CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: knowledge discovery techniques</title> <meta name="description" content="Search results for: knowledge discovery techniques"> <meta name="keywords" content="knowledge discovery techniques"> <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 ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="knowledge discovery techniques" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="knowledge discovery techniques"> <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> 13975</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: knowledge discovery techniques</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13975</span> Knowledge Discovery from Production Databases for Hierarchical Process Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pavol%20Tanuska">Pavol Tanuska</a>, <a href="https://publications.waset.org/abstracts/search?q=Pavel%20Vazan"> Pavel Vazan</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Kebisek"> Michal Kebisek</a>, <a href="https://publications.waset.org/abstracts/search?q=Dominika%20Jurovata"> Dominika Jurovata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20process%20control" title="hierarchical process control">hierarchical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20from%20databases" title=" knowledge discovery from databases"> knowledge discovery from databases</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a> </p> <a href="https://publications.waset.org/abstracts/2816/knowledge-discovery-from-production-databases-for-hierarchical-process-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2816.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">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13974</span> Data Mining As A Tool For Knowledge Management: A Review </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maram%20Saleh">Maram Saleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20Mining" title="Data Mining">Data Mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20management" title=" Knowledge management"> Knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20discovery" title=" Knowledge discovery"> Knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20creation." title=" Knowledge creation."> Knowledge creation.</a> </p> <a href="https://publications.waset.org/abstracts/137030/data-mining-as-a-tool-for-knowledge-management-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137030.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">208</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13973</span> Application of Data Mining Techniques for Tourism Knowledge Discovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Teklu%20Urgessa">Teklu Urgessa</a>, <a href="https://publications.waset.org/abstracts/search?q=Wookjae%20Maeng"> Wookjae Maeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Joong%20Seek%20Lee"> Joong Seek Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title="classification algorithms">classification algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a> </p> <a href="https://publications.waset.org/abstracts/59419/application-of-data-mining-techniques-for-tourism-knowledge-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59419.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">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13972</span> Algorithms used in Spatial Data Mining GIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Bairami%20Rad">Vahid Bairami Rad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20base" title="spatial data base">spatial data base</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20database" title=" knowledge discovery database"> knowledge discovery database</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20relationship" title=" spatial relationship"> spatial relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20data%20mining" title=" predictive data mining"> predictive data mining</a> </p> <a href="https://publications.waset.org/abstracts/29004/algorithms-used-in-spatial-data-mining-gis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29004.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">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13971</span> Data Mining and Knowledge Management Application to Enhance Business Operations: An Exploratory Study </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeba%20Mahmood">Zeba Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge" title="knowledge">knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=business" title=" business"> business</a>, <a href="https://publications.waset.org/abstracts/search?q=operational" title=" operational"> operational</a>, <a href="https://publications.waset.org/abstracts/search?q=information" title=" information"> information</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/82255/data-mining-and-knowledge-management-application-to-enhance-business-operations-an-exploratory-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82255.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">538</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13970</span> Knowledge-Driven Decision Support System Based on Knowledge Warehouse and Data Mining by Improving Apriori Algorithm with Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pejman%20Hosseinioun">Pejman Hosseinioun</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Shakeri"> Hasan Shakeri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghasem%20Ghorbanirostam"> Ghasem Ghorbanirostam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title="decision support system">decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20discovery" title=" data discovery"> data discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a> </p> <a href="https://publications.waset.org/abstracts/48155/knowledge-driven-decision-support-system-based-on-knowledge-warehouse-and-data-mining-by-improving-apriori-algorithm-with-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48155.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">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13969</span> Machine Learning Methods for Network Intrusion Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouhammad%20Alkasassbeh">Mouhammad Alkasassbeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Almseidin"> Mohammad Almseidin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=DDoS" title=" DDoS"> DDoS</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=KDD" title=" KDD"> KDD</a> </p> <a href="https://publications.waset.org/abstracts/93688/machine-learning-methods-for-network-intrusion-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93688.