Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data.
Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reli...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317914 |
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| _version_ | 1823856866197766144 |
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| author | Tian Xia Lanju Zhou Khalil Ahmad |
| author_facet | Tian Xia Lanju Zhou Khalil Ahmad |
| author_sort | Tian Xia |
| collection | DOAJ |
| description | Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. This research included the development of abnormal categorisation models, assessment of data recognition quality, and detection of anomalies. The research methodology consisted of the following steps: formulation of the problem, selection of data and labelling, construction of the model for prediction, installation, and testing. The data tagging technique was based on the requirements set by the Global Aviation Organisation for business jet-engine aircraft, which expert business pilots then validated. The 93% precision demonstrated an excellent match for the most effective prediction model, linear dipole testing. Furthermore, the "good fit" of the model was verified by its achieved area-under-the-curve ratios of 0.97 for abnormal identification and 0.96 for daily detection. |
| format | Article |
| id | doaj-art-e5df4cf43b3b4b808ee098188c4c53ce |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e5df4cf43b3b4b808ee098188c4c53ce2025-02-12T05:30:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031791410.1371/journal.pone.0317914Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data.Tian XiaLanju ZhouKhalil AhmadRegarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. This research included the development of abnormal categorisation models, assessment of data recognition quality, and detection of anomalies. The research methodology consisted of the following steps: formulation of the problem, selection of data and labelling, construction of the model for prediction, installation, and testing. The data tagging technique was based on the requirements set by the Global Aviation Organisation for business jet-engine aircraft, which expert business pilots then validated. The 93% precision demonstrated an excellent match for the most effective prediction model, linear dipole testing. Furthermore, the "good fit" of the model was verified by its achieved area-under-the-curve ratios of 0.97 for abnormal identification and 0.96 for daily detection.https://doi.org/10.1371/journal.pone.0317914 |
| spellingShingle | Tian Xia Lanju Zhou Khalil Ahmad Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. PLoS ONE |
| title | Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. |
| title_full | Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. |
| title_fullStr | Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. |
| title_full_unstemmed | Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. |
| title_short | Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. |
| title_sort | machine learning based anomaly detection and prediction in commercial aircraft using autonomous surveillance data |
| url | https://doi.org/10.1371/journal.pone.0317914 |
| work_keys_str_mv | AT tianxia machinelearningbasedanomalydetectionandpredictionincommercialaircraftusingautonomoussurveillancedata AT lanjuzhou machinelearningbasedanomalydetectionandpredictionincommercialaircraftusingautonomoussurveillancedata AT khalilahmad machinelearningbasedanomalydetectionandpredictionincommercialaircraftusingautonomoussurveillancedata |