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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Taglout, Ramdane
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Computer Science & IT Engineering

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Enhanced object tracking with artificial bee colony, motion modeling, and deep learning Taglout, Ramdane; Saoud, Bilal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5344-5354

Abstract

As a fundamental aspect of computer vision, visual object tracking supports a wide array of applications, notably in transport infrastructure and advanced industrial automation. Although correlation filter-based trackers demonstrate robust performance, they face persistent limitations including scale changes, object occlusion, boundary artifacts, and complex background interference. To address these issues, we have introduced an approach that combines artificial bee colony (ABC) optimization, deep neural architectures, and Kalman filtering techniques. Our methodology begins with reliability assessment of the tracking pipeline, proceeding to compute target confidence measures at the predicted position, followed by an adaptive update mechanism. The proposed system leverages ABC optimization for dynamic scale adaptation while employing Kalman filtering to model inter-frame target motion dynamics. Comprehensive evaluation across multiple benchmark datasets demonstrates our method's efficacy, precision, and resilience, achieving enhanced performance relative to existing state-of-the art approaches.
Enhancing AODV protocol for black hole attack detection and mitigation in VANETs: a lightweight dual-confirmation approach Abderraouf, Ahmed; Taglout, Ramdane; Boukli-Hacene, Sofiane
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp252-262

Abstract

Vehicular ad hoc networks (VANETs) represent a specialized category of Mobile ad hoc networks that are specifically designed to enable communication among autonomous (self-driving or partially self-driving) vehicles. These vehicles are equipped with onboard computers, network interfaces, and sophisticated sensors for data capture and processing. Within a VANET, vehicles have the ability to communicate with each other as well as with surrounding infrastructure, thereby exchanging critical messages aimed at enhancing road safety, reducing traffic congestion, and enabling new services and applications for drivers and passengers. Due to its unique characteristics, VANETs have succeeded in enhancing transportation efficiency and safety. However, VANETs are vulnerable to black hole attacks, where malicious nodes discard packets, compromising safety. Existing solutions suffer from high overhead or infrastructure dependence. We propose a lightweight enhancement to AODV using dual-confirmation (RepAck/Info packets) to detect and isolate attackers in real time. Simulations show a 98% improvement in packet delivery ratio under attack, with minimal protocol modifications. While routing overhead increases by 25%, this trade-off ensures reliable communication in dynamic VANETs.
Co-Authors Abderraouf, Ahmed Boukli-Hacene, Sofiane Saoud, Bilal