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data</title> <meta name="description" content="Search results for: accurate data"> <meta name="keywords" content="accurate 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 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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="accurate 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> 26519</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: accurate data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26519</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">26518</span> Accurate HLA Typing at High-Digit Resolution from NGS Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yazhi%20Huang">Yazhi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Yang"> Jing Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dingge%20Ying"> Dingge Ying</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Zhang"> Yan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Vorasuk%20Shotelersuk"> Vorasuk Shotelersuk</a>, <a href="https://publications.waset.org/abstracts/search?q=Nattiya%20Hirankarn"> Nattiya Hirankarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Pak%20Chung%20Sham"> Pak Chung Sham</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Lung%20Lau"> Yu Lung Lau</a>, <a href="https://publications.waset.org/abstracts/search?q=Wanling%20Yang"> Wanling Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20leukocyte%20antigens" title="human leukocyte antigens">human leukocyte antigens</a>, <a href="https://publications.waset.org/abstracts/search?q=next%20generation%20sequencing" title=" next generation sequencing"> next generation sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=whole%20exome%20sequencing" title=" whole exome sequencing"> whole exome sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=HLA%20typing" title=" HLA typing"> HLA typing</a> </p> <a href="https://publications.waset.org/abstracts/26433/accurate-hla-typing-at-high-digit-resolution-from-ngs-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26433.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">663</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">26517</span> Accurate Position Electromagnetic Sensor Using Data Acquisition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Ezzouine">Z. Ezzouine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Nakheli"> A. Nakheli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a high position electromagnetic sensor system (HPESS) that is applicable for moving object detection. The authors have developed a high-performance position sensor prototype dedicated to students’ laboratory. The challenge was to obtain a highly accurate and real-time sensor that is able to calculate position, length or displacement. An electromagnetic solution based on a two coil induction principal was adopted. The HPESS converts mechanical motion to electric energy with direct contact. The output signal can then be fed to an electronic circuit. The voltage output change from the sensor is captured by data acquisition system using LabVIEW software. The displacement of the moving object is determined. The measured data are transmitted to a PC in real-time via a DAQ (NI USB -6281). This paper also describes the data acquisition analysis and the conditioning card developed specially for sensor signal monitoring. The data is then recorded and viewed using a user interface written using National Instrument LabVIEW software. On-line displays of time and voltage of the sensor signal provide a user-friendly data acquisition interface. The sensor provides an uncomplicated, accurate, reliable, inexpensive transducer for highly sophisticated control systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20sensor" title="electromagnetic sensor">electromagnetic sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=accurately" title=" accurately"> accurately</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20measurement" title=" position measurement"> position measurement</a> </p> <a href="https://publications.waset.org/abstracts/66493/accurate-position-electromagnetic-sensor-using-data-acquisition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66493.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">26516</span> Developing an Information Model of Manufacturing Process for Sustainability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Hyun%20Lee">Jae Hyun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Manufacturing companies use life-cycle inventory databases to analyze sustainability of their manufacturing processes. Life cycle inventory data provides reference data which may not be accurate for a specific company. Collecting accurate data of manufacturing processes for a specific company requires enormous time and efforts. An information model of typical manufacturing processes can reduce time and efforts to get appropriate reference data for a specific company. This paper shows an attempt to build an abstract information model which can be used to develop information models for specific manufacturing processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20information%20model" title="process information model">process information model</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=OWL" title=" OWL"> OWL</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacturing" title=" manufacturing"> manufacturing</a> </p> <a href="https://publications.waset.org/abstracts/5611/developing-an-information-model-of-manufacturing-process-for-sustainability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5611.