zulqarnain

Biography

Dr. Muhammad Zulqarnain is an Assistant Professor (BPS-19) in the Department of Computer Science & IT, Faculty of Bio-Sciences, Cholistan University of Veterinary and Animal Sciences (CUVAS), Bahawalpur, Pakistan. He graduated with a Ph.D degree in Information Technology from the University Tun Hussein Onn Malaysia (UTHM), Johor, Malaysia in December 2020. Earlier, in 2014, he completed his Master's degree in Computer Science from The Islamia University of Bahawalpur (IUB), Pakistan. He received his Bachelor degree in Computer Science from The Islamia University of Bahawalpur (IUB) in 2012. His research areas include machine learning, deep learning, natural language processing, data mining, and time series prediction. He has an approved Ph.D and master supervisor from the Higher Education Commission of Pakistan. He has successfully supervised a number of PhD and master's students and published more than 35 articles in various international journals and conference proceedings. He is a reviewer for various journals and conferences and a co-editor of a Springer conference proceeding. He has also served as a technical committee member for numerous international conferences.

Publications

Zulqarnain, M., Al Saedi, A.K.A., Ghazali, R., Ghouse, M.G., Sharif, W., & Husaini N.A. (2021). A comparative analysis of the question classification task based on deep learning approaches. PeerJ Computer Science. Vol. 7, Impact Factor = 3.09
Zulqarnain, M., Ghazali, R., Hassim, Y.M.M., & Aamir, M. (2021). An Enhanced Gated Recurrent Unit with Auto-Encoder for Solving Text Classification Problems. Arabian Journal for Science and Engineering (AJSE). Vol. 10, No. 6. Impact Factor = 2.711
Aamir, M., Nawi, N.M., Naseem, R., & Zulqarnain, M., (2021). Hybrid Contractive Auto-encoder with Restricted Boltzmann Machine for Multiclass Classification. Arabian Journal for Science and Engineering (AJSE). Impact Factor = 2.711
Zulqarnain, M., Ghazali, R., Shah. H., Ismail. L. H., Alsheddy. A., & Muhmud. M. (2021). A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting. Computers, Materials & Continua (CMC).Vol. 71, No.2, pp.3051-3068. Impact Factor = 3.772
Zulqarnain, M., Ghazali, R., Aamir, M., & Hassim, Y.M.M. (2024) “An efficient two-state GRU based on feature attention mechanism for sentiment analysis”. International Journal of Multimedia Tools and Applications. Impact Factor = 2.757
Zulqarnain, M., Shah, H., Ghazali, R., Alqahtani, O., Sheikh, R., & Asadullah, M. (2023). Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model. Brain Sciences, 13(7), 994. Impact Factor = 3.333
Zulqarnain, M., Razzaq, H. H., Gifal, A. S., & Aftab, M. N. (2025). An optimized EEG-based hybrid deep learning framework for schizophrenia detection. Biomedical Engineering Letters, 1-15. Impact Factor = 2.8
Almutairi, S., Abohashrh, M., Razzaq, H. H., Zulqarnain, M., Namoun, A., & Khan, F. (2024). A Hybrid Deep Learning Model for Predicting Depression Symptoms From Large-Scale Textual Dataset. IEEE Access. Vol. 12, pp.168477-168499. Impact Factor = 3.90 DOI: 10.1109/ACCESS.2024.3496741
Javid I., Al Saedi, A.K.A.,Ghazali, R., Hassim, Y.M.M., & Zulqarnain M. (2022). “Optimally Organized GRU-Deep Learning model with Chi2 Feature Selection for Heart Disease Prediction”. Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4083-4094.Impact Factor = 1.953
Javid I., Ghazali, R., Syed I., Zulqarnain M., and Husaini N A. (2022) “Study on the Pakistan Stock Market using a New Stock Crisis Prediction Method:” PLoS ONE 17(10): e0275022. https://doi.org/10.1371/journal.pone.0275022. Impact Factor = 3.752
Javid I., Ghazali, R., Zulqarnain M. and Norlida H. “Data pre-processing for cardiovascular disease classification:” A systematic literature review. International Journal of Intelligent & Fuzzy Systems, vol. 44, no. 01, pp. 1525-1545, 2023. Impact Factor = 2.0
Razzaq, H. H., Ghazali, R., George, L. E., & Zulqarnain, M. (2024). A Novel Framework For Breast Cancer Scoring Based On Machine Learning Technique Using Immunohistochemistry Images. Vol. 06, pp. 1-12 Computational Methods for Differential Equations. Impact Factor = 0.9
Aftab, M. N., Khan, D. M., Zulqarnain, M., & Akram, M. R. (2025). An Enhanced LSTM Model Based on Feature Attention Mechanism and Emotional Intelligence for Advanced Sentiment Analysis. International Journal of Advanced Computer Science & Applications, 16(6). Impact Factor = 0.8
Zulqarnain, M., Ghazali, R., Hassim, Y.M.M., and Rehan, M. “Text classification based on gated recurrent unit combines with support vector machine,” International Journal of Electrical and Computer Engineering (IJECE)., Vol. 10, No. 4, pp. 3734–3742, 2020.
Zulqarnain, M., Ghazali, R., and Hassim, Y.M.M. (2020). “A comparative review on deep learning models for text classification.” Indonesian Journal of Electrical Engineering and Computer Science. Vol. 19, No. 1, pp. 325-335.