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">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13968</span> Application of Knowledge Discovery in Database Techniques in Cost Overruns of Construction Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mai%20Ghazal">Mai Ghazal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Hammad"> Ahmed Hammad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cost overruns in construction projects are considered as worldwide challenges since the cost performance is one of the main measures of success along with schedule performance. To overcome this problem, studies were conducted to investigate the cost overruns' factors, also projects' historical data were analyzed to extract new and useful knowledge from it. This research is studying and analyzing the effect of some factors causing cost overruns using the historical data from completed construction projects. Then, using these factors to estimate the probability of cost overrun occurrence and predict its percentage for future projects. First, an intensive literature review was done to study all the factors that cause cost overrun in construction projects, then another review was done for previous researcher papers about mining process in dealing with cost overruns. Second, a proposed data warehouse was structured which can be used by organizations to store their future data in a well-organized way so it can be easily analyzed later. Third twelve quantitative factors which their data are frequently available at construction projects were selected to be the analyzed factors and suggested predictors for the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20management" title="construction management">construction management</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20projects" title=" construction projects"> construction projects</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20overrun" title=" cost overrun"> cost overrun</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20performance" title=" cost performance"> cost performance</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehousing" title=" data warehousing"> data warehousing</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/85161/application-of-knowledge-discovery-in-database-techniques-in-cost-overruns-of-construction-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85161.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">371</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13967</span> Recent Advances in Data Warehouse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Hanash%20Alzahrani">Fahad Hanash Alzahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20analytical%20processing" title=" on-line analytical processing"> on-line analytical processing</a> </p> <a href="https://publications.waset.org/abstracts/63299/recent-advances-in-data-warehouse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63299.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">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13966</span> Evaluation of Classification Algorithms for Diagnosis of Asthma in Iranian Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taha%20SamadSoltani">Taha SamadSoltani</a>, <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Rezaei%20Hachesu"> Peyman Rezaei Hachesu</a>, <a href="https://publications.waset.org/abstracts/search?q=Marjan%20GhaziSaeedi"> Marjan GhaziSaeedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Zolnoori"> Maryam Zolnoori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asthma" title="asthma">asthma</a>, <a href="https://publications.waset.org/abstracts/search?q=datamining" title=" datamining"> datamining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/30919/evaluation-of-classification-algorithms-for-diagnosis-of-asthma-in-iranian-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30919.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">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13965</span> Research on Construction of Subject Knowledge Base Based on Literature Knowledge Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yumeng%20Ma">Yumeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Wang"> Fang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinxia%20Huang"> Jinxia Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Researchers put forward higher requirements for efficient acquisition and utilization of domain knowledge in the big data era. As literature is an effective way for researchers to quickly and accurately understand the research situation in their field, the knowledge discovery based on literature has become a new research method. As a tool to organize and manage knowledge in a specific domain, the subject knowledge base can be used to mine and present the knowledge behind the literature to meet the users' personalized needs. This study designs the construction route of the subject knowledge base for specific research problems. Information extraction method based on knowledge engineering is adopted. Firstly, the subject knowledge model is built through the abstraction of the research elements. Then under the guidance of the knowledge model, extraction rules of knowledge points are compiled to analyze, extract and correlate entities, relations, and attributes in literature. Finally, a database platform based on this structured knowledge is developed that can provide a variety of services such as knowledge retrieval, knowledge browsing, knowledge q&a, and visualization correlation. Taking the construction practices in the field of activating blood circulation and removing stasis as an example, this study analyzes how to construct subject knowledge base based on literature knowledge extraction. As the system functional test shows, this subject knowledge base can realize the expected service scenarios such as a quick query of knowledge, related discovery of knowledge and literature, knowledge organization. As this study enables subject knowledge base to help researchers locate and acquire deep domain knowledge quickly and accurately, it provides a transformation mode of knowledge resource construction and personalized precision knowledge services in the data-intensive research environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20model" title="knowledge model">knowledge model</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20knowledge%20extraction" title=" literature knowledge extraction"> literature knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20knowledge%20services" title=" precision knowledge services"> precision knowledge services</a>, <a href="https://publications.waset.org/abstracts/search?q=subject%20knowledge%20base" title=" subject knowledge base"> subject knowledge base</a> </p> <a href="https://publications.waset.org/abstracts/103587/research-on-construction-of-subject-knowledge-base-based-on-literature-knowledge-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103587.