Journal Articles

  1. Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira, “Sentence embedding approach using LSTM auto-encoder for discussion threads summarization“, Computer Science and Information Systems 2023 OnLine-First Issue 00, Pages: 55-55 [Paper]
  2. Omar Bin Samin, Nasir Ahmed Abdulkhader Algeelani, Ammar Bathich, Abdul Qadus, and Adnan Amin, Malicious Agricultural IoT Traffic Detection and Classification: A Comparative Study of ML Classifiers, Journal of advances in technology (JAIT) 2023, Vol. 14(4): 811–820, doi: 10.12720/jait.14.4.811-820 [Paper ]
  3. Wasim, M., Al-Obeidat, F., Amin, A., Gul, H., Moreira, F., Enhancing link prediction efficiency with the shortest path and structural attributes. Intelligent Data Analysis, vol. Pre-press, no. Pre-press, pp. 1–17, 2023. DOI: 10.3233/IDA-230030 [Code & Paper]
  4. Amin, A., Adnan, A., & Anwar, S. (2023). An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes. Applied Soft Computing, Vol. 137, 110103. [Code & Datasets]
  5. Gul, H., Al-Obeidat, F., Amin, A., Tahir, M., & Huang, K. (2022). Efficient link prediction model for real-world complex networks using matrix-forest metric with local similarity features. Journal of Complex Networks10 (5), cnac039.
  6. Gul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected GraphsMathematics10(22), 4265.
  7. Gul, H., Amin, A., Adnan, A., & Huang, K. (2021). A systematic analysis of link prediction in complex network. IEEE Access9, 20531-20541.
  8. Ahmad, S., Anwar, M. S., Ebrahim, M., Khan, W., Raza, K., Adil, S. H., & Amin, A. (2020). Deep network for the iterative estimations of students’ cognitive skills. IEEE Access8, 103100-103113.
  9. Amin, A., Al-Obeidat, F., Shah, B., Tae, M. A., Khan, C., Durrani, H. U. R., & Anwar, S. (2020). Just-in-time customer churn prediction in the telecommunication sectorThe Journal of Supercomputing76, 3924-3948.
  10. Amin, A., Shah, B., Khattak, A. M., Moreira, F. J. L., Ali, G., Rocha, A., & Anwar, S. (2019). Cross-company customer churn prediction in telecommunication: A comparison of data transformation methodsInternational Journal of Information Management46, 304-319.
  11. Shah, S., Shah, B., Amin, A., Al-Obeidat, F., Chow, F., Moreira, F. J. L., & Anwar, S. (2019). Compromised user credentials detection in a digital enterprise using behavioral analytics. Future Generation Computer Systems93, 407-417.
  12. Ahmad, S., Li, K., Amin, A., & Khan, S. (2018). A novel technique for the evaluation of posterior probabilities of student cognitive skills. IEEE Access6, 53153-53167.
  13. Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research94, 290-301.
  14. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach.  Neurocomputing237, 242-254.
  15. Amin, A., Shah, B., Anwar, S., Al-Obeidat, F., Khattak, A. M., & Adnan, A. (2018). A prudent based approach for compromised user credentials detection. Cluster Computing21, 423-441.
  16. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawala, A., & Hussain, A. (2016). Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. Ieee Access4, 7940-7957.
  17. Khan, C., Anwar, S., Bashir, S., Rauf, A., & Amin, A. (2015). Site selection for food distribution using rough set approach and TOPSIS method. Journal of Intelligent & Fuzzy Systems29(6), 2413-2419.
  18. Rauf, Amin, A., Mahfooz, S., & Khusro, S. (2013). The Performance of MapReduce Over the Varying Nature of Data. Life Science Journal10(4).

Conference Papers

  1. Zainab, Z., Al-Obeidat, F., Moreira, F., Gul, H., Amin, A. (2023). Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore.
  2. Al-Obeidat, F., Ishaq, M., Shuhaiber, A., & Amin, A. (2022, December). Twitter sentiment analysis to understand students’ perceptions about online learning during the Covid’19. In 2022 International Conference on Computer and Applications (ICCA) (pp. 1-7). IEEE.
  3. Gul, H., Al-Obeidat, F., Amin, A., Tahir, M., & Moreira, F. (2022). A systematic analysis of community detection in complex networks. Procedia Computer Science201, 343-350.
  4. Amin, A., Shah, B., Abbas, A., Anwar, S., Alfandi, O., & Moreira, F. (2019). Features weight estimation using a genetic algorithm for customer churn prediction in the telecom sector. In New Knowledge in Information Systems and Technologies: Volume 2 (pp. 483-491). Springer International Publishing.
  5. Ahmad, S., Li, K., Amin, A., Anwar, M. S., & Khan, W. (2018). A multilayer prediction approach for the student cognitive skills measurementIEEE Access6, 57470-57484.
  6. Ahmad, S., Li, K., Amin, A., & Faheem, M. Y. (2018, July). Simulation of student skills: The novel technique based on quantization of cognitive skills outcomes. In 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 97-102). IEEE.
  7. Amin, A., Shah, B., Khattak, A. M., Baker, T., & Anwar, S. (2018, July). Just-in-time customer churn prediction: With and without data transformation. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-6). IEEE.
  8. Amin, A., Anwar, S., Shah, B., & Khattak, A. M. (2017, February). Compromised user credentials detection using temporal features: A prudent based approach. In Proceedings of the 9th International Conference on Computer and Automation Engineering (pp. 104-110).
  9. Amin, A., Anwar, S., Adnan, A., Khan, M. A., & Iqbal, Z. (2015, November). Classification of cyber attacks based on rough set theory. In 2015 First International Conference on Anti-Cybercrime (ICACC) (pp. 1-6). IEEE.
  10. Amin, A., Rahim, F., Ali, I., Khan, C., & Anwar, S. (2015). A comparison of two oversampling techniques (smote vs mtdf) for handling class imbalance problem: A case study of customer churn prediction. In New Contributions in Information Systems and Technologies: Volume 1 (pp. 215-225). Springer International Publishing.
  11. Amin, A., Rahim, F., Ramzan, M., & Anwar, S. (2015). A prudent based approach for customer churn prediction. In Beyond Databases, Architectures and Structures: 11th International Conference, BDAS 2015, Ustroń, Poland, May 26-29, 2015, Proceedings 11 (pp. 320-332). Springer International Publishing.
  12. Amin, A., Shehzad, S., Khan, C., Ali, I., & Anwar, S. (2015). Churn prediction in telecommunication industry using rough set approachNew trends in computational collective intelligence, 83-95.

Book Chapters

  1. Gul, H., Amin, A., Nasir, F., Ahmad, S.J., Wasim, M. (2021). Link Prediction Using Double Degree Equation with Mutual and Popular Nodes. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1368. Springer, Cham.
  2. Amin, A., Khan, C., Ali, I., & Anwar, S. (2014). Customer churn prediction in telecommunication industry: With and without counter-example. In Nature-Inspired Computation and Machine Learning: 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Tuxtla Gutiérrez, Mexico, November 16-22, 2014. Proceedings, Part II 13 (pp. 206-218). Springer International Publishing.