EXTERNAL PROFILES
Lecturer
Md. Jubayar Alam Rafi
In learning you will teach, and in teaching you will learn.
Institute of Information Technology (IIT)
BIOGRAPHY
I am a Lecturer at the Institute of Information Technology (IIT), Noakhali Science and Technology University (NSTU), Bangladesh. My academic and professional interests center on advancing education, research, and innovation in Computer Science and Software Engineering. As an educator, I am committed to fostering a dynamic and intellectually stimulating learning environment that promotes critical thinking, analytical problem-solving, and lifelong learning. In addition to teaching undergraduate courses, I actively supervise student research and project work, contribute to curriculum development, and engage in initiatives aimed at enhancing academic quality and institutional excellence.
RESEARCH INTERESTS
2022 - 2023
Master of Science (MSc)
Computer Science and Telecommunication Engineering
Noakhali Science and Technology University.
2017 - 2021
Bachelor of Science (BSc Honours)
Computer Science and Telecommunication Engineering
Noakhali Science and Technology University.
2014 - 2016
Higher Secondary School
Science
Birshreshtha Munshi Abdur Rouf Public College.
2009 - 2014
Secondary School
Science
Kasba Government Girls High school.
Last updated on 2026-05-11 18:18:42
Lecturer
Department of Computer Science and Engineering, Daffodil International University (DIU), Dhaka, Bangladesh.
2025-01-01 to
2025-12-31
2025-12-31
Lecturer
Department of Computer Science and Engineering, University of Global Village (UGV), Barisal, Bangladesh.
2023-07-18 to
2024-12-31
2024-12-31
Intern
Research & Development, Tappware Solutions Limited, Dhaka, Bangladesh.
2022-11-01 to
2023-06-30
2023-06-30
No Training information...
Last Updated: N/A
No workshop information...
Last Updated: N/A
AWARDS AND ACHIEVEMENTS
No award information...
2025
Journal
Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-Based Model and Direct Imaging Model.
Authors: Farida Siddiqi Prity, Mirza Raquib, Saydul Akbar Murad, Md Jubayar Alam Rafi, Md Khairul Bashar Bhuiyan, Anupam Kumar Bairagi
Journal: Wiley
DOI: https://doi.org/10.1002/fsn3.71350
2025
Journal
UGV-NBWASTE: An oriented dataset for non-biodegradable waste in Bangladesh.
Authors: Md. Riadul Isalm, Nabil Bin Mahabub, Md. Jubayar Alam Rafi, Pronoy Kanti Roy, Turjo Roy, Md. Tariqul Islam, Md. Abdur Razzak
Journal: Data in Brief
DOI: https://doi.org/10.1016/j.dib.2025.111559
2024
Journal
NSTU-BDTAKA: An open dataset for Bangladeshi paper currency detection and recognition.
Authors: Md. Jubayar Alam Rafi, Mohammad Rony, Nazia Majadi
Journal: Data in Brief
DOI: https://doi.org/10.1016/j.dib.2024.110701
2025
Conference
VashaNet-V2: Bangla Handwritten Character Recognition Using a Novel Deep Convolutional Neural Network and an Extended Original Dataset
Authors: Mirza Raquib, Mohammad Amzad Hossain, Md. Jubayar Alam Rafi, Iftehaz Newaz, Md. Maruf Hossain, Mohammad Rony, Farida Siddiqi Prity & Md. Bipul Hossain
DOI: https://doi.org/10.1007/978-981-96-2721-9_32
2024
Conference
Non-Biodegradable Plastic Waste Detection and Classification Using Deep Learning: Bangladeshi Environmental Scenario.
Authors: Pronoy Kanti Roy, Md. Riadul Islam, Md.Jubayar Alam Rafi
DOI: https://doi.org/10.1109/ICEEICT62016.2024.10534312
2024
Conference
Non-permissible Mobile Detection to Enhance Security in Bangladeshi Museum: A Multiprocess YOLO-Based Approach.
Authors: MD. Omar Faruk Maruf, Md. Riadul Islam, Md.Jubayar Alam Rafi, Md Abu Sufian Shake, Md.Tariqul Islam
DOI: https://doi.org/10.1109/ICEEICT62016.2024.10534325
2024
Conference
Citrus Leaf Disease Classification Using Different Deep Learning Models: A Survey of Southern part of Bangladesh.
