Publications

Niloy, N.T. and Islam, M.S., 2020, August. In 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (ICIVPR) (pp. 1-6). IEEE.

Rahman, M.A., Mahmud, O., Niloy, N.T. and Siddik, M.S., 2020. ASM Sci. J., 13, pp.21-30.

Ismot Ara Emu, Nishat Tasnim Niloy, Bhuyan Md Anowarul Karim, Anindya Chowdhury, Fatema Tuj Johora, Mahamudul Hasan, Tanni Mittra, Mohammad Rifat Ahmmad Rashid, Taskeed Jabid, Maheen Islam, Md Sawkat Ali

Nishat Tasnim Niloy, Md Rayhan Ahmed, Sinthia Sarkar Ananna, Sanjida Kater, Iffat Jahan Shorna, Sadika Islam Sneha, Md Hasanul Ferdaus, Mohammad Manzurul Islam, Mohammad Rifat Ahmmad Rashid, Taskeed Jabid, Md Sawkat Ali

Mohammad Manzurul Islam, Sanjida Rahman, Nahida Hoque, Md Al Mamun, Md Sultan Moheuddin, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Saleh Masum, Md Hasanul Ferdaus, Nishat Tasnim Niloy, Md Atiqur Rahman

Islam, M., Niloy, N.T., Hasan, I., Rupin, R.J., Chowdhury, M., Fahim, S.F., Ashhab, M.M., Islam, M.M., Ali, M.S. and Rashid, M.R.A., 2024. An Extensive Photographic Dataset to Classify Laptop Components for Automating E-waste Management by Recycling Old Laptops. Data in Brief, p.111122.

Fahim, S.F., Sounok, S.A., Shaeed, N., Orpa, M.H. and Niloy, N.T., 2024, June. Analyzing User Sentiment in Google Play Store Reviews: A Natural Language Processing Approach. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Islam, M., Islam, M.M., Ali, M.S., Niloy, N.T., Chowdhury, A. and Avik, S.C., 2023, December. Ensemble Method for Predicting Student Performance and Dropout Risk. In International Conference on Recent Advances in Artificial Intelligence & Smart Applications (pp. 269-278). Singapore: Springer Nature Singapore.

Rani J., Saha S., Ferdous F., Rahman M.A. (2024), Assessment of the Bangladeshi antibiotic market: Implications of the WHO AWaRe classification and dosage form availability on antimicrobial resistance. Journal of Infection and Public Health, 17(12), 1-10. Article 102587. No.12:102587.

Rani J., Sadik G. (2024). “Isolation and characterization of cholinesterase inhibitors from Wedelia trilobata”, International Conference on Pharmaceutical and Health Sciences (ICPHS). (6-7 December 2024).

Rani J., Saha S., Rahman M.A. (2024). “Inappropriate marketing of Watch and Reserve class antibiotics in Bangladesh is posing big threat to the evolution of multidrug-resistant bacteria”, International Conference on Advances and Challenges through Translational Research in Biological Sciences. (27 April 2024).

Rani J., Saha S., Rahman M.A. (2023). “An analysis of antibiotic marketing of AWaRe (Access, Watch and Reserve) class antibiotics in Bangladesh”, International Conference on The Role of Science and Technology Towards 4IR. (5-6 October 2023).

• Jahan, M.R., Aziz, F.I., Ema, M.B.I., Islam, A.B. and Islam, M.N., 2019, June. A wearable system for path finding to assist elderly people in an indoor environment. In Proceedings of the XX International Conference on Human Computer Interaction (pp. 1-7). [Click Here]
• Islam, M.N., Jahan, M.R., Ali, A., Rony, S., Anannya, T.T., Aziz, F.I., Bayzed, M., Yeazdani, A. and Rabbi, M.F., 2019. Design and development of an intelligent seed germination system based on iot. In Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II: Proceedings of the 2nd International Conference of ICT for Adapting Agriculture to Climate Change (AACC'18), November 21-23, 2018, Cali, Colombia (pp. 146-161). Springer International Publishing. [Click Here]
• Rahman, M.A., Liu, S., Li, R., Wu, H., Jahan, M.R. and Kwok, N., 2018, April. A review on brightness preserving contrast enhancement methods for digital image. In Ninth International Conference on Graphic and Image Processing (ICGIP 2017) (Vol. 10615, pp. 794-804). SPIE. [Click here]

