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All Journal International Journal of Electrical and Computer Engineering International Journal of Engineering, Science and Information Technology
Abeer Salim Jamil
Al-Mansour University College
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Real-time face detection in digital video-based on Viola-Jones supported by convolutional neural networks Tameem Hameed Obaida; Abeer Salim Jamil; Nidaa Flaih Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3083-3091

Abstract

Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.
Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Laith S. Ismail; Abeer Salim Jamil; Taghreed Alaa Mohammed Ali; Ibraheem Hatem Mohammed Al-Dosari; Khdier Salman; Siti Sarah Maidin
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1397

Abstract

Autonomous electric vehicles (AEVs) require real-time decision-making, low-latency computation, and energy-aware coordination to operate effectively. Traditional centralised cloud computing struggles to meet these demands due to inherent delays and scalability issues in large-scale AEV networks. This paper proposes a novel hybrid edge–fog computing architecture to address these challenges. Our framework utilises a three-tier system (vehicle-edge, roadside-fog, and cloud) governed by a deep reinforcement learning agent that manages energy-aware task offloading. Extensive simulations demonstrate the framework's effectiveness, achieving significant end-to-end latency reductions of up to 56% during urban peak hours and decreasing energy consumption by 20% under high-load conditions. The deep reinforcement learning agent successfully adapts control policies to dynamic road conditions, while the architecture proves highly scalable and resilient, maintaining high task success rates and recovering from node failures in seconds. These findings confirm that a hybrid edge–fog architecture, guided by reinforcement learning, is a highly effective solution for scalable, adaptive, and energy-efficient AEV operations. This study's primary contribution is an empirically validated framework that uniquely integrates predictive control and energy-aware scheduling at the edge, providing a deployable model for next-generation intelligent transportation systems.
Co-Authors Ibraheem Hatem Mohammed Al-Dosari Khdier Salman Laith S. Ismail Nidaa Flaih Hassan Siti Sarah Maidin Taghreed Alaa Mohammed Ali Tameem Hameed Obaida