Artificial Intelligence, Machine Learning, Deep Learning, Privacy-Preserving Machine Learning, Differential Privacy, Hybrid Ensemble Learning, Optimization-Based Modeling, Natural Language Processing, Computer Vision and Data Science.

Ongoing Research:
I spearheaded a project capturing 3000 images of various winter vegetable plant leaves, focusing on tomatoes, potatoes, and beans, both healthy and diseased. My objective is to conduct research utilizing machine learning and explainable artificial intelligence techniques to develop an Android application. This app aims to accurately identify plant diseases and provide users with remedies and potential solutions.

Publications in Journal:
1. Mamun, M., Chowdhury, S. H., Hossain, M. M., Khatun, M. R., & Iqbal, S. (2025). Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based
random forest model. Informatics and Health, 2(1), 17-40. https://doi.org/10.1016/j.infoh.2025.01.001
2. Chowdhury, S. H., Mamun, M., Shaikat, T. A., Hussain, M. I., Iqbal, S., & Hossain, M. M. (2025). An Ensemble Approach for Artificial Neural Network-Based Liver Disease Identification from
Optimal Features through Hybrid Modeling Integrated with Advanced Explainable AI. Medinformatics, 2(2), 107-119. https://doi.org/10.47852/bonviewMEDIN52024744
3. Hossain, M. M., Mamun, M., Munir, A., Rahman, M. M., & Chowdhury, S. H. (2025). A Secure Bank Loan Prediction System by Bridging Differential Privacy and Explainable Machine
Learning. Electronics, 14(8), 1691. https://doi.org/10.3390/electronics14081691
4. Hussain, M. I., Munir, A., Mamun, M., Chowdhury, S. H., Uddin, N., & Hossain, M. M. (2025). A Transparent House Price Prediction Framework Using Ensemble Learning, Genetic
Algorithm-Based Tuning, and ANOVA-Based Feature Analysis. FinTech, 4(3), 33. https://doi.org/10.3390/fintech4030033
5. Hussain, M. I., Chowdhury, S. H., Hossain, M. M., & Mamun, M. (2025). NeuroBlend-3: Hybrid Deep and Machine Learning Framework with Explainable AI for Multi-Class Brain Tumor
Detection Using MRI Scans. Medinformatics. https://doi.org/10.47852/bonviewMEDIN52026540
6. M. Mamun, S. H. Chowdhury, M. O. Faruq, et al., “Identification of Maternal Health Risk From Optimal Features Using Explainable Machine Learning,” Engineering Reports 7, no. 11 (2025):
e70491, https://doi.org/10.1002/eng2.70491
7. Tasnim, S., Mamun, M., Chowdhury, S. H., Hussain, M. I., & Hossain, M. M. (2025). Advancing Interpretable AI for Cardiovascular Risk Assessment: A Stacking Regression Approach in
Clinical Data from Bangladesh. Medinformatics. https://doi.org/10.47852/bonviewMEDIN52027812

