IJRES Team

Followers

161

Following

Co-authors

Public Views

The International Journal of Reconfigurable and Embedded Systems (IJRES), p-ISSN 2089-4864, e-ISSN 2722-2608, is aimed at providing a vehicle for academics, industrial professionals, educators, and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable systems, embedded systems, very large-scale integration (VLSI) design, and the internet of things (IoT). As it is well-known, the center of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable cores in system-on-chip (SoC) and embedded IoT devices. Reconfigurable and embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons, etc. The journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU). The IJRES is an OPEN ACCESS and PEER-REVIEWED journal distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License. Published papers since the 2021 issues of this journal have been indexed by Scopus.

less

Interests

Uploads

Papers by IJRES Team

Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities

International Journal of Reconfigurable and Embedded Systems (IJRES), 2025

The number of deaths has increased as a direct result of the increased frequency of traffic accid... more The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.

IJRES Team

Uploads

Papers by IJRES Team

Log In