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Huong, Truong Thu,Bac, Ta Phuong,Thang, Bui Doan,Long, Dao Minh,Quang, Le Anh,Dan, Nguyen Minh,Hoang, Nguyen Viet International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.6
Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.
AN EFFECTIVE SEGMENT PRE-FETCHING FOR SHORT-FORM VIDEO STREAMING
Nguyen Viet Hung,Truong Thu Huong International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.3
The popularity of short-form video platforms like TikTok has increased recently. Short-form videos are significantly shorter than traditional videos, and viewers regularly switch between different types of content to watch. Therefore, a successful prefetching strategy is essential for this novel type of video. This study provides a resource-effective prefetching technique for streaming short-form videos. The suggested solution dynamically adjusts the quantity of prefetched video data based on user viewing habits and network traffic conditions. The results of the experiments demonstrate that, in comparison to baseline approaches, our method may reduce data waste by 21% to 83%, start-up latency by 50% to 99%, and the total time of Re-buffering by 90% to 99%.