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">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13964</span> A Review on Existing Challenges of Data Mining and Future Research Perspectives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hema%20Bhardwaj">Hema Bhardwaj</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Srinivasa%20Rao"> D. Srinivasa Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques" title=" knowledge discovery techniques"> knowledge discovery techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining%20challenges" title=" data mining challenges"> data mining challenges</a> </p> <a href="https://publications.waset.org/abstracts/160836/a-review-on-existing-challenges-of-data-mining-and-future-research-perspectives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160836.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">110</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13963</span> Intuitional Insight in Islamic Mysticism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Bakhtyar">Maryam Bakhtyar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pegah%20Akrami"> Pegah Akrami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intuitional insight or mystical cognition is a different insight from common, concrete and intellectual insights. This kind of insight is not achieved by visionary contemplation but by the recitation of God, self-purification, and mystical life. In this insight, there is no distance or medium between the subject of cognition and its object, and they have a sort of unification, unison, and incorporation. As a result, knowledgeable consider this insight as direct, immediate, and personal. The goal of this insight is God, cosmos’ creatures, and the general inner and hidden aspect of the world that is nothing except God’s manifestations in the view of mystics. AS our common cognitions have diversity and stages, intuitional insight also has diversity and levels. As our senses are divided into concrete and rational, mystical discovery is divided into superficial discovery and spiritual one. Based on Islamic mystics, the preferable way to know God and believe in him is intuitional insight. There are two important criteria for evaluating mystical intuition, especially for beginner mystics of intellect and revelation. Indeed, the conclusion and a brief evaluation of Islamic mystics’ viewpoint is the main subject of this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intuition" title="intuition">intuition</a>, <a href="https://publications.waset.org/abstracts/search?q=discovery" title=" discovery"> discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=mystical%20insight" title=" mystical insight"> mystical insight</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20knowledge" title=" personal knowledge"> personal knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=superficial%20discovery" title=" superficial discovery"> superficial discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=spiritual%20discovery" title=" spiritual discovery"> spiritual discovery</a> </p> <a href="https://publications.waset.org/abstracts/155560/intuitional-insight-in-islamic-mysticism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155560.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">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13962</span> A General Framework for Knowledge Discovery from Echocardiographic and Natural Images </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Nandagopalan">S. Nandagopalan</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Pradeep"> N. Pradeep</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20contour" title="active contour">active contour</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=echocardiographic%20image" title=" echocardiographic image"> echocardiographic image</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20vector" title=" feature vector"> feature vector</a> </p> <a href="https://publications.waset.org/abstracts/42868/a-general-framework-for-knowledge-discovery-from-echocardiographic-and-natural-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42868.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">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13961</span> Performance Comparison of Outlier Detection Techniques Based Classification in Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayadi%20Aya">Ayadi Aya</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghorbel%20Oussama"> Ghorbel Oussama</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Obeid%20Abdulfattah"> M. Obeid Abdulfattah</a>, <a href="https://publications.waset.org/abstracts/search?q=Abid%20Mohamed"> Abid Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20networks" title="bayesian networks">bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=classification-based%20approaches" title=" classification-based approaches"> classification-based approaches</a>, <a href="https://publications.waset.org/abstracts/search?q=KPCA" title=" KPCA"> KPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20SVM" title=" one-class SVM"> one-class SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier%20detection" title=" outlier detection"> outlier detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/66531/performance-comparison-of-outlier-detection-techniques-based-classification-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66531.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">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13960</span> A General Framework for Knowledge Discovery Using High Performance Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Nandagopalan">S. Nandagopalan</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Pradeep"> N. Pradeep</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20contour" title="active contour">active contour</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=echocardiographic%20image" title=" echocardiographic image"> echocardiographic image</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20vector" title=" feature vector"> feature vector</a> </p> <a href="https://publications.waset.org/abstracts/42632/a-general-framework-for-knowledge-discovery-using-high-performance-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42632.