Predicting failures in fiber optic information transmission systems with support of machine learning
The use of machine learning methods in fiber-optic information transmission systems (FOITS) is considered. The article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes...
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| Format: | Article |
| Language: | Spanish |
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Universidad Nacional de San Martín
2025-07-01
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| Series: | Revista Científica de Sistemas e Informática |
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| Online Access: | https://revistas.unsm.edu.pe/index.php/rcsi/article/view/907 |
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| author | Nafisa Juraeva Dilmurod Davronbekov Ulugbek Turdiev |
| author_facet | Nafisa Juraeva Dilmurod Davronbekov Ulugbek Turdiev |
| author_sort | Nafisa Juraeva |
| collection | DOAJ |
| description |
The use of machine learning methods in fiber-optic information transmission systems (FOITS) is considered. The article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in FOITS to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of FOITS, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. This paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. The results obtained allow us to draw conclusions about the most effective methods for predicting failures, which is of great practical importance for ensuring the reliability of communication networks and minimizing downtime.
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| format | Article |
| id | doaj-art-ba5017f1b5864dffb416107d1bc49f1e |
| institution | Kabale University |
| issn | 2709-992X |
| language | Spanish |
| publishDate | 2025-07-01 |
| publisher | Universidad Nacional de San Martín |
| record_format | Article |
| series | Revista Científica de Sistemas e Informática |
| spelling | doaj-art-ba5017f1b5864dffb416107d1bc49f1e2025-08-20T03:38:12ZspaUniversidad Nacional de San MartínRevista Científica de Sistemas e Informática2709-992X2025-07-015210.51252/rcsi.v5i2.907Predicting failures in fiber optic information transmission systems with support of machine learningNafisa Juraeva0Dilmurod Davronbekov1Ulugbek Turdiev2Tashkent University of Information Technology Tashkent University of Information Technology University of Information Technologies and Management The use of machine learning methods in fiber-optic information transmission systems (FOITS) is considered. The article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in FOITS to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of FOITS, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. This paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. The results obtained allow us to draw conclusions about the most effective methods for predicting failures, which is of great practical importance for ensuring the reliability of communication networks and minimizing downtime. https://revistas.unsm.edu.pe/index.php/rcsi/article/view/907extra tree regressorfailure predictionmachine learningrandom forestregression algorithmssupport vector regression |
| spellingShingle | Nafisa Juraeva Dilmurod Davronbekov Ulugbek Turdiev Predicting failures in fiber optic information transmission systems with support of machine learning Revista Científica de Sistemas e Informática extra tree regressor failure prediction machine learning random forest regression algorithms support vector regression |
| title | Predicting failures in fiber optic information transmission systems with support of machine learning |
| title_full | Predicting failures in fiber optic information transmission systems with support of machine learning |
| title_fullStr | Predicting failures in fiber optic information transmission systems with support of machine learning |
| title_full_unstemmed | Predicting failures in fiber optic information transmission systems with support of machine learning |
| title_short | Predicting failures in fiber optic information transmission systems with support of machine learning |
| title_sort | predicting failures in fiber optic information transmission systems with support of machine learning |
| topic | extra tree regressor failure prediction machine learning random forest regression algorithms support vector regression |
| url | https://revistas.unsm.edu.pe/index.php/rcsi/article/view/907 |
| work_keys_str_mv | AT nafisajuraeva predictingfailuresinfiberopticinformationtransmissionsystemswithsupportofmachinelearning AT dilmuroddavronbekov predictingfailuresinfiberopticinformationtransmissionsystemswithsupportofmachinelearning AT ulugbekturdiev predictingfailuresinfiberopticinformationtransmissionsystemswithsupportofmachinelearning |