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rely on indexing web pages so that the information obtained by the tourist is still unfavorable because it only shows a web page with keywords that exist on the article. A support system to recognize tourism places on the web pages is required to produce better information presentation. In this study, the recognition
Search By Multiple Examples (SBME) is a new search paradigm that allows users to specify their information needs as a set of relevant documents rather than as a set of keywords. In this study, we propose a Transductive Positive Unlabeled learning (TPU learning) based framework for SBME. The framework consists of two
done on a set of data is chosen to form the basis as done with keywords. If the base data is chosen arbitrarily, it is automatic, whereas some 'knowledge' or 'background' is put in the choice it is adaptive. Statistical features of the images are extracted from the pixel map of the image. The extracted features are
The web mining is a cutting edge technology, which includes information gathering and classification of information over web. This paper puts forth the concepts of document pre-processing, which is achieved by extraction of keywords from the documents fetched from the web, processing it and generating a term-document
the websites into their most appropriate category. Several parameters like the weight applied to each feature and the keywords used to classify the websites were tuned to yield better results. The experimental evaluation revealed that the method implemented provides very high accuracy. In particularly, we obtained an
this problem, the application of TF-IDF algorithm in words weight calculation was researched in this paper. Combining the relevant knowledge of information theory and analyzing the distribution of keywords within a class, the article proposed improving TF-IDF algorithm and applying it in term weight calculation. To test
Multi-label image annotation has received significant attention in the research community over the past few years. Multi-label automatic image annotation assigns keywords to the image based on low level features automatically. In this paper, we present an extensive survey on the research work carried out in the area
Today location technologies are integrated into many devices enabling location-based services. Movement data recorded with these devices can be uploaded to web sites and shared with others. Movement data can be organized using keywords and semantic tags, e.g. walking and running. Our main goal is to automatically
Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework. In region-based image annotation, keywords are usually
to keeping the original idea in TWSVM, still the edges of our method lie in considerably less computing time with respect to TWSVM, which is comparable to that of GEPSVM. Experiments tried out on standard datasets disclose the effectiveness of our method. Keywords: TWSVM; dual QPPs; approximate.
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
likelihood in the entire training documents where the training and test data are split randomly into k-subsets like 2/3 for training and 1/3 for test data. In addition, it also utilizes two level hierarchy structures for training documents like features from title, keywords and content with the predefined knowledge available
to describe a document instead of traditional keywords vector, which is based on merging words with high similarity and using a concept to describe the semantic feature rather than a series of words. It not only reduces feature dimension but also adds semantic information to the vector. We also use sentence (document
method has an important characteristic that it can suggest multiple keywords per image, which improves the accuracy. Experimental results on pre-diagnosed database of brain images shows high accuracy (up to 95%), allowing us to claim that the use of associative classifier is an efficient technique to assist in the
to search and retrieve components. Proposed technique helps re-user to identify and retrieve software component. In its first step it matches keywords, their synonyms and their interrelationships. And then makes use of ant colony optimization, a probabilistic approach to generate rule for matching the component against
performance improvement. Third, a comprehensive method of annotation refinement is developed to remove the noisy keywords. Finally, experimental results demonstrate the effectiveness of our proposed system.
is to stem and eliminate common words. The aim of this research is to stem words from Persian documents to make their use more efficient in text summarization, the present method is to eliminate words and stem keywords. The compound of existing techniques in the words network was used to create a Persian database using
This work identifies relevant songs from a user's personal music collection to accompany pictures of an event. The event's pictures are analyzed to extract aggregated semantic concepts in a variety of dimensions, including scene type, geospatial information, and event type, along with user-provided keywords. These
into the server. Each of the file data or Web data is viewed as a memex event that can be described by 4W1H form. The memex event ontology is used to transform the various types of data to the standard 4W1H form. Users can view their life log chronologically and search them by keywords. Moreover, the life logs can be
Automatic mood information acquiring from music data is an important topic of music retrieval area. In this paper, we try to find the strongest emotional expression of the song in large music databases. By analyzing hundreds of credible reviews from website, a 7 keywords mood model is constructed. 217 songs were
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