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                <text>Conference Papers</text>
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              <text>Vehicular Propagation Velocity Forecasting Using Open CV</text>
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              <text>Computer Vision; Convolutional Neural Network; Machine Learning; Vehicle Speed Detection</text>
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              <text>This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety.   2023 IEEE.</text>
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          <name>Creator</name>
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              <text>Das U.; Sharma V.; Das M.; Mishra S.; Iwendi C.; Osamor J.</text>
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              <text>2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023</text>
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          <name>Publisher</name>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICCAKM58659.2023.10449587" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICCAKM58659.2023.10449587&lt;/a&gt;
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          <name>Rights</name>
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              <text>All Open Access; Green Open Access</text>
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              <text>ISBN: 979-835039324-8</text>
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              <text>Online</text>
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              <text>Das U., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Das M., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 0BA, United Kingdom</text>
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