Predicting LAN switch failures: an integrated approach with DES and machine learning techniques (RF/LR/DT/SVM)

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dc.authorscopusidİlhami Çolak / 6602990030
dc.authorwosidİlhami Çolak / KVO-7460-2024
dc.contributor.authorMyrzatay, Ali
dc.contributor.authorRzayevac, Leila
dc.contributor.authorBandini, Stefania
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorSaoud, Bilal
dc.contributor.authorÇolak, İlhami
dc.contributor.authorKayisli, Korhan
dc.date.accessioned2025-04-18T10:37:23Z
dc.date.available2025-04-18T10:37:23Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractThis research paper introduces an innovative approach to predicting failures in Local Area Network (LAN) switches, combining Double Exponential Smoothing (DES) with a suite of Machine Learning (ML) algorithms including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM). The primary objective of this study is to enhance the accuracy and timeliness of LAN switch failure predictions, thereby facilitating more proactive and effective network management. Our methodology involves the integration of DES for trend analysis and forecasting in time -series data, with the advanced predictive capabilities of the aforementioned ML algorithms. This hybrid approach not only leverages the strengths of DES in identifying underlying patterns in failure data but also capitalizes on the diverse predictive models to handle various aspects of failure prediction more robustly. The paper details the process of data collection, preprocessing, and the specific application of DES and each ML algorithm to the dataset. A notable contribution of this research is the development of a framework that effectively combines the output of DES with ML models, leading to a significant improvement in predictive accuracy as compared to traditional methods. Through rigorous testing and validation; the proposed approach demonstrated a marked increase in the precision and reliability of failure predictions. The results indicate that the integration of DES with ML algorithms can substantially aid in preemptive maintenance and decision -making processes in LAN management. The implications of these findings are profound, suggesting that such a combined approach can greatly enhance network stability and efficiency. While the focus of this study is on LAN switches, the methodology has the potential for broader applications in various fields of network management and predictive maintenance.
dc.identifier.citationMyrzatay, A., Rzayeva, L., Bandini, S., Shayea, I., Saoud, B., Çolak, I., & Kayisli, K. (2024). Predicting LAN Switch Failures: An Integrated Approach with DES and Machine Learning Techniques (RF/LR/DT/SVM). Results in Engineering, 102356.
dc.identifier.doi10.1016/j.rineng.2024.102356
dc.identifier.endpage14
dc.identifier.issn2590-1230
dc.identifier.scopus2-s2.0-85196117426
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.rineng.2024.102356
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7149
dc.identifier.volume23
dc.identifier.wosWOS:001258989200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇolak, İlhami
dc.institutionauthoridİlhami Çolak / 0000-0002-6405-5938
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofResults in engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBPMN
dc.subjectDouble Exponential Smoothing
dc.subjectDecision-Making Systems
dc.subjectLAN
dc.subjectMachine Learning
dc.subjectNetwork
dc.titlePredicting LAN switch failures: an integrated approach with DES and machine learning techniques (RF/LR/DT/SVM)
dc.typeArticle

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