| An integrated mediapipe-optimized GRU model for Indian sign language recognition B Subramanian, B Olimov, S Naik. M, S Kim, KH Park, J Kim Scientific Reports 12 (1), 11964, 2022 | 72 | 2022 |
| Missing data imputation for geolocation-based price prediction using KNN–MCF method K Sanjar, O Bekhzod, J Kim, A Paul, J Kim ISPRS International Journal of Geo-Information 9 (4), 227, 2020 | 52 | 2020 |
| FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers B Olimov, K Sanjar, S Din, A Ahmad, A Paul, J Kim Multimedia Systems, 1-14, 2021 | 36 | 2021 |
| Weight initialization based‐rectified linear unit activation function to improve the performance of a convolutional neural network model B Olimov, S Karshiev, E Jang, S Din, A Paul, J Kim Concurrency and Computation: Practice and Experience 33 (22), e6143, 2021 | 33 | 2021 |
| Improved U-Net: Fully convolutional network model for skin-lesion segmentation K Sanjar, O Bekhzod, J Kim, J Kim, A Paul, J Kim Applied Sciences 10 (10), 3658, 2020 | 26 | 2020 |
| UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method BA Ugli Olimov, KC Veluvolu, A Paul, J Kim Computers & Industrial Engineering 171, 108313, 2022 | 23 | 2022 |
| REF-Net: Robust, efficient, and fast network for semantic segmentation applications using devices with limited computational resources B Olimov, J Kim, A Paul IEEE Access 9, 15084-15098, 2021 | 17 | 2021 |
| Consecutive multiscale feature learning-based image classification model B Olimov, B Subramanian, RAA Ugli, JS Kim, J Kim Scientific Reports 13 (1), 3595, 2023 | 13 | 2023 |
| AEDCN-Net: Accurate and efficient deep convolutional neural network model for medical image segmentation B Olimov, SJ Koh, J Kim IEEE Access 9, 154194-154203, 2021 | 13 | 2021 |
| Dcbt-net: Training deep convolutional neural networks with extremely noisy labels B Olimov, J Kim, A Paul IEEE Access 8, 220482-220495, 2020 | 11 | 2020 |
| Extensive knowledge distillation model: An end-to-end effective anomaly detection model for real-time industrial applications AAU Rakhmonov, B Subramanian, B Olimov, J Kim IEEE Access, 2023 | 7 | 2023 |
| An efficient deep convolutional neural network for semantic segmentation B Olimov, J Kim, A Paul, B Subramanian 2020 8th International Conference on Orange Technology (ICOT), 1-9, 2020 | 7 | 2020 |
| An integrated mediapipe-optimized GRU model for Indian sign language recognition. Sci Rep 12, 11964(2022) B Subramanian, B Olimov, SM Naik | 5 | |
| Deepcleannet: Training deep convolutional neural network with extremely noisy labels B Olimov, J Kim J. Korea Multimed. Soc 23, 1349-1360, 2020 | 3 | 2020 |
| Deepmednet: Deep learning based medical image segmentation model B Olimov, B Subramanian, J Kim 한국정보과학회 학술발표논문집, 576-578, 2021 | 2 | 2021 |
| Fast Convergence GRU Model for Sign Language Recognition B Subramanian, B Olimov, J Kim 멀티미디어학회논문지 25 (9), 1257-1265, 2022 | 1 | 2022 |
| Unsupervised deep learning-based end-to-end network for anomaly detection and localization B Olimov, B Subramanian, J Kim 2022 Thirteenth International Conference on Ubiquitous and Future Networks …, 2022 | 1 | 2022 |
| CMSFL-Net: Consecutive Multi-scale Feature Learning-based Image Classification Model B Olimov, B Subramanian, RAA Ugli, JS Kim, J Kim | | 2022 |
