Publication

[1] F. M. Javed Mehedi Shamrat, T. Islam, X. zhou, M. Y. Idna, R. Shakil, A. Sutradhar, R. Gururajan, “MammoSegNet: a convolutional network analysis for segmenting tumor tissue masses in digital mammograms of breast cancer patients,” Neural Comput & Applic, Sept. 2025, doi: 10.1007/s00521-025-11631-6. [Paper]

[2] F. M. Mehedi Shamrat, R. Shakil, B. Akter, M. Z.  Ahmed, K. Ahmed, M. B. Francis, and M. A. Moni, “An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection,” Healthcare Analytics, vol. 5, p. 100303, Jun. 2024. Doi: 10.1016/j.health.2024.100303. [Paper]

[3] R. Shakil, S. Islam, and B. Akter, “A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI,” Journal of Pathology Informatics, vol. 15, p. 100398, Dec. 2024, doi: 10.1016/j.jpi.2024.100398. [Paper]

[4] R. Shakil, S. Islam, Y. A. Shohan, A. Mia, A. Rajbongshi, M. H. Rahmand, B. Akter, “Addressing agricultural challenges: An identification of best feature selection techniques for dragon fruit disease recognition,” Array, p. 100326, 2023, Doi: 10.1016/j.array.2023.100326. [Paper]

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

[6] F. M. J. M. Shamrat, R. Shakil, M. Y. I. Idris, B. Akter, and X. Zhou, “FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification,” Data in Brief, p. 110821, Aug. 2024, doi: 10.1016/j.dib.2024.110821.  [Paper]

[7] 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]

[8] 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]

[9] 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]

[10] 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]

[11] 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]

[12] 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. J. M. Shamrat, S. Sharmin, B. Akter, M. A. Rubi, and A. Dutta, “Toward Precision Diagnosis of Otitis Media: Introducing A Novel EARnet-AR Model,” in 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC), Nov. 2023, pp. 1–6. doi: 10.1109/ICAIHC59020.2023.10431468. [Paper]