http://depot.umc.edu.dz/handle/123456789/14517 Mon, 01 Jun 2026 15:58:02 GMT 2026-06-01T15:58:02Z http://depot.umc.edu.dz/handle/123456789/14652 Application Vision Transformers On Face Age Regression Chami, Ahmed Chaouki; Ajgou, Riadh Transformers have recently gained signifcant attention in machine learning due to their self-attention mechanisms, which allow models to dynamically assess the importance of different input elements. Although originally designed for Natural Language Processing (NLP), the application of transformers in computer vision tasks, such as image classifcation, has been gaining traction. This work explores the use of Vision Transformers (ViT) in the context of face age regression, focusing on three well-known datasets: MORPH II, AFAD, and CACD. By leveraging ViT in a regression setting, we aim to predict the age of individuals based on facial images. We evaluate the model’s performance using the Mean Absolute Error (MAE) on each of these datasets and compare it to traditional models like Convolutional Neural Networks (CNNs). Furthermore, we investigate the computational efciency and performance gains from transfer learning using pre-trained ViT models on the ImageNet dataset. Our experiments demonstrate that Vision Transformers offer a competitive alternative to CNNs for face age regression, with promising results across all three datasets, showing their potential for future applications in age estimation and facial analysis. Fri, 25 Oct 2024 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/14652 2024-10-25T00:00:00Z http://depot.umc.edu.dz/handle/123456789/14651 From Data to Prediction: Comparative Analysis of Machine Learning Classifiers for Type 2 Diabetes Samet, Sarra; Samet, Ahmed Timely identification and diagnosis of medical conditions hold paramount im portance in averting severe health complications and optimizing healthcare effica-cy. Machine Learning, an offshoot of Artificial Intelligence, possesses considera-ble potential in anticipatory analysis through the integration of Data Mining. The objective of our investigation is to establish a streamlined mechanism for the prompt and precise identification of Type 2 diabetes by utilizing the widely rec-ognized Pima dataset, which encompasses eight clinical parameters. To ensure equitable consideration of all features, we employ the "Standard scaler" technique for feature scaling. Our primary focus lies in enhancing the accuracy of diabetes prognosis by employing supervised machine learning methods, namely Decision Tree, Random Forest, Gradient Boosting algorithms, and Support Vector Ma-chine. Performance evaluation encompasses various metrics such as F1-score, MCC, and other relevant indicators. Notably, Random Forest emerges as the most accurate model, attaining an impressive accuracy rate of 95.24%. Moreover, to mitigate overfitting, we conduct a 5-fold cross-validation, which further af-firms an accuracy rate of 92.55%. It is worth highlighting that our proposed models exhibit superior accuracy in predicting diabetes mellitus when compared to previous endeavors employing the Pima dataset. Fri, 25 Oct 2024 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/14651 2024-10-25T00:00:00Z http://depot.umc.edu.dz/handle/123456789/14650 Designing an AI-Powered Safety System for Industrial Protection Raid Diaeddine, Boukhrissa This study investigates the application of artificial intelligence (AI) in improving industrial safety through computer vision for motion detection, integrated with a Programmable Logic Controller (PLC) system for real-time responses. The primary aim is to design a system that captures and analyzes live video using computer vision algorithms programmed in Python, which subsequently sends safety alerts to the PLC based on the analysis results. By integrating AI-based image analysis with industrial communication protocols, such as Modbus or Ethernet, this system offers a secure and responsive safety solution tailored to industrial environments. Fri, 25 Oct 2024 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/14650 2024-10-25T00:00:00Z http://depot.umc.edu.dz/handle/123456789/14649 Learner autonomy and artificial intelligence (AI): a possible alliance. Khainnar, Yasmina YK While education has always aimed to cultivate the autonomy of the individual, the emergence of artificial intelligence raises new questions. Indeed, by offering customized solutions and automating certain tasks, AI might seem to restrict individuals' ability to develop their own critical thinking. However, it is essential to consider AI not as a substitute for autonomy, but as a tool that can strengthen it. By freeing learners from repetitive tasks, AI allows them to focus on more complex and creative activities, fostering the development of their critical thinking skills and ability to solve complex problems. Fri, 25 Oct 2024 00:00:00 GMT http://depot.umc.edu.dz/handle/123456789/14649 2024-10-25T00:00:00Z