Introduction: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD remains incurable, highlighting the critical need for early diagnosis and intervention to improve patient outcomes. Timely detection plays a crucial role in managing the disease more effectively. Pretrained convolutional neural networks (CNNs) trained on large-scale datasets, such as ImageNet, have been employed for AD classification, providing a head start for developing more accurate models.Methods: This paper proposes a novel hybrid deep learning approach that combines the strengths of two specific pretrained architectures. The proposed model enhances the representation of AD-related patterns by leveraging the feature extraction capabilities of both networks. We validated this model using a large dataset of MRI images from AD patients. Performance was evaluated in terms of classification accuracy and robustness against noise, and the results were compared to several commonly used models in AD detection.Results: The proposed hybrid model demonstrated significant performance improvements over individual models, achieving an accuracy classification rate of 99.85%. Comparative analysis with other models further revealed the superiority of the new architecture, particularly in terms of classification rate and resistance to noise interference.Discussion: The high accuracy and robustness of the proposed hybrid model suggest its potential utility in early AD detection. By improving feature representation through the combination of two pretrained networks, this model could provide clinicians with a more reliable tool for early diagnosis and monitoring of AD progression. This approach holds promise for aiding in timely diagnoses and treatment decisions, contributing to better management of Alzheimer’s disease.

Introduction: Knee osteoarthritis (KOA) is a major health issue affecting millions worldwide. This study employs machine learning algorithms to analyze human gait using kinematic data, aiming to enhance the diagnosis and detection of KOA. By adopting this approach, we contribute to the development of an effective diagnostic methods for KOA, a prevalent joint condition.Methods: The methodology is structured around several critical steps to optimize the model’s performance. These steps include extracting kinematic features from video data to capture essential gait dynamics, applying data filtering and reduction techniques to remove noise and enhance data quality, and calculating key gait parameters to boost the model’s predictive power. The machine learning model trains on these refined features, validates through cross-validation for robust performance assessment, and tests on unseen data to ensure generalizability.Results: Our approach demonstrates significant improvements in classification accuracy, highlighting its potential for early and precise KOA detection. The model achieves a high classification accuracy, indicating its effectiveness in distinguishing KOA-related gait patterns.Discussion: Furthermore, a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method, suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows.

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

This comprehensive paper delves into two vital facets of contemporary higher education. The first segment investigates the transformative force of mobile learning (m-learning), elucidating its far-reaching impact on the educational landscape. Offering unprecedented flexibility and accessibility, m-learning redefines the dynamics between students and educators. The exploration navigates through potential benefits, challenges, and broader implications, providing a nuanced understanding of the evolving landscape of higher education in the digital age. The second segment focuses on the impact of active teaching in a first-year class at the National Engineering School of Carthage (ENICarthage) in Tunisia. Active teaching, recognized for engaging students in the learning process, is scrutinized for its effectiveness in enhancing theoretical understanding and graded assignment performance. This research introduces a dynamic dimension by incorporating mobile learning and tutorials as integral components of active teaching strategies. With its technological leverage, mobile learning aims to enhance student engagement, while advanced tutorials feature simulation functionality for an immersive learning experience. Through statistical analysis, the paper contributes to a nuanced comprehension of the positive influence of active teaching on academic achievements, offering practical insights into the application of active teaching methods and their potential benefits for enhancing student learning outcomes. This synthesis thus presents a holistic view of the dynamic interplay between mobile learning and active teaching in shaping the contemporary higher education landscape.

Fingerprint matching is currently the subject of intense research by both private and academic institutions. So fingerprints are emerging as the most common and trusted biometric for personal identification. For the fingerprint matching problem, the input is some fingerprint images and the output is the probability that the fingerprints were captured from the same finger. This paper presents an improved feature for fingerprint identification, it based on the Euclidian distance between the center point and their nearest neighbor bifurcation and ridge ending minutiae's. These new works overcome the problem of geometric rotation and translation over the acquisition phase of image fingerprints. Fingerprint matching results from the proposed method are validated and the similarity score for the test data available is evaluated. We have tested this technique on more than 1000 images fingerprint taken from “FVC2004”. The experimental results demonstrate the superiority, the effectiveness and the robustness of the proposed method.

In this paper, we propose a new method for fingerprint identification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutiae's. The main advantage of the new method is the reduced number of features vectors used to characterize fingerprint, compared with the classic characterization method based on the spatial coordinate position of bifurcation minutiae points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase of image fingerprints. Whatever the degree of fingerprint rotation, the extraction features used to characterize the fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate position of fingerprint minutiae. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. Extensive experiments prove that the Fingerprint Characterization based on the Euclidean distance between the center point and their nearest neighbor bifurcation minutiae's gives better results in fingerprint classification than several other features.