1. Rasel, A.A.S., Robin, M.T.I., Islam, M.S. and Hasan, M., 2026. Sentence classification in medical abstracts using quantized transformer and BiLSTM architecture. International Journal of Intelligent Systems and Applications (IJISA), 18(2), pp.156–166.  
2. Ferdaus, M.H., Prito, R.H., Rasel, A.A.S., Ahmed, M., Saykot, M.J.H., Shanta, S.S., Akter, S., Das, A.C., Islam, M.M., Hasan, M. and Ali, M.S., 2025. BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in Bangladesh. Data in Brief, 58, p.111239.
3. Islam, M.M., Sarker, A., Choudhury, A., Ahmed, N., Rasel, A.A.S., Niloy, N.T., Hossain, M.S., Ali, M.S., Chowdhury, A. and Ferdaus, M.H., 2025. ShrimpDiseaseBD: An image dataset for detecting shrimp diseases in the aquaculture sector of Bangladesh. Data in Brief, 60, p.111553.
4. Ferdaus, M.H., Prito, R.H., Ahmed, M., Islam, M.M., Ali, M.S., Ibrahim, M., Rasel, A.A.S., Islam, M., Jabid, T., Rahman, M.A. and Rahoman, M.M., 2025. MangoImageBD: An extensive mango image dataset for identification and classification of various mango varieties in Bangladesh. Data in Brief, p.111908.
5. Islam, M.M., Ahmed, M.J., Shafi, M.B., Das, A., Hasan, M.R., Al Rafi, A., Rashid, M.R.A., Niloy, N.T., Ali, M.S., Chowdhury, A. and Rasel, A.A.S., 2025. BDMANGO: An image dataset for identifying the variety of mango based on the mango leaves. Data in Brief, 58, p.111241.
6. Rasel, A.A.S. and Yousuf, M.A., 2019. An efficient framework for hand gesture recognition based on histogram of oriented gradients and support vector machine. International Journal of Information Technology and Computer Science (IJITCS), 11(12), pp.50–56.

Hasan, M. M., Momen, S. H., Akter, M., Eshaque, N. H., Uddin, M. S., Hasan, T., ... & Hossen, S. M. (2026). Hepatoprotective Effects of Baccaurea motleyana Fruit Extract Against CCl4‐Induced Liver Injury in Rats: In Vivo Evaluation and GC–MS Phytochemical Profiling. Chemistry & Biodiversity, 23(7), e71464. https://doi.org/10.1002/cbdv.71464. IF: 2.9, Q2

Uddin, M., Naher, S., Rasel, M. H., Mamun, M. J. I., Hasan, M. N., Eshaque, N. H., & Hasan, M. M. (2026). Bioactivity Assessment and In Silico Target Engagement of Clerodendrum infortunatum Extracts: Correlating Experimental Outcomes With DFT and Docking Predictions. Food Science & Nutrition, 14(7), e72047. IF: 5, Q1

Proma, N. M., Hasan, M. M., Chowdhury, M. T., Ali, M. L., Hoque, N., Meem, J. N., ... & Hossain, M. K. (2026). A comprehensive review on mushrooms with potent antileukemic activity: current status and prospects. Precision Nutrition, 5(2), e00138. IF: 2.44, Q2

Chowdhury, S., Azme, E., Hasan, M. M., Rahman, M., Rahman, M. A., Pallab, M. R. A., ... & Amin, M. N. (2026). Mechanistic insights of clinically investigated natural products as anti-colorectal cancer agents. Precision Nutrition, 5(2), e00137. IF: 2.44, Q2

