Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PD

Synthesis and Biological Evaluation of New Chromenes and Chromeno[2,3-d] pyrimidines

A simple and efficient approach has been developed to synthesise novel and functionalised 5H-chromeno[2,3-d] pyrimidines derivatives (4a–h). This  approach entails treating 2-amino-3-cyano-4H-chromenes (3a–h) with formamidine acetate under microwave irradiations and solvent-free conditions. All  structures of new compounds obtained in this study were characterised by IR, MS, 1H and 13C NMR analysis. Additionally, the synthesised compounds  were investigated for their antibacterial and antioxidant potential. Compounds 3b, 3c, 3e, 4c and 4e showed significant activities

Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512

Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512

Abrupt introduction of distance learning during the covid-19 pandemic: what psychological impact on teachers?

Background: As soon as the COVID-19 pandemic appeared, the Moroccan education ministry decided to adopt distance learning (DL). Our target was to study the psychological impact of DL on Moroccan teachers during the pandemic. Method: This cross-sectional study used an online questionnaire based on the Hospital Anxiety and Depression Scale. Results: Among 148 responses,64.9% were women, and the average age was 41.1±11.5 years. 79.1% participated in DL, 58.8% were required to acquire DL tools and 71.6% had never received DL training. Between the start and the end of confinement, we noticed a decrease in the motivation of teachers.36.2% had definite depressive symptomatology and 41.3% had certain anxiety symptomatology with a significant predominance in women. The frequencies of depression and anxiety were higher in those who had participated in DL, but the association was not significant. Depression was significantly frequent among teachers who were obliged to acquire tools to practice DL p=0.02, those who had never received training DL p=0.046, and those who were not satisfied with the situation p=0.03. Conclusion: We didn’t find a direct association between DL and anxiety and depression, which the small sample size may explain, but we did find an association with the variables related to DL

Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

In the field of online learning, the development of learning objects (LOs) has increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, the lack of a conceptual model for sharing suitable LOs between learners makes multiple challenges. In this regard, multi-label classification plays a significant role to make high-quality LOs, which can be accessible and reusable. This article highlights a new way of using learning objects based on Multi-Label Classification (MLC) and clustering algorithms with feature selection techniques. It suggests a new system that makes the most suitable choice among many alternative sources based on the Sharable Content Object Reference Model (SCORM). The proposed algorithm has been tested on a real-world application dataset related to the data analysis service for the learning science community. The experimental results show that our algorithm outperforms the traditional approach and produces good results

Dynamic Reconfigurable Component for Cloud Computing Resources

In recent years a new concept of IT organization emerged, the Cloud Computing. With This new concept, the resources are dynamically scalable, virtualized and provided to users as a service on the Internet. It is primarily intended to meet the demands of users and allow them access to virtually unlimited resources. This model motivates many academic institutions and non-academics as well to develop open-source solutions to improve performance. Among these techniques, dynamic reconfiguration of cloud resources has to take an interest. In this paper an approach for optimization resources is presented, based on dynamic reconfiguration techniques. In fact, a Dynamic Reconfigurable Component (DRC) is proposed to be added to the cloud system, that optimize the use of cloud resources and enable dynamic resource allocation. Then, the implementation of this DRC component is provided

Implantation d’un modèle conceptuel avec multi-représentation

info:eu-repo/semantics/publishe