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">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13959</span> Ontology-Driven Knowledge Discovery and Validation from Admission Databases: A Structural Causal Model Approach for Polytechnic Education in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernard%20Igoche%20Igoche">Bernard Igoche Igoche</a>, <a href="https://publications.waset.org/abstracts/search?q=Olumuyiwa%20Matthew"> Olumuyiwa Matthew</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Bednar"> Peter Bednar</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Gegov"> Alexander Gegov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=admission%20databases" title="admission databases">admission databases</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology-driven%20knowledge%20discovery" title=" ontology-driven knowledge discovery"> ontology-driven knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=polytechnic%20education" title=" polytechnic education"> polytechnic education</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20causal%20model" title=" structural causal model"> structural causal model</a> </p> <a href="https://publications.waset.org/abstracts/184064/ontology-driven-knowledge-discovery-and-validation-from-admission-databases-a-structural-causal-model-approach-for-polytechnic-education-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184064.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">64</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13958</span> Presenting a Knowledge Mapping Model According to a Comparative Study on Applied Models and Approaches to Map Organizational Knowledge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Aslizadeh">Ahmad Aslizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Farid%20Ghaderi"> Farid Ghaderi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mapping organizational knowledge is an innovative concept and useful instrument of representation, capturing and visualization of implicit and explicit knowledge. There are a diversity of methods, instruments and techniques presented by different researchers following mapping organizational knowledge to reach determined goals. Implicating of these methods, it is necessary to know their exigencies and conditions in which those can be used. Integrating identified methods of knowledge mapping and comparing them would help knowledge managers to select the appropriate methods. This research conducted to presenting a model and framework to map organizational knowledge. At first, knowledge maps, their applications and necessity are introduced because of extracting comparative framework and detection of their structure. At the next step techniques of researchers such as Eppler, Kim, Egbu, Tandukar and Ebner as knowledge mapping models are presented and surveyed. Finally, they compare and a superior model would be introduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20mapping" title="knowledge mapping">knowledge mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20study" title=" comparative study"> comparative study</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20and%20management" title=" business and management"> business and management</a> </p> <a href="https://publications.waset.org/abstracts/29967/presenting-a-knowledge-mapping-model-according-to-a-comparative-study-on-applied-models-and-approaches-to-map-organizational-knowledge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29967.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">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13957</span> Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walid%20Cherif">Walid Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20classification" title=" supervised classification"> supervised classification</a> </p> <a href="https://publications.waset.org/abstracts/79268/hybrid-reliability-similarity-based-approach-for-supervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79268.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">465</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13956</span> Review and Comparison of Associative Classification Data Mining Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Wedyan">Suzan Wedyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=associative%20classification" title="associative classification">associative classification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20ranking" title=" rule ranking"> rule ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20pruning" title=" rule pruning"> rule pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/4191/review-and-comparison-of-associative-classification-data-mining-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4191.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">537</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13955</span> Lightweight Cryptographically Generated Address for IPv6 Neighbor Discovery </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amjed%20Sid%20Ahmed">Amjed Sid Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosilah%20Hassan"> Rosilah Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Effendy%20Othman"> Nor Effendy Othman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Limited functioning of the Internet Protocol version 4 (IPv4) has necessitated the development of the Internetworking Protocol next generation (IPng) to curb the challenges. Indeed, the IPng is also referred to as the Internet Protocol version 6 (IPv6) and includes the Neighbor Discovery Protocol (NDP). The latter performs the role of Address Auto-configuration, Router Discovery (RD), and Neighbor Discovery (ND). Furthermore, the role of the NDP entails redirecting the service, detecting the duplicate address, and detecting the unreachable services. Despite the fact that there is an NDP’s assumption regarding the existence of trust the links’ nodes, several crucial attacks may affect the Protocol. Internet Engineering Task Force (IETF) therefore has recommended implementation of Secure Neighbor Discovery Protocol (SEND) to tackle safety issues in NDP. The SEND protocol is mainly used for validation of address rights, malicious response inhibiting techniques and finally router certification procedures. For routine running of these tasks, SEND utilizes on the following options, Cryptographically Generated Address (CGA), RSA Signature, Nonce and Timestamp option. CGA is produced at extra high costs making it the most notable disadvantage of SEND. In this paper a clear description of the constituents of CGA, its operation and also recommendations for improvements in its generation are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CGA" title="CGA">CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=IPv6" title=" IPv6"> IPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=NDP" title=" NDP"> NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a> </p> <a href="https://publications.waset.org/abstracts/31309/lightweight-cryptographically-generated-address-for-ipv6-neighbor-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31309.