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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Predicting the availability of power line communication nodes using semi-supervised learning algorithms.

Kareem Moussa^1,2, Khaled Mostafa Elsayed^3,4, M Saeed Darweesh^5,6

¹University of Science and Technology, Zewail City, Giza, 12578, Egypt. p-kareem.moussa@zewailcity.edu.eg.

Scientific Reports

|May 21, 2025

Summary

This summary is machine-generated.

This study enhances Power Line Communication (PLC) node availability prediction using self-training machine learning. Label Spreading achieved 94.67% accuracy, optimizing data transmission efficiency.

Keywords:

Label Propagation Label Spreading Light Gradient Boosting Machine (LGBM)Machine Learning Power Line Communication (PLC)Self Training Classifier Semi Supervised Learning Support Vector Machine (SVM)

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Area of Science:

Electrical Engineering
Computer Science
Data Science

Background:

Power Line Communication (PLC) networks face challenges with data transmission to unavailable nodes, causing delays.
Machine learning offers a solution by predicting node availability, improving network efficiency.

Purpose of the Study:

To investigate the effectiveness of self-training machine learning algorithms for predicting node availability in PLC networks.
To compare the performance of various self-training and supervised learning models.

Main Methods:

A dataset of 2000 instances from a 500-node PLC network was collected, featuring CINR, SNR, and RSSI.
Self-training was applied to LGBM, SVM, Label Propagation, and Label Spreading algorithms.
Supervised learning models (Random Forest, logistic regression) were used for comparison.

Main Results:

Label Spreading demonstrated superior performance with 94.67% accuracy, 0.947 f1-score, 0.946 precision, and 0.947 recall.
The best model achieved this with minimal training time (0.018 sec) and memory consumption (0.99 MB).

Conclusions:

Self-training algorithms, particularly Label Spreading, are highly effective for predicting node availability in PLC networks.
Optimized node availability prediction significantly reduces data transmission delays and improves overall network performance.