Pal Varga

AI and ML techniques in various fields of Infocommunications – in the autumn issue of ICJ  

WELCOME to the September 2024 issue of the Infocom munications Journal. Let’s have an brief overview of the papers. The paper by A. S. Jagmagji and his co-authors presents the application of machine learning models to predict network per formance metrics for the Self-Clocked Rate Adaptation for Mul timedia (SCReAM) congestion control algorithm. By employing a systematic approach, including regression models, hyperpa rameter tuning, and ensemble learning, the study achieved high accuracy in predicting key metrics such as network throughput, queue delay, and smoothed Round Trip Time (sRTT). The Light GBM and CatBoost models outperformed others in predicting these metrics, demonstrating the effectiveness of the applied techniques. The study also highlights areas for improvement, including more advanced hyperparameter tuning and ensemble methods, and calls for rigorous statistical testing to validate minor performance differences.

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Please cite this paper the following way:

Pal Varga, "AI and ML techniques in various fields of Infocommunications – in the autumn issue of ICJ  ", Infocommunications Journal, Vol. XVI, No 3, September 2024, pp. 1.