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: indexing data</title> <meta name="description" content="Search results for: indexing data"> <meta name="keywords" content="indexing data"> <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="indexing data" 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="indexing data"> <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> 25165</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: indexing data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24955</span> The Maximum Throughput Analysis of UAV Datalink 802.11b Protocol</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inkyu%20Kim">Inkyu Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=SangMan%20Moon"> SangMan Moon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UAV%20datalink" title="UAV datalink">UAV datalink</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV%20formation%20flight%20datalink" title=" UAV formation flight datalink"> UAV formation flight datalink</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV%20WLAN%20datalink%20application" title=" UAV WLAN datalink application"> UAV WLAN datalink application</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV%20IEEE%20802.11b%20datalink%20application" title=" UAV IEEE 802.11b datalink application"> UAV IEEE 802.11b datalink application</a> </p> <a href="https://publications.waset.org/abstracts/1538/the-maximum-throughput-analysis-of-uav-datalink-80211b-protocol" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1538.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">392</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">24954</span> Methods for Distinction of Cattle Using Supervised Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radoslav%20%C5%BDidek">Radoslav Židek</a>, <a href="https://publications.waset.org/abstracts/search?q=Veronika%20%C5%A0idlov%C3%A1"> Veronika Šidlová</a>, <a href="https://publications.waset.org/abstracts/search?q=Radovan%20Kasarda"> Radovan Kasarda</a>, <a href="https://publications.waset.org/abstracts/search?q=Birgit%20Fuerst-Waltl"> Birgit Fuerst-Waltl</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20data" title="genetic data">genetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinzgau%20cattle" title=" Pinzgau cattle"> Pinzgau cattle</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</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/9320/methods-for-distinction-of-cattle-using-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9320.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">550</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">24953</span> Router 1X3 - RTL Design and Verification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nidhi%20Gopal">Nidhi Gopal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20packets" title="data packets">data packets</a>, <a href="https://publications.waset.org/abstracts/search?q=networking" title=" networking"> networking</a>, <a href="https://publications.waset.org/abstracts/search?q=router" title=" router"> router</a>, <a href="https://publications.waset.org/abstracts/search?q=routing" title=" routing"> routing</a> </p> <a href="https://publications.waset.org/abstracts/39921/router-1x3-rtl-design-and-verification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39921.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">814</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">24952</span> Noise Reduction in Web Data: A Learning Approach Based on Dynamic User Interests</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Julius%20Onyancha">Julius Onyancha</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentina%20Plekhanova"> Valentina Plekhanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=web%20log%20data" title="web log data">web log data</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20user%20profile" title=" web user profile"> web user profile</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20interest" title=" user interest"> user interest</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20web%20data%20learning" title=" noise web data learning"> noise web data learning</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/77482/noise-reduction-in-web-data-a-learning-approach-based-on-dynamic-user-interests" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77482.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">265</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">24951</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">24950</span> Analyzing Large Scale Recurrent Event Data with a Divide-And-Conquer Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jerry%20Q.%20Cheng">Jerry Q. Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title="big data analytics">big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=divide-and-conquer" title=" divide-and-conquer"> divide-and-conquer</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20event%20data" title=" recurrent event data"> recurrent event data</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20computing" title=" statistical computing"> statistical computing</a> </p> <a href="https://publications.waset.org/abstracts/100777/analyzing-large-scale-recurrent-event-data-with-a-divide-and-conquer-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100777.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">166</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">24949</span> Adoption of Big Data by Global Chemical Industries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashiff%20Khan">Ashiff Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Seetharaman"> A. Seetharaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhijit%20Dasgupta"> Abhijit Dasgupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemical%20engineering" title="chemical engineering">chemical engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20revolution" title=" industrial revolution"> industrial revolution</a>, <a href="https://publications.waset.org/abstracts/search?q=professional%20competence" title=" professional competence"> professional competence</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a> </p> <a href="https://publications.waset.org/abstracts/151938/adoption-of-big-data-by-global-chemical-industries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151938.