Modeling the operation of climate control system in premises based on fuzzy controller

Sansyzbay, L. Zh; Orazbayev, Batyr

doi:10.1088/1742-6596/1399/4/044017

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…It is noteworthy that most of the listed systems employ the simple network management protocol (SNMP) to acquire real-time information on the status of L2 network equipment, including the temperature of switch processors, the level of processing of requests between devices, incoming and outgoing traffic, power supply status, and other network device components. Access to this information enables the optimization of L2 network device performance through the application of machine learning algorithms for predictive analytics regarding possible future malfunctions [9,10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Rzayeva,

Myrzatay,

Abitova

et al. 2023

Electronics

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Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

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Section: Introductionmentioning

confidence: 99%