I am a Computer Science graduate from Ahsanullah University of Science and Technology (AUST) with a CGPA of 3.336/4.00 and four peer-reviewed research publications. My academic journey has been driven by a deep curiosity in Artificial Intelligence, Machine Learning, and Cybersecurity, which I have explored through both academic research and industry experience.
My published work spans image enhancement using reinforcement learning, Bangla news popularity prediction, machine learning-based music genre classification, and soft computing applications — presented at international IEEE conferences and published in IEEE Xplore and SSRN.
With a blend of academic rigor and industry insight from my role as a Software Quality Assurance Engineer, I am seeking to pursue a PhD in Computer Science to further explore intelligent systems and secure computing, aiming to contribute novel insights at the intersection of AI and digital security.
Developed a reinforcement learning-based approach for dark image enhancement through color feature balancing. Proposed comprehensive image processing techniques that outperform traditional methods.
Investigated machine learning classifiers for categorizing and predicting popularity of Bangla news articles. Compared performance across multiple ML algorithms for NLP tasks on Bengali text.
Applied machine learning and deep learning techniques for genre classification of Bangla music, extracting audio features and comparing classification algorithms for optimal performance.
2022 IEEE International Conference on Smart Information Systems and Technologies (SIST), pp. 1-6
2022 4th International Conference on Natural Language Processing (ICNLP), pp. 376-379
2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), pp. 1-6
Available at SSRN (April 28, 2022)
2022 7th International Conference on Image, Vision and Computing (ICIVC)
CGPA: 3.336 / 4.00
Thesis: Reinforcement Learning Based Dark Image Enhancement Through Color Feature Balancing
Supervisor: Ms. Raqeebir Rab [Profile]
GPA: 5.00 / 5.00
GPA: 5.00 / 5.00
Research-oriented and technically significant projects
Built ML classification model to identify genuine and fake banknotes using wavelet-transformed image features. Compared Logistic Regression, KNN, Decision Trees, Random Forest, and SVM.
Developed ML model to classify music genres from extracted audio features (MFCCs, chroma, spectral centroid) using Random Forest, SVM, KNN, and Decision Trees with Librosa.
Performed performance testing on the BlazeDemo web application using Apache JMeter to evaluate response time, throughput, and scalability under load. Generated detailed reports and CSV logs to analyze bottlenecks and validate system stability.
Designed and documented comprehensive test scenarios and test cases covering functional, UI, and integration testing of the Gorilla Move Grocery web-responsive and mobile-responsive application. Deliverables include Test Plan, Test Case Documentation, Bug Reports, and Test Metrics.
