A Fuzzy Near Neighbors Approach for Arabic Text Categorization Based on Web Mining Technique

Gouiouez, Mounir

doi:10.1007/978-3-030-73882-2_52

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…The LSA framework uses the semantically similar terms extracted from the web-mining techniques to group the LOs together and classify them based on their context. Also, in the metadata of each LO the set of words that co-occur in a snippet with a target term can be calculated with FCM based on the distance between words similarity (Gouiouez, 2021). In our method, we are used the following steps of LSA to extract relevant information of LOs:Text pre-processing: The text documents are cleaned, tokenised and stemmed to remove noise and make the text more consistent.Term-metadata matrix: A matrix is created where each row represents a term and each column represents a metadata.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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New perspective of learning objects in e-learning system

Amane

Aissaoui

Berrada

2023

IJILT

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PurposeTogether, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.Design/methodology/approachThe development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.FindingsTo achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.Originality/valueFinally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

New perspective of learning objects in e-learning system

Amane

Aissaoui

Berrada

2023

IJILT

View full text Add to dashboard Cite

show abstract

“…Web mining techniques refer to all the techniques aimed at exploring, processing, and analyzing large masses amount of information related to web activity, while FCM divides numerical data into clusters. This idea may be implemented on the learning objects by taking the metadata of each object, which contains a set of words that co-occur in a snippet with a target term, and applying FCM to calculate the distance between the words' similarity [28]. For the second question, we thought of using multilabel classification in order to reuse it in different contexts.…”

Section: -Our Approachmentioning

confidence: 99%

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

2023

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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: PDF

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A Fuzzy Near Neighbors Approach for Arabic Text Categorization Based on Web Mining Technique

Cited by 2 publications

References 13 publications

New perspective of learning objects in e-learning system

New perspective of learning objects in e-learning system

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

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