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

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Enhancing the smart parking assignment system through constraints optimization Elkhalidi, Nihal; Benabbou, Faouzia; Sael, Nawal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2374-2385

Abstract

Traffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hinders urban mobility, particularly in big cities and other congested areas. This leads to an increase in accidents, a big consumption of fuel, and a spectacular augmentation of pollution. In this paper, we introduce a parking assignment system grounded in constraint programming to address the growing demand for efficient parking management in smart cities. Our system is designed to meet the requirements of groups of drivers seeking to reserve parking spaces simultaneously within the same period and geographical area. This entails imposing constraints on the desired parking type, including considerations such as walking and driving distances, parking costs, and availability. Within the scope of this study, we propose two formulations: constraint satisfaction programming (CSP) with an objective function and mixed-integer linear programming (MILP). Evaluation shows Choco, a CSP solver, is effective for smaller requests but slower for larger ones, while MILP excels for larger scenarios. Both solvers produce high-quality solutions meeting real-time response requirements. Our research offers innovative solutions for smart city management, considering parking type preferences, costs, and availability. We contribute significantly to parking space assignment methodologies, aiming to alleviate the time-consuming search for parking, reduce accidents, fuel consumption, and pollution.
Driver inattention detection system using multi-task cascaded convolutional networks Soultana, Abdelfettah; Benabbou, Faouzia; Sael, Nawal; Bouhsissin, Soukaina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4249-4262

Abstract

Driver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system.
Efficient cross-lingual plagiarism detection using bidirectional and auto-regressive transformers Bouaine, Chaimaa; Benabbou, Faouzia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4619-4629

Abstract

The pervasive availability of vast online information has fundamentally altered our approach to acquiring knowledge. Nevertheless, this wealth of data has also presented significant challenges to academic integrity, notably in the realm of cross-lingual plagiarism. This type of plagiarism involves the unauthorized copying, translation, ideas, or works from one language into others without proper citation. This research introduces a methodology for identifying multilingual plagiarism, utilizing a pre-trained multilingual bidirectional and auto-regressive transformers (mBART) model for document feature extraction. Additionally, a siamese long short-term memory (SLSTM) model is employed for classifying pairs of documents as either "plagiarized" or "non-plagiarized". Our approach exhibits notable performance across various languages, including English (En), Spanish (Es), German (De), and French (Fr). Notably, experiments focusing on the En-Fr language pair yielded exceptional results, with an accuracy of 98.83%, precision of 98.42%, recall of 99.32%, and F-score of 98.87%. For En-Es, the model achieved an accuracy of 97.94%, precision of 98.57%, recall of 97.47%, and an F-score of 98.01%. In the case of En-De, the model demonstrated an accuracy of 95.59%, precision of 95.21%, recall of 96.85%, and F-score of 96.02%. These outcomes underscore the effectiveness of combining the MBART transformer and SLSTM models for cross-lingual plagiarism detection.
Enhancing machine learning algorithm performance through feature selection for driver behavior classification Bouhsissin, Soukaina; Sael, Nawal; Benabbou, Faouzia; Soultana, Abdelfettah
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp354-365

Abstract

Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.
Co-Authors Bouaine, Chaimaa Bouhsissin, Soukaina Elkhalidi, Nihal Sael, Nawal Soultana, Abdelfettah