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A keyword choice and analysis approach in SEO is studied to deal with the issues such as low efficiency, poor reliability and instability optimization in artificial SEO processing in this paper. A keyword expansion method is proposed by reversing search engine's related search keywords to meet user's requirements
HTML documents and the visual characteristics of the images. By converting and pruning this learned tree, a set of rules with high estimated accuracy which determines whether or not a word can be the keyword of an image can be generated. Upon experimental results, the proposed method made 57 rules and the precision and
traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages
small number of HTML input elements extracted from user input HTML forms and a few keywords. It utilizes pre-query technique and post-query technique in a hierarchical manner. Decision trees and multi layer artificial neural networks were used to obtain the classification rates over 91% to classify search forms and non
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.