Ibrahim, M., Hossain, M. S., Ooi, L., Hasan, M. M., Ahsan, S., Salem, A. K., ... & Ahsan, F. (2026). Targeting the NAD+–PARP1–XRCC1 axis in ALS. Trends in molecular medicine. https://doi.org/10.1016/j.molmed.2026.04.004. IF: 18.1, Q1

Moon, N. J., Mohammad, M., Mamun, M. J. I., Nova, F. B. A., Eshaque, N. H., Rasel, M. H., ... Hasan, H.H. & Hossen, S. M. (2026). Pharmacological evaluation of Adenostemma lavenia acetone extract in Swiss Albino mice: Analgesic, anti‐inflammatory, and thrombolytic insights from in vivo, in vitro, density functional theory, and molecular docking studies. Animal Models and Experimental Medicine. https://doi.org/10.1002/ame2.70200, IF: 3.3, Q1

Hasan, M. M., Bhuyan, E. A., Ekram, M. E. H., Proma, N. M., & Hossain, M. K. (2026). Acute and Subacute Oral Toxicity Evaluation of Baccaurea motleyana Fruit Extract in Wistar Rats. Food Science & Nutrition, 14(4), e71744. IF: 5, Q1, https://doi.org/10.1002/fsn3.71744

Tahrim, K. S., Rahman, M. M., Rahman, M. S., Sayem, M. A., Momen, M. S. H., Hasan, T., ... & Hasan, M. M.* (2026). Wound‐Healing Efficacy of Garcinia pedunculata Fruit Extract: In Vivo and In Silico Validation of Regenerative Mechanisms With Histological Analysis. Food Science & Nutrition, 14(4). IF: 5, Q1, PMID: 41948382 

Mohammad, Mahathir, Nusrat Jahan Moon, Md Hossain Rasel, Md Anisul Islam, Nazmul Hasan Eshaque, Md Jahirul Islam Mamun, Md Zamil Hossain, Md Mahmudul Hasan, Md Tanvir Chowdhury, and S. M. Hossen. "Multifaceted Evaluation of Syngonium podophyllum Flower Extracts: Anthelmintic, Antidiabetic, Antipyretic, and Antidiarrheal Potentials via Experimental, In Silico, and DFT Approaches." Food Science & Nutrition 14, no. 4 (2026): 1. IF: 5, Q1, https://doi.org/10.1002/fsn3.71701

Hasan, M. M*., Azme, E., Ekram, M. E. H., Momen, M. S. H., & Hasan, T. (2026). Nature’s Defense Against Lead Toxicity: Baccaurea motleyana Fruit Extract Reverses Oxidative Stress and Hematological Damage in Rats. Pharmacological Research-Natural Products, 100605. https://doi.org/10.1016/j.prenap.2026.100605, Q3

Faruk, Omar, Kausar Uddin Ahmed, Nabila Ishrat, Sabrina Alam Mrittika, Sakibul Alam, Md. Mahmudul Hasan, Farjana Afrin Tanjum, Nor Mohammad, Ashiq Mahmud, and Mohammad Nurul Amin. "Liver cirrhosis: epidemiology, risk factors, potential complications, and possible treatment strategies." Gastroenterology & Endoscopy (2026). https://doi.org/10.1016/j.gande.2026.03.001

Mamun, M. J. I., Rasel, M. H., Chowdhury, M. T., Hasan, M. M., Chowdhury, M., Mohaimeen, T., ... & Hossen, S. M. (2026). Biological and Computational Exploration of Ulva flexuosa: Antioxidant, Anti-inflammatory, Antidiabetic, Antipyretic, and Cytotoxic Insights for Drug Discovery. Pharmacological Research-Natural Products, 100533. https://doi.org/10.1016/j.prenap.2026.100533

