Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Buletin Ilmiah Sarjana Teknik Elektro Control Systems and Optimization Letters
Tarik, Benmessaoud Mohammed
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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

Found 2 Documents
Search

 1 
A Systematic Design of a Low-Cost Real-Time Vehicle Tracking System for Enhanced Security and Location Monitoring Marhoon, Hamzah M.; Sabah, Sabah Ali; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Mohammed, Raghad Jassim; Fadhil Abbas, Riyam
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.219

Abstract

The rapid development of vehicle tracking technology has significantly enhanced the safety and security of vehicles worldwide. This paper presents the design and implementation of a real-time vehicle tracking system utilising Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technologies, based on the Arduino Uno platform. The proposed system enables vehicle owners to continuously monitor their vehicle's location, receiving instant SMS notifications for unauthorised movement, speed violations, and historical location data. The system's core components include an Arduino Uno microcontroller, interfaced with a SIM900A GSM module and a NEO-6 M GPS module, enabling real-time tracking via SMS alerts and Google Maps integration. Key features include automatic alerts for unauthorised car startups, exceeding speed limits, live tracking requests, and location history retrieval. The system stores the last five GPS coordinates in EEPROM memory and offers a user-friendly interface for retrieving data via SMS. The integration of Google Maps enhances the tracking experience by providing a visual representation of the vehicle’s location. This solution offers a cost-effective and reliable means of vehicle monitoring, contributing to improved vehicle security and owner peace of mind.
Metaheuristic-Driven Optimisation of Support Vector Regression Models for Precision Control in Unmanned Aerial Vehicle Systems Marhoon, Hamzah M.; Omar, Rasha Khalid; Al-Rammahi, Hussein; Al-Tahir, Sarah O.; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Agajie, Takele Ferede
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14251

Abstract

Unmanned Aerial Vehicle (UAV) systems are deployed in dynamic and uncertain environments where many traditional control structures, including Proportional–Integral–Derivative (PID) and Linear Quadratic Regulator (LQR) controllers, are unable to provide stability and adaptation. In order to overcome these shortcomings, this work presents a hybrid Support Vector Regression (SVR) model optimised with the Eagle Strategy-Particle Swarm Optimisation (ES-PSO). The proposed framework is tested with high-fidelity simulated flight data on a quadcopter platform, in which throttle, pitch, roll and yaw are provided as control variables and altitude, velocity and orientation are provided as outputs. The ES-PSO algorithm is an algorithm that optimises the global and local hyperparameters of the SVR and makes it more effective at capturing nonlinear dynamics of the input-output process under both nominal and perturbed flight conditions. To compare with benchmarking, standalone SVR, Neural Networks, Decision Trees, Naive Bayes and K-Nearest Neighbour models were executed using the same simulation parameters with no metaheuristic optimisation, and it was made fair. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Percentage Error (MPE) quantitative assessments illustrate that the ES-PSO-SVR model has the lowest error in prediction and the highest tracking accuracy compared to all baseline techniques. These results demonstrate how metaheuristic-based learning systems can be used to drive forward the creation of adaptive and intelligent UAV control systems that can perform effectively in challenging operational conditions.
Co-Authors Agajie, Takele Ferede Al-Rammahi, Hussein Al-Tahir, Sarah O. Basil, Noorulden Fadhil Abbas, Riyam Marhoon, Hamzah M. Mohammed, Raghad Jassim Omar, Rasha Khalid Sabah, Sabah Ali