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">85</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">24948</span> Secure Multiparty Computations for Privacy Preserving Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Sumana">M. Sumana</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Hareesha"> K. S. Hareesha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=homomorphic%20property" title="homomorphic property">homomorphic property</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20product" title=" secure product"> secure product</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20mean%20and%20variance" title=" secure mean and variance"> secure mean and variance</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20dot%20product" title=" secure dot product"> secure dot product</a>, <a href="https://publications.waset.org/abstracts/search?q=vertically%20partitioned%20data" title=" vertically partitioned data"> vertically partitioned data</a> </p> <a href="https://publications.waset.org/abstracts/34716/secure-multiparty-computations-for-privacy-preserving-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34716.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">412</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">24947</span> Cross Project Software Fault Prediction at Design Phase</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pradeep%20Singh">Pradeep Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shrish%20Verma"> Shrish Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20metrics" title="software metrics">software metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20prediction" title=" fault prediction"> fault prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20project" title=" cross project"> cross project</a>, <a href="https://publications.waset.org/abstracts/search?q=within%20project." title=" within project. "> within project. </a> </p> <a href="https://publications.waset.org/abstracts/27206/cross-project-software-fault-prediction-at-design-phase" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27206.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">344</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">24946</span> Comparing Emotion Recognition from Voice and Facial Data Using Time Invariant Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vesna%20Kirandziska">Vesna Kirandziska</a>, <a href="https://publications.waset.org/abstracts/search?q=Nevena%20Ackovska"> Nevena Ackovska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Madevska%20Bogdanova"> Ana Madevska Bogdanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</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/42384/comparing-emotion-recognition-from-voice-and-facial-data-using-time-invariant-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42384.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">316</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">24945</span> Cryptosystems in Asymmetric Cryptography for Securing Data on Cloud at Various Critical Levels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sartaj%20Singh">Sartaj Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Amar%20Singh"> Amar Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Sharma"> Ashok Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Kaur"> Sandeep Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptography" title="cryptography">cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20key%20cryptography" title=" symmetric key cryptography"> symmetric key cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20key%20cryptography" title=" asymmetric key cryptography"> asymmetric key cryptography</a> </p> <a href="https://publications.waset.org/abstracts/152445/cryptosystems-in-asymmetric-cryptography-for-securing-data-on-cloud-at-various-critical-levels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152445.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">124</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">24944</span> Data Recording for Remote Monitoring of Autonomous Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rong-Terng%20Juang">Rong-Terng Juang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicle" title="autonomous vehicle">autonomous vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20compression" title=" data compression"> data compression</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20monitoring" title=" remote monitoring"> remote monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=controller%20area%20networks%20%28CAN%29" title=" controller area networks (CAN)"> controller area networks (CAN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidar" title=" Lidar"> Lidar</a> </p> <a href="https://publications.waset.org/abstracts/99577/data-recording-for-remote-monitoring-of-autonomous-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99577.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">24943</span> Multimedia Data Fusion for Event Detection in Twitter by Using Dempster-Shafer Evidence Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samar%20M.%20Alqhtani">Samar M. Alqhtani</a>, <a href="https://publications.waset.org/abstracts/search?q=Suhuai%20Luo"> Suhuai Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Brian%20Regan"> Brian Regan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title="data fusion">data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=Dempster-Shafer%20theory" title=" Dempster-Shafer theory"> Dempster-Shafer theory</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=event%20detection" title=" event detection"> event detection</a> </p> <a href="https://publications.waset.org/abstracts/34741/multimedia-data-fusion-for-event-detection-in-twitter-by-using-dempster-shafer-evidence-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34741.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">410</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">24942</span> Legal Issues of Collecting and Processing Big Health Data in the Light of European Regulation 679/2016</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Iglezakis">Ioannis Iglezakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Theodoros%20D.%20Trokanas"> Theodoros D. Trokanas</a>, <a href="https://publications.waset.org/abstracts/search?q=Panagiota%20Kiortsi"> Panagiota Kiortsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20health%20data" title="big health data">big health data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20subject%20rights" title=" data subject rights"> data subject rights</a>, <a href="https://publications.waset.org/abstracts/search?q=GDPR" title=" GDPR"> GDPR</a>, <a href="https://publications.waset.org/abstracts/search?q=pandemic" title=" pandemic"> pandemic</a> </p> <a href="https://publications.waset.org/abstracts/133310/legal-issues-of-collecting-and-processing-big-health-data-in-the-light-of-european-regulation-6792016" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133310.