Master Informatique
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Item Brain MRI Image Segmentation Using YOLO and U-Net Deep Learning Models(university of bordj bou arreridj, 2025) Dhikra YOUSFIThe precise detection of brain tumors is crucial in medical practice, and the use of au tomated segmentation techniques has the potential to improve this significantly. The paper presents a comparative evaluation of U-Net and YOLOv8 deep learning models for automatic brain tumor segmentation from magnetic resonance imaging (MRI). U-Net, with its ability to achieve pixel-level accuracy, performed superiorly in terms of Intersection over Union (IoU) and Dice coefficient, indicating its robustness in boundary delineation. In contrast, YOLOv8 had better precision and recall, thus being more appropriate for fast segmentation. The com parison was made with the standard metrics of precision, recall, IoU, Dice coefficient, and F1 score, providing a well-rounded evaluation. The findings show that U-Net is better at gener ating precise segmentation boundaries, whereas YOLOv8 performs better when it comes to detecting tumors quickly and accurately. This comparison provides important insights for the determination of the most appropriate model depending on specific application requirements.Item Classification des émotions à partir de signaux EEGàl’aide de techniques d’apprentissage profond(university of bordj bou arreridj, 2025) BELARBI Chaima; KEDJOUTI AmelIn recent years, human emotion recognition from EEG signals has seen substantial progress, largely driven by advances in deep learning techniques. Unlike traditional methods based on facial expressions, EEG signals provide greater objectivity and robustness against voluntary manipulations. In this study, we propose an automatic emotion classification approach based on EEGsignals, utilizing the EEG Brainwave Dataset and the eeg-dataset-emotions. Two deep learning architectures—1D Convolutional Neural Network (1D CNN) and Long Short-Term Memory (LSTM) networks—were implemented to extract spatial and temporal features, res pectively. The experimental results demonstrate that the combined use of these models en hances the accuracy and reliability of emotional state recognition.Item Deep Learning Algorithms for Remote Sensing(university of bordj bou arreridj, 2024) Bouketir Hadda; Habbeche DouniaDeep learning has revolutionized the analysis of data collected from unmanned aerial vehicle (UAV) imagery, allowing for more profound insights, precise analysis, and enhanced data extraction. This advancement has significantly contributed to the refinement of semantic segmentation techniques. Particularly convolutional neural networks (CNNs) have emerged as powerful tools in this domain, outperforming traditional methodologies. Nonetheless, challenges persist, including feature extraction, class imbalance issues, overfitting, and vanishing gradients that hinder deep neural network training, consequently impacting segmentation performance. To address these challenges, we propose a novel approach by integrating the U-Net architecture with ResNet34 backbone leveraging its strong feature extraction capabilities. These features are further improved by using trained weights from the ImageNet dataset. We train and evaluate the proposed model on several UAV datasets, including Aerial Semantic Segmentation, LandCover.ai, UAVid, and AeroScapes. We achieve remarkable performance, higher accuracy, precision, recall, F1-score, and miou compared to other methods.Item Suivre les rumeurs dans les réseaux sociaux(2024) LAALAOUI Mouna; LAHRI SarahThe emergence of the Internet has transformed global communications, making information accessible at unprecedented speeds. With the emergence of social media, people can now connect, share and interact immediately with a variety of content. However, the ease with which information can be exchanged also facilitates the rapid spread of unverified rumors and false information. In this work, we aim to track and detect rumors, and to this end, we will present a model based on a deep learning approach using LSTM and RNN algorithms in order to obtain the best possible classification and more accurate and valid results.