This paper proposes an adaptive hybrid machine learning model for remote fault detection in wireless data transmission network elements, fault-severity and root-cause assessment, and automated response generation. The model dynamically combines probabilistic outputs of SVM, Random Forest, HistGradientBoosting and a multilayer perceptron according to validation quality, sample-wise confidence and inference latency. Temperature scaling is used for probability calibration, Isolation Forest identifies previously unseen faults, and the response action is selected by minimizing the expected loss defined by a cost matrix of erroneous interventions.
| Mualliflar | Omonov, Ibratbek, Matyokubov, O’tkir Karimovich |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-08-23 |
| Son | 3 |
| Betlar | 92-97 |
| Til | Rus |
wireless network, remote diagnosis, machine learning, adaptive ensemble, беспроводная сеть, дистанционная диагностика, машинное обучение, адаптивный ансамбль, выявление неисправностей, Simsiz tarmoq, masofaviy diagnostika, mashinali o‘qitish, adaptiv model
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