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All Journal Indonesian Journal of Innovation and Applied Sciences (IJIAS) Journal of Scientific Insights Frontiers in Sustainable Science and Technology
H. Abdelati, Mohamed
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology 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.
Modeling the Impact of Traffic Density on Critical Gap Distribution at Unsignalized Intersections Al-Hussein Matar; Hazem Gamal; H. Abdelati, Mohamed
Journal of Scientific Insights Vol. 2 No. 1 (2025): February
Publisher : Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jsi.v2i1.287

Abstract

This study constructs mathematical models to evaluate the connection between critical gap dimensions and traffic flow volumes at traffic intersections without signals. Drivers need critical gap as the briefest gap between vehicles to safely move through the road. Traditional theoretical models maintain fixed traffic density patterns even though actual conditions from urban roads typically exhibit changing densities. Including fluctuating traffic density in this model will improve accuracy when studying how changing traffic conditions influence critical gap measurements. The model applies exponential distribution to compute acceptance probabilities of different gap sizes while traffic density adjusts. In more congested traffic conditions drivers accept shorter gaps because the critical gap becomes smaller. The conclusion matches what existing traffic flow theories have previously demonstrated. This model delivers meaningful information regarding traffic management procedures and road intersection design standards particularly for city areas experiencing shifting traffic congestion throughout the day.
Long-Term Impact of COVID-19 on Global Air Transport: Analyzing Recovery Patterns and Strategic Responses H. Abdelati, Mohamed
Frontiers in Sustainable Science and Technology Vol. 2 No. 1 (2025): June
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/fsst.v2i1.339

Abstract

The outbreak of COVID-19 has tremendously affected the world’s aviation business by causing significant losses and organizational difficulties. Specifically, in this paper, the analysis of the changes in the conditions of the pandemic’s effect on the financial and traffic indicators of the companies in the aviation field and patterns of their operational strategies is considered. Based on the data, which covers January 2019- December 2021, it is established that there are revenue and passenger indicators that decrease with only partial restoration by the end of 2021. Various financial records obtained from the airline reports reveal massive reductions in revenue and increased expenses on cancellations and the grounding of aircraft. Using the passenger traffic data from IATA and ACI, it is possible to study the passenger volumes and analyze the time series to determine main recovery tendencies. Primary data from the surveys conducted in 2020 and 2021 to understand the passengers’ confidence and travel inclination revealed a gradual recovery. This paper also focuses on other adaptive actions by airlines, like route changes and operation strategies to minimize losses. The results imply a focus on effective crisis management and policy measures to promote the improvement of resilience in businesses and industries. The issues and guidelines for future preparation are the creation of financial buffers, the implementation of contingent organizational structures, and the development of additional funding sources. Based on the current study, the following recommendations are proposed to policymakers, airline operators, and other related stakeholders.
Optimizing Simple Exponential Smoothing for Time Series Forecasting in Supply Chain Management H. Abdelati, Mohamed; Hilal A. Abdelwali
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 4 No. 3 (2024): October-January
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v4i3.1591

Abstract

This paper deals with optimizing Simple Exponential Smoothing for time series forecasting in supply chain management, particularly in the transport and automotive sectors. This paper attempts to enhance the accuracy of the forecast by estimating an optimal smoothing constant α with the Mean Squared Error as the objective function. This optimization exercise will be done using MATLAB's fminsearch function. Indeed, results realize substantial improvements in the accuracy of the forecast, validated using different error metrics and graphical representations.
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 Hazem Gamal Hilal A. Abdelwali M. Rabie Matar, Al-Hussein