Authors: Md. Masud, Arpita Chokroborty, Md. Masudur Rahman, Partho Sarathi Sarker, MD. Jubayar Alam Rafi, Mirza Raquib
DOI: https://doi.org/10.1109/ICCIT64611.2024.11022384
No Research Project Found
Last Updated: 2026-07-19 18:05:39
2024-05-24
to
2025-11-30
Project
An Intelligent Paper Currency Recognition System for Blind and Visually Impaired Persons in Bangladesh.
Abstract: Low vision and blindness have become a global public health concern due to the rapid growing population, especially in low income nations. They frequently use banknotes for daily purchases and bill payment. The similarity of paper surface and size across different denominations makes recognizing currency one of the other major challenges faced by them. Furthermore, the sizes and colors of banknotes are causing major problems for blind or visually impaired people. Thus, a real-time paper currency recognition system is necessary for visually challenged people to avoid depending on others for financial transactions. This project aims to introduce a deep learning-based assistance framework, named Vision-Assist, for detecting and recognizing Bangladeshi paper currency in real-time, specifically for individuals who are blind or visually impaired. To achieve this, the YOLOv5 model was employed to detect currency regions. The YOLOv5 model achieves an impressive mean average precision score of 0.95, indicating its superior performance in accurately identifying currency areas compared to other potential detection models. The experiment was conducted on the NSTU-BDTAKA dataset, which includes 28875 diverse images of Bangladeshi paper currency. Achieving a testing accuracy of 94.94%, the proposed Vision-Assist approach demonstrated excellent performance. Results show that Vision-Assist outperforms other current methods and provides greater applicability in the unrestricted setting of currency recognition tasks. The model demonstrates strong potential for enhancing financial independence among visually impaired individuals.
Funding Agency: ICT Innovation Fund, Ministry of Information & Communication Technology, Dhaka, Bangladesh.
Funding Amount: 10 Lakh
Position: Research Assistant
2023-10-09
to
2024-12-19
Project
Predicting Student Engagement in Collaborative Learning using Computer Vision: In the Perspective of Bangladesh.
Abstract: Monitoring student engagement in real time is increasingly vital in educational research, especially as intelligent classroom technologies proliferate. While previous research focused on single components such as gaze tracking or emotion recognition, a few studies offer unified approaches to holistic involvement assessment. The lack of comprehensive, contextually appropriate datasets is the main cause of this. This paper presents a multimodal deep learning framework that integrates face detection, gaze estimation, and facial expression recognition to comprehensively detect and classify student engagement levels. To accomplish this, a novel multimodal dataset has been created: a gaze detection dataset with 8191 original images augmented to 24573 samples, and a facial expression recognition dataset with 9008 original images expanded to 27023 samples. Both datasets were collected in actual classroom settings to ensure ecological validity. The proposed framework, which uses YOLOv8 for face detection and LightGBM for expression and gaze classification, is designed for interpretability and real-time responsiveness. Unlike previous approaches prone to overfitting or unsuited for dynamic settings, the framework demonstrates strong generalization, as evidenced by minimal divergence between training and validation curves. It achieves classification accuracies of 96\% and 97\% for expression and gaze recognition, respectively, and an F1-score of 0.99 for face detection. These results underscore the value of purpose-built datasets in enabling nuanced, robust detection of engagement states such as attentiveness, boredom, or distraction.
Funding Agency: National Science & Technology ( NST Fellowship ), Ministry of Science & Technology, Dhaka, Bangladesh.
Funding Amount: 54000 tk
Last Updated: 2026-07-19 18:05:39
C, C++, Java, Python, Data Structure, Algorithm, Object-Oriented Programming, Database Management System, Data Mining, Machine Learning
No Course Materials Found....
No Supervision Found...
- Institutional Email: rafi.iit@nstu.edu.bd
- Personal Email: jobayaralamrafi27093@gmail.com
- Mobile number: +8801707587089
- Emergency Contact: +8801317631551
- PABX:N/A
- Website:N/A
Institute
Institute of Information Technology (IIT)
Noakhali Science and Technology University