Publication

[1] R. Shakil, S. Islam; Y. A. Shohan, A. Mia, M.H. Rahman, B. Akter, “Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition,” Array, vol. 20, p. 100326, Dec. 2023, doi: 10.1016/j.array.2023.100326. [Paper]

[2] A. Rajbongshi, S. Sazzad, R. Shakil, B. Akter, and U. Sara, “A comprehensive guava leaves and fruits dataset for guava disease recognition,” Data in Brief, vol. 42, p. 108174, Jun. 2022, doi: 10.1016/j.dib.2022.108174. [Paper]

[3] U. Sara, A. Rajbongshi, R. Shakil, B. Akter, S. Sazzad, and M. S. Uddin, “An extensive sunflower dataset representation for successful identification and classification of sunflower diseases,” Data in Brief, vol. 42, p. 108043, Jun. 2022, doi: 10.1016/j.dib.2022.108043. [Paper]

[4] S. Sazzad, A. Rajbongshi, R. Shakil, B. Akter, and M. S. Kaiser, “RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose,” Data in Brief, vol. 44, p. 108497, Oct. 2022, doi: 10.1016/j.dib.2022.108497. [Paper]

[5] U. Sara, A. Rajbongshi, R. Shakil, B. Akter, and M. S. Uddin, “VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system,” Data in Brief, vol. 43, p. 108422, Aug. 2022, doi: 10.1016/j.dib.2022.108422. [Paper]

[6] R. Shakil, B. Akter, F. M. Javed Mehedi Shamrat, S. R. H. Noori, "A Novel Automated Feature Selection Based Approach to Recognize Cauliflower Disease," Bulletin of Electrical Engineering and Informatics, vol. 12, p. 3541~3551, Dec. 2023, Doi: 10.11591/eei.v12i6.5359. [Paper]

[7] A. Rajbongshi, R. Shakil, B. Akter, M. A. Lata, M.A. and M.M.A. Joarder, "A comprehensive analysis of feature ranking-based fish disease recognition," Array, vol. 21, p.100329, 2024. doi: 10.1016/j.array.2023.100329 [Paper]

[8] F. M. Javed Mehedi Shamrat, R. Shakil, S. Sharmin, B. Akter, M. Z. Ahmed, K. Ahmed, M. A. Moni, “Enhancing Diabetic Retinopathy Detection with DRNet13: An Advanced Deep Neural Network for Fundus Image Analysis " Healthcare Analytics, vol. 5, pp.100303, 2024. doi: doi.org/10.1016/j.health.2024.100303 [Paper]

[9] A. Rajbongshi, A. Raian, Md. K. Islam, R. Shakil, Md. Ashrafuzzaman, F.  Akter, “A Comprehensive Review of Federated Learning Integration in the Blended Learning Landscape: Safeguarding Privacy in a Centralized Learning Management System” in IEEE Transactions on Learning Technologies. (Submitted)

[10] B. Akter, R. Shakil, F. M. Javed Mehedi Shamrat, “EARNet-14: A Deep Learning Efficient Approach to classify Ear Diseases” (work in progress).

[1] A. Rajbongshi, A. A. Biswas, J. Biswas, R. Shakil, B. Akter, and M. R. Barman, “Sunflower Diseases Recognition Using Computer Vision-Based Approach,” in 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Sep. 2021, pp. 1–5. doi: 10.1109/R10-HTC53172.2021.9641588. [Paper]

[2] R. Shakil, B. Akter, F. M. Javed Mehedi Shamrat, N. Jahan, S. Hasan, and A. Khater, “Systematic Analysis of Several Deep Learning Approaches for COVID-19 Detection Using X-ray Images,” in 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Oct. 2022, pp. 1301–1307. doi: 10.1109/ICOSEC54921.2022.9952147. [Paper]

[3] R. Shakil, B. Akter, F. Faisal, T. R. Chowdhury, T. Roy, and A. Khater, “A Promising Prediction of Diabetes Using a Deep Learning Approach,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Mar. 2022, pp. 923–927. doi: 10.1109/ICCMC53470.2022.9753763. [Paper]

[4] B. Akter, R. Shakil, A. Rajbongshi, U. Sara, and M. R. Barman, “Utilization of Five-Distinct Dataset to Diagnose and Predict Heart Disease: A Machine Learning Approach,” in 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Oct. 2022, pp. 1–6. doi: 10.1109/ICCCNT54827.2022.9984443. [Paper]

[5] B. Akter, A. Rajbongshi, S. Sazzad, R. Shakil, J. Biswas, and U. Sara, “A Machine Learning Approach to Detect the Brain Stroke Disease,” in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Jan. 2022, pp. 897–901. doi: 10.1109/ICSSIT53264.2022.9716345. [Paper]

[6] R. Shakil, B. Akter, A. Rajbongshi, U. Sara, M. R. Barman, and A. Dhali, “A Transfer Learning Approach to the Development of an Automation System for Recognizing Guava Disease Using CNN Models for Feasible Fruit Production,” in Hybrid Intelligent Systems, Springer, Cham, 2023, pp. 127–141. doi: 10.1007/978-3-031-27409-1_12.  [Paper]

[7] R. Shakil, F. M. Javed Mehedi Shamrat, S. Sharmin, B. Akter, M. A. Rubi, A. Khater, “Toward Precision Diagonsis of Otitis Media: Introducing A Novel EARnet-AR Model” in International Conference on Ambient Intelligence in Health Care 2023. (Accepted).