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">129</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">24941</span> Adaptive Data Approximations Codec (ADAC) for AI/ML-based Cyber-Physical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong-Kyu%20Jung">Yong-Kyu Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20codec" title="adaptive codec">adaptive codec</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber-physical" title=" cyber-physical"> cyber-physical</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a> </p> <a href="https://publications.waset.org/abstracts/160223/adaptive-data-approximations-codec-adac-for-aiml-based-cyber-physical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160223.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">78</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">24940</span> Real-Time Visualization Using GPU-Accelerated Filtering of LiDAR Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sa%C5%A1o%20Pe%C4%8Dnik">Sašo Pečnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Borut%20%C5%BDalik"> Borut Žalik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=filtering" title="filtering">filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=graphics" title=" graphics"> graphics</a>, <a href="https://publications.waset.org/abstracts/search?q=level-of-details" title=" level-of-details"> level-of-details</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20visualization" title=" real-time visualization"> real-time visualization</a> </p> <a href="https://publications.waset.org/abstracts/16857/real-time-visualization-using-gpu-accelerated-filtering-of-lidar-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16857.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">308</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">24939</span> Estimating Destinations of Bus Passengers Using Smart Card Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasik%20Lee">Hasik Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Young%20Kho"> Seung-Young Kho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=destination%20estimation" title="destination estimation">destination estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20density%20estimation" title=" Kernel density estimation"> Kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20card%20data" title=" smart card data"> smart card data</a>, <a href="https://publications.waset.org/abstracts/search?q=validation" title=" validation"> validation</a> </p> <a href="https://publications.waset.org/abstracts/80452/estimating-destinations-of-bus-passengers-using-smart-card-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80452.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">352</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">24938</span> Evaluated Nuclear Data Based Photon Induced Nuclear Reaction Model of GEANT4</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Won%20Shin">Jae Won Shin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ENDF%2FB-VII.1" title="ENDF/B-VII.1">ENDF/B-VII.1</a>, <a href="https://publications.waset.org/abstracts/search?q=GEANT4" title=" GEANT4"> GEANT4</a>, <a href="https://publications.waset.org/abstracts/search?q=photoneutron" title=" photoneutron"> photoneutron</a>, <a href="https://publications.waset.org/abstracts/search?q=photonuclear%20reaction" title=" photonuclear reaction"> photonuclear reaction</a> </p> <a href="https://publications.waset.org/abstracts/61592/evaluated-nuclear-data-based-photon-induced-nuclear-reaction-model-of-geant4" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61592.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">275</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">24937</span> Optimizing Communications Overhead in Heterogeneous Distributed Data Streams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashi%20Bhalla">Rashi Bhalla</a>, <a href="https://publications.waset.org/abstracts/search?q=Russel%20Pears"> Russel Pears</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Asif%20Naeem"> M. Asif Naeem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types. <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=bayesian%20inference" title=" bayesian inference"> bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20stream%20mining" title=" distributed data stream mining"> distributed data stream mining</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous-distributed%20data" title=" heterogeneous-distributed data"> heterogeneous-distributed data</a> </p> <a href="https://publications.waset.org/abstracts/124886/optimizing-communications-overhead-in-heterogeneous-distributed-data-streams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124886.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">161</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">24936</span> Data Privacy: Stakeholders’ Conflicts in Medical Internet of Things</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benny%20Sand">Benny Sand</a>, <a href="https://publications.waset.org/abstracts/search?q=Yotam%20Lurie"> Yotam Lurie</a>, <a href="https://publications.waset.org/abstracts/search?q=Shlomo%20Mark"> Shlomo Mark</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MIoT" title="MIoT">MIoT</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20privacy" title=" data privacy"> data privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=stakeholders" title=" stakeholders"> stakeholders</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20healthcare" title=" home healthcare"> home healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20privacy" title=" information privacy"> information privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a> </p> <a href="https://publications.waset.org/abstracts/153239/data-privacy-stakeholders-conflicts-in-medical-internet-of-things" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153239.