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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Journal of Computing Theories and Applications
Alabi, Oluwaseyi Omotayo
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Design and implementation of an alcohol detection driver system Alabi, Oluwaseyi Omotayo; Adeaga, Oyetunde Adeoye; Ajagbe, Sunday Adeola; Adekunle, Esther Oluwayemisi; Adigun, Matthew Olusegun
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp278-285

Abstract

A technology called an alcohol detection driver system is used to stop drunk driving by identifying alcohol in a motorist's breath or blood. This technology correctly measures the amount of alcohol a driver has in their system using sensors and algorithms, and it stops the car from starting if the amount is more than the legal limit. The number of fatal accidents and traffic fatalities caused by drinking could be greatly decreased thanks to this technology. The main focus of this project is to carry out the experiment in lowering the number of alcohol-related incidents on the road. Alcohol detection devices come in a variety of forms right now, including ignition interlocks, passive alcohol sensors, and in-car breathalyzers. Although these systems have reduced the number of drunk driving accidents, there remain questions about their efficiency, dependability, and cost. According to the sensor's specs, the output voltage of the MQ-3 sensor reduces by 69% during the sensor's recovery period of 30 seconds at 69% of baseline resistance. To assess the long-term viability and efficiency of these systems in lowering alcohol-related accidents and enhancing traffic safety, more research is required.
RETRACTED: The Use of AI to Analyze Social Media Attacks for Predictive Analytics Adekunle, Temitope Samson; Alabi, Oluwaseyi Omotayo; Lawrence, Morolake Oladayo; Ebong, Godwin Nse; Ajiboye, Grace Oluwamayowa; Bamisaye, Temitope Abiodun
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10120

Abstract

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.
A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis Akande, Timileyin Opeyemi; Alabi, Oluwaseyi Omotayo; Oyinloye, Julianah B.
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10125

Abstract

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.
Co-Authors Adeaga, Oyetunde Adeoye Adekunle, Esther Oluwayemisi Adekunle, Temitope Samson Adigun, Matthew Olusegun Ajagbe, Sunday Adeola Ajiboye, Grace Oluwamayowa Akande, Timileyin Opeyemi Bamisaye, Temitope Abiodun Ebong, Godwin Nse Lawrence, Morolake Oladayo Oyinloye, Julianah B.