[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119663-en":3,"doc-seo-119663-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119663,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",6,"Technology","Fiber Break Prevention Using Machine Learning Approaches","Modern fiber-optic communication relies on optical fibers to transmit information through infrared light pulses, yet their intrinsic fragility creates operational risk during construction. Construction impacts and crushing pressures can physically damage fibers and threaten signal integrity, leading to outages and costly, time-consuming repairs. This study proposes preventative and mitigating actions using machine learning to classify abnormal fiber signals. Data are collected using an optical fiber sensor and distributed acoustic sensing, filtered with a bandpass Butterworth filter, converted to spectrograms, and classified. Experimental results reach 99.78% accuracy.","Journal of Informatics and Web Engineering  \nVol. 4 No. 1 (February 2025) eISSN: 2821-370X  \nFiber Break Prevention Using Machine Learning Approaches  \nZhan Heng Ng1, Tee Connie2*, Kan Yeep Choo3, Michael Kah Ong Goh4, Nurul Ain binti Abdul  \nAziz5, Hong Yeap Ngo6  \n1,2,4Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia.  \n3,5,6Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Malaysia.  \n*corresponding author: ([tee.connie@mmu.edu.my](tee.connie@mmu.edu.my); ORCiD: 0000-0002-0901-3831)  \nAbstract-Modern fiber-optic communication systems are built around optical fiber, which allows data to be sent by emitting infrared light pulses. It is widely used by telecommunications firms and is essential to the smooth transmission of information in internet communication as well as the transmission of telephone signals. Nonetheless, optical fibers intrinsic fragility raises a problem, especially in areas where building projects are taking place. Especially nowadays construction-related impact and crushing pressures can cause physical damage that jeopardizes the fiber optic's integrity. Therefore, this research emphasizes the necessity of taking preventative and mitigating actions to reduce the possibilities of fiber optic breakages in response to these difficulties by using machine learning approaches. The data collected by an optical fiber sensor and a distributed acoustic sensing interrogator unit (DAS) . Five tools are used to simulate fiber break threats on the road surface and the fiber optic signal is denoised by using the bandpass Butterworth filter. The filtered data is then transformed into spectrogram representation and trained by using the machine learning approaches. The results of the experiments in the research achieves the accuracy 99.78% which is a high accuracy which can be potentially applied in classifying the signals of the tools and preventing the breakage of the fiber optic cables.  \nKeywords—Fiber Optic Communications, Signal Processing, Machine Learning, Spectrogram, Road Surface Impact Classification  \nReceived: 30 August 2024; Accepted: 24 December 2024; Published: 16 February 2025 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nOptical fiber is a main component in fiber-optic communication which can transmit data between locations with the aid of optical fiber to emit infrared light pulses [1], [2], [3] . It is also widely used by the telecommunication companies to transmit the telephone signals and contribute to the internet communication. Telemetries through the local area or long-distance networks, voice and video can be transmitted well due to fiber has a high bandwidth and immunity to the electromagnetic inference. However, as optical fiber is fragile, it can break easily especially when there is construction project nearby. The impact and crushing which are from the construction area will cause the physical damages of the fiber optic. Besides this, the vibrations which are continuously from the machinery at the  \nconstructions will include a huge pressure on the fiber optic and hence the breakage of the fiber optic will occur. The consequences of fiber optic damages lead to the outage of the information when the transmission of the data is carried on. Furthermore, the repairs of the fiber optic are costly and time consuming.  \nHence, precaution approaches are required to prevent and reduce the risk or possibilities of the breakage of the fiber optic. This study presents proactive measures to prevent fiber break occurrences from happening in the first place as proactive measures in detecting and responding to them promptly. A robust method to detect the signals of fiber break is developed using machine learning methods. In this research, the data is collected in a chosen open area of Multimedia University, Cyberjaya which is quiet and with less population. Five tools s","cbCaitcj1LICAkHA","https://ap.wps.com/l/cbCaitcj1LICAkHA","pdf",1824730,1,21,"English","en",105,"# INTRODUCTION\n## Research motivation and problem context\n## Proposed sensing and signal-processing pipeline\n# LITERATURE REVIEW\n## Conventional methods","[{\"question\":\"What problem does the study address in fiber-optic communication?\",\"answer\":\"It addresses the high likelihood of fiber breakage caused by construction-related impacts, crushing pressures, and continuous vibrations, which can damage fiber integrity and disrupt data transmission.\"},{\"question\":\"How is the collected data processed before machine learning classification?\",\"answer\":\"Raw signals are denoised using a bandpass Butterworth filter, then transformed into spectrogram representations for model training and classification.\"},{\"question\":\"Which machine learning models are used to classify the spectrograms?\",\"answer\":\"The study implements Support Vector Machine, Random Forest Classifier, KNN Classifier, Convolutional Neural Network, and Artificial Neural Network to classify the five categories of fiber-break-related signals.\"}]","Fiber Break Prevention Using Machine Learning Approaches | 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problem does the study address in fiber-optic communication?","Question",{"text":75,"@type":76},"It addresses the high likelihood of fiber breakage caused by construction-related impacts, crushing pressures, and continuous vibrations, which can damage fiber integrity and disrupt data transmission.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the collected data processed before machine learning classification?",{"text":80,"@type":76},"Raw signals are denoised using a bandpass Butterworth filter, then transformed into spectrogram representations for model training and classification.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are used to classify the spectrograms?",{"text":84,"@type":76},"The study implements Support Vector Machine, Random Forest Classifier, KNN Classifier, Convolutional Neural Network, and Artificial Neural Network to classify the five categories of fiber-break-related 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