Efficient Relational Interest Feature Selection for Improving the Quality of M-distance Education Using Content-Based Information Similarity Measure
DOI:
https://doi.org/10.26438/ijcse/v6i10.845855Keywords:
knowledge learning, content mining, interest analysis, feature analysis, similarity measureAbstract
The advanced development of education needs the distance learning for improving the student knowledge based on the relational content providence. E-learning improvements are based on M-learning techniques through the knowledge learning process without providing the right content of subjectivity resource to the student be to create the problems. The Mlearning process contains digital information with subjectivity reference of content based on the student interest. The content analysis techniques doesn’t create relational subjectivity interest measure on multimedia content services. To intake the challenge approach, we propose an Efficient Relational interest feature selection for improving the quality of M-distance education using content-based information similarity measure(RIF: MDEISM). This initially analyses the interest in multimedia content information to extract the relation feature on the subjectivity. Further, the extracted features are observed by relative semantic analysis using information similarity measure to get the optimized result from web learning resources. The resultant proves the higher efficient relational content analysis to improve the m-learning distance education
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