Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Aptisi Transactions on Technopreneurship (ATT)
Awang, Suryanti
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Artificial intelligence in the United Arab Emirates public sector: a systematic literature review Akhoirshieda, Modafar Shaker; Naim Ku Khalif, Ku Muhammad; Awang, Suryanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2472-2481

Abstract

This systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.
Vision-Based Vehicle Classification for Smart City Ismail, Ahsiah; Ismail, Amelia Ritahani; Shaharuddin, Nur Azri; Ara, Muhammad Afiq; Puzi, Asmarani Ahmad; Awang, Suryanti; Ramli, Roziana
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.446

Abstract

Vehicle detection systems are essential for improving traffic management, enhancing safety, supporting law enforcement, facilitating toll collection, and contributing to smart city initiatives through real-time monitoring and data analysis. With the rapid growth of smart city technologies, the need for efficient, scalable, and high-accuracy vehicle detection models has become increasingly critical. This study aims to propose an advanced vehicle detection system using Convolutional Neural Networks (CNNs) in combination with the YOLOv5 model, which is known for its high-speed performance and superior accuracy in image recognition tasks. The proposed model is evaluated using a custom-trained YOLOv5s model, tested on a dataset comprising 1460 images of vehicles. These images are divided into five classes which are cars, motorcycles, trucks, ambulances, and buses. Performance evaluation metrics such as precision, recall, and mean Average Precision (mAP50-95) are used to assess the model's effectiveness. The results indicate that the YOLOv5-based model achieved impressive detection accuracy, with precision, recall, and mAP values exceeding 87%. The proposed system demonstrates its robustness in detecting and classifying various vehicle types across different conditions, including small, partially visible, and distant vehicles. The findings suggest that this model holds significant potential for real-world applications in urban traffic management and smart city infrastructure.
Co-Authors Akhoirshieda, Modafar Shaker Ara, Muhammad Afiq Ismail, Ahsiah Ismail, Amelia Ritahani Naim Ku Khalif, Ku Muhammad Puzi, Asmarani Ahmad Ramli, Roziana Shaharuddin, Nur Azri