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This paper explores a unique way in which the thinking algorithm adds an extra logical substrate to a Web search query using artificial intelligence. Instead of just going after keyword searching, the algorithm tries to assess the motives of the user behind entering a query. The algorithm tries to find the reasons as
. Healthcare data is easily available or can be recorded at low cost. The proposed method is used to show various relevant videos for a given user's need by keyword based label matching. The proposed method performs video data collection and speech to text conversion to create the transcription snippets. Finally, keyword based
the word attributes are trained by the labeled training weblogs, and some keywords of a testing weblog are extracted as one part of the tags based on the probability distributions. Then the other part of the tags are obtained from the first part ones with the help of Latent Semantic Indexing (LSI) model. Experiments on a
With the increasing popularity of Web 2.0, a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.