Faculté des mathématiques et de l'informatique
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Item Using Multi-objective Meta-heuristics for Data Mining(university of bordj bou arreridj, 2025-01-05) Soumaia KAHLOULThe ability to extract knowledge from large datasets is essential for innovation and informed decision-making, a process known as knowledge extraction or data mining. Traditional methods often fall short in fully utilizing data potential, necessitating the development of new algorithms for better insights. This thesis explores an innovative approach by integrating deep learning with advanced feature selection techniques to improve the classification accuracy of COVID-19 cases from chest X-ray images. The dataset includes X-ray images categorized as COVID-19, pneumonia, and normal. We employ the Binary Multi-Objective Henry Gas Solubility Optimization Algorithm (B-MOHGSO) for feature selection and leverage models like AlexNet, VGG19, GoogleNet, and ResNet for feature extraction. Eight versions of B-MOHGSO were tested, with k-nearest neighbors (k-NN) as the classifier. The study highlights the significant impact of S-shaped and V-shaped transfer functions on binary transformations and classifier performance in high-dimensional medical imaging. Notably, B-MOHGSO algorithms, particularly those using V-shaped transfer functions, excelled in selecting relevant features while maintaining high accuracy. When combined with the VGG19 model and SVM classifier, B-MOHGSO significantly reduced the feature set without sacrificing performance. The application of B-MOHGSO in COVID-19 classification is crucial for identifying key features that enhance diagnostic processes and treatment strategies. By adapting MOHGSO for discrete optimization, this research aims to address the complexities of high-dimensional medical data and improve healthcare analytics outcomes.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 Towards biometric recognition system based on explainable classifier methods(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) Djouamai, Zineber the past decade, biometric systems have advanced significantly, achieving high classifica tion accuracy and minimal equal error rates (EER). However, many conventional methods lack transparency and explainability, which are critical in areas like security and identity verifica tion, where trust is paramount. This limitation restricts the ability to understand these systems’ decision-making processes, making it difficult to ensure reliability and accountability in sensi tive applications. To address these challenges, we propose the development of an efficient biometric system based on explainable, rule-based classifiers. Unlike traditional approaches, our method incorpo rates explainability at its core, offering clear insights into the system’s decision-making process while maintaining high performance. This approach ensures that the system is not only accurate but also adaptable and user-friendly, enabling its application across a range of classification and predictive tasks. By prioritizing transparency alongside performance, the proposed system aims to meet the growing demand for trust and usability in biometric applications. Its dual focus on achieving low EER and delivering explainable outcomes ensures it is suitable for deployment in critical domains. This balance between accuracy and explainability positions the system as a reliable and advanced solution for high-stakes environments like security and identity managementItem outils de la programmation mathématique ( Maple)(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) Attia, AbdelouahabItem Mathématiques 1 ( Math01)(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2022) Khaled, HamidiItem Contribution à la fouille de données imparfaites(UNIVERSITY BBA, 2024) SEDDIKI, ImaneSequential rule mining is a technique in the field of data mining that allows one for the discovery of temporal relationships between events and for making predictions. The discovery of temporal relationships between events stored in large databases is important in many areas such as stock market analysis, e-learning, etc. In many real-world applications, such as wireless sensor networks, databases contain uncertain data due to factors such as missing, incomplete, or inaccurate information. Data uncertainty is modeled by an existential probability that is associated with each element in each sequence in the database. In this thesis, we focus on the extraction of uncertain sequential rules. We propose a new approach for extracting sequential rules from uncertain sequence databases, by evaluating the performance of the developed methods on synthetic and real data. ملخص استخراج القواعد التسلسلیة، المعروف أیضًا باسم استخراج الأنماط التسلسلیة، ھو تقنیة في مجال استكشاف البیانات تسمح باكتشاف العلاقات الزمنیة بین الأحداث والتنبؤ بھا. یعد اكتشاف العلاقات الزمنیة بین الأحداث المخزنة في قواعد البیانات الكبیرة مھمًا في العدید من المجالات مثل تحلیل سوق الأسھم والتعلم الإلكتروني وما إلى ذلك. في العدید من التطبیقات الواقعیة، مثل شبكات المستشعرات اللاسلكیة، تحتوي قواعد البیانات على بیانات غیر مؤكدة بسبب عوامل مثل المعلومات المفقودة أو غیر المكتملة أو غیر الدقیقة. یتم نمذجة عدم الیقین في البیانات من خلال احتمال وجودي مرتبط بكل عنصر في كل تسلسل في قاعدة البیانات. تركز ھذه الأطروحة على استخراج القواعد التسلسلیة غیر المؤكدة. نقترح نھجًا جدیدًا لاستخراج القواعد التسلسلیة من قواعد بیانات التسلسلات غیر المؤكدة، من خلال تقییم أداء الطرق المتطورة على البیانات الاصطناعیة والواقعیة.Item 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.Item Suivre les rumeurs dans les réseaux sociaux(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) LAALAOUI, Moun; 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 L'autonomie de l'apprentissage : le e-learning à travers la plateforme Moodle. Cas des étudiants de première année licence français Université Mohamed El Bachir El Ibrahimi BBA(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) -Dounia, MEZHOUD; - Amel BEN, REDOUANE; - Maria, RAHMANIItem L’Apprentissage Automatique Pour La Prédiction De Lien Dans Les Réseaux Complexes(Université de Bordj Bou Arreridj Faculty of Mathematics and Computer Science, 2024) BOUABDALLAH, Maroua; DRIAI, IbtissemThis work explores graph theory concepts to model and analyze complex networks with an emphasis on the use of machine learning. Methods examined include similarity measures based on common neighbors, measures based on the length of paths, We also evaluated the effectiveness of different classification algorithms, such as Support Vector Machine (SVM), K Nearest Neighbors (KNN)…Our results show that certain combinations of these methods and algorithms make it possible to obtain accurate predictions of link classes in complex networks, thus opening new perspectives for their analysis and application in various fiel