DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels. - Bekhzod Olimov; Jeonghong Kim; Anand Paul 001 (IEEE Access, 2020)

Missing Data Imputation for Geolocation-based Price Prediction Using KNN-MCF Method. - Karshiev Sanjar; Bekhzod Olimov; Jaesoo Kim; Anand Paul 001; Jeonghong Kim (ISPRS Int. J. Geo Inf., 2020)

REF-Net: Robust, Efficient, and Fast Network for Semantic Segmentation Applications Using Devices With Limited Computational Resources. - Bekhzod Olimov; Jeonghong Kim; Anand Paul 001 (IEEE Access, 2021)

AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation. - Bekhzod Olimov; Seok-Joo Koh; Jeonghong Kim (IEEE Access, 2021)

Weight initialization based-rectified linear unit activation function to improve the performance of a convolutional neural network model. - Bekhzod Olimov; Karshiev Sanjar; Eungyeong Jang; Sadia Din; Anand Paul 001; Jeonghong Kim (Concurr. Comput. Pract. Exp., 2021)

FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers. - Bekhzod Olimov; Karshiev Sanjar; Sadia Din; Awais Ahmad 001; Anand Paul 001; Jeonghong Kim (Multim. Syst., 2021)

Unsupervised Deep Learning-based End-to-end Network for Anomaly Detection and Localization. - Bekhzod Olimov; Barathi Subramanian; Jeonghong Kim (ICUFN, 2022)

UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method. - Bekhzod Alisher ugli Olimov; Kalyana C. Veluvolu; Anand Paul 001; Jeonghong Kim (Comput. Ind. Eng., 2022)

Extensive Knowledge Distillation Model: An End-to-End Effective Anomaly Detection Model for Real-Time Industrial Applications. - Akhrorjon Akhmadjon Ugli Rakhmonov; Barathi Subramanian; Bekhzod Olimov; Jeonghong Kim (IEEE Access, 2023)