International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fault Detection and Localization in Optical Transport Network Using Machine Learning Technique: A Review |
|---|---|
| ABSTRACT | Optical transport networks (OTNs) play an important role in achieving high-speed data transport. But rising complexity and traffic increase inefficiency in fault management using conventional rule-based approaches due to the inability of precise fault detection and identification. Machine learning (ML) can serve as a promising tool for the automation of fault analysis. This paper provides a comprehensive literature review of fault detection and localization methods in OTNs based on machine learning techniques, including supervised learning, unsupervised learning, deep learning, ensemble learning, and graph-based learning approaches. Several machine learning algorithms such as SVM, ANN, CNN, LSTM, random forest, and XGBoost are compared by network parameters, like BER, OSNR, QoT. Also, several open research questions are discussed, along with possible ways to solve them in future research. |
| AUTHOR | VINAY TATYARAO BIRADAR, PROF. V. A. DESHMUKH, PROF. VIDYA A. NEMADE Student, Department of Computer Engineering, P.E.S. Modern College of Engineering (Affiliated to Savitribai Phule Pune University), Pune, India Assistant Professor, Department of Computer Engineering, P.E.S. Modern College of Engineering (Affiliated to Savitribai Phule Pune University), Pune, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405056 |
| pdf/56_Fault Detection and Localization in Optical Transport Network Using Machine Learning Technique A Review.pdf | |
| KEYWORDS | |
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