Analysis of Document Summarization and Word Classification in a Smart Environment

Background/Objectives: The need for digital learning is rapidly increasing with the effective granularity of learning objects. Retrieval of learning materials from smart environment helps the learner to have a personalized experience using classification algorithms. Methods/Statistical Analysis: The digital learning objects are stored in a local repository in the smart architecture. The framework involves mobile agents which play a vital role in predicting the intuitive nature of the learner which helps the learner to get the targeted content. Content retrieval is based on the document summarisation and word classification which is proposed using classification algorithms like content similarity algorithm. Effective personalization is obtained by better accuracy in terms of precision and recall. Findings: Document summarization is done using classification algorithm initially based on the type of document retrieved by the learner. The word classification based on the retrieved document is further processed with a pre processing method which is used in the domain ontology. The similarity of the topical terms retrieved helps in classifying the document and presenting the content to the learner for more personalized experience. Further these contents which are stored in a smart framework include mobile agents which help in providing synchronous communication between the users working on similar problems at the same time and gives uninterrupted content to the user based on the query even while the user is moving in different locations. The content similarity algorithm provides better accuracy of retrieved data thereby providing the exact document requested by the learner. The samples of the data classification provide the expected the result of the retrieved data which involve document summarization based on the word classification. The results have proved that the learner has a better intuitive experience with the targeted document wherein the learner gets better personalization. Applications/Improvements: More intelligent agents can be involved for learner as well as author to make the system more personalized and provide better collaboration. The classification algorithms along with clustering of documents obtained after summarization can yield better accuracy for large data sets.