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">430</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">26515</span> An Automated Approach to Consolidate Galileo System Availability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marie%20Bieber">Marie Bieber</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Cosson"> Fabrice Cosson</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Schmitt"> Olivier Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Europe's Global Navigation Satellite System, Galileo, provides worldwide positioning and navigation services. The satellites in space are only one part of the Galileo system. An extensive ground infrastructure is essential to oversee the satellites and ensure accurate navigation signals. High reliability and availability of the entire Galileo system are crucial to continuously provide positioning information of high quality to users. Outages are tracked, and operational availability is regularly assessed. A highly flexible and adaptive tool has been developed to automate the Galileo system availability analysis. Not only does it enable a quick availability consolidation, but it also provides first steps towards improving the data quality of maintenance tickets used for the analysis. This includes data import and data preparation, with a focus on processing strings used for classification and identifying faulty data. Furthermore, the tool allows to handle a low amount of data, which is a major constraint when the aim is to provide accurate statistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=availability" title="availability">availability</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20quality" title=" data quality"> data quality</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20performance" title=" system performance"> system performance</a>, <a href="https://publications.waset.org/abstracts/search?q=Galileo" title=" Galileo"> Galileo</a>, <a href="https://publications.waset.org/abstracts/search?q=aerospace" title=" aerospace"> aerospace</a> </p> <a href="https://publications.waset.org/abstracts/107165/an-automated-approach-to-consolidate-galileo-system-availability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107165.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">167</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">26514</span> Information Management Approach in the Prediction of Acute Appendicitis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Shahin">Ahmad Shahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Moudani"> Walid Moudani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Bekraki"> Ali Bekraki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=healthcare%20management" title="healthcare management">healthcare management</a>, <a href="https://publications.waset.org/abstracts/search?q=acute%20appendicitis" title=" acute appendicitis"> acute appendicitis</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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/8697/information-management-approach-in-the-prediction-of-acute-appendicitis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8697.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">350</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">26513</span> Model for Introducing Products to New Customers through Decision Tree Using Algorithm C4.5 (J-48)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komol%20Phaisarn">Komol Phaisarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Anuphan%20Suttimarn"> Anuphan Suttimarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Vitchanan%20Keawtong"> Vitchanan Keawtong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kittisak%20Thongyoun"> Kittisak Thongyoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Chaiyos%20Jamsawang"> Chaiyos Jamsawang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible. <p class="card-text"><strong>Keywords:</strong> <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=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=customers" title=" customers"> customers</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20insurance%20pay%20package" title=" life insurance pay package"> life insurance pay package</a> </p> <a href="https://publications.waset.org/abstracts/11724/model-for-introducing-products-to-new-customers-through-decision-tree-using-algorithm-c45-j-48" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11724.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">428</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">26512</span> JavaScript Object Notation Data against eXtensible Markup Language Data in Software Applications a Software Testing Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theertha%20Chandroth">Theertha Chandroth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study on how to check JSON (JavaScript Object Notation) data against XML (eXtensible Markup Language) data from a software testing point of view. JSON and XML are widely used data interchange formats, each with its unique syntax and structure. The objective is to explore various techniques and methodologies for validating comparison and integration between JSON data to XML and vice versa. By understanding the process of checking JSON data against XML data, testers, developers and data practitioners can ensure accurate data representation, seamless data interchange, and effective data validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=XML" title="XML">XML</a>, <a href="https://publications.waset.org/abstracts/search?q=JSON" title=" JSON"> JSON</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20comparison" title=" data comparison"> data comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=integration%20testing" title=" integration testing"> integration testing</a>, <a href="https://publications.waset.org/abstracts/search?q=Python" title=" Python"> Python</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL"> SQL</a> </p> <a href="https://publications.waset.org/abstracts/170435/javascript-object-notation-data-against-extensible-markup-language-data-in-software-applications-a-software-testing-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170435.