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">102</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">24935</span> Optimizing Data Integration and Management Strategies for Upstream Oil and Gas Operations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Singh">Deepak Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rail%20Kuliev"> Rail Kuliev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master%20data%20management" title="master data management">master data management</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=AI%26ML" title=" AI&ML"> AI&ML</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20Computing" title=" cloud Computing"> cloud Computing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20optimization" title=" data optimization"> data optimization</a> </p> <a href="https://publications.waset.org/abstracts/171495/optimizing-data-integration-and-management-strategies-for-upstream-oil-and-gas-operations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171495.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">70</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">24934</span> Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi">Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi"> Mohammad Javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aref%20Ghafouri"> Aref Ghafouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20nonlinear%20estimation" title="local nonlinear estimation">local nonlinear estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=LWPR%20algorithm" title=" LWPR algorithm"> LWPR algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20training%20method" title=" online training method"> online training method</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20projection%20regression%20method" title=" locally weighted projection regression method"> locally weighted projection regression method</a> </p> <a href="https://publications.waset.org/abstracts/14554/influence-of-parameters-of-modeling-and-data-distribution-for-optimal-condition-on-locally-weighted-projection-regression-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14554.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">502</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">24933</span> Big Data Strategy for Telco: Network Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Amin">F. Amin</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Feizi"> S. Feizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance. <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=next%20generation%20networks" title=" next generation networks"> next generation networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20transformation" title=" network transformation"> network transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=strategy" title=" strategy"> strategy</a> </p> <a href="https://publications.waset.org/abstracts/4387/big-data-strategy-for-telco-network-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4387.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">360</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">24932</span> REDUCER: An Architectural Design Pattern for Reducing Large and Noisy Data Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apkar%20Salatian">Apkar Salatian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20pattern" title="design pattern">design pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=compression" title=" compression"> compression</a>, <a href="https://publications.waset.org/abstracts/search?q=architectural%20design" title=" architectural design"> architectural design</a> </p> <a href="https://publications.waset.org/abstracts/2130/reducer-an-architectural-design-pattern-for-reducing-large-and-noisy-data-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2130.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">212</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">24931</span> Fuzzy Expert Systems Applied to Intelligent Design of Data Centers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20M.%20Figueroa%20de%20la%20Cruz">Mario M. Figueroa de la Cruz</a>, <a href="https://publications.waset.org/abstracts/search?q=Claudia%20I.%20Solorzano"> Claudia I. Solorzano</a>, <a href="https://publications.waset.org/abstracts/search?q=Raul%20Acosta"> Raul Acosta</a>, <a href="https://publications.waset.org/abstracts/search?q=Ignacio%20Funes"> Ignacio Funes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telecommunications" title="telecommunications">telecommunications</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20center" title=" data center"> data center</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20systems" title=" expert systems"> expert systems</a> </p> <a href="https://publications.waset.org/abstracts/7297/fuzzy-expert-systems-applied-to-intelligent-design-of-data-centers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7297.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">345</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">24930</span> Genetic Testing and Research in South Africa: The Sharing of Data Across Borders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amy%20Gooden">Amy Gooden</a>, <a href="https://publications.waset.org/abstracts/search?q=Meshandren%20Naidoo"> Meshandren Naidoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-border" title="cross-border">cross-border</a>, <a href="https://publications.waset.org/abstracts/search?q=data" title=" data"> data</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20testing" title=" genetic testing"> genetic testing</a>, <a href="https://publications.waset.org/abstracts/search?q=law" title=" law"> law</a>, <a href="https://publications.waset.org/abstracts/search?q=regulation" title=" regulation"> regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=research" title=" research"> research</a>, <a href="https://publications.waset.org/abstracts/search?q=sharing" title=" sharing"> sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/153426/genetic-testing-and-research-in-south-africa-the-sharing-of-data-across-borders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153426.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">161</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">24929</span> Design of a Low Cost Motion Data Acquisition Setup for Mechatronic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baris%20Can%20Yalcin">Baris Can Yalcin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design" title="design">design</a>, <a href="https://publications.waset.org/abstracts/search?q=mechatronics" title=" mechatronics"> mechatronics</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20sensor" title=" motion sensor"> motion sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a> </p> <a href="https://publications.waset.org/abstracts/10243/design-of-a-low-cost-motion-data-acquisition-setup-for-mechatronic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10243.