- Faculty
- Faculty Faculty of Sciences and Engineering
- Research Interest
- Blockchain, Machine Learning, AI and Computer Vision.
- Teaching Materials
- no content
- Academic Background
- B.Sc. In Computer Science and Engineering, East West University M.Sc. In Computer Science and Engineering, East West University
- Awards & Achievements
- no content
- Selected Publications
- Education Certification and Verified Documents Sharing System by Blockchain An ML-based decision support system for reliable diagnosis of ovarian cancer by leveraging explainable AI A CNN Based Model for Plant Disease Classification using Transfer Learning A Transformer Based Model for Twitter Sentiment Analysis using RoBERTa Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding 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 dis
- Professional Experience
- Academic Experience 2024 (October) - current: East West University, Lecturer. Key responsibility: Lecture and hands-on lab activities, thesis/project supervision, assessment grading, marking, student consultation. Courses taught: Discrete Mathematics, Database, Operating System and Advance Database. 2024 (January – September) : University of Liberal Arts Bangladesh, Lecturer. Key responsibility: Teach hands-on lab activities, assessment grading, marking, student consultation. Courses taught: Computer Architecture, Operating System, Introduction to Programming, Algorithm (Lab), Database(Lab). 2023 ( September – December ) : Primeasia University, Lecturer. Key responsibility: Teach hands-on lab activities, assessment grading, marking, student consultation. Courses taught: Computer Architecture, Digital Logic Design, System Analysis and Design. 2022 - 2023 (August) : East West University, Graduate Teaching Assistant. Course : Discrete Mathematics . 2020 - 2021: East West University, Undergraduate Teaching Assistant. Course : Physics.
- Affiliation Professional Membership
- no content