Zulqarnain, M., Ghouse, M.G., Sharif, W., Jilanie, G., & Shifa, A., (2021). An Efficient Method of Data Hiding for Digital Color Images Based on Variant Expansion and Modulus Function. Journal of Engineering Science and Technology (JESTEC). Vol.16, No. 5. ISI & WoS
Zulqarnain, M., Ghazali, R., M. G. Ghouse, and M. F. Mushtaq, “Efficient Processing of GRU Based on Word Embedding for Text Classification,” International Journal Informatics Visualization., vol. 3, no. 4, pp. 377–383, 2019.
Zulqarnain, M., Ghazali, R., Ghouse, M. G., Hassim, Y.M.M., & Javid, I., (2020). Predicting Financial Prices of the Stock Market using Recurrent Convolutional Neural Networks. International Journal of Modern Education and Computer Science, 12(6), pp 16-28. https://DOI: 10.5815/ijmecs.2020.06.02
Zulqarnain, M., Ghazali, R., S. H. Khaleefah, and Rehan. A. (2019). “An Improved the Performance of GRU Model based on Batch Normalization for Sentence Classification,” International Journal of Computer Science and Network Security., vol. 19, no. 9, pp. 176–186, 2019. ISI Indexing Impact = 2.139
Zulqarnain M, Ishak SA., Ghazali R, Nawi NM, Aamir M, Mazwin Y. (2020). An improved Deep Learning Approach based on Variant Two-State GRU and Word Embedding for Sentiment Classification. International Journal of Advanced Computer Science and Applications(IJACSA); vol. 11, no. 1; pp. 593-603. Impact Factor = 0.9
Mushtaq, M. F., Akram, U., Aamir, M., Ali, H., & Zulqarnain, M. (2019). Neural Network Techniques for Time Series Prediction: A Review. JOIV: International Journal on Informatics Visualization, 3(3), 314-320.
Akram, U., Ghazali, R., Ismail, L. H., Zulqarnain, M., Husaini, N. A., & Mushtaq, M. F. (2019). An Improved Pi-Sigma Neural Network with Error Feedback for Physical Time Series Prediction. International Journal of Advanced Trends in Computer Science and Engineering, 8(3), 231-240. https://doi.org/10.30534/ijatcse/2019/5381.32019
Mushtaq, M. F., Akram, U., Tariq, A., Khan, I., Zulqarnain, M., & Iqbal, U. (2017). An Innovative Cognitive Architecture for Humanoid Robot. International Journal of Advanced Computer Science and Applications, 8(8), 60-67. Impact Factor = 0.9
Aamir, M., Nawi, N.M., Mahdin, H.B., Naseem, R. and Zulqarnain, M., (2020). Auto-encoder variants for solving handwritten digits classification problem. International Journal of Fuzzy Logic and Intelligent Systems, 20(1), pp.8-16. Impact Factor = 1.50
Ahmad, N., Harun, A., Khizar, H.M.U., Othman, B. and Zulqarnain, M., (2020). The Effect of Electronic Word of Mouth Communication on Purchase Intention Moderated by Trust: A Case Online Consumer of Bahawalpur Pakistan. International Journal of Advanced Science and Technology, 29(9), pp.4995-5008.
Dayan, F., Javaid, M., Zulqarnain, M., Ali, M. T., & Ahmad, B. (2018). Computing banhatti indices of hexagonal, honeycomb and derived networks. American Journal of Mathematical and Computer Modelling, 3(2), 38-45.
Ghouse, M. G., Jamel, S., Aamir, M., Zulqarnain, M., & Deris, M. M. (2021, May). i-AEGIS-128: An Improved Authenticated Encryption Based on AEGIS-128. In 2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1-7). IEEE.
Javid I., Ghazali, R., Zulqarnain M. Husaini N.A. “Deep learning GRU model and Random Forest for screening out key attributes of Cardiovascular Disease. Lecture Notes in Network and System (LNNS) Book Series, Vol.457, pp 160-170. Malaysia, 2022.
Javid, I., Ghazali, R., Syed, I., Husaini, N. A., & Zulqarnain, M. (2022, October). Developing Novel T-Swish Activation Function in Deep Learning. In 2022 International Conference on IT and Industrial Technologies (ICIT) (pp. 1-7). IEEE Explore.
Zulqarnain, M., Alsaedi, A.K.Z., Sheikh, R. et al. An improved gated recurrent unit based on auto-encoder for sentiment analysis. International Journal of Information Technology. (2023). https://doi.org/10.1007/s41870-023-01600-4
Zulqarnain, M., Sheikh, R., Hussain, S., Sajid, M., Abbas, S. N., Majid, M., & Ullah, U. (2024). Text Classification Using Deep Learning Models: A Comparative Review. Cloud Computing and Data Science, 80-96.
Sharif, W., Zulqarnain, M., Ayyub, I., Mukram, M., Ali, R., Shaheen, M., & Mumtaz, Q. U. A. (2024, September). TS-GRU-CBOW: Identification of Suspicious Language for Sentiment Analysis. In International Conference on Artificial Intelligence and Networking (pp. 585-602). Singapore: Springer Nature Singapore.
Razzaq, H. H., Al-Rammahi, L. F., Almousawy, A. M., & Zulqarnain, M. (2025). An Optimized Hybrid Deep Learning Approach for Accurate Fruit Image Classification. JOIV: International Journal on Informatics Visualization, 9(4), 1688-1696.
Shoaib, M., Zulqarnain, M., Ahmed, A., Abdullah, S., & Alturki, N. (2025). A Review on Network Intrusion detection systems based on Machine Learning, Deep Learning and Blockchain for IoT-based healthcare systems. VFAST Transactions on Software Engineering, 13(4), 200-216.