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Can keyword-hashtag networks, derived from Big Data environments such as Twitter, yield clinicians a powerful tool to extrapolate patterns that may lead to development of new medical therapy and/or drugs? In our paper, we present a systematic network mining method to answer this question. We present HashnetMiner, a
matching algorithm will give best performance for IDPS is not known at hand. So in this work four pattern matching algorithms namely Brute-force, RabinKarp, Boyer-Moore and Knuth-Morris-Pratt has been selected for the analysis. These single keyword matching algorithms are mainly used. Performance of pattern matching
Latent Dirichlet Allocation, which is a non-supervised learning method, can be used for topic detection, automatic text categorization, keyword extraction and so on. It only focuses on the text itself, not considering other external correlation properties. External association property refers to some structured
all cases, reliable taxonomies considering precision and recall along with F-measure. For the experiment, we use Reuters (RCV1) dataset and the results show that we improve the discovering pattern as compared to previous text mining methods. The results of the experiment setup show that the keyword-based methods not give
task of ad hoc information retrieval is, finding documents within a corpus like Bible, that are relevant to the user remains a hard challenge. Sometimes the relevant documents may not contain the specified keyword. The lack of the given term in a document does not necessarily mean that the document is not a relevant
media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes
Due to the large quantity of digital information now available, information search engines provide a popular and important Internet service. Issues involved in the improvement of digital content search efficiency include: keyword filtering, inefficient search filtering, and existence search queries. Internet services
keyword and documents, and search the versioned objects that are consistent in the top-k results throughout a given query interval. Finally, we use data from Wikipedia to demonstrate the efficiency and performance of our algorithm.
transformations. While interprocedural alias analysis is still being studied and in some cases rejected by developers for resource consumption or compile time considerations, other techniques can be used to enhance performance. This paper proposes a practical and simple way of alias-property (restrict keyword) propagation after
This paper surveys Audio Information Retrieval (AIR) using a literature review and classification of articles from 1994 to 2010 with a keyword index and article abstract in order to explore how AIR methodologies and applications have developed during this period. Based on the scope of many papers and journals of AIR
A mind map is a diagram used to represent words, ideas, or other items linked to and arranged around a central keyword or idea. Mind maps are used to generate, visualize, structure, and classify ideas, and as an aid in organization, study, project management, problem solving, decision making, and writing. It has been
. Especially as a part of the interest router table, each K-bucket stores a certain number of the peers' information that have high interest similarity. The query can be executed in the appropriate k-bucket by calculating interest similarity and interest keyword. Through mining the latent interest, we found that two peers having
consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multidimensional querying approach to semi-structured data searches in personal information systems by allowing users to provide fuzzy structure and metadata conditions in addition to traditional keyword
Domain Assets are the domain knowledge constructed according to the common requirements in the domain. In order to reuse the domain assets effectively, a domain assets search algorithm is proposed in this paper. Compared with the keyword search, this algorithm is based on semantic similarity, and the domain assets
the two parties, computational complexity, execution time and correctness of the matching algorithm. Although the paper focus on a music matching application, the principles can be easily adapted to perform other tasks, such as speaker verification and keyword spotting.
special data record and new record model on fuzzy set are given. By calculating the membership of keyword, new fuzzy closeness functions are proposed to classify the information. Finally, examples prove that this algorithm can effectively and automatically classify input information of database, the accuracy and intelligence
Currently, Web of Things is based on keyword matching which is not beneficial to the development regarding Web of Things. Accordingly, "Semantic Web of Things" is proposed. As far as Semantic Web of Things concerned, the information of things should be represented as ontology-based semantic annotation
We created Mini PGP application which has the function as encryption/description and digital signatures. The other functions of PGP had been removed in the pursuit of facilitating everybody in checking its security. Asymmetry algorithm utilizes the RSA 4096 bit only while symmetry algorithm utilizes Twofish only. We use SHA256 as hash function. This paper shows the security analysis of mini PGP. Comprehensive...
Research on cross-language information retrieval (CLIR) increasingly concentrates in candidate translation selection of the keywords in the query. The accuracy of translation has a direct impact on accurate rate and recalled rate. This thesis presents three methods based on HowNet to resolve query translation
implement an indexing infrastructure supporting range queries on top of Distributed Hash Tables. The algorithm supports hierarchical keywords, and is especially designed for situations where a few keywords are very common while others are very uncommon. The design of our algorithm perfectly matches the requirements found in a
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