2 research outputs found
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
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
Multi-label recognition (MLR) with incomplete labels is very challenging.
Recent works strive to explore the image-to-label correspondence in the
vision-language model, \ie, CLIP, to compensate for insufficient annotations.
In spite of promising performance, they generally overlook the valuable prior
about the label-to-label correspondence. In this paper, we advocate remedying
the deficiency of label supervision for the MLR with incomplete labels by
deriving a structured semantic prior about the label-to-label correspondence
via a semantic prior prompter. We then present a novel Semantic Correspondence
Prompt Network (SCPNet), which can thoroughly explore the structured semantic
prior. A Prior-Enhanced Self-Supervised Learning method is further introduced
to enhance the use of the prior. Comprehensive experiments and analyses on
several widely used benchmark datasets show that our method significantly
outperforms existing methods on all datasets, well demonstrating the
effectiveness and the superiority of our method. Our code will be available at
https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR) 202