Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier
Emerging nations grapple with a pressing issue the surge in road accidents. To curb this trend, there is a need for a cost-effective, labor-efficient smart accidental management system. The proposed model, named Smart Accidental Management Using a Multi-Criterion Asynchronous Classifier, employs the...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Transportation Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X2500003X |
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| author | Rajesh Yadav Pankaj Agarwal |
| author_facet | Rajesh Yadav Pankaj Agarwal |
| author_sort | Rajesh Yadav |
| collection | DOAJ |
| description | Emerging nations grapple with a pressing issue the surge in road accidents. To curb this trend, there is a need for a cost-effective, labor-efficient smart accidental management system. The proposed model, named Smart Accidental Management Using a Multi-Criterion Asynchronous Classifier, employs the Least Squares prediction technique to foresee accident points based on road characteristics and geographical locations. Analysis features include latitude, longitude, and object hits, amplifying classifier accuracy. Signal collision during emergency transmissions is tackled by integrating Multilayered Capsnet, utilizing robust acoustic signals with precise frequency administration to prevent overfitting. Unlike existing models, statistical relevance is boosted with the Multi-Criterion Asynchronous Neural Network, which identifies accident hotspots like steep roads and T-regions without clustering. It also gauges right turning areas and surface plane identification for improved stability. Implemented in Python with the Cupcarbon simulator, this model offers practical convenience. It significantly outperforms existing systems, providing heightened accuracy in accident detection. This innovation marks a substantial stride in advancing road safety technology, addressing the critical challenges faced by emerging nations in managing and mitigating the impact of road accidents. |
| format | Article |
| id | doaj-art-61605724563a49f88dcd17a9ba0d2eb2 |
| institution | DOAJ |
| issn | 2666-691X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj-art-61605724563a49f88dcd17a9ba0d2eb22025-08-20T03:16:35ZengElsevierTransportation Engineering2666-691X2025-03-011910030310.1016/j.treng.2025.100303Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifierRajesh Yadav0Pankaj Agarwal1Corresponding author: Rajesh Yadav; K.R. Mangalam University Sohna Road, Gurugram, Haryana 122103, IndiaK.R. Mangalam University Sohna Road, Gurugram, Haryana 122103, IndiaEmerging nations grapple with a pressing issue the surge in road accidents. To curb this trend, there is a need for a cost-effective, labor-efficient smart accidental management system. The proposed model, named Smart Accidental Management Using a Multi-Criterion Asynchronous Classifier, employs the Least Squares prediction technique to foresee accident points based on road characteristics and geographical locations. Analysis features include latitude, longitude, and object hits, amplifying classifier accuracy. Signal collision during emergency transmissions is tackled by integrating Multilayered Capsnet, utilizing robust acoustic signals with precise frequency administration to prevent overfitting. Unlike existing models, statistical relevance is boosted with the Multi-Criterion Asynchronous Neural Network, which identifies accident hotspots like steep roads and T-regions without clustering. It also gauges right turning areas and surface plane identification for improved stability. Implemented in Python with the Cupcarbon simulator, this model offers practical convenience. It significantly outperforms existing systems, providing heightened accuracy in accident detection. This innovation marks a substantial stride in advancing road safety technology, addressing the critical challenges faced by emerging nations in managing and mitigating the impact of road accidents.http://www.sciencedirect.com/science/article/pii/S2666691X2500003XLeast square predictionCapsule neural networkAsynchronous neural networkAccidental managementGeographical mapping systemAccidental alert |
| spellingShingle | Rajesh Yadav Pankaj Agarwal Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier Transportation Engineering Least square prediction Capsule neural network Asynchronous neural network Accidental management Geographical mapping system Accidental alert |
| title | Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier |
| title_full | Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier |
| title_fullStr | Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier |
| title_full_unstemmed | Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier |
| title_short | Least square predicted acoustic signal based smart accidental management using multi-criterion asynchronous classifier |
| title_sort | least square predicted acoustic signal based smart accidental management using multi criterion asynchronous classifier |
| topic | Least square prediction Capsule neural network Asynchronous neural network Accidental management Geographical mapping system Accidental alert |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X2500003X |
| work_keys_str_mv | AT rajeshyadav leastsquarepredictedacousticsignalbasedsmartaccidentalmanagementusingmulticriterionasynchronousclassifier AT pankajagarwal leastsquarepredictedacousticsignalbasedsmartaccidentalmanagementusingmulticriterionasynchronousclassifier |