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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Waga, Abderrahim
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Computer Science & IT Engineering

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Efficient autonomous navigation for mobile robots using machine learning Waga, Abderrahim; Ba-ichou, Ayoub; Benhlima, Said; Bekri, Ali; Abdouni, Jawad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3061-3071

Abstract

The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory to do heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.
Explainable deep learning for scalable record linkage: a TabNet-based framework for structured data integration Zahrae Saber, Fatima; Choukri, Ali; Amnai, Mohamed; Waga, Abderrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp725-743

Abstract

Record linkage is considered a fundamental process for ensuring data quality and reliability, with critical applications in domains such as healthcare, finance, and commerce. A machine learning-based approach for optimizing record linkage in structured datasets is presented in this paper. By integrating hybrid blocking methods (combining standard blocking and sorted neighborhood approaches) with advanced similarity measures, computational overhead is significantly reduced while high accuracy is maintained. The performance of TabNet, a deep learning model designed for tabular data, is compared with traditional deep neural networks (DNNs) in the classification phase. Experimental results on a synthetic dataset of 5,000 records demonstrate that comparable precision and recall are achieved by TabNet to DNNs while execution time is reduced by over 79%. The scalability and efficiency of the proposed method are highlighted by these findings, making it well-suited for large-scale data management tasks. Practical and computationally efficient solutions for record linkage in the era of big data are contributed to by this work.
Co-Authors Abdouni, Jawad Amnai, Mohamed Ba-ichou, Ayoub Bekri, Ali Benhlima, Said Choukri, Ali Zahrae Saber, Fatima