Publications in Conference:
1. Chowdhury, S. H., Mamun, M., Hossain, M. M., Hossain, M. I., Iqbal, M. S., & Kashem, M. A. (2024, April). Newborn Weight Prediction And Interpretation Utilizing Explainable Machine
Learning. In 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICAEEE62219.2024.10561798
2. Mamun, M., Chowdhury, S. H., Hussain, M. I., & Iqbal, M. S. (2024, October). Early-Stage Diabetes Risk Prediction Utilizing Machine Learning with Explainable AI from Polynomial and
Binning Feature Generation. In 2024 2nd International Conference on Information and Communication Technology (ICICT) (pp. 26-30). IEEE.
https://doi.org/10.1109/ICICT64387.2024.10839710
3. Chowdhury, S. H., Mamun, M., Hussain, M. I., & Iqbal, M. S. (2024, October). Brain Stroke Prediction using Explainable Machine Learning and Time Series Feature Engineering. In 2024
2nd International Conference on Information and Communication Technology (ICICT) (pp. 16-20). IEEE. https://doi.org/10.1109/ICICT64387.2024.10839683
4. Shaikat, M. T. A., Chowdhury, S. H., Shovon, M., Hossain, M. M., Hussain, M. I., & Mamun, M. (2025, February). Explainability Elevated Obstructive Pulmonary Disease Care: Severity
Classification, Quality of Life Prediction, and Treatment Impact Assessment. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6).
IEEE. https://doi.org/10.1109/ECCE64574.2025.11013299
5. Chowdhury, S. H., Mamun, M., Shaikat, M. T. A., Hussain, M. I., & Hossain, M. M. (2025, February). Improving Network Classification Accuracy through Feature Clustering and
Ensemble Machine Learning with Explainable AI. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
https://doi.org/10.1109/ECCE64574.2025.11013433
6. Mamun, M., Ali, M. S., Chowdhury, M. S. A., Chowdhury, S. H., Hussain, M. I., & Hossain, M. M. (2025, February). A Differential Privacy and TOPSIS Enhanced Explainable Machine
Learning Framework for Diabetes Risk Diagnosis. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
https://doi.org/10.1109/ECCE64574.2025.11013439
7. Chowdhury, S. H., Hussain, M. I., Chowdhury, M. S. A., Ali, M. S., Hossain, M. M., & Mamun, M. (2025, June). Hepatitis C Detection from Blood Donor Data Using Hybrid Deep Feature
Synthesis and Interpretable Machine Learning. In 2025 2nd International Conference on Next- Generation Computing, IoT and Machine Learning (NCIM) (pp. 1-6). IEEE.
https://doi.org/10.1109/NCIM65934.2025.11160156
8. Mamun, M., Hussain, M. I., Ali, M. S., Chowdhury, M. S. A., Hossain, M. M., & Chowdhury, S. H. (2025, June). Interpretable Heart Failure Identification Utilizing Auto Machine Learning
Tools. In 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM) (pp. 1-6). IEEE. https://doi.org/10.1109/NCIM65934.2025.11159854
9. Chowdhury, S. H., Hussain, M. I., Shovon, M., Morzina, M. S., Hossain, M. M., & Mamun, M. (2025, July). LoRA and ReFT Optimized Explainable Machine Learning and Deep Learning
Framework for SMS Spam Detection. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE.
https://doi.org/10.1109/QPAIN66474.2025.11171842 Best Paper Award (Runner Up)
10. Mamun, M., Hussain, M. I., Ali, M. S., Chowdhury, M. S. A., Chowdhury, S. H., & Hossain, M. M. (2025, July). An Explainable Ensemble Learning Framework with Feature Optimization for
Accurate Maternal Health Risk Prediction. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE.
https://doi.org/10.1109/QPAIN66474.2025.11172243
11. Das, K., Mamun, M., Safat, Y., Hussain, M. I., Hossain, M. M., & Chowdhury, S. H. (2025, July). Optimized Feature-Driven Dengue Diagnosis Using Explainable Machine Learning
Approaches. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE. https://doi.org/10.1109/QPAIN66474.2025.11171726
12. Hussain, M. I., Chowdhury, S. H., Shovon, M., Morzina, M. S., Hossain, M. M., & Mamun, M. (2025, July). SENet-Augmented Explainable Deep Feature Framework with Machine Learning
for Breast Tumor Detection in Ultrasound Imaging. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-6). IEEE.
https://doi.org/10.1109/QPAIN66474.2025.11171635
13. Onti, W. H., Chowdhury, S. H., Hossain, M. M., & Mamun, M. (2025). Hybrid Artificial Intelligence for Forecasting Renewable Energy Consumption with Ensemble Machine Learning
and Time Series Models. In International Conference on Data Science, AI and Applications (pp. 377- 392). Springer, Cham. https://doi.org/10.1007/978-3-032-11335-1_26 Book Chapter
(Springer)
14. Mamun, M., Hussain, M. I., Ali, M. S., Chowdhury, M. S. A., Chowdhury, S. H., & Hossain, M. M. (2025). Privacy-Preserving Prediction of Chronic Kidney Disease Using Ensemble Machine
Learning with Laplacian Differential Privacy and Explainable AI. In International Conference on Data Science, AI and Applications (pp. 346-361). Springer, Cham. https://doi.org/10.1007/978-3-
032-11335-1_24 Book Chapter (Springer)
15. Hussain, M. I., Ali, M. S., Chowdhury, S. H., Hossain, M. M., Chowdhury, S. A., & Mamun, M. (2025). Multi-stage framework for lung cancer identification from histopathological images
using deep convolutional architectures with advanced feature engineering and ensemble machine learning. In Proceedings of the 28th International Conference on Computer and
Information Technology (ICCIT 2025) (pp. 1–6). (Accepted)
16. Hussain, M. I., Mamun, M., Ali, M. S., Hossain, M. M., & Chowdhury, M. S. A & Chowdhury, S. H. (2025). Privacy-preserving dengue prediction using explainable machine learning and
Laplacian differential privacy mechanisms. In Proceedings of the 28th International Conference on Computer and Information Technology (ICCIT 2025) (pp. 1–6). (Accepted)
17. Hussain, M. I., Hossain, M. M., Sojib, M. I. H., Parvez, A. H. M. S., Chowdhury, S. H., & Mamun, M. (2025). NOVA-Tab: A neural orthogonal variational additive network for medical tabular
data with explainable AI validation against clinical decisions. In Proceedings of the 28th International Conference on Computer and Information Technology (ICCIT 2025) (pp. 1–6).
(Accepted)
18. Hussain, M. I., Mamun, M., Shovon, M., Hossain, M. M., Parvez, A. H. M. S., & Chowdhury, S.H. (2025). A comparative study of CNN and vision transformer methods for rice variety
classification with XAI. In Proceedings of the 28th International Conference on Computer and Information Technology (ICCIT 2025) (pp. 1–6). (Accepted)
19. Haque Onti, W., Hossain, M. M., Chowdhury, S. H., & Mamun, M. (2025). A deep learning and explainable AI framework to rank solar irradiance zones in southern Bangladesh. In
Proceedings of the 2025 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) (pp. 1–6). (Accepted)

