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Nazneen Akhter

Nazneen Akhter

Assistant Professor

Department of Computer Science and Engineering
Faculty of Science & Technology (FST)

nazneen.akhter@bup.edu.bd

BANGLADESH UNIVERSITY OF PROFESSIONALS
Mirpur Cantonment, Dhaka-1216

Biography

She completed her BSc in Information and Communication Engineering and MSc in Information and Communication Engineering from the Department of Information and Communication Technology of Bangladesh University of Professionals. She began her academic career as a Lecturer in the Department of Information and Communication Technology in 2021 and is currently serving as an Assistant Professor in the same department. Her research interests include Software Engineering, Artificial Intelligence applications, and Natural Language Processing. She also serves as the Advisor of the IEEE BUP Student Branch Women in Engineering (WIE) Affinity Group.

Last Updated: 25 Feb 2026

Education
S.S.C, B A F Shaheen College Kurmitola, Dhaka
H.S.C, Shaheed Bir Uttam Lt Anwar Girls’ College
Bachelor of Science (BSc), Bangladesh University of Professionals (BUP)
Master of Science (Tech), Bangladesh University of Professionals (BUP)

Last Updated: 25 Feb 2026

Journal Publication

1. Investigation of Depression Using Context Analysis SA Asma, S Hasan, N Akhter, M Afrin, A Khatun, KA Taher Journal of FST 1 (01), 47

2. Analysis of Duplicate Bug Report Detection Techniques A Khatun, SFM Al Gabid, N Akhter, KA Taher, T Karim Journal of FST 1 (01), 1

3. Metu, M.A., Akhter, N., Nasrin, S., Anzum, T., Khatun, A. and Mazumder, R., 2024. Hybrid SVM-Bidirectional Long Short-Term Memory Model for Fine-Grained Software Requirement Classification. Journal of Advances in Information Technology, 15(8).

4.

Asma, S.A., Akhter, N., Sharmin, S., Rahman, M.S., Hosen, A.S., Lee, O.S. and Ra, I.H., 2024. Hierarchical Explainable Network for Investigating Depression from Multilingual Textual Data. IEEE Access.

5.

Akhter, N., Khatun, A., Rahman, M.S., Hosen, A.S, and Islam, M.S, 2024. A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code. International Journal of Artificial Intelligence (IJ-AI). Available: https://ijai.iaescore.com/index.php/IJAI/article/view/25013

6. Akhter, N, Shiraj, T.B, Tabassum, M. A, 2024. Text Feature-Based CNN Approach to Detect Fake News. Journal of FST, 2(01).

7. Nazneen Akhter, S. A. Tuba, Afrina Khatun “A Systematic Analysis of Machine Learning and Deep Learning Techniques to Detect Fake News,” Journal of FST, vol. 3, no. 1.

Last Updated: 25 Feb 2026

Conference Papers

1. Akhter, N., Rahman, S. and Taher, K.A., 2021, February. An Anti-Pattern Detection Technique Using Machine Learning to Improve Code Quality. In 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 356-360). IEEE.

2. Afrin, M., Asma, S.A., Akhter, N., Ridoy, J.H., Sauda, S.S. and Taher, K.A., 2022, December. A Hybrid Approach to Investigate Anti-pattern from Source Code. In 2022 25th International Conference on Computer and Information Technology (ICCIT) (pp. 888-892). IEEE.

3.

Asma, S.A., Akhter, N., Afrin, M., Hasan, S., Mia, M.S. and Ali, K.A., 2023, May. Hybrid HAN Model to Investigate Depression from Twitter Posts. In International Conference on Information, Communication and Computing Technology (pp. 104-116). Cham: Springer Nature Switzerland.

4. Zaman, A., Ferdous, S.S., Akhter, N., Ena, T.I., Nabi, M.M. and Asma, S.A., 2023, September. A Multilevel Depression Detection from Twitter using Fine-Tuned RoBERTa. In 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 280-284). IEEE.

5. Tasnim, A., Akhter, N., Khanam, M., Rimi, N.J., 2023. An Attention Based LSTM Model: Automated Requirement Classification from User Story. In: Proceedings of the 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 21-23 September 2023. 

6. Puja, R.S., Fatema, T., Akhter, N. and Khatun, A., 2023, September. Prediction of Code Smell from Source Code: A Hybrid Approach. In 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 315-319). IEEE.

7. Zaman, A., Ferdous, S.S., Akhter, N., Tagore, T., Nabi, M.M. and Ali, K.A., 2023, December. DARN: Dual-Attention RoBERTa Network for Depression Severity Detection from Twitter. In 2023 26th International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.

8. Tasnim, A., Akhter, N. and Ali, K.A., 2023, December. A Fine Tuned Ensemble Approach to Classify Requirement from User Story. In 2023 26th International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.

9. Shiraj, T.B., Dipta, M.S.I. and Akhter, N., 2023, December. A Hybrid Transfer Learning Approach to Detect Parkinson's Disease. In 2023 26th International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.

10. K. N. Jahan Suchi, N. Jahan, A. Tasnim and N. Akhter, "Explainable attention based BiLSTM-SVM for Software Requirement Classification: Integrating Generative Artificial Intelligence," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 2404-2409, doi: 10.1109/ICCIT64611.2024.11022439.

11.

A. T. Rina, N. Akhter and M. N. Uddin, "Depression Detection in Bangla: A Hybrid Model with FastText and XLM-RoBERTa,"  2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 3152-3157, doi:10.1109/ICCIT64611.2024.11022553.

12. S. Rahman, T. B. Tariq, N. Jahan, N. H. Naime and N. Akhter, "HAN-DistilBERT for Depression Detection from Twitter Data: Integrating Hierarchical Attention with Transformer-Based Model," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 3092-3097, doi: 10.1109/ICCIT64611.2024.11022022.

13. Tasnim, A., Mehda, R., Rahman, M.S., Akhter, N. (2026). Dynamic Feature Selection with Attention Mechanism: BiLSTM-CNN Hybrid Approach for Network Intrusion Detection. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Sixth International Conference on Trends in Computational and Cognitive Engineering. TCCE 2024. Lecture Notes in Networks and Systems, vol 1588. Springer, Singapore. https://doi.org/10.1007/978-981-95-1069-6_36

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