Singha, S., Ekram, M. E. H., Ali, M. L., Hasan, M. M., & Ahsan, M. T. (2026). Unraveling the Therapeutic Potential of Firmiana colorata Bark Extract: GC-MS Profiling, Pharmacological Studies, and Protein-Ligand Interaction Analysis for Pain, Fever, and Inflammation. Pharmacological Research-Natural Products, 100523. https://doi.org/10.1016/j.prenap.2026.100523

Mamun, M. J. I., Akter, S., Islam, Z., Hossen, M. I., Rasel, M. H., Eshaque, N. H., ... & Hasan, M. M. (2026). Pharmacological Investigation of Syzygium diospyrifolium Fruit Extract: Antimicrobial and Diuretic Evidence From Experimental, DFT and Molecular Docking Studies. Journal of Food Biochemistry, 2026(1), 4037376. https://doi.org/10.1155/jfbc/4037376, IF: 2.3, Q1

Akter, F., Sovon, M. S. I., Hossain, B., Islam, M. A., Das, S., Biswas, A., ... & Hasan, M. M*. (2026). Maesa ramentacea Leaf Extract Protects Against Gentamicin‐Induced Nephrotoxicity in Wistar Rats by Modulating Renal Oxidative Stress, Inflammation and Apoptosis. Veterinary Medicine International, 2026(1), 6127118. https://doi.org/10.1155/vmi/6127118, IF: 2.3, Q1

Mahmudul Hasan, M., Tahrim, K. S., Hasan, T., Ekram, E. H., Momen, S. H., Proma, N. M., ... & Hossain, M. K. (2026). Multitarget Anti‐Obesity Potential of Baccaurea motleyana Fruit: Insights From Biochemical, Histological, and In Silico Analyses. Journal of Food Biochemistry, 2026(1), 3495881. IF: 2.3, Q1. https://doi.org/10.1155/jfbc/3495881

Jafrin, S., Arnab, M. K. H., Hasan, M. M., Hossain, M. S., Millat, M. S., Mohammad, N., ... & Amin, M. N. (2025). Clinically investigated natural products against prostate cancer, with mechanistic insights. Precision Nutrition, 4(4), e00118. DOI: 10.1097/PN9.0000000000000118, IF: 2.44, Q2

Millat, M. S., Hasan, M. M., Uddin, M. S., Salam, M. A., Aziz, M. A., Akhter, I., ... & Islam, M. S. (2025). Inflammatory cytokines and specific factors influencing lung cancer progression. Cancer Pathogenesis and Therapy, 3(06), 484-500. https://mednexus.org/doi/full/10.1016/j.cpt.2025.04.002, IF: 5.9, Q1

Hasan, M. M., Momen, M. S. H., Haque, A., Afroja, F., Mim, S. A., Pallab, M. R. A., ... & Amin, M. N. (2025). Unlocking the mechanistic insights of clinically proven natural products against osteoporosis. Clinical Phytoscience, 11(1), 18. https://link.springer.com/article/10.1186/s40816-025-00403-3, 

Siddiqui, S. A., Aka, T. D., Hossain, M. M., Millat, M. S., Pallab, M. R. A., Mohammad, N., ..Hasan, M.M.. & Amin, M. N. (2025). The molecular mechanisms of dietary supplements and potential bioactive compounds in the management of obesity: A review. Pharmacological Research-Natural Products, 100417. https://doi.org/10.1016/j.prenap.2025.100417

Islam, M. R., Hasan, M. M., Ali, M. L., Chowdhury, M. T., Chowdhury, M. M., & Hossain, M. K. (2025). Phytochemical profiling and neuropharmacological assessment of Ipomoea purpurea: Integrated experimental and computational approaches. South African Journal of Botany, 186, 123-135. https://doi.org/10.1016/j.sajb.2025.09.007, IF: 3.1, Q2

Hasan, M. M., Azme, E., Ekram, M. E. H., Momen, M. S. H., Hossain, M. K., & Proma, N. M. (2025). Exploring the anti-inflammatory and wound-healing potential of Baccaurea motleyana fruit: Preliminary insights from GC–MS metabolomic profiling, molecular docking, and ADMET studies. Fitoterapia, 106930. https://doi.org/10.1016/j.fitote.2025.106930, IF: 2.9, Q2

