transformation,” in IEEE CEC 2018, Rio de Janeiro, Brazil ., 2018, pp. 1–7. [View] [PDF]

[17]      A. Amin, S. Anwar, A. Adnan, M. A. Khan, and Z. Iqbal, “Classification of cyber attacks based on rough set theory,” 2015, doi: 10.1109/Anti-Cybercrime.2015.7351952. [View] [PDF]

[18]      A. Amin, R. Faisal, R. Muhammad, and A. Sajid, “A Prudent Based Approach for Customer Churn Prediction,” in 11th International Conference, BDAS 2015, Ustroń, Poland, 2015, pp. 320–332. [View] [PDF]

[19]      A. Amin, S. Anwar, B. Shah, and A. M. Khattak, “Compromised user credentials detection using temporal features: A prudent based approach,” in ACM International Conference Proceeding Series, 2017, vol. Part F1278, doi: 10.1145/3057039.3057051. [View] [PDF]

[20]      S. Ahmad, K. Li, A. Amin, and M. Y. Faheem, “Simulation of Student Skills : The Novel Technique Based on Quantization of Cognitive Skills Outcomes,” in In 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), 2018, pp. 97–102. [View] [PDF]

Book Chapters Published

[21]      A. Amin, F. Rahim, I. Ali, C. Khan, and S. Anwar, “A comparison of two oversampling techniques (SMOTE vs MTDF) for handling class imbalance problem: A case study of customer churn prediction,” in Advances in Intelligent Systems and Computing, 2015, vol. 353, doi: 10.1007/978-3-319-16486-1_22. [View] [PDF]

[22]      A. Amin, B. Shah, A. Abbas, S. Anwar, O. Alfandi, and F. Moreira, “Features weight estimation using a genetic algorithm for customer churn prediction in the telecom sector,” in Advances in Intelligent Systems and Computing, 2019, vol. 931, pp. 483–491, doi: 10.1007/978-3-030-16184-2_46. [View] [PDF]

[23]      G. Haji, A. Amin, N. Furqan, S. J. Ahmad, and W. Muhammad, “Link Prediction Using Double Degree Equation with Mutual and Popular Nodes,” 2021, doi: 10.1007/978-3-030-72654-6_32. [View]