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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Hadhrami Ab Ghani
University Malaysia Kelantan
Computer Science & IT Electrical & Electronics Engineering

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Advances in lane marking detection algorithms for all-weather conditions Hadhrami Ab Ghani; Rosli Besar; Zamani Md Sani; Mohd Nazeri Kamaruddin; Syabeela Syahali; Atiqullah Mohamed Daud; Aerun Martin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3365-3373

Abstract

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.
Intrusion detection system for imbalance ratio class using weighted XGBoost classifier Januar Al Amien; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Edi Ismanto; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24735

Abstract

The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances.
Co-Authors Aerun Martin Atiqullah Mohamed Daud Edi Ismanto Gunawan, Rahmad Januar Al Amien Mohd Nazeri Kamaruddin Nurul Izrin Md Saleh Rosli Besar Syabeela Syahali Zamani Md Sani