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">140</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">26511</span> Experiments on Weakly-Supervised Learning on Imperfect Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Cheng">Yan Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yijun%20Shao"> Yijun Shao</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Rudolph"> James Rudolph</a>, <a href="https://publications.waset.org/abstracts/search?q=Charlene%20R.%20Weir"> Charlene R. Weir</a>, <a href="https://publications.waset.org/abstracts/search?q=Beth%20Sahlmann"> Beth Sahlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing%20Zeng-Treitler"> Qing Zeng-Treitler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weakly-supervised%20learning" title="weakly-supervised learning">weakly-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=delirium" title=" delirium"> delirium</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/99362/experiments-on-weakly-supervised-learning-on-imperfect-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99362.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">199</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">26510</span> An Interpretable Data-Driven Approach for the Stratification of the Cardiorespiratory Fitness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.Mendes">D.Mendes</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Henriques"> J. Henriques</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Carvalho"> P. Carvalho</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Rocha"> T. Rocha</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Paredes"> S. Paredes</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Cabiddu"> R. Cabiddu</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Trimer"> R. Trimer</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Mendes"> R. Mendes</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Borghi-Silva"> A. Borghi-Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Kaminsky"> L. Kaminsky</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Ashley"> E. Ashley</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Arena"> R. Arena</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Myers"> J. Myers</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiorespiratory%20fitness" title="cardiorespiratory fitness">cardiorespiratory fitness</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20models" title=" data-driven models"> data-driven models</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</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/77099/an-interpretable-data-driven-approach-for-the-stratification-of-the-cardiorespiratory-fitness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77099.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">286</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">26509</span> Cascaded Neural Network for Internal Temperature Forecasting in Induction Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hidir%20S.%20Nogay">Hidir S. Nogay </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cascaded%20neural%20network" title="cascaded neural network">cascaded neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20temperature" title=" internal temperature"> internal temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=inverter" title=" inverter"> inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=three-phase%20induction%20motor" title=" three-phase induction motor"> three-phase induction motor</a> </p> <a href="https://publications.waset.org/abstracts/62890/cascaded-neural-network-for-internal-temperature-forecasting-in-induction-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62890.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">26508</span> Implementation of Data Science in Field of Homologation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shubham%20Bhonde">Shubham Bhonde</a>, <a href="https://publications.waset.org/abstracts/search?q=Nekzad%20Doctor"> Nekzad Doctor</a>, <a href="https://publications.waset.org/abstracts/search?q=Shashwat%20Gawande"> Shashwat Gawande</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the use and the import of Keys and ID Transmitter as well as Body Control Modules with radio transmission in a lot of countries, homologation is required. Final deliverables in homologation of the product are certificates. In considering the world of homologation, there are approximately 200 certificates per product, with most of the certificates in local languages. It is challenging to manually investigate each certificate and extract relevant data from the certificate, such as expiry date, approval date, etc. It is most important to get accurate data from the certificate as inaccuracy may lead to missing re-homologation of certificates that will result in an incompliance situation. There is a scope of automation in reading the certificate data in the field of homologation. We are using deep learning as a tool for automation. We have first trained a model using machine learning by providing all country's basic data. We have trained this model only once. We trained the model by feeding pdf and jpg files using the ETL process. Eventually, that trained model will give more accurate results later. As an outcome, we will get the expiry date and approval date of the certificate with a single click. This will eventually help to implement automation features on a broader level in the database where certificates are stored. This automation will help to minimize human error to almost negligible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=homologation" title="homologation">homologation</a>, <a href="https://publications.waset.org/abstracts/search?q=re-homologation" title=" re-homologation"> re-homologation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</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=ETL%20%28extract%20transform%20loading%29" title=" ETL (extract transform loading)"> ETL (extract transform loading)</a> </p> <a href="https://publications.waset.org/abstracts/151431/implementation-of-data-science-in-field-of-homologation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151431.