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Ebram F.F. Mokbel
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Revolutionizing Automotive Engineering with Artificial Neural Networks: Applications, Challenges, and Future Directions H. Abdelati, Mohamed; Ebram F.F. Mokbel; Hilal A. Abdelwali; Al-Hussein Matar; M. Rabie
Journal of Scientific Insights Vol. 1 No. 4 (2024): December
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i4.232

Abstract

Artificial neural networks (ANNs) have emerged as the technology that provides solutions to key issues arising in the field of automobile engineering regarding autonomous driving, predictive maintenance, energy control, and vehicle protection. This paper aims to present various uses of ANNs in car industry concerning data handling for continuous decision-making and adaptation. Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs) are all explored in relation to their ANN specific relevance to automobiles. The identified limitation also responds to issues associated with the integration of ANN such as data dependency, the computational load required, and questions related to the ethical use of AI decision making. This paper compares ANN techniques in an automotive context, explaining where they excel and where they could use improvement in terms of the tasks they are applied to. The strategies for phased implementation of the ANN framework, the performance evaluation for each stage of implementation, and the optimization methodologies are discussed below. Future direction highlights the future development of transformers, energy efficient models and raising concerns of ethical regulatory frameworks with regards to ANN driven systems. Thus, by such barriers overcoming, ANNs have a potential to significantly influence the further development of automotive engineering and make automobiles safer, more efficient and environmentally friendly. This study advances the discussion around intelligent mobility and provides the foundation on which future research in the field can build from.
Data-Driven Road Safety: A Machine Learning Framework Utilizing Open Traffic Data H. Abdelati, Mohamed; Al-Hussein Matar; Hilal A. Abdelwali; Ebram F.F. Mokbel; M. Rabie
Journal of Scientific Insights Vol. 1 No. 4 (2024): December
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v1i4.237

Abstract

Road traffic accidents continue to be a problem across the world and according to statistics cause high mortality and economic losses. This research work conceptualizes an idea that will use open traffic data and machine learning models to forecast accidents on roads in order to promote road safety. Based on the presented literature review, the framework incorporates a step-by-step procedure to analyze risk factors for targeted safety interventions, including data pre-processing and feature selection, application of a chosen model for high-risk zones identification, and improving the result by altering related factors. The findings show the applicability of open data and predictive analysis in traffic safety matters, with special emphasis on temporal, spatial, and environmental features. Resources allocation, urban traffic control, and monitoring are cases used to illustrate the framework's applicability. Although this is a conceptual model, the challenges, such as data quality, data privacy issues, and practical issues with implementation, are also included in the framework, along with suggestions for future research, such as the use of stream data and improved modeling techniques. This investigation contributes to the literature as a robust theoretical model from which practical solutions for road traffic safety interventions can be derived to reduce and ultimately eliminate traffic accidents and fatalities worldwide.
Co-Authors Al-Hussein Matar H. Abdelati, Mohamed Hilal A. Abdelwali M. Rabie