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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Nur Asyiqin Amir Hamzah
Multimedia University
Computer Science & IT Engineering

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Ant-colony and nature-inspired heuristic models for NOMA systems: a review Law Poh Liyn; Hadhrami Ab. Ghani; Farah Najwa Roslim; Nur Asyiqin Amir Hamzah; Saeed Mohammed Abdulghani Mohammed; Nor Hidayati Abdul Aziz; Azlan Abd. Aziz; Tan Kim Geok; Azizul Azizan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14995

Abstract

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.
The impact of weather data on traffic flow prediction models Hatem Fahd Al-Selwi; Azlan Bin Abd Aziz; Fazly Salleh Abas; Nur Asyiqin Amir Hamzah; Azwan Bin Mahmud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Traffic flow prediction is an integral part of the intelligent transportation system (ITS) that helps in making well-informed decisions. Traffic flow prediction helps in alleviating traffic congestion as well as in some connected vehicles applications such as resources allocation. However, most of the existing models do not consider external factors such as weather data. Traffic flow in road networks is affected by weather conditions which affects the periodicity of traffic. These effects introduce some irregularity to the traffic pattern, making traffic flow prediction a challenging issue. In this paper, we present a detailed investigation on the impact of weather data on different traffic flow prediction models. The investigation presented in this paper demonstrates how adding weather data could improve the models’ prediction accuracy and efficiency.
Co-Authors Azizul Azizan Azlan Abd. Aziz Azlan Bin Abd Aziz Azwan Bin Mahmud Farah Najwa Roslim Fazly Salleh Abas Hadhrami Ab. Ghani Hatem Fahd Al-Selwi Law Poh Liyn Nor Hidayati Abdul Aziz Saeed Mohammed Abdulghani Mohammed Tan Kim Geok