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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Journal of Applied Engineering and Technological Science (JAETS)
Khambari, Najwan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Real time hand gesture detection by using convolutional neural network for in-vehicle infortainment systems Yaakob Wan Bejuri, Wan Mohd; Asmai, Siti Azira; Ikram, Raja Rina Raja; Rahim, Nur Raidah; Khambari, Najwan; Azmi, Mohd Sanusi; Sholva, Yus
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp42-49

Abstract

Nowadays, a variety of technologies on autonomous vehicles have been extensively developed, including in-vehicle infotainment (IVI). It have been noted as one of the key services in the automobile industry. In the near future, people will be able to watch some virtual reality (VR) movies through the streaming service provided in the vehicle. However, a person sometime not tend to be joy while watching espcially when the remote controller or audio sensory controller lack of battery or too far from IVI panel. Thus, the purpose of this research is to design a scheme of real time hand gesture detection for in-vehicle infotainment system, in order to create human computer experience. In this research, the image of human palm hand will be taken by using camera for recognize the hand gesture action. This proposed scheme will recognize human gesture and convert to be computer intruction, that can be understood by IVI device. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving better user experience. Furthermore, the proposed scheme is anticipated to contribute significantly to the IVI field, benefiting both academia and societal outcomes.
Swarm Intelligence Optimisation Vs Deep Learning: Energy-Aware Strategy for Disaster Communication Networks Mansor, Norhisham; Md Shah, Wahidah; Khambari, Najwan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7937

Abstract

In disaster-prone environments, communication networks must sustain operation under severe constraints such as limited energy, damaged infrastructure, and uncertain topology. This study compares Deep Learning (DL) and Swarm Intelligence Optimisation (SIO) as energy-aware strategies for disaster communication. While DL excels in data-rich prediction and situational analysis, its reliance on high-performance hardware and stable connectivity restricts its feasibility during real-time emergencies. In contrast, SIO provides decentralised coordination, lightweight computation, and adaptive routing, making it better suited to infrastructure-independent device-to-device (D2D) networks when central control collapses. A comparative conceptual framework was developed to evaluate both paradigms across five criteria: energy efficiency, adaptability, computational demand, response time, and scalability, based on recent literature between 2023 and 2025. Findings show that SIO demonstrates superior suitability for energy-limited and time-critical operations, while DL remains valuable for pre-disaster prediction and post-event analysis. Hybrid DL–SIO frameworks bridge both paradigms, enabling predictive–adaptive synergy across the disaster lifecycle. The study contributes a context-aware guideline for algorithm selection, shifting the focus from technology-centric performance toward environment-centric deployment in future energy-efficient, resilient, and adaptive disaster communication systems.
Co-Authors Asmai, Siti Azira Azmi, Mohd Sanusi Ikram, Raja Rina Raja Mansor, Norhisham Md Shah, Wahidah Rahim, Nur Raidah Sholva, Yus Yaakob Wan Bejuri, Wan Mohd