Preprints:
1. Chowduhury, S. H., & Mamun, M. Hrnet-Xgb: A Comprehensive Hybrid Framework for Automated and Interpretable Skin Cancer Detection with Multi-Scale Feature Fusion. Available
at SSRN 5167462. https://dx.doi.org/10.2139/ssrn.5167462

Presentation at Conferences and Seminars:
1. 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE-2024), DUET, Gazipur, Bangladesh.
a. Newborn Weight Prediction and Interpretation Utilizing Explainable Machine Learning.
Presenter: Mohammad Mamun

2. 2nd International Conference on Information and Communication Technology (ICICT-2024), BUET, Dhaka, Bangladesh.
a. Brain Stroke Prediction using Explainable Machine Learning and Time Series FeatureEngineering.
Presenter: Mohammad Mamun
b. Early-Stage Diabetes Risk Prediction Utilizing Machine Learning with Explainable AI from Polynomial and Binning Feature Generation.
Presenter: Mohammad Mamun

3. 4th International Conference on Electrical, Computer and Communication Engineering (ECCE-2025), CUET, Chittagong, Bangladesh.
a. Improving Network Classification Accuracy through Feature Clustering and Ensemble Machine Learning with Explainable AI.
Presenter: Mohammad Mamun.

4. 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM-2025), DUET, Gazipur, Bangladesh.
a. Interpretable Heart Failure Identification Utilizing Auto Machine Learning Tools
Presenter: Mohammad Mamun.

