Master Informatique
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Item Fonctionnalités approfondies pour les systèmes de vérification Palmaire(university of bordj bou arreridj, 2024) - KACIMI lina; - TABET chaimaBiometrics is the automated identification of individuals based on their physical and behavioral characteristics. It helps provide certainty when interacting with familiar or unfamiliar people, authorizing the granting of specific rights or the denial of certain privileges. The underlying principle of biometrics is the assumption that each individual has unique physical and behavioral characteristics that distinguish them from others. Improving human identification techniques currently focuses on exploring new and emerging methods. This development is driven by growing security concerns and the emergence of tampering techniques. The goal is to leverage distinct parts of the human body that can be used for accurate identification, such as fingerprints, palm prints, iris and lips. However, many existing systems and methods suffer from slow processing or require expensive technical equipment. Palmprints have proven to be a promising biometric modality for personal identification due to their uniqueness and stability. This master's dissertation presents an in-depth study on the use of deep features for palm print identity verification systems. We have experimented with CNN models for pre-processing of TANTRIGGS, DOG methods and for feature extraction such as BSIF, GABOR. For classification, we used K-Nearest Neighbors (KNN), Support Vector Machines (SVM), ALMO.Item Deep Learning-based Anomaly Detection in Network Traffic Patterns(university of bordj bou arreridj, 2024) HEDJAM Lidia; BELOUAHRI AyaThe anomaly in network traffic is a crucial issue that can cause significant losses in network security and performance. This prompted us to undertake this work to detect these anomalies accurately and promptly using deep learning techniques. This thesis investigates the use of long short-term memory (LSTM) neural networks, one of the deep learning methods, to detect anomalies in network data flows. LSTMs are well suited to this task thanks to their ability to capture long-term temporal dependencies. Our approach is distinguished by its ability to detect complex and varied anomalies, thus improving the security and efficiency of computer networks. The results show a significant improvement over traditional methodsItem Fouille d’épisodes à partir de séquences d’événements : Application à l’analyse de l’historique des visites de pages web(university of bordj bou arreridj, 2024) HEBBOUL AHLEM; MEKHOUKH LAMISIn a constantly evolving digital environment, the analysis of user navigation on websites represents a major challenge for companies wishing to optimize their digital strategy. This dissertation explores the application of the EMDO (Episode Mining under Distinct Occurrence) algorithm to predict user behavior on the Web by analyzing their web page visit sequences. The algorithm offers an innovative approach to extracting episode-based rules based on distinct occurrences, thus improving the accuracy of predictionsItem Secure P2P(peer-to-peer) Escrow Platform(university of bordj bou arreridj, 2024) 1. Mekkari Zakarya; 2. Drahmoun Wahid; 3. Bounabi Ilyes; 4. Rehahela OussamIn this dissertation, we focus on designing and implementing a platform that offers a unique experience for service providers, freelance contractors, and customers. Our platform, named "Khedemni," has been created to streamline service operations be tween providers and clients. Customers can search for the right professional for their required services, while service providers can showcase their offerings and interact directly with clients. Our platform features a secure payment system, acting as a financial intermediary between the two parties. Once a service is selected and requirements are set, the client pays an amount that is held until the service is completed. Our platform contributes to developing a safe and reliable environment for both parties. As a startup in the Algerian market, it brings mutual benefits to both service providers and customerItem Etude Comparative Des Racinisateurs Arabes.(university of bordj bou arreridj, 2024) MAOUCHE Farah Hiba; KHALFA LyndaArabic language has a complex morphological structure, which presents a unique challenge in natural language processing (NLP). The derivational system of Arabic is based on roots, which are frequently modified to create new words, employing an extensive set of Arabic morphemes affixes such as prefixes, suffixes and more. Stemming is a fundamental task in text processing, plays a crucial role in information retrieval (IR) and text analysis, it reduces words to their basic or root form, facilitating text normalization for easier processing. However, no stemming algorithm for this language is perfect. In this work, we are going to focus on comparing and evaluating the performance of several Arabic stemmers namely, ISRI, Tashaphyne and Snowball. We intend to assess their performance on two distinct datasets using advanced techniques such as neural networks and machine learning classifiers. Additionally, we aim to determine which combination of stemmer and classifier yields the best results, providing invaluable insights for Arabic text processing applicationsItem 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 Classification automatique de la maladie de Parkinson à partir de la voix(university of bordj bou arreridj, 2024) Dahili Ahlem; Benmessahel SamiraSpeech analysis is a promising approach for early and automated diagnosis of Parkinson's disease. This non-invasive and inexpensive method relies on the characteristic voice changes of the disease, present from the early stages, to identify patients. Automated systems based on artificial intelligence can analyze these voice changes and effectively discriminate Parkinson's disease patients from healthy subjects. Despite challenges such as voice variability and background noise, speech analysis has great potential to improve the diagnosis and management of Parkinson's disease. Ongoing research aims to refine this technology and make it a valuable tool for improving the quality of life for patients.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.Item Autism Detection Using Machine Learning(2024-09-23) Youcef Kouadria; Sakhraoui ZineddineThis research investigates the use of machine learning, specifically deep learning, for the detection of Autism Spectrum Disorder (ASD) using facial image data. By employing the VGG16 model with transfer learning, the project achieved high classification accuracy, demonstrating the potential of machine learning in supporting early ASD diagnosis. The use of automated image analysis provides a non-invasive, scalable solution that could complement traditional diagnostic methods. The results indicate that machine learning can significantly contribute to healthcare by enabling quicker and more accurate ASD diagnoses. Future improvements could include the use of larger datasets and multimodal data such as voice and behavioral analysisItem Classification automatique de la maladie de Parkinson à partir de la voix(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) Dahili, Ahlem; Benmessahel, SamiraAbstract Speech analysis is a promising approach for early and automated diagnosis of Parkinson's disease. This non-invasive and inexpensive method relies on the characteristic voice changes of the disease, present from the early stages, to identify patients. Automated systems based on artificial intelligence can analyze these voice changes and effectively discriminate Parkinson's disease patients from healthy subjects. Despite challenges such as voice variability and background noise, speech analysis has great potential to improve the diagnosis and management of Parkinson's disease. Ongoing research aims to refine this technology and make it a valuable tool for improving the quality of life for patients.