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">588</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">24928</span> Speed Characteristics of Mixed Traffic Flow on Urban Arterials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Dhamaniya">Ashish Dhamaniya</a>, <a href="https://publications.waset.org/abstracts/search?q=Satish%20Chandra"> Satish Chandra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85<sup>th</sup> and 50<sup>th</sup> percentile speed to the difference in 50<sup>th</sup> and 15<sup>th</sup> percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title="normal distribution">normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=percentile%20speed" title=" percentile speed"> percentile speed</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20spread%20ratio" title=" speed spread ratio"> speed spread ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20volume" title=" traffic volume"> traffic volume</a> </p> <a href="https://publications.waset.org/abstracts/1902/speed-characteristics-of-mixed-traffic-flow-on-urban-arterials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1902.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">422</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">24927</span> An Exploratory Analysis of Brisbane's Commuter Travel Patterns Using Smart Card Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming%20Wei">Ming Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken. <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=smart%20card%20data" title=" smart card data"> smart card data</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20pattern" title=" travel pattern"> travel pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a> </p> <a href="https://publications.waset.org/abstracts/54782/an-exploratory-analysis-of-brisbanes-commuter-travel-patterns-using-smart-card-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54782.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">285</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">24926</span> Developing a Decision-Making Tool for Prioritizing Green Building Initiatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tayyab%20Ahmad">Tayyab Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Gerard%20Healey"> Gerard Healey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sustainability in built environment sector is subject to many development constraints. Building projects are developed under different requirements of deliverables which makes each project unique. For an owner organization, i.e., a higher-education institution, involved in a significant building stock, it is important to prioritize some of the sustainability initiatives over the others in order to align the sustainable building development with organizational goals. The point-based green building rating tools i.e. Green Star, LEED, BREEAM are becoming increasingly popular and are well-acknowledged worldwide for verifying a sustainable development. It is imperative to synthesize a multi-criteria decision-making tool that can capitalize on the point-based methodology of rating systems while customizing the sustainable development of building projects according to the individual requirements and constraints of the client organization. A multi-criteria decision-making tool for the University of Melbourne is developed that builds on the action-learning and experience of implementing Green Buildings at the University of Melbourne. The tool evaluates the different sustainable building initiatives based on the framework of Green Star rating tool of Green Building Council of Australia. For each different sustainability initiative the decision-making tool makes an assessment based on at least five performance criteria including the ease with which a sustainability initiative can be achieved and the potential of a sustainability initiative to enhance project objectives, reduce life-cycle costs, enhance University’s reputation, and increase the confidence in quality construction. The use of a weighted aggregation mathematical model in the proposed tool can have a considerable role in the decision-making process of a Green Building project by indexing the Green Building initiatives in terms of organizational priorities. The index value of each initiative will be based on its alignment with some of the key performance criteria. The usefulness of the decision-making tool is validated by conducting structured interviews with some of the key stakeholders involved in the development of sustainable building projects at the University of Melbourne. The proposed tool is realized to help a client organization in deciding that within limited resources which sustainability initiatives and practices are more important to be pursued than others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=higher%20education%20institution" title="higher education institution">higher education institution</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-criteria%20decision-making%20tool" title=" multi-criteria decision-making tool"> multi-criteria decision-making tool</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20values" title=" organizational values"> organizational values</a>, <a href="https://publications.waset.org/abstracts/search?q=prioritizing%20sustainability%20initiatives" title=" prioritizing sustainability initiatives"> prioritizing sustainability initiatives</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20aggregation%20model" title=" weighted aggregation model"> weighted aggregation model</a> </p> <a href="https://publications.waset.org/abstracts/85042/developing-a-decision-making-tool-for-prioritizing-green-building-initiatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85042.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">234</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=7" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=2">2</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=indexing%20data&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=7">7</a></li> <li class="page-item active"><span class="page-link">8</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=10">10</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=11">11</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=indexing%20data&page=838">838</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=839">839</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=indexing%20data&page=9" 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>