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">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13954</span> Medical Knowledge Management since the Integration of Heterogeneous Data until the Knowledge Exploitation in a Decision-Making System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadjat%20Zerf%20Boudjettou">Nadjat Zerf Boudjettou</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahima%20Nader"> Fahima Nader</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Chalal"> Rachid Chalal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20database" title=" knowledge discovery in database"> knowledge discovery in database</a>, <a href="https://publications.waset.org/abstracts/search?q=KDD" title=" KDD"> KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20knowledge%20management" title=" medical knowledge management"> medical knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title=" Bayesian networks"> Bayesian networks</a> </p> <a href="https://publications.waset.org/abstracts/14543/medical-knowledge-management-since-the-integration-of-heterogeneous-data-until-the-knowledge-exploitation-in-a-decision-making-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14543.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">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13953</span> Performance Analysis with the Combination of Visualization and Classification Technique for Medical Chatbot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shajida%20M.">Shajida M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakthiyadharshini%20N.%20P."> Sakthiyadharshini N. P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamalesh%20S."> Kamalesh S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Aswitha%20B."> Aswitha B.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentimental%20analysis" title="sentimental analysis">sentimental analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20chatbot" title=" medical chatbot"> medical chatbot</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20bayes" title=" naïve bayes"> naïve bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20cloud" title=" word cloud"> word cloud</a> </p> <a href="https://publications.waset.org/abstracts/165924/performance-analysis-with-the-combination-of-visualization-and-classification-technique-for-medical-chatbot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165924.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">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13952</span> Knowledge Discovery and Data Mining Techniques in Textile Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filiz%20Ersoz">Filiz Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Taner%20Ersoz"> Taner Ersoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Erkin%20Guler"> Erkin Guler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=textile%20production" title=" textile production"> textile production</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/75461/knowledge-discovery-and-data-mining-techniques-in-textile-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75461.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">351</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13951</span> Analysis and Rule Extraction of Coronary Artery Disease Data Using Data Mining </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rezaei%20Hachesu%20Peyman">Rezaei Hachesu Peyman</a>, <a href="https://publications.waset.org/abstracts/search?q=Oliyaee%20Azadeh"> Oliyaee Azadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Salahzadeh%20Zahra"> Salahzadeh Zahra</a>, <a href="https://publications.waset.org/abstracts/search?q=Alizadeh%20Somayyeh"> Alizadeh Somayyeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Safaei%20Naser"> Safaei Naser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=coronary%20artery%20disease" title=" coronary artery disease"> coronary artery disease</a>, <a href="https://publications.waset.org/abstracts/search?q=data-mining" title=" data-mining"> data-mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery" title=" knowledge discovery"> knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=extract" title=" extract "> extract </a> </p> <a href="https://publications.waset.org/abstracts/1268/analysis-and-rule-extraction-of-coronary-artery-disease-data-using-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1268.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">658</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13950</span> Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriela%20V.%20Angeles%20Perez">Gabriela V. Angeles Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Castillejos%20Lopez"> Jose Castillejos Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Araceli%20L.%20Reyes%20Cabello"> Araceli L. Reyes Cabello</a>, <a href="https://publications.waset.org/abstracts/search?q=Emilio%20Bravo%20Grajales"> Emilio Bravo Grajales</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriana%20Perez%20Espinosa"> Adriana Perez Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20L.%20Quiroz%20Fabian"> Jose L. Quiroz Fabian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20accidents" title=" road traffic accidents"> road traffic accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=Waze" title=" Waze"> Waze</a>, <a href="https://publications.waset.org/abstracts/search?q=Weka" title=" Weka"> Weka</a> </p> <a href="https://publications.waset.org/abstracts/83804/road-traffic-accidents-analysis-in-mexico-city-through-crowdsourcing-data-and-data-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83804.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">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13949</span> Estimation of Coefficients of Ridge and Principal Components Regressions with Multicollinear Data </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajeshwar%20Singh">Rajeshwar Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conflicting%20claim%20on%20credit%20of%20discovery%20of%20ridge%20regression" title="conflicting claim on credit of discovery of ridge regression">conflicting claim on credit of discovery of ridge regression</a>, <a href="https://publications.waset.org/abstracts/search?q=multicollinearity" title=" multicollinearity"> multicollinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20components%20and%20ridge%20regressions" title=" principal components and ridge regressions"> principal components and ridge regressions</a>, <a href="https://publications.waset.org/abstracts/search?q=variance%20inflation%20factor" title=" variance inflation factor"> variance inflation factor</a> </p> <a href="https://publications.waset.org/abstracts/31600/estimation-of-coefficients-of-ridge-and-principal-components-regressions-with-multicollinear-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31600.