Faculté des mathématiques et de l'informatique
Permanent URI for this communityhdl:123456789/17
Browse
459 results
Search Results
Item INTRODUCTION TO DYNAMICAL SYSTEMS(university of bordj bou arreridj, 2025) Aziza BerbacheItem Algorithmic and Data Structures 2(university of bordj bou arreridj, 2023) SAIFI LyndaItem Algorithmic and Data Structures 1 With Solved exercises in algorithmic language and C language(university of bordj bou arreridj, 2023) SAIFI LyndaItem Cognitive Psychology in Service of the Machine: Towards the Study of the Human Cognition(university of bordj bou arreridj, 2025) AGTI NadiaThe cognitive space plays an important role in structuring an action and the possible actions to be executed; therefore, actions are not only governed by a dynamic perception of knowledge but also by previously acquired knowledge. The neurophysiologist Alain Berthoz sees the brain as “a simulator of action and an emulator of reality.” He believes that it is through action—and not language—that we construct our perception of the world. The goal here is to integrate knowledge about actions/events from memory or past experiences and combine it with infor mation perceived from the environment. The complexity of the problem increases when we take into account the unpredictable nature of human behavior. Today, technological breakthroughs are attempting to enable direct communication between machines and the human brain, with the aim of performing actions through thought. The question now is whether it is possible to understand the physical or mental state that led a person to: 1) undertake actions they should have avoided, or conversely, 2) be encouraged to carry out actions they had previously abandoned. We are interested in these theories in order to reproduce a person’s psychological state, with the aim of understanding and explaining the reasons behind the two points above. This is done by relying on semantic interdependencies and the properties of actions carried out in the past or present.Item Narrative Approaches and Machine Learning for Health Knowledge Management: Themes Extraction and Texts Classification(university of bordj bou arreridj, 2025) Hafida TiaibaThe healthcare sector generates a massive volume of heterogeneous information every day, a large portion of which takes a narrative form, such as medical records, hospitalisation reports, and patient testimonies. In the context of exponential growth in textual medical data, it has become essential to transform this information into machine-readable knowledge to support personalised diagnoses, therapeutic monitoring, and the practice of precision medicine. This thesis proposes an interdisciplinary approach that combines symbolic arti f icial intelligence, machine learning—including deep learning techniques—and medical on tologies to enhance the representation, organisation, and utilisation of healthcare knowledge. Ontologies structure and provide meaning to specific terms, abbreviations, and contextual dependencies present in medical texts, while advanced machine learning techniques facilitate their automatic classification into disease categories, treatment plans, or predefined diag nostic groups. The integration of these methods provides a robust framework for managing narrative knowledge in healthcare, optimising the retrieval of relevant information, support ing clinical decision-making, and enabling treatment planning tailored to individual patient needs. Thus, combining narrative formalisms, statistical methods, and ontological resources offers a powerful means to fully exploit the potential of textual medical data.Item On the efficiency of the transformation-based template protection algorithms(university of bordj bou arreridj, 2025) Zineb MaarefWith the intense proliferation of biometric identification systems (BIS), several security con cerns about the vulnerability of the user templates have emerged. Indeed, unlike the conven tional security systems that are based on passwords or tokens which are renewable, a biometric template is not renewable once compromised. In addition, a compromised template can reveal the original biometric data, which constitutes a clear threat, as it can be used to track the user from one application to another. In fact, new algorithms have been proposed in the literature to reconstitute the original biometric trait by using the extracted features, attacking the stored features makes it possible to reconstruct the user’s fingerprint, face, or other biometric trait in order to usurp the identity of another person or entity. For these reasons, cancelable biometric template protection methods have been proposed to overcome these problems. Their basic idea is to transform the biometric data and achieve the matching in the transformed domain. The transform function must simultaneously fulfill the following four properties: non-invertibility, cancelability, accuracy and diversity. The non-invertibility property guarantees that the origi nal biometric data can’t, or it is hard to, be recovered even if some parameters of the transform function are known. This is generally ensured by the non-existence of the inverse transform function (one-to-many inverse-transformation) or by, simply, making the search-space size very large to escape to a brute-force attack. This thesis aims to investigate the robustness of cancelable biometric systems by analyzing and classifying various attacks targeting these systems based on well-defined criteria to enhance their security level. Additionally, it proposes a comprehensive evaluation framework grounded in stringent standards to assess the effectiveness of protection schemes against such attacks. Furthermore, a protection system for palmprint templates is implemented using irreversible transformation, ensuring a high level of security while ensuring the practical characteristics of these systems. On the other hand, we are interested in transformation-based techniques that establish a map ping between the original biometric template points and the transformed template. An attack against a cancelable fingerprint scheme is conducted to demonstrate the possibility of inverting the transformation function and to analyze the impact of correlation between multiple instances of protected templates generated from the same biometric trait on the efficiency of such algo rithms.Item Sur les équations intégo-différentielles et la méthode des éléments finis(university of bordj bou arreridj, 2025) ZERAIBI KhalissaThe main objective of this thesis is to propose a theoretical and numerical study on lin ear integro-differential equations. we used Lax Milgram theorems to provide existence and uniqueness results for linear integro-differentials. In addition, we applied the finite element method for the approximate solution of some linear integro-differential equations. Finally, several numerical examples are given to show the effectiveness of our approaches.Item Issues de la simulation basée crowdsourcing pour l’informatique verte(university of bordj bou arreridj, 2025) Bilal BENCHARIFIn this thesis, we propose an innovative approach to optimize logistics systems by integrating crowdsourced real-time data collected via the Google Distance Matrix API. By leveraging a probabilistic tabu search algorithm, we have developed a framework ai med at improving the management of delivery routes in the context of green computing. Experiments conducted on both simulated and real data reveal that speed has a decisive impact on energy consumption. Furthermore, the analysis demonstrates that the trave led distance alone is not a reliable indicator of energy usage—indeed, a longer route does not necessarily imply higher energy consumption, especially when coupled with optimized traffic conditions. These results underscore the potential of collaborative approaches in transforming sustainable logistics and open up new perspectives for optimizing transpor tation systeItem Accélération de la convergence de méthodes numériques pour résoudre des équations intégrales(university of bordj bou arreridj, 2025) ABDENNEBI IssamThe research work presented in this thesis focuses on improving the convergence speed of numerical methods for solving integral equations. These equations often introduce a very complex behavior, posing significant challenges to traditional numerical techniques, par ticularly in terms of convergence and accuracy. To address these challenges, we have de veloped and analyzed an adaptive spectral collocation method for Fredholm and Volterra integral equations of the second kind, which can achieve fast convergence and high ac curacy despite the fact that its solution exhibits localized rapid variations, steep gradi ents, or a steep front. Adaptivity is implemented using a suitable family of one-to-one mappings to generate a new equation with smoother behavior that can be approximated more accurately. The proposed method can achieve exponential accuracy by adjusting a parameter-dependent mapping in the modal approximation according to the given data. Finally, several numerical examples are given to show that the proposed method is prefer able to its classical method and some other existing approaches with a relatively smaller number of degrees of freedomItem Sur quelques problèmes d’évolution paraboliques(university of bordj bou arreridj, 2024) ILEAM HEDDADJIThe aim of this thesis is to study the theory of sums of linear operators and the theory of semigroups in Banach space and how to apply them to differential equations and partial differential equations