| 1D CNN based network intrusion detection with normalization on imbalanced data M Azizjon, A Jumabek, W Kim 2020 international conference on artificial intelligence in information and …, 2020 | 237 | 2020 |
| Performance evaluation of deep learning based network intrusion detection system across multiple balanced and imbalanced datasets A Meliboev, J Alikhanov, W Kim Electronics 11 (4), 515, 2022 | 81 | 2022 |
| W-net: a CNN-based architecture for white blood cells image classification C Jung, M Abuhamad, J Alikhanov, A Mohaisen, K Han, DH Nyang arXiv preprint arXiv:1910.01091, 2019 | 35 | 2019 |
| Rule based metadata extraction framework from academic articles J Azimjonov, J Alikhanov arXiv preprint arXiv:1807.09009, 2018 | 18 | 2018 |
| Exploring human pose estimation and the usage of synthetic data for elderly fall detection in real-world surveillance S Juraev, A Ghimire, J Alikhanov, V Kakani, H Kim IEEE Access 10, 94249-94261, 2022 | 17 | 2022 |
| Toward data-driven digital therapeutics analytics: Literature review and research directions U Lee, G Jung, EY Ma, J San Kim, H Kim, J Alikhanov, Y Noh, H Kim IEEE/CAA Journal of Automatica Sinica 10 (1), 42-66, 2023 | 16 | 2023 |
| Investigating the effect of traffic sampling on machine learning-based network intrusion detection approaches J Alikhanov, R Jang, M Abuhamad, D Mohaisen, D Nyang, Y Noh IEEE Access 10, 5801-5823, 2021 | 14 | 2021 |
| CatBoost-based network intrusion detection on imbalanced CIC-IDS-2018 dataset A Jumabek, SS Yang, YT Noh 한국통신학회논문지 46 (12), 2191-2197, 2021 | 8 | 2021 |
| Transfer learning using multiple convnet layers activation features with principal component analysis for image classification B Byambajav, J Alikhanov, Y Fang, S Ko, GS Jo 지능정보연구 24 (1), 205-225, 2018 | 8 | 2018 |
| Performance comparison of backbone networks for multi-tasking in self-driving operations S Abdigapporov, S Miraliev, J Alikhanov, V Kakani, H Kim 2022 22nd International Conference on Control, Automation and Systems (ICCAS …, 2022 | 5 | 2022 |
| W-Net: A CNN-based architecture for white blood cells image classification. arXiv 2019 C Jung, M Abuhamad, J Alikhanov, A Mohaisen, K Han, D Nyang arXiv preprint arXiv:1910.01091, 0 | 5 | |
| Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods J Alikhanov, H Kim IEEE Access, 2023 | 4 | 2023 |
| Edge device deployment of multi-tasking network for self-driving operations S Miraliev, S Abdigapporov, J Alikhanov, V Kakani, H Kim arXiv preprint arXiv:2210.04735, 2022 | 4 | 2022 |
| Improved CS-RANSAC Algorithm Using K-Means Clustering S Ko, UN Yoon, J Alikhanov, GS Jo KIPS Transactions on Software and Data Engineering 6 (6), 315-320, 2017 | 4 | 2017 |
| Transfer Learning Based on AdaBoost for Feature Selection from Multiple ConvNet Layer Features J Alikhanov, MH Ga, S Ko, GS Jo arXiv preprint arXiv:1602.00417, 2016 | 1 | 2016 |
| Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction J Alikhanov, P Zhang, Y Noh, H Kim IEEE Internet of Things Journal, 2023 | | 2023 |
| 전이학습 기반 다중 컨볼류션 신경망 레이어의 활성화 특징과 주성분 분석을 이용한 이미지 분류 방법 B Byambajav, J Alikhanov, Y Fang, S Ko, GS Jo 지능정보연구 24 (1), 205-225, 2018 | | 2018 |
| Unsupervised feature learning for classification M Abdullaev, J Alikhanov, S Ko, GS Jo 한국컴퓨터정보학회 학술발표논문집 24 (2), 51-54, 2016 | | 2016 |
| Obtaining Better Image Representations by Combining Complementary Activation Features of Multiple ConvNet Layers for Transfer Learning. J Alikhanov, S Ko, JG Sik arXiv preprint arXiv:1602.00417, 2016 | | 2016 |
| 다중 신경망 레이어에서 특징점을 선택하기 위한 전이 학습 기반의 AdaBoost 기법 주마벡， 가명현， 고승현， 조근식 한국정보처리학회 학술대회논문집 23 (1), 633-635, 2016 | | 2016 |