Islam, M. R., Chowdhury, M. T., Chowdhury, M. M., Khanam, B. H., Ali, M. L., Hasan, M. M., & Hossain, M. K. (2025). Investigating the Secondary Metabolite Profile and Neuropharmacological Activities of Ipomoea purpurea: A Multi‐Method Approach Using GC–MS, In Vivo, and In Silico Techniques. Chemistry & Biodiversity, 22(9), e202500560. https://doi.org/10.1002/cbdv.202500560 IF: 2.9, Q2

Hasan, M. M., Azme, E., Alam, R., Mamun, M. J. I., Chowdhury, M. T., Rasel, M. H., ... & Chung, H. J. (2025). Mushrooms as Potent Autophagy Modulators in Cancer Therapy: Current Evidence and Therapeutic Prospects. Cancer Pathogenesis and Therapy, 3, E01-E58. https://mednexus.org/doi/full/10.1016/j.cpt.2025.08.001, IF: 5.9, Q1

Hasan, M. M., Alim, M. A., Momen, M. S. H., Islam, M. S., Chowdhury, S. H., Rashed, M., ... & Hossen, S. M. (2025). Harnessing the multidimensional bioactivity of Chaetomorpha aerea: Integrative phytochemical profiling with in vitro, in vivo, and in silico insights. Animal Models and Experimental Medicine, 8(8), 1416-1427. https://doi.org/10.1002/ame2.70064, IF: 3.3, Q1

Hasan, M. M., Wasin, S. M., Rahman, M., Azme, E., Mostaq, M. S., Nahid, M. M. H., ... & Amin, M. N. (2025). Clinically proven natural products against breast cancer, with mechanistic insights. Oncology Research, 33(7), 1611. https://pubmed.ncbi.nlm.nih.gov/40612872/ IF: 4.6, Q1

Ali, M. L., Noushin, F., Sadia, Q. A., Metu, A. F., Meem, J. N., Chowdhury, M. T., .Hasan, M.M... & Azme, E. (2025). Spices and culinary herbs for the prevention and treatment of breast cancer: a comprehensive review with mechanistic insights. Cancer Pathogenesis and Therapy, 3(03), 197-214. IF: 5.9, Q1. https://mednexus.org/doi/full/10.1016/j.cpt.2024.07.003

Azme, E., Hasan, M. M., Ali, M. L., Alam, R., Hoque, N., Noushin, F., ... & Chung, H. J. (2025). Computational identification of potential natural terpenoid inhibitors of MDM2 for breast cancer therapy: molecular docking, molecular dynamics simulation, and ADMET analysis. Frontiers in Chemistry, 13, 1527008. https://doi.org/10.3389/fchem.2025.1527008, IF: 5.3, Q2

Mohammad, M., Hossain, M. R., Hasan, M. M., Richi, F. T., Bhuiya, A. M., Chowdhury, S., ... & Hossain, R. (2025). A novel insight into the anti-diabetic and diuretic potentials of the Colocasia esculenta L.(Taro) vegetable flower extract accentuating its ethnobotanical importance. Pharmacological Research-Natural Products, 6, 100177. https://doi.org/10.1016/j.prenap.2025.100177

Hasan, M. M., Momen, M. S. H., Alim, M. A., Chowdhury, S. H., Chowdhury, M., Mamun, M. A., ... & Hossen, S. M. (2025). Neuropharmacological, Antidiarrheal, and Antimicrobial Effects of Chaetomorpha aerea Acetone Extract: GC‐MS Profiling and In Silico Analysis. Scientifica, 2025(1), 6745529. https://doi.org/10.1155/sci5/6745529, IF: 3.6, Q1

Ali, M. L., Hoque, N., Hasan, M. M., Azme, E., & Noushin, F. (2024). Exploration of marine natural compounds as promising MDM2 inhibitors for treating triple-negative breast cancer: insights from molecular docking, ADME/T studies, molecular dynamics simulation and MM-PBSA binding free energy calculations. Discover Chemistry, 1(1), 61.

