Research papers

Peer-reviewed research from our team

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  arXiv · 2024

  LITE: The Lightweight Integrated Tracking-Feature Extraction paradigm

  Jumabek Alikhanov, Dilshod Obidov, Hakil Kim

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  IEEE · 2024

  PRIME: Practical Evaluation Framework for Real-Time Multi-Object Tracking

  Jumabek Alikhanov, Dilshod Obidov, Mirsaid Abdurasulov, Hakil Kim

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  IEEE · 2023

  Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction

  Jumabek Alikhanov, Panyu Zhang, Youngtae Noh; Hakil Kim

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  MDPI · 2022

  Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets

  Meliboev Azizjon, Alikhanov Jumabek, Wooseong Kim

• 
  IEEE · 2021

  Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches

  Jumabek Alikhanov, Rhongho Jang, Mohammed Abuhamad, David Mohaisen, Daehun Nyang, Youngtae Noh

• 
  IEEE · 2020

  1D CNN based network intrusion detection with normalization on imbalanced data

  Meliboev Azizjon, Alikhanov Jumabek, Wooseong Kim

• 
  arXiv · 2019

  W-Net: A CNN-based Architecture for White Blood Cells Image Classification

  Changhun Jung, Mohammed Abuhamad, Jumabek Alikhanov, Aziz Mohaisen, Kyungja Han, DaeHun Nyang