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For each treatment plan, patient adherence can be managed, audited, and improved by the Patient Adherence Management System applying Intelligent Keyword (PAMSIK) featuring the use of intelligent keywords to navigate users to the target in-time knowledge and also leverage the collective power - peer learning to
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
We consider topic detection without any prior knowledge of category structure or possible categories. Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with
overlapping communities. Inspired by natural societies, a forum is deemed as a complex network in which all entities (keywords, posts and user) of an online forum are grouped into a series of communities that can share members with each other. To enable this, a kind of keyword association graph is constructed based on the co
This paper proposes a novel method to generate labels for grouping and organizing the search results returned by auxiliary search engines. It has applied statistical techniques to measure the quantities of co-occurrence keywords for forming the label matrix of them, and then agglomerated them into higher-level
The amount of information on the Web is growing at an exponential rate. Information overload has brought a heavy burden for modern life. Keyword based search engines no long fill the needs of many people. This paper introduces an approach towards intelligent information retrieval by providing clustered Web pages and
Since keyword-based search engine usually return large amount of results in which there are many unrelated documents and many documents with same content, automatic clustering technology is used to classify the retrieval results. While there are large amount of Web retrieval results, the clustering process usually
This paper proposes a system for finding a userpsilas interests on the Internet. It is based on his browsing behaviors and the contents of his visited pages. The system has two features. One is building userpsilas browsing interests implicitly, multiple keyword vectors, one per interest. The other is that it can
The background of this paper is the issue of how to overview the knowledge of a given query keyword. Especially, we focus on concerns of those who search for Web pages with a given query keyword. The Web search information needs of a given query keyword is collected through search engine suggests. Given a query
title, keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been
In this paper, we examine the significance of expansion of the user query by two techniques namely Efficient Clustering-By-Direction and Theme Clustering. These two techniques produce the clusters of keywords extracted from the set of retrieved documents for the user query. The former clustering is based on
More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text
images are to be re-ranked using visual features after the initial text-based search. Here first query keywords are utilize for separating the dataset images into two group of relevant image and irrelevant image then all the images are ranked base on the image different modality of image features as the similar images need
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