Ali, M. L., Noushin, F., Azme, E., Hasan, M. M., Hoque, N., & Metu, A. F. (2024). Marine natural compounds as potential CBP bromodomain inhibitors for treating cancer: an in-silico approach using molecular docking, ADMET, molecular dynamics simulations and MM-PBSA binding free energy calculations. In silico pharmacology, 12(2), 85. https://link.springer.com/article/10.1007/s40203-024-00258-5

Artificial Intelligence and Applications.

(2022, October 10).

https://ojs.bonviewpress.com/index.php/AIA/article/view/354

 IEEE SMC 2021, The Papercept

Conference Manuscript Management System

Abstract:
Threat and abusive languages spread quickly through social media which can be controlled if we can detect and remove them. Since there exist many social media like Facebook, Twitter, Instagram etc and a huge number of social media users, we need a robust and effective automatic system to identify threat and abusive languages. In our proposed system Machine Learning and Natural Language Processing techniques have been implemented to build an automatic system. Previous research on Bengali abusive language detection used Multinomial Näıve Bayes (MNB), Support Vector Machine(SVM) algorithms and considered Bengali Unicode characters to build their system. We considered both Unicode emoticons and Unicode Bengali characters as valid input in our proposed system. Besides MNB and SVM algorithm, we implemented Convolutional Neural Network (CNN) with Long Short Term Memory(LSTM). Among three algorithms, SVM with linear kernel performed best with 78% accuracy. 

Link: https://ieeexplore.ieee.org/document/8934609

Abstract:

Article information: Objective: The primary intent of this paper is to review related studies that are more corresponding to the detection of five variants of cyberbullying text, such as abusive, hateful, aggressive, bully, and toxic comments or texts of Bengali language as a sample of low-resource language, to gain a comprehensive understanding of the challenges and state-of-the-art approaches used to identify these types of text. Materials: We have searched the associated articles on cyberbullying text detection in the Bengali language published from 2017 to July 2021 since there was no research being detected before the year 2017 on this domain-specific paradigm. After that, we scrutinize the different levels of aspects by inspecting the title, abstract, and entire text to enlist the subsequent research in this review study. Results: After applying different levels of filtering, from the initial search results, 28 domain-centric papers are considered out of 2,745 documents. At first, we deeply analyze the context of each study and then narrate a clear comparative review in case of research challenges and approaches, as well as providing the direction for the future work on the road to the detection of cyberbullying text for the Bengali language. Conclusion: In this paper, we discuss five variants of cyberbullying text, such as abusive text, hateful speech, aggressive text, bully text, and toxic comments over the web, and their detection process by studying existing literature in this domain. We present advice on dataset preparation, pre-process and feature extraction tasks, and classier selection that may aid in comprehensive research for better detection. 

Link: https://ph01.tci-thaijo.org/index.php/ecticit/article/view/248039

Abstract:

This paper manifests the experimentation with sentiment polarity detection over Stanford's IMDB movie review dataset using a Support Vector Machine classifier (SVM). Our prime motivation was to find out the best possible combinations of classic features and preprocessing techniques for the classification of positive and negative opinions. We also explored two variants of kernels with numerous parameter settings for the classifier in the hope of getting the best SVM model. Our best model achieved an accuracy score of 85.45%. The results indicate that a model with a non-linear Radial Basis Function (RBF) kernel leads to the highest accuracy. The features that contributed the most are stemmed word n-grams.

