Identification and Recognition of Rice Diseases and Pests Using Deep Convolutional Neural Networks R Rahman, P Arko, E Ali, M Khan, A Wasif, MR Jani, MS Kabir https://arxiv.org/pdf/1812.01043v1.pdf, 27, 2019 | 411* | 2019 |
Cusboost: Cluster-based under-sampling with boosting for imbalanced classification F Rayhan, S Ahmed, A Mahbub, R Jani, S Shatabda, DM Farid 2017 2nd International Conference on Computational Systems and Information …, 2017 | 110 | 2017 |
PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences R Muhammod, S Ahmed, D Md Farid, S Shatabda, A Sharma, A Dehzangi Bioinformatics 35 (19), 3831-3833, 2019 | 104 | 2019 |
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides S Ahmed, R Muhammod, ZH Khan, S Adilina, A Sharma, S Shatabda, ... Scientific Reports 11 (1), 1-15, 2021 | 49 | 2021 |
Hybrid methods for class imbalance learning employing bagging with sampling techniques S Ahmed, A Mahbub, F Rayhan, R Jani, S Shatabda, DM Farid 2017 2nd International Conference on Computational Systems and Information …, 2017 | 49 | 2017 |
iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features MS Rahman, U Aktar, MR Jani, S Shatabda Molecular Genetics and Genomics 294 (1), 69-84, 2019 | 41 | 2019 |
iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier MS Rahman, U Aktar, MR Jani, S Shatabda Genomics 111 (5), 1160-1166, 2019 | 40 | 2019 |
Meboost: Mixing estimators with boosting for imbalanced data classification F Rayhan, S Ahmed, A Mahbub, MR Jani, S Shatabda, DM Farid, ... 2017 11th international conference on software, knowledge, information …, 2017 | 30 | 2017 |
LIUBoost: locality informed under-boosting for imbalanced data classification S Ahmed, F Rayhan, A Mahbub, MR Jani, S Shatabda, DM Farid Emerging Technologies in Data Mining and Information Security, 133-144, 2019 | 27 | 2019 |
iRecSpot-EF: effective sequence based features for recombination hotspot prediction MR Jani, MTK Mozlish, S Ahmed, NS Tahniat, DM Farid, S Shatabda Computers in biology and medicine 103, 17-23, 2018 | 23 | 2018 |
CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data MO Miah, R Muhammod, KA Al Mamun, DM Farid, S Kumar, A Sharma, ... Journal of Neuroscience Methods 364, 109373, 2021 | 12 | 2021 |
SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences HMF Haque, M Rafsanjani, F Arifin, S Adilina, S Shatabda Computational Biology and Chemistry, 107489, 2021 | 7 | 2021 |
Prediction of motor imagery tasks from multi-channel eeg data for brain-computer interface applications MO Miah, MM Rahman, R Muhammod, DM Farid BioRxiv, 2020.04. 08.032201, 2020 | 5 | 2020 |
Revisiting CNN for Highly Inflected Bengali and Hindi Language Modeling CR Rahman, MD Rahman, M Rafsan, S Zakir, ME Ali, R Muhammod arXiv preprint arXiv:2110.13032, 2021 | 1 | 2021 |
CNN for Modeling Sanskrit Originated Bengali and Hindi Language C Rahman, MDH Rahman, M Rafsan, ME Ali, S Zakir, R Muhammod Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the …, 2022 | | 2022 |
Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language CR Rahman, MD Rahman, M Rafsan, S Zakir, ME Ali, R Muhammod arXiv preprint arXiv:2110.13032, 2021 | | 2021 |
Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language C Rafeed Rahman, H Rahman, M Rafsan, S Zakir, M Eunus Ali, ... arXiv e-prints, arXiv: 2110.13032, 2021 | | 2021 |