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in the emergent ocean of information. The upcoming demand for data storage in petabytes and exabytes of data has also resulted in putting pressure in organizing the file structure in such a way that retrieval results of searching a keyword should match with the growing pace of data storage. As a result, there is an
repository and the associated search techniques viz. keyword-based search and signature matching in a reasoned manner by exemplifying the working methodology of these techniques via its automated implementation. To address the purpose a new tool has been developed name ARE (Automated Repository Exploration) ver. 1.0.0. Readers
clustering genes is done in two steps: First, keywords corresponding to all genes of interest from a subset of MEDLINE database were extracted automatically using TF-IDF and Z-scores. In the second step, the classic K-means algorithm was used to group genes into clusters of genes based on the keyword features.
With the gradual improvement of the level of educational information, the idea of education big data has gone deep into people's minds. More and more teaching staff and researchers have the consciousness of the data needs, and expect to do the data mining and learning analysis of education big data. However, the most realistic and basic problem they have in the process of carrying out research is...
In this paper, an intelligent concept based search engine has been presented that can be used as a multilingual platform for different search queries. It retrieves those results pages also which don't have directly the keywords but contains the synonyms or related words. In response to a query for the word “car
Keyword-based search engines are becoming increasingly sophisticated, and yet navigating the ever-increasing collection of academic knowledge remains an arduous task. Keeping abreast of relevant scientific literature is often a fragmented process that breaks the workflow of academic writing.
requires knowledge of appropriate keywords due to different context and language of the past. Thus, non-professional users may have difficulties with conceptualizing suitable queries, as, typically, their knowledge of the past is limited. In this paper, we propose a novel approach for the temporal correspondence
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