Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles
Autonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing and detecting numerous targets in real-time, mainly in city arrangements in adversarial environmental conditio...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241693 |
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| author | Khaled Tarmissi Hanan Abdullah Mengash Noha Negm Yahia Said Ali M. Al-Sharafi |
| author_facet | Khaled Tarmissi Hanan Abdullah Mengash Noha Negm Yahia Said Ali M. Al-Sharafi |
| author_sort | Khaled Tarmissi |
| collection | DOAJ |
| description | Autonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing and detecting numerous targets in real-time, mainly in city arrangements in adversarial environmental conditions, has become a significant challenge for AVs. The effectiveness of vehicle detection has been measured as a crucial stage in intelligent visual surveillance or traffic monitoring. After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. However, according to statistical learning, conventional AI is fundamental, facing restrictions due to manual feature engineering and restricted flexibility in adaptive environments. This study presents the explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection for autonomous vehicles (XAIFTL-AWCDAV) method. The XAIFTL-AWCDAV model's main aim is to detect and classify weather conditions for AVs in challenging scenarios. In the preprocessing stage, the XAIFTL-AWCDAV model utilizes a non-local mean filtering (NLM) method for noise reduction. Besides, the XAIFTL-AWCDAV model performs feature extraction by fusing three models: EfficientNet, SqueezeNet, and MobileNetv2. The denoising autoencoder (DAE) technique is employed to classify adverse weather conditions. Next, the DAE method's hyperparameter selection uses the Levy sooty tern optimization (LSTO) approach. Finally, to ensure the transparency of the model's predictions, XAIFTL-AWCDAV integrates explainable AI (XAI) techniques, utilizing SHAP to visualize and interpret each feature's impact on the model's decision-making process. The efficiency of the XAIFTL-AWCDAV method is validated by comprehensive studies using a benchmark dataset. Numerical results show that the XAIFTL-AWCDAV method obtained a superior value of 98.90% over recent techniques. |
| format | Article |
| id | doaj-art-0c043a73ad234973b69dbc11ac32a2b6 |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-0c043a73ad234973b69dbc11ac32a2b62025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912356783570110.3934/math.20241693Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehiclesKhaled Tarmissi0Hanan Abdullah Mengash1Noha Negm2Yahia Said3Ali M. Al-Sharafi4Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, Applied College at Mahayil, King Khalid University, Saudi ArabiaCenter for Scientific Research and Entrepreneurship, Northern Border University, 73213, Arar, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi ArabiaAutonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing and detecting numerous targets in real-time, mainly in city arrangements in adversarial environmental conditions, has become a significant challenge for AVs. The effectiveness of vehicle detection has been measured as a crucial stage in intelligent visual surveillance or traffic monitoring. After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. However, according to statistical learning, conventional AI is fundamental, facing restrictions due to manual feature engineering and restricted flexibility in adaptive environments. This study presents the explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection for autonomous vehicles (XAIFTL-AWCDAV) method. The XAIFTL-AWCDAV model's main aim is to detect and classify weather conditions for AVs in challenging scenarios. In the preprocessing stage, the XAIFTL-AWCDAV model utilizes a non-local mean filtering (NLM) method for noise reduction. Besides, the XAIFTL-AWCDAV model performs feature extraction by fusing three models: EfficientNet, SqueezeNet, and MobileNetv2. The denoising autoencoder (DAE) technique is employed to classify adverse weather conditions. Next, the DAE method's hyperparameter selection uses the Levy sooty tern optimization (LSTO) approach. Finally, to ensure the transparency of the model's predictions, XAIFTL-AWCDAV integrates explainable AI (XAI) techniques, utilizing SHAP to visualize and interpret each feature's impact on the model's decision-making process. The efficiency of the XAIFTL-AWCDAV method is validated by comprehensive studies using a benchmark dataset. Numerical results show that the XAIFTL-AWCDAV method obtained a superior value of 98.90% over recent techniques.https://www.aimspress.com/article/doi/10.3934/math.20241693explainable artificial intelligencetransfer learningadverse weather conditions detectionlevy sooty tern optimizationautonomous vehicles |
| spellingShingle | Khaled Tarmissi Hanan Abdullah Mengash Noha Negm Yahia Said Ali M. Al-Sharafi Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles AIMS Mathematics explainable artificial intelligence transfer learning adverse weather conditions detection levy sooty tern optimization autonomous vehicles |
| title | Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| title_full | Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| title_fullStr | Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| title_full_unstemmed | Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| title_short | Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| title_sort | explainable artificial intelligence with fusion based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles |
| topic | explainable artificial intelligence transfer learning adverse weather conditions detection levy sooty tern optimization autonomous vehicles |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241693 |
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