Link: https://ieeexplore.ieee.org/document/9396874

1. "Convolutional Neural Networks(CNN) for Detecting Fruit Information using Machine Learning Techniques", In: IOSR Journal of Computer Engineering, Volume-22, Issue-02, (Mar-Apr 2020) PP 01-13. Authors: Fouzia Risdin, Pronab Kumar Mondal, Kazi Mahmudul Hassan

2. "Extracting Text Information from Digital Images", In: International Journal of Science & Engineering Research, Volume 10, Issue-06, year-2019.  Authors: Md. Mijanur Rahman, Mahnuma Rahman Rinty, Fouzia Risdin

We introduce a transfer learning technique, correlation alignment, in software defect prediction.

Manually selection of optimal hyper parameter in regression (Lass, Ridge, Elastic net) is time consuming as well as error prone. In this work we introduce "divide and conquer" based approach here to select hyper parameter automatically and efficiently.

The emergence of new and improved technological advances created severe problems in the security state of the educational certification system. Throughout this paper, a proposal has been made to improve security. Here, Blockchain technology has been introduced as reliable secure storage for the educational certification system, providing an additional facility to the users. That is the validation and authentication of the student’s academic records. Moreover, for security purposes, Blockchain technology can replace the traditional academic certification system and contribute to a new model for sharing student information. After completion of data inclusion and hashing, the blocks will be inserted into the Blockchain network. This proposed model enhances document security and fraud reduction and additionally reduces a significant amount of authentication time almost up to double the current speed. With this system, we will get a certification process in which all data will be digitalized and secured in an unbreakable database with proper authentication and with a noticeable amount of time efficiency.

Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Genetic algorithm has been utilized to identify the most significant biomarkers. The XGBoost classifier is then trained on the selected features and high ROC-AUC scores of 0.864 and 0.911 have been obtained for the premenopausal and postmenopausal populations, respectively. Lack of explainability is one major limitation of current AI systems. The stochastic nature of the ML algorithms raises concerns about the reliability of the system as it is difficult to interpret the reasons behind the decisions. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework. SHAP is employed to quantify the contributions of the selected biomarkers and determine the most discriminative features. Merging SHAP with the ML models enables clinicians to investigate individual decisions made by the model and gain insights into the factors leading to that prediction. Thus, a hybrid decision support system has been established that can eliminate the bottlenecks caused by the black-box nature of the ML algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin which signifies its potential to be an effective tool in the differential diagnosis of OC.

Global food security is seriously threatened by plant diseases, which annually cause large losses in agricultural productivity. Early diagnosis and accurate classification of plant diseases are required for disease management programs to be implemented promptly and efficiently. In the area of plant disease classification, Convolutional Neural Networks (CNN) have demonstrated encouraging results in recent years. In this study, we propose a CNN based approach for plant disease classification using a MobileNetV2 based model and transfer learning. The proposed model leverages the MobileNetV2 architecture, known for its lightweight and efficient design, making it well-suited for resource-constrained environments. The pre-trained MobileNetV2 model is modified using transfer learning to accommodate the goal of classifying plant diseases. The model benefits from the characteristics that have been learned from a large-scale dataset through the use of pre-trained weights, leading to improved generalization and reduced training time. We use a standard plant disease dataset with a filtering method as a preprocessing strategy in extended trials to assess the efficiency of the proposed approach. The performance of the model is compared using several cutting-edge techniques, including VGG16, AlexNet and InceptionV3. The experimental findings show that the suggested model performs competitively in classifying plant diseases, surpassing other approaches with an accuracy of 98.56%.

In recent years, social media platforms, particularly twitter, have emerged as crucial sources of public opinion and sentiment. Analyzing sentiment on twitter data presents a significant challenge due to the platform's inherent characteristics, such as brevity, informality, and the prevalence of slang and emojis. This research paper proposes a method for twitter sentiment analysis by leveraging the power of a transformer-based model called RoBERTa. The proposed strategy employs RoBERTa due to its exceptional performance in various natural language processing tasks. Our system captures intricate contextual information and semantic nuances in tweets, making it well-suited for sentiment analysis on this challenging platform. To build an effective sentiment analysis system, the architecture is fine-tuned using a large corpus of twitter data, annotated with sentiment labels. Additionally, we explore various strategies to handle the unique characteristics of twitter data, including tokenization, handling hashtags, user mentions, and URLs, as well as the incorporation of emojis and emoticons. We compare the performance of our model with three other standard machine learning and deep learning models, such as Decision Tree (DT), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) in order to show that our model is superior at correctly analyzing twitter sentiment. The model showcases an exceptional accuracy of 96.78%, highlighting its effectiveness in understanding and classifying sentiment within the context of tweets.

