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All Journal Indonesian Journal of Innovation and Applied Sciences (IJIAS) Journal of Scientific Insights
M. Rabie
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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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.
Transforming Waste Management in Egypt Through Extended Producer Responsibility in Automotive and Transportation H. Abdelati, Mohamed; Abdelwali, Hilal A.; Matar, Al-Hussein; M. Rabie
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 5 No. 1 (2025): February-May
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v5i1.1806

Abstract

The policy proposal of Extended Producer Responsibility in Egypt’s automotive and transportation industry is discussed in this research as a viable solution for major waste management issues. From the best practices observed on the international level and having regard to the country-specific socio-economic environment of Egypt, the study suggests comprehensive and step-by-step anatomy that might be best adapted to adoption in the given country context. Simulations for hypothetical modeling on some of the main automobile components including tires and batteries, indicated that recycling rates could rise from an initial 10% to over 50% after 10 years under EPR. The framework also points to the creation of approximately 15,000 new jobs and the achievement of 20% cost savings on municipal waste management. One of the most important elements is the capacity to support the participation of informal waste pickers in the formal system, providing them with the training and motivation, that will secure an environmentally sound approach, as well as social inclusion. This work therefore provides a clear guideline for policymakers and stakeholders to SMADE to implement an effective, reflective, and fair manner of waste management which complements Egypt’s Vision 2025 and other Universal sustainable developments.  
Co-Authors Abdelwali, Hilal A. Al-Hussein Matar Ebram F.F. Mokbel H. Abdelati, Mohamed Hilal A. Abdelwali Matar, Al-Hussein