Search results for: text-mining.

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text-mining.</title> <meta name="description" content="Search results for: text-mining."> <meta name="keywords" content="text-mining."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="text-mining."> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: text-mining.</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Classifier Based Text Mining for Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Govindarajan">M. Govindarajan</a>, <a href="https://publications.waset.org/search?q=R.%20M.%20Chandrasekaran"> R. M. Chandrasekaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Back%20propagation" title="Back propagation">Back propagation</a>, <a href="https://publications.waset.org/search?q=classification%20accuracy" title=" classification accuracy"> classification accuracy</a>, <a href="https://publications.waset.org/search?q=textmining" title=" textmining"> textmining</a>, <a href="https://publications.waset.org/search?q=time%20complexity." title=" time complexity."> time complexity.</a> </p> <a href="https://publications.waset.org/4305/classifier-based-text-mining-for-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4305/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4305/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4305/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4305/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4305/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4305/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4305/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4305/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4305/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4305/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4305.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4218</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Full-genomic Network Inference for Non-model organisms: A Case Study for the Fungal Pathogen Candida albicans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J%C3%B6rg%20Linde">J枚rg Linde</a>, <a href="https://publications.waset.org/search?q=Ekaterina%20Buyko"> Ekaterina Buyko</a>, <a href="https://publications.waset.org/search?q=Robert%20Altwasser"> Robert Altwasser</a>, <a href="https://publications.waset.org/search?q=Udo%20Hahn"> Udo Hahn</a>, <a href="https://publications.waset.org/search?q=Reinhard%20Guthke"> Reinhard Guthke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Pathogen" title="Pathogen">Pathogen</a>, <a href="https://publications.waset.org/search?q=network%20inference" title=" network inference"> network inference</a>, <a href="https://publications.waset.org/search?q=text-mining" title=" text-mining"> text-mining</a>, <a href="https://publications.waset.org/search?q=Candida%20albicans" title=" Candida albicans"> Candida albicans</a>, <a href="https://publications.waset.org/search?q=LASSO" title=" LASSO"> LASSO</a>, <a href="https://publications.waset.org/search?q=mutual%20information" title=" mutual information"> mutual information</a>, <a href="https://publications.waset.org/search?q=reverse%20engineering" title=" reverse engineering"> reverse engineering</a>, <a href="https://publications.waset.org/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/search?q=modelling." title=" modelling."> modelling.</a> </p> <a href="https://publications.waset.org/7979/full-genomic-network-inference-for-non-model-organisms-a-case-study-for-the-fungal-pathogen-candida-albicans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7979/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7979/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7979/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7979/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7979/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a 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Search results for: text-mining.