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Recent Submissions
توظيف الترجمة اآللية في التعلم المدمج ـــ تالميذ السنة األولى ثانوي جذع مشترك علوم وتكنولوجيا نموذجا
(university of Tlemcen, 2026-02-17) دلباز نادية
Machine translation is used within blended learning to facilitate the learning of
English, particularly vocabulary comprehension, in an integrated educational
environment that combines face-to-face and digital learning, thereby enhancing
learner interaction and effectively developing their language skills.
English in Algerian E-commerce: Practices and Attitudes on Instagram
(University of Tlemcen, 2026-02-17) Nawal Abid; Farah Berrezoug
Algeria is characterized by a diverse linguistic landscape with Arabic, Berber, and French widely used. However, in last
years, English has been progressively gaining ground, especially in education, digital spaces, and among youth. With the
increasing popularity of social media, Instagram has become an important platform for e -commerce, where English is
increasingly present on sales pages. This study investigates how English has been integrated into Algerian e-commerce
platforms by analyzing sellers’ motivations for using it and customers’ attitudes toward it using a mixed-methods approach
combining questionnaires, interviews, and content analysis, the results clearly show that English is starting to establish
itself as a strategic linguistic tool in this rapidly expanding sector.
Les interférences langagières chez les étudiants du Zimbabwe en mobilité académique externe en Algérie
(University of Tlemcen, 2026-02-17) MABEDHLA Charles
Ce travail s’inscrit dans le champ des Sciences du langage et porte sur l’étude des interférences
langagières dans les productions orales des étudiants zimbabwéens anglophones en situation de
mobilité académique à l’Université de Tlemcen en Algérie. Bien qu’au Zimbabwe existe seize
langues officielles c’était inévitable que les étudiants du Zimbabwe manifestent des
interférences en face la langue française donc notre objectif était de dégager les types des
interférences et leurs originaires et la forme qui dominé les autres. Nous avons adopté une
approche contrastive. Au niveau de la méthodologie, nous avons opté pour la méthode
qualitative et nous avons utilisé l’entretien semi-directif comme notre outil de collecter des
données. Nous avons sélectionné notre échantillon par raison et deux étudiants qui ont plus que
deux ans en Algérie étaient sélectionnés des deux spécialités différentes, de foresterie et de
biochimie. Nous avons utilisé un téléphone portable pour enregistrer les discours oraux pendant
l’enquête, après nous avons fait la convention de transcription. Grâce à la convention
phonétique nous avons identifié les interférences et ils ont classées en trois grandes catégories :
la phonétique, la lexicale, et aussi la morphosyntaxique. Les analyses ont révélé que les
interférences phonétiques et morphosyntaxiques sont les plus fréquentes, bien que leur
répartition varie selon les locuteurs, aussi nous avons noté que les résultats obtenus mettent en
évidence une influence plus marquée de l’anglais que du shona. Ce travail contribue ainsi a une
meilleure compréhension des phénomènes de pratiques langagières et des mécanismes
d’interférence dans le cadre de mobilité académique des étudiants anglophones aux contextes
francophones
Development of a diagnostic aid system for neurodegenerative pathologies: Application to the detection of Alzheimer’s disease
(University of Tlemcen, 2025-05-15) Saim, Meriem
Alzheimer’s disease (AD) is a degenerative disorder and one of the most widespread
forms of dementia, with no current cure available. This absence of a cure has led
the medical field to focus on managing the symptoms of the disease. However, its
progressive nature complicates the identification of disease stages, often requiring
years of expertise. Consequently, computer-aided diagnostic systems are essential
to assist clinicians in accurately defining disease stages, enabling more targeted and
effective treatments. Given the lack of a cure, early detection of AD, particularly
at the Mild Cognitive Impairment (MCI) stage, is crucial to slow or halt disease
progression. However, distinguishing MCI symptoms from normal aging remains
challenging, even with MRI imaging, due to the subtle differences across MCI substages.
This thesis focuses on accurately classifying AD stages, emphasizing early-stage
detection. Two MRI databases were utilized, leading to four methodologies addressing
specific challenges while fulfilling the research objectives. The first system
combines the Histogram of Oriented Gradients (HOG) with the Bias Correction
Fuzzy C Means algorithm and machine learning classifiers, achieving an accuracy of
96.8% for the first database and 96% for the second. The second system shifts to
the frequency domain, employing the Fast Finite Shearlet Transform (FFST) and
Gray Level Co-occurrence Matrix (GLCM) angles, resulting in 72% accuracy for the
first database. Building on these approaches, the third system leverages inductive
transfer learning with layer-wise fine-tuning of ten pre-trained models. The best
results were achieved using the Xception architecture, yielding 85.19% accuracy for
the first database and 77.23% for the second using VGG19.
Finally, the fourth methodology integrates machine learning and deep learning
by automatically extracting features and refining them using Bayesian optimization.
It achieves an accuracy of 98.45% for the first database and 78.54% for the
second. These methodologies address critical research questions and highlight the
importance of feature quality, hyperparameter optimization, and data augmentation
techniques in medical imaging. The findings underscore the potential of advanced
computer-aided diagnostic systems to enhance the detection and staging of
Alzheimer’s disease.
Early detection of pathological behaviors by studying heart sounds
(University of Tlemcen, 2025) Hakkoum, Khaoula Nour El Houda
This thesis investigates advanced signal processing techniques for the early detection and
management of cardiac pathologies, focusing on the analysis of phonocardiogram (PCG)
signals and intracardiac pressure estimation. It emphasizes the significance of accurate
cardiovascular health assessment as a critical factor in improving patient outcomes. The
research employs artificial intelligence (AI) methodologies, leveraging machine learning
algorithms to enhance the accuracy of pathological identification in PCG signals. Additionally,
it explores non-invasive methods for estimating cardiac pressures, highlighting
their crucial role in diagnosing conditions such as heart failure, valvular disorders, and pulmonary
hypertension. The findings demonstrate that integrating AI with traditional cardiac
monitoring can significantly improve diagnostic precision, facilitating timely clinical
interventions and paving the way for more effective cardiovascular disease management.