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">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13948</span> Concept for Knowledge out of Sri Lankan Non-State Sector: Performances of Higher Educational Institutes and Successes of Its Sector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jeyarajan">S. Jeyarajan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Concept of knowledge is discovered from conducted study for successive Competition in Sri Lankan Non-State Higher Educational Institutes. The Concept discovered out of collected Knowledge Management Practices from Emerald inside likewise reputed literatures and of Non-State Higher Educational sector. A test is conducted to reveal existences and its reason behind of these collected practices in Sri Lankan Non-State Higher Education Institutes. Further, unavailability of such study and uncertain on number of participants for data collection in the Sri Lankan context contributed selection of research method as qualitative method, which used attributes of Delphi Method to manage those likewise uncertainty. Data are collected under Dramaturgical Method, which contributes efficient usage of the Delphi method. Grounded theory is selected as data analysis techniques, which is conducted in intermixed discourse to manage different perspectives of data that are collected systematically through perspective and modified snowball sampling techniques. Data are then analysed using Grounded Theory Development Techniques in Intermix discourses to manage differences in Data. Consequently, Agreement in the results of Grounded theories and of finding in the Foreign Study is discovered in the analysis whereas present study conducted as Qualitative Research and The Foreign Study conducted as Quantitative Research. As such, the Present study widens the discovery in the Foreign Study. Further, having discovered reason behind of the existences, the Present result shows Concept for Knowledge from Sri Lankan Non-State sector to manage higher educational Institutes in successful manner. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adherence%20of%20snowball%20sampling%20into%20perspective%20sampling" title="adherence of snowball sampling into perspective sampling">adherence of snowball sampling into perspective sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=Delphi%20method%20in%20qualitative%20method" title=" Delphi method in qualitative method"> Delphi method in qualitative method</a>, <a href="https://publications.waset.org/abstracts/search?q=grounded%20theory%20development%20in%20intermix%20discourses%20of%20analysis" title=" grounded theory development in intermix discourses of analysis"> grounded theory development in intermix discourses of analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management%20for%20success%20of%20higher%20educational%20institutes" title=" knowledge management for success of higher educational institutes"> knowledge management for success of higher educational institutes</a> </p> <a href="https://publications.waset.org/abstracts/98296/concept-for-knowledge-out-of-sri-lankan-non-state-sector-performances-of-higher-educational-institutes-and-successes-of-its-sector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98296.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">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13947</span> Network Word Discovery Framework Based on Sentence Semantic Vector Similarity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ganfeng%20Yu">Ganfeng Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuefeng%20Ma"> Yuefeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Yang"> Shanliang Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The word discovery is a key problem in text information retrieval technology. Methods in new word discovery tend to be closely related to words because they generally obtain new word results by analyzing words. With the popularity of social networks, individual netizens and online self-media have generated various network texts for the convenience of online life, including network words that are far from standard Chinese expression. How detect network words is one of the important goals in the field of text information retrieval today. In this paper, we integrate the word embedding model and clustering methods to propose a network word discovery framework based on sentence semantic similarity (S³-NWD) to detect network words effectively from the corpus. This framework constructs sentence semantic vectors through a distributed representation model, uses the similarity of sentence semantic vectors to determine the semantic relationship between sentences, and finally realizes network word discovery by the meaning of semantic replacement between sentences. The experiment verifies that the framework not only completes the rapid discovery of network words but also realizes the standard word meaning of the discovery of network words, which reflects the effectiveness of our work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20information%20retrieval" title="text information retrieval">text information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20word%20discovery" title=" new word discovery"> new word discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a> </p> <a href="https://publications.waset.org/abstracts/153917/network-word-discovery-framework-based-on-sentence-semantic-vector-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153917.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">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13946</span> CERD: Cost Effective Route Discovery in Mobile Ad Hoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anuradha%20Banerjee">Anuradha Banerjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A mobile ad hoc network is an infrastructure less network, where nodes are free to move independently in any direction. The nodes have limited battery power; hence, we require energy efficient route discovery technique to enhance their lifetime and network performance. In this paper, we propose an energy-efficient route discovery technique CERD that greatly reduces the number of route requests flooded into the network and also gives priority to the route request packets sent from the routers that has communicated with the destination very recently, in single or multi-hop paths. This does not only enhance the lifetime of nodes but also decreases the delay in tracking the destination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ad%20hoc%20network" title="ad hoc network">ad hoc network</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=flooding" title=" flooding"> flooding</a>, <a href="https://publications.waset.org/abstracts/search?q=node%20lifetime" title=" node lifetime"> node lifetime</a>, <a href="https://publications.waset.org/abstracts/search?q=route%20discovery" title=" route discovery"> route discovery</a> </p> <a href="https://publications.waset.org/abstracts/20336/cerd-cost-effective-route-discovery-in-mobile-ad-hoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20336.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">347</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=465">465</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=466">466</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20techniques&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>