Publications

Journal Articles

  1. Haji gull Feras Al-Obeidat, Adnan Amin, and Fernando Moreira, Analyzing complex networks: Extracting key characteristics and measuring structural similarities, Expert System, October 24, 2023. https://doi.org/10.1111/exsy.13470.
  2. Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, and Fernando Moreira, “Sentence embedding approach using LSTM auto-encoder for discussion threads summarization“, Computer Science and Information Systems 2023 OnLine-First Issue 00, Pages: 42-55
    https://doi.org/10.2298/CSIS221210055K [Paper]
  3. 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 ]
  4. 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]
  5. 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. https://doi.org/10.1016/j.asoc.2023.110103 [Code & Datasets]
  6. 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.
  7. 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.
  8. Gul, H., Amin, A., Adnan, A., & Huang, K. (2021). A systematic analysis of link prediction in complex network. IEEE Access9, 20531-20541.
  9. 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.
  10. 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. https://doi.org/10.1007/s11227-017-2149-9
  11. 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. https://doi.org/10.1016/j.ijinfomgt.2018.08.015
  12. 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.
  13. 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.
  14. 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. https://doi.org/10.1016/j.jbusres.2018.03.003
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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. Laiq, F. Al-Obeidat, A. Amin and F. Moreira, “DDoS Attack Detection in Edge-IIoT using Ensemble Learning,” 2023 7th Cyber Security in Networking Conference (CSNet), Montreal, QC, Canada, 2023, pp. 204-207, doi: 10.1109/CSNet59123.2023.10339784.
  2. Wasim, M., Al-Obeidat, F., Moreira, F., Gul, H., Amin, A., Forecasting Networks Links with Laplace Characteristic and Geographical Information in Complex Networks, Procedia Computer Science, Volume 224, 2023, Pages 357-364, https://doi.org/10.1016/j.procs.2023.09.048
  3. Wasim, M., Ubaid, U., Al-Obeidat, F., Amin, A., and Moreira, F., Effectiveness of internal evaluation matrices for community detection based on clustering, 17th International Conference on Information Technology and Applications (ICITA) Turin 2023, Springer.
  4. 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. https://doi.org/10.1007/978-981-19-9331-2_11
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. 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.
  13. 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.
  14. 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.
  15. 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. https://doi.org/10.1007/978-3-030-72654-6_32.
  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.