An intelligent risk management framework for monitoring vehicular engine health
The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through r...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34931/ http://umpir.ump.edu.my/id/eprint/34931/7/An%20Intelligent%20Risk%20Management%20Framework%20for%20Monitoring%20Vehicular%20Engine%20Health.pdf |
| _version_ | 1848824640695173120 |
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| author | Rahim, Md. Abdur Rahman, Md. Arafatur Rahman, Md. Mustafizur Zaman, Nafees Moustafa, Nour Razzak, Imran |
| author_facet | Rahim, Md. Abdur Rahman, Md. Arafatur Rahman, Md. Mustafizur Zaman, Nafees Moustafa, Nour Razzak, Imran |
| author_sort | Rahim, Md. Abdur |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date. |
| first_indexed | 2025-11-15T03:16:15Z |
| format | Article |
| id | ump-34931 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:16:15Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-349312022-10-27T03:05:55Z http://umpir.ump.edu.my/id/eprint/34931/ An intelligent risk management framework for monitoring vehicular engine health Rahim, Md. Abdur Rahman, Md. Arafatur Rahman, Md. Mustafizur Zaman, Nafees Moustafa, Nour Razzak, Imran HD Industries. Land use. Labor HD28 Management. Industrial Management TA Engineering (General). Civil engineering (General) The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date. Institute of Electrical and Electronics Engineers Inc. 2022-05 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34931/7/An%20Intelligent%20Risk%20Management%20Framework%20for%20Monitoring%20Vehicular%20Engine%20Health.pdf Rahim, Md. Abdur and Rahman, Md. Arafatur and Rahman, Md. Mustafizur and Zaman, Nafees and Moustafa, Nour and Razzak, Imran (2022) An intelligent risk management framework for monitoring vehicular engine health. IEEE Transactions on Green Communications and Networking, 6 (3). pp. 1298-1306. ISSN 2473-2400. (Published) https://doi.org/10.1109/TGCN.2022.3179350 https://doi.org/10.1109/TGCN.2022.3179350 |
| spellingShingle | HD Industries. Land use. Labor HD28 Management. Industrial Management TA Engineering (General). Civil engineering (General) Rahim, Md. Abdur Rahman, Md. Arafatur Rahman, Md. Mustafizur Zaman, Nafees Moustafa, Nour Razzak, Imran An intelligent risk management framework for monitoring vehicular engine health |
| title | An intelligent risk management framework for monitoring vehicular engine health |
| title_full | An intelligent risk management framework for monitoring vehicular engine health |
| title_fullStr | An intelligent risk management framework for monitoring vehicular engine health |
| title_full_unstemmed | An intelligent risk management framework for monitoring vehicular engine health |
| title_short | An intelligent risk management framework for monitoring vehicular engine health |
| title_sort | intelligent risk management framework for monitoring vehicular engine health |
| topic | HD Industries. Land use. Labor HD28 Management. Industrial Management TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/34931/ http://umpir.ump.edu.my/id/eprint/34931/ http://umpir.ump.edu.my/id/eprint/34931/ http://umpir.ump.edu.my/id/eprint/34931/7/An%20Intelligent%20Risk%20Management%20Framework%20for%20Monitoring%20Vehicular%20Engine%20Health.pdf |