Md Majedul Islam

Graduate Research Assistant, Kennesaw State University

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Md Majedul Islam | Graduate Research Assistant, Kennesaw State University

Conference Papers

2024

Rabby, A. K. M., Ali, H., Islam, M. M., Abujar, S., & Rahman, F. (2024). Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 1102-1109). (https://openaccess.thecvf.com/content/WACV2024W/WVLL/html/Rabby_Enhancement_of_Bengali_OCR_by_Specialized_Models_and_Advanced_Techniques_WACVW_2024_paper.html)

2023

Rabby, A. S. A., Ali, H., Islam, M. M., & Rahman, F. (2023, December). Versatile Bengali OCR: Document Analysis Technique for Varied Document Styles and Content. In 2023 IEEE International Conference on Big Data (BigData) (pp. 1965-1969). IEEE. (https://ieeexplore.ieee.org/abstract/document/10386582)

Ali, H., Rabby, A. S. A., Islam, M. M., Mahamud, A., Hasan, N., & Rahman, F. (2023, December). Gold Standard Bangla OCR Dataset: An In-Depth Look at Data Preprocessing and Annotation Processes. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track (pp. 460-470).(https://aclanthology.org/2023.emnlp-industry.44/)

2021

Islam, M. M., Das, A., Kowsar, I., Rabby, A. S. A., Hasan, N., & Rahman, F. (2021, December). Towards building a bangla text recognition solution with a multi-headed cnn architecture. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 1061-1067). IEEE.(https://ieeexplore.ieee.org/abstract/document/9671653)

Rahman, F., Rahman, A., Rabby, A. S. A., Rifat, M. J. R., Banik, M., Islam, M. M., … & Goldblatt, S. (2021, December). Modeling Influenza with a Forest Deep Neural Network Utilizing a Virtualized Clinical Semantic Network. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4753-4760). IEEE. (https://ieeexplore.ieee.org/abstract/document/9671507)

Rabby, A. S. A., Islam, M. M., Islam, Z., Hasan, N., & Rahman, F. (2021, December). Towards Building A Robust Large-Scale Bangla Text Recognition Solution Using A Unique Multiple-Domain Character-Based Document Recognition Approach. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1393-1399). IEEE.(https://ieeexplore.ieee.org/abstract/document/9680139)

Rahman, F., Rahman, A., Rabby, A. S. A., Rifat, M. J. R., Banik, M., Islam, M. M., … & Goldblatt, S. (2021, November). Disease modeling with a forest deep neural network utilizing nlp and a virtualized clinical semantic network. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 935-942). IEEE.(https://ieeexplore.ieee.org/abstract/document/9643218)

Rabby, A. S. A., Islam, M. M., Hasan, N., Nahar, J., & Rahman, F. (2021, May). A Deep Learning Solution to Detect Text-Types Using a Convolutional Neural Network. In Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020 (pp. 727-736). Singapore: Springer Singapore.(https://link.springer.com/chapter/10.1007/978-981-33-4087-9_58)

Foysal, M. F. A., Sultana, N., Rimi, T. A., & Rifat, M. H. (2021). Convolutional neural network hyper-parameter optimization using particle swarm optimization. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 2 (pp. 363-373). Springer Singapore. (https://link.springer.com/chapter/10.1007/978-981-33-4367-2_35)

Islam, M. M., Rabby, A. S. A., Hasan, N., Nahar, J., & Rahman, F. (2021). Text-type extraction using a deep learning solution at the character level. In Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, Volume 1 (pp. 253-262). Springer Singapore.(https://link.springer.com/chapter/10.1007/978-981-33-6912-2_23)

Masum, A. K. M., Majedul Islam, M., Abujar, S., Sorker, A. K., & Hossain, S. A. (2021). Bengali news headline generation on the basis of sequence to sequence learning using bi-directional RNN. In Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 491-501). Springer Singapore.(https://link.springer.com/chapter/10.1007/978-981-15-7394-1_45)

Majedul Islam, M., Khushbu, S. A., & Islam, M. S. (2021). Predicting the Appropriate Category of Bangla and English Books for Online Book Store Using Deep Learning. In Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 409-421). Springer Singapore.( https://link.springer.com/chapter/10.1007/978-981-15-7394-1_39 )

2020

Rabby, A. S. A., Islam, M. M., Hasan, N., Nahar, J., & Rahman, F. (2020, December). A novel deep learning character-level solution to detect language and printing style from a bilingual scanned document. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5218-5226). IEEE.(https://ieeexplore.ieee.org/abstract/document/9117477)

Rabby, A. S. A., Islam, M. M., Hasan, N., Nahar, J., & Rahman, F. (2020, October). Borno: Bangla handwritten character recognition using a multiclass convolutional neural network. In Proceedings of the Future Technologies Conference (pp. 457-472). Cham: Springer International Publishing. (https://link.springer.com/chapter/10.1007/978-3-030-63128-4_35)

Rabby, A. S. A., Islam, M. M., Hasan, N., Nahar, J., & Rahman, F. (2020, July). Language detection using convolutional neural network. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.(https://ieeexplore.ieee.org/abstract/document/9225610)

2019

Islam, M. M., Masum, A. K. M., Rabbani, M. G., Zannat, R., & Rahman, M. (2019, November). Performance measurement of multiple supervised learning algorithms for Bengali news headline sentiment classification. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 235-239). IEEE. (https://ieeexplore.ieee.org/abstract/document/9117477)

Islam, M. M., Rabby, A. S. A., Arfin, M. H. R., & Hossain, S. A. (2019, July). PataNET: A convolutional neural networks to identify plant from leaf images. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE. (https://ieeexplore.ieee.org/abstract/document/8944667)