Mrittika Mahbub
Lecturer
| M.Sc Eng, Computer Science and Engineering. , University of Rajshahi. |
| B.Sc Eng. Computer Science and Engineering. , University of Rajshahi |
| Lecturer, Department of Computer Science & Engineering, Pundra University of Science & Technology. |
| Lecturer, Department of Computer Science & Engineering TMSS Engineering College. |
| 1. Mst. Rehena Khatun, Md. Palash Tai, Mrittika Mahbub, Md. Easir Arafat (2023) “Conventional Machine Learning approaches for rice plant diseases classification and detection usingSupport Vector Machine” TIJER || ISSN 2349-9249 || © December 2023, Volume 10, Issue12 ||” https://tijer.org/tijer/papers/TIJER2312038.pdf’’ |
| 2. Mst. Rehena Khatun, Md. Palash Tai, Mrittika Mahbub, Md. Easir Arafat (2024)“Detection of Crop Diseases using different Machine Learning Approaches” TIJER ||ISSN 2349-9249 || © January 2024, Volume 11, Issue 1 ||https://tijer.org/tijer/papers/TIJER2401051.pdf |
| 3. Mrittika Mahbub, Md. Habib Ehsanul Hoque, Mst. Rehena Khatun (2024) “Smart Farming in Bangladesh: Mobile Application for Tomato Leaf Disease Detection Using a Hybrid VGG16-CNN Model” IJLTEMAS || ISSN 2278-2540 || © December 2024, Volume 13, Issue 12 || DOI : https://doi.org/10.51583/IJLTEMAS.2024.131220 |
| 4. Mrittika Mahbub, Md. Habib Ehsanul Hoque (2024) “Optimizing YOLOv10 for Real-Time Traffic Sign Detection and Recognition: A Bangladeshi Perspective” IJARCCE || ISSN (O) 2278-1021|| © January 2025, Volume 14, Issue 1|| DOI: 10.17148/IJARCCE.2025.14101 |
| 5. Md. Habib Ehsanul Hoque, Mrittika Mahbub, Mohd Razali Bin Md Tomari, Rezaul Bashar, Dipankar Das, Md. Golam Rashed “Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8” Conference: 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS2024) Sydney, Australia.https://www.researchgate.net/publication/386170240_Real_time_Detection_of_Diverse_Bangladeshi_Traffic_Signs_Using_YOLOv8 |
| Machine Learning and Deep Learning-Based Computer Vision: This research focuses on developing advanced machine learning models, particularly deep learning approaches, to improve computer vision tasks such as object detection, image classification, and segmentation. |
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