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

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Blockchain and smart contracts based system for criminal record management Jlil, Manal; Jouti, Kaoutar; Loqman, Chakir
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp365-379

Abstract

Reducing crime rate in a country is the most important concern of developing robust systems to automate the criminal record-obtaining process. Generally, the criminal record is managed manually, which makes the information collection from other criminal records very difficult. Therefore, investigations that could be carried out using criminal records to understand the purpose of crime and countering it are outdated. However, the integrity, security, and traceability of data exchange, especially for the judicial sector are the most frequent issues faced by information systems of public organizations. In this paper, we present a study of using blockchain technology and smart contracts to design a new architecture for a decentralized system to manage criminal record storage. This proposed architecture automates the process of getting a criminal record by moving past the techniques employed in developing traditional systems of data management such as centralized systems. In this study, blockchain technology is used to ensure data security, integrity, and traceability as well as ensure timely access to criminal records, and smart contracts are used to allow traceability and authenticity. This architecture will significantly reduce the impact of corruption in law enforcement by eliminating fraud cases, which will revolutionize E-governance in the Moroccan country.
Enhancing traffic flow through multi-agent reinforcement learning for adaptive traffic light duration control Faqir, Nada; Boumhidi, Jaouad; Loqman, Chakir; Oubenaalla, Youness
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp500-515

Abstract

This study addresses urban traffic congestion through deep learning for traffic signal control (TSC). In contrast to previous research on single traffic light controllers, our approach is tailored to the TSC challenge within a network of two intersections. Employing convolutional neural networks (CNN) in a deep Q-network (DQN) model, our method adopts centralized training and distributed execution (CTDE) within a multi-agent reinforcement learning (MARL) framework. The primary aim is to optimize traffic flow in a twointersection setting, comparing outcomes with baseline strategies. Overcoming scalability and partial observability challenges, our approach demonstrates the efficacy of the CTDE-based MARL framework. Experiments using urban mobility simulation (SUMO) exhibit a 68% performance enhancement over basic traffic light control systems, validating our solution across diverse scenarios. While the study focuses on two intersections, it hints at broader applications in complex settings, presenting a promising avenue for mitigating urban traffic congestion. The research underscores the importance of collaboration within MARL frameworks, contributing significantly to the advancement of adaptive traffic signal control (ATSC) in urban environments for sustainable transportation solutions.
Co-Authors Boumhidi, Jaouad Faqir, Nada Jlil, Manal Jouti, Kaoutar Oubenaalla, Youness