Hassan, M, Kanwal, A, Jarrah, M, Pradhan, M R, Hussain, A and Mago, B (2022) Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 16-17 Feb. 2022, Dubai, United Arab Emirates.
Full text not available from this repository. (Request a copy)Abstract
Congestion-free traffic management has been a top priority for Machine Learning (ML) in the smart city sector for the past decade. Machine learning Algorithms are superfluous although working with the increased amount of data but these improve the capability and intelligence at a level cost. In this research, we propose a model based on a deep learning framework with a multi-layer Extreme Learning Machine (ELM) is proposed considering congestion information at all possible connection points to smooth a signal working over that recorded information. A more desirable outcome will be achieved by the proposed method, and traffic flow and congestion will improve.
Affiliation: | Skyline University College |
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SUC Author(s): | Jarrah, M, Pradhan, M R ORCID: https://orcid.org/0000-0002-0115-2722 and Mago, B ORCID: https://orcid.org/0000-0003-1537-1202 |
All Author(s): | Hassan, M, Kanwal, A, Jarrah, M, Pradhan, M R, Hussain, A and Mago, B |
Item Type: | Conference or Workshop Item (Paper) |
Uncontrolled Keywords: | smart city,extreme learning machine, machine learning algorithms |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 25 May 2022 08:16 |
Last Modified: | 25 May 2022 08:16 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/223 |
Publisher URL: | https://doi.org/10.1109/ICBATS54253.2022.9759073 |
Publisher OA policy: | |
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