5. International Conference on Data Science, AI and Applications (ICDSAIA-2025), EATL Innovation Hub, Gazipur Hitech City, Bangladesh.
a. Privacy-Preserving Prediction of Chronic Kidney Disease Using Ensemble Machine Learning with Laplacian Differential Privacy and Explainable AI.
Presenter: Mohammad Mamun.

6. 2025 IEEE International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN-2025), Rangpur, Bangladesh.
a. LoRA and ReFT Optimized Explainable Machine Learning and Deep Learning Framework for SMS Spam Detection.
Presenter: Mohammad Mamun.
Best Paper Award (Runner Up/2nd out of 500 papers presented).
b. SENet-Augmented Explainable Deep Feature Framework with Machine Learning for Breast Tumor Detection in Ultrasound Imaging.
Presenter: Mohammad Mamun.

7. IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS-2025), Islamic University, Kushtia, Bangladesh.
a. Explainable AI-Driven Tree-Selection Stacking Random Forest with Hybrid Feature Synthesis for Lung Cancer Survival Time Prediction.
Presenter: Mohammad Mamun.

8. “AI Interactive Voice Classroom: Enhancing Learning with AI” – Seminar Presentation at
Bangladesh University, Dhaka, Bangladesh.
Presenter: Mohammad Mamun.

9. 28th International Conference on Computer and Information Technology (ICCIT-2025), Long
Beach Hotel, Cox’s Bazar, Bangladesh.
a. Multi-Stage Framework for Lung Cancer Identification from Histopathological Images Using Deep Convolutional Architectures with Advanced Feature Engineering and
Ensemble Machine Learning.
Presenter: Mohammad Mamun.
b. NOVA-Tab: A Neural Orthogonal Variational Additive Network for Medical Tabular
Data with Explainable AI Validation Against Clinical Decisions.
Presenter: Mohammad Mamun.
c. Comparative Study of CNN and Vision Transformer Methods for Rice Variety Classification with XAI.
Presenter: Mohammad Mamun.

Publication in progress:
1. Mamun, M., Hossain, M. M., Hussain, M. I., Chowdhury, S. H., Alahmadi, T. J., & Moni, M. A.
(2025). Privacy-Preserving Maternal Health Risk Prediction Utilizing Differential Privacy with
Explainable Machine Learning. Applied Soft Computing. Elsevier. (Under Review).

2. Chowdhury, S. H., Mamun, M., Hossain, M. M., Khatun, M. R., Hassan, M. R., Rahman, M. M.,
& Munir, A. (2025). Liver Disease Diagnosis Using Explainable Machine Learning with
Differential Privacy. SN Computer Science. Springer. (Under Review).

3. Mamun, M., & Chowdhury, S. H. (2025). An Explainable Machine Learning Framework with
Differential Privacy for Secure and Accurate Bank Loan Status Prediction Using Polynomial
Feature Generation. International Journal of Information Management Data Insights. Elsevier.
(Under Review).

4. Shaikat, M. T. A., Mamun, M., Chowdhury, S. H., Shaha, P., Hossain, M. M., & Iqbal, S. (2025).
An Explainable Machine Learning Framework with BERT for Multi-Source AI Generated Text
Classification. Telematics and Informatics Reports. Elsevier. (Third Revision).

5. Hussain, M. I., Chowdhury, S. H., Mamun, M., Hossain, M. M., Parvez, A. H. M. S., & Munir,
A. (2025). Multi-Objective Optimized Differential Privacy with Interpretable Machine Learning
for Brain Stroke and Heart Disease Diagnosis. SN Computer Science. Springer. (Submitted).

6. Hussain, M. I., Chowdhury, S. H., Mamun, M., Hossain, M. M., Parvez, A. H. M. S., & Munir, A. (2025). Identification of the Source of AI-Generated Text Using Explainable Machine
Learning with Manual and Deep Learning Fusion Feature Extraction Techniques. Electronics. MDPI. (Submitted).

