JOURNAL PAPER

1. Md Ahsan Habib, Md Anwar Hussen Wadud, Md Fazlul Karim Patwary, Mohammad Motiur Rahman, MF Mridha, Yuichi Okuyama, Jungpil Shin, Exploring Progress in Text-to-Image Synthesis: An In-Depth Survey on the Evolution of Generative Adversarial Networks, IEEE Access, 2024. doi: https://doi.org/10.1109/ACCESS.2024.3435541
2. Tasnim, F., Ahmed, T., Ahmed, K., Patwary, M. F. K., & Newaz, M. K., Potential Risk Factors and Association of Significant Factors of Blood Cancer in Bangladesh Using Data Mining Techniques., Journal of Engineering Science,, 13(1), pp.9-20, 2022.
Rahman, E., Bardhan, N., Curtin, M., Islam, M., Patwary, M., Karim, F., & Kumar Das, S., An assessment of disability and quality of life in people with spinal cord injury upon discharge from a Bangladesh rehabilitation unit. S, pinal Cord,, 1-6, 2022.
1. Ehsanur Rahman, Nirupom Bardhan, Michael Curtin, Md. Shofiqul Islam, Md. Fazlul Karim Patwary & Shazal Kumar Das, An assessment of disability and quality of life in people with spinal cord injury upon discharge from a Bangladesh rehabilitation unit., Spinal Cord 61, pp.37–42, 2023. doi: https://doi.org/10.1038/s41393-022-00852-4
Shamima Islam Nipa, Thanyaluck Sriboonreung, Aatit Paungmali, Chailert Phongnarisorn, Md Fazlul Karim Patwary, Validity and Reliability Measurement of Bengali Translated Questionnaire for Urinary Incontinence Diagnosis, Journal of Health Science and Medical Research, 2021. doi: 10.31584/jhsmr.2021813

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CONFERENCE PAPER

M. Chanda, O. Mazumder and M. F. Karim Patwary, Smoker Recognition from Lung X-ray Images using ML, 26th International Conference on Computer and Information Technology (ICCIT), pp.1-6, Cox's Bazar, Bangladesh, 2023. doi: 10.1109/ICCIT60459.2023.10441360

Smoking is the leading cause of death in Bangladesh, accounting for one in every five fatalities, according to scientific research. A number of researchers have developed cutting-edge techniques based on Deep Learning methods to ascertain a man's smoking status through image processing. As far as we are aware, no CNN-based system can distinguish between a smoker and a non-smoker from a lung X-ray image. In this work, we offer a novel CNN-based system that can detect, in real time, with high sensitivity and specificity, whether a man smokes or not by examining lung X-ray pictures. The employed data-set is divided into two groups: smokers and non-smokers. In this study, we provide a novel ML-based system that uses images of the guy's lungs taken in real time to detect, with high sensitivity and specificity, whether or not the man smokes. Hospitals may occasionally need to utilize this method to draw blood from a nonsmoker. Additionally, men who vape are not allowed to take college admissions exams or the army selection process. The efficacy of the proposed method for Smoker and Non-Smoker prediction was evaluated and contrasted with previous CNN systems based on multiple performance metrics. The proposed technique accurately detects smokers and nonsmokers from lung X-ray pictures with 91.50% accuracy, 92% precision, and 91% recall. We are certain that real-time system performance won't be affected, even if the recommended approach was trained on a picture dataset. This device might be used in a hospital, while choosing applicants for the police or army, or for university admission.

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BOOK CHAPTER

SP Sagar, K Oliullah, K Sohan, MFK Patwary, PRCMLA: Product Review Classification Using Machine Learning Algorithms, International Conference on Trends in Computational and Cognitive Engineering, 978-981-33-4673-4, pp.65-75, Jahangirnagar University, 2020. doi: https://doi.org/10.1007/978-981-33-4673-4_6
Md Mahfuzur Rahman, Sheikh Shah Mohammad Motiur Rahman, Shaikh Muhammad Allayear, Md Fazlul Karim Patwary, Md Tahsir Ahmed Munna, A Sentiment Analysis Based Approach for Understanding the User Satisfaction on Android Application, Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing book series, AISC, volume 1079, pp.397-407, Hyderabad, India, 2020. doi: https://doi.org/10.1007/978-981-15-1097-7_33

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