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain.

Text classification is known to be a supervised machine learning technique used in one or more predefined categories to classify sentences or text archives. To be a perfect phrase to convey one's feelings or to be significant, a Bengali sentence must have three properties i.e. Compatibility, Proximity and Expectancy. In this paper, we have proposed a method that is able to detect whether a Bengali sentence has compatibility, proximity and expectancy using Long Short Term Memory network. Our model is trained with word embedding layer and LSTM layer for the detection of Compatibility of a sentence but POS tagging is included for assuring the syntactic structure in case of Proximity and Expectancy detection. The model is tested on around 75000 Bengali simple sentences. The proposed framework achieves an accuracy of 97.5 percent, 85.5 percent and 97 percent for Compatibility, Proximity and Expectancy respectively. The result analysis proves that our model gives better performance.

1. M. A. K. Rifat, A. Kabir, and A. Huq, “An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction,” Procedia Computer Science, vol. 246, pp. 1905–1914, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.704. ‌[Presented at the 28th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2024), as part of a special issue.]

1. Rabea Khatun, Maksuda Akter, Md Manowarul Islam, Md Ashraf Uddin, Md Alamin Talukder, Joarder Kamruzzaman, A. K. M. Azad et al. "Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data." Genes 14, no.9 (2023): 1802.(Link)
2. Saurav Chandra Das, Wahia Tasnim, Humayan Kabir Rana, Dr. Uzzal Kumar Acharjee, Md Manowarul Islam, Rabea Khatun. "Comprehensive Bioinformatics and Machine Learning Analysis for Breast Cancer Staging Using TCGA Dataset". Briefings in bioinformatics 26.1 (2024): bbae628.(Link)
3. Rabea Khatun, Wahia Tasnim, Maksuda Akter, Md Manowarul Islam, Dr. Md. Ashraf Uddin, Saurav Chandra Das, Dr. Md. Zulfiker Mahmud. "Integrative Machine Learning and Bioinformatics Approach for Identifying Key Biomarkers in Gallbladder Cancer Diagnosis and Progression". IET Systems Biology 19.1 (2025): e70022.(Link)
4. Maksuda Akter, Rabea Khatun, Md. Alamin Talukder, Md Manowarul Islam, Dr. Md. Ashraf Uddin. "An integrated deep learning model for skin cancer detection using hybrid feature fusion technique".Biomedical Materials & Devices 3, 1433–1447 (2025).(Link)

1. Nabanita Saha Joya, Sagar Biswas Sadia Jannat Mitu, Rabea Khatun, Md Manowarul Islam. "An Efficient Machine Learning Framework for Panic Disorder Detection: Addressing Data Imbalance with SMOTE to Reduce False Negatives." 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, 2025, pp. 1-6.(Link)
2. Jahad Hasan, Md. Lutfur Rahman, Satayjit Biswas, Md. Solaiman Mia, Rabea khatun. "A Smart Decision-Maker by Monitoring Progress of Student’s Study using Machine Learning Algorithms." (Accepted at the International Conference on Big Data, IoT, and Machine Learning (BIM 2025)).
3. Sagufta Sabah Nakshi, Wahia Tasnim, Rabea Khatun. (2025), “A Lightweight Federated–Ensemble Deep Learning Approach for Early Detection of Alzheimer’s Disease Using MRI Imaging”. Paper accepted at 2025 28th International Conference on Computer and Information Technology (ICCIT).