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

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Enhancing convolutional neural network based model for cheating at online examinations detection Ouahabi, Sara; Aboudihaj, Rihab; Sael, Nawal; El Guemmat, Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp843-852

Abstract

In the last few years, e-learning has revolutioning education, giving students access to diverse and adaptable on-line resources, but it has also face a major challenge: cheating on online exams. Students now use variant cheating methods include consulting unauthorized documents, communicating with others during the exam, searching for information on the internet. Combating these cheating practices has become crucial to preserving the integrity of academic assessments. In this context, artificial intelligence (AI) has emerged as an essential tool for mitigating this fraudulent behavior. Equipped with advanced machine learning capabilities, AI can examine a wide range of data to detect student suspicious behavior. This study develops an approach based on a convolutional neural network (CNN) model designed to detect cheating by analyzing candidates' head movements during online exams. By exploiting the FEI dataset, this model achieves an interesting accuracy of 97.28%. In addition, we compare this model to the well-known transfer learning models used in the literature namely, ResNet50, VGG16, DenseNet21, MobileNetV2, and EfficientNetB0 demonstrating the out performance of our approach in detecting cheating during online exams.
Enhancing emotion recognition model for a student engagement use case through transfer learning Qarbal, Ikram; Sael, Nawal; Ouahabi, Sara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1576-1586

Abstract

Distance education has been prevalent since the late 1800s, but its rapid expansion began in the late 1990s with the advent of the online technological revolution. Distance learning encompasses all forms of training conducted without the physical presence of learners or teachers. While this mode of education offers great flexibility and numerous advantages for both students and teachers, it also presents challenges such as reduced concentration and commitment from students, and difficulties in course supervision for teachers. This article aims to study student engagement on distance learning platforms by focusing on emotion detection. Leveraging various existing datasets, including the Facial Expression Recognition 2013 (FER2013), the Karolinska Directed Emotional Faces (KDEF), the extended Cohn-Kanade (CK+), and the Kyung Hee University Multimodal Facial Expression Database (KMU-FED), the proposed approach utilizes transfer learning. Specifically, it exploits the large number and diversity of images from datasets like FER2013, and the high-quality images from datasets like KDEF, CK+, and KMU-FED. The model can effectively learn and generalize emotional cues from varied sources by combining these datasets. This comprehensive method achieved a performance accuracy of 96.06%, demonstrating its potential to enhance understanding of student engagement in online learning environments.
Evaluating hybrid and standard deep learning models for maximum temperature forecasting in a semi-arid region Zemnazi, Oussama; El Filali, Sanaa; Ouahabi, Sara; Mouhtadi, Abderrahim
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.pp183-193

Abstract

Temperature forecasting is important for industries affected by climate, especially in semi-arid regions where the weather can change quickly and is hard to predict over time. Many studies have examined various deep learning models, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNNs), and transformer-based hybrids. However, their performance in data-limited semi-arid environments is often unclear and inconsistent. This study compares six deep learning methods for predicting daily maximum temperatures in Settat, Morocco. It uses 11 years of ground-observed meteorological data. The models examined include a baseline artificial neural network (ANN) and five hybrid structures: ANN-LSTM, ANN-GRU, ANN-CNN, ANN–random forest (RF), and ANN-transformer. The results indicate that the ANN performs the best overall, with MAE = 0.0432, root mean square error (RMSE) = 0.0543, and R² = 0.8820. It surpasses all hybrid models. When using a relative improvement metric, the ANN shows accuracy gains of 32% to 42% compared to the recurrent, convolutional, and attention-based hybrids. These results suggest that in semi-arid climates, where maximum temperature mainly depends on the same-day atmospheric conditions, simpler feedforward models work better than more complex temporal models. The study underscores the need to match model complexity with climatic factors and dataset size, offering a useful benchmark for temperature forecasting in regions with limited data.
Co-Authors Aboudihaj, Rihab El Filali, Sanaa El Guemmat, Kamal Mouhtadi, Abderrahim Qarbal, Ikram Sael, Nawal Zemnazi, Oussama