7. Chowdhury, S. H. & Mamun, M. (2025). HRNet-XGB: A Comprehensive Hybrid Framework for Automated and Interpretable Skin Cancer Detection with Multi-Scale Feature Fusion.
Information Sciences. Elsevier. (Under Review).

8. Hussain, M. I., Chowdhury, S. H., Hossain, M. M., & Mamun, M. (2025). Explainable AI-Driven
Tree-Selection Stacking Random Forest with Hybrid Feature Synthesis for Lung Cancer
Survival Time Prediction. 2nd International Conference on Computing, Applications, and
Systems (COMPAS 2025). Islamic University, Kushtia, Bangladesh: IEEE. (Submitted).

9. Hussain, M. I., Shovon, M., Parvez, A. H. M. S., Mamun, M., & Chowdhury, S. H. (2025). A
Comparative Study of CNN and Vision Transformer Methods for Rice Variety Classification
with XAI. In 2nd International Conference on Computing, Applications, and Systems
(COMPAS 2025). Islamic University, Kushtia, Bangladesh: IEEE. (Submitted).

10. Dipu, A., Chowdhury, S. H., Hussain, M. I., Hossain, M. M., & Mamun, M. (2025). Comparative
Evaluation of Machine Learning Models for Non-Invasive Hypoglycemia Detection with XAI
Methods. In 2nd International Conference on Computing, Applications, and Systems
(COMPAS 2025). Islamic University, Kushtia, Bangladesh: IEEE. (Submitted).

11. Enhancing flood probability prediction through interpretable ensemble machine learning
models,” Proceedings of the IEEE QPAIN Conference 2026, under review.

12. Predicting brain stroke risk from clinical data using optimized machine learning models with
random stochastic hyperparameter optimization,” Proceedings of the IEEE QPAIN Conference
2026, under review.

13. A hybrid feature fusion framework of RoBERTa and count vectorizer with explainable boosting
models for fake news detection,” Proceedings of the IEEE QPAIN Conference 2026, under review.

14. A transparent and accurate machine learning framework for crop suitability analysis from soil
properties,” Proceedings of the IEEE QPAIN Conference 2026, under review.

15. Hypertension risk prediction using optimized and interpretable learning models,” Proceedings of the IEEE QPAIN Conference 2026, under review.

16. Interpreting early thalassemia risk prediction using CAON-optimized explainable machine
learning paradigms,” Proceedings of the IEEE QPAIN Conference 2026, under review.

17. Machine learning-based migraine classification using structured clinical and neurological
features,” Proceedings of the IEEE QPAIN Conference 2026, under review.

18. An MG-PSO–based feature optimization and explainable machine learning framework for
dengue disease prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

19. A novel explainable enhanced opposition-based learning optimized logistic regression
framework for early lung cancer prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

20. An explainable ensemble learning framework optimized by MSSO for kidney disease
prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

21. Explainable ensemble modeling with feature optimization for reliable lung cancer risk
prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

22. An interpretable LightGBM-based machine learning framework for robust multiclass hepatitis C virus prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

23. Bayesian-optimized interpretable machine learning for heart disease detection,” Proceedings
of the IEEE QPAIN Conference 2026, under review.

24. An explainable ensemble learning framework with feature optimization for accurate thyroid
disease prediction,” Proceedings of the IEEE QPAIN Conference 2026, under review.

25. A comparative and explainable machine learning study for accurate calorie burn prediction,”
Proceedings of the IEEE QPAIN Conference 2026.

Public Outreach & Media Contributions:
1. Mamun, M. (2023, November 28). Machine learning adventure with explainable artificial
intelligence. The Daily Observer. https://www.observerbd.com/news/448178
2. Mamun, M. (2024, May 29). Leveraging ML with AI could help us cope with natural calamities. The
Daily Observer. https://www.observerbd.com/news/474534
3. Mamun, M. (2024, February 5). Importance of quantum learning and XAI in computer science. The
Daily Observer. https://www.observerbd.com/news/458623