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">26507</span> Issues on Determination of Accurate Fajr and Dhuha Prayer Times According to Fiqh and Astronomical Perspectives in Malaysia: A Bibliography Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raihana%20Abdul%20Wahab">Raihana Abdul Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Norihan%20Kadir"> Norihan Kadir</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhamad%20Hazwan%20Mustafa"> Muhamad Hazwan Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The determination of accurate times for Fajr and Dhuha prayers in Malaysia is faced with issues of differing views in the fixation of the parameters of the sun’s altitude used in the calculation of astronomy, especially in Malaysia. Therefore, this study aims to identify issues and problems in the methods used in determining the accurate times for both these prayers through a literature review of previous research studies. The results show the need to review the parameters of sun altitude used in calculating prayer times for both these prayers through observations in changes in the brightness of the early morning light for distinguish of true dawn and false dawn for the Fajr prayers and the length of the shadow for Dhuha payer by collecting data from all the states throughout Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fajr" title="fajr">fajr</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhuha" title=" Dhuha"> Dhuha</a>, <a href="https://publications.waset.org/abstracts/search?q=sky%20brightness" title=" sky brightness"> sky brightness</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20of%20shadows" title=" length of shadows"> length of shadows</a>, <a href="https://publications.waset.org/abstracts/search?q=astronomy" title=" astronomy"> astronomy</a>, <a href="https://publications.waset.org/abstracts/search?q=Islamic%20jurisprudence" title=" Islamic jurisprudence"> Islamic jurisprudence</a> </p> <a href="https://publications.waset.org/abstracts/53844/issues-on-determination-of-accurate-fajr-and-dhuha-prayer-times-according-to-fiqh-and-astronomical-perspectives-in-malaysia-a-bibliography-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53844.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">258</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">26506</span> Data Driven Infrastructure Planning for Offshore Wind farms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isha%20Saxena">Isha Saxena</a>, <a href="https://publications.waset.org/abstracts/search?q=Behzad%20Kazemtabrizi"> Behzad Kazemtabrizi</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20C.%20M.%20Troffaes"> Matthias C. M. Troffaes</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Crabtree"> Christopher Crabtree</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The calculations done at the beginning of the life of a wind farm are rarely reliable, which makes it important to conduct research and study the failure and repair rates of the wind turbines under various conditions. This miscalculation happens because the current models make a simplifying assumption that the failure/repair rate remains constant over time. This means that the reliability function is exponential in nature. This research aims to create a more accurate model using sensory data and a data-driven approach. The data cleaning and data processing is done by comparing the Power Curve data of the wind turbines with SCADA data. This is then converted to times to repair and times to failure timeseries data. Several different mathematical functions are fitted to the times to failure and times to repair data of the wind turbine components using Maximum Likelihood Estimation and the Posterior expectation method for Bayesian Parameter Estimation. Initial results indicate that two parameter Weibull function and exponential function produce almost identical results. Further analysis is being done using the complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can be helpful for the engineers to schedule maintenance and repairs to decrease the downtime of the turbine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20parameter%20inference" title=" bayesian parameter inference"> bayesian parameter inference</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=weibull%20function" title=" weibull function"> weibull function</a>, <a href="https://publications.waset.org/abstracts/search?q=SCADA%20data" title=" SCADA data"> SCADA data</a> </p> <a href="https://publications.waset.org/abstracts/172809/data-driven-infrastructure-planning-for-offshore-wind-farms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172809.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">86</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">26505</span> Big Data and Analytics in Higher Education: An Assessment of Its Status, Relevance and Future in the Republic of the Philippines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byron%20Joseph%20A.%20Hallar">Byron Joseph A. Hallar</a>, <a href="https://publications.waset.org/abstracts/search?q=Annjeannette%20Alain%20D.%20Galang"> Annjeannette Alain D. Galang</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Visitacion%20N.%20Gumabay"> Maria Visitacion N. Gumabay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines. <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%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=republic%20of%20the%20philippines" title=" republic of the philippines"> republic of the philippines</a>, <a href="https://publications.waset.org/abstracts/search?q=assessment" title=" assessment"> assessment</a> </p> <a href="https://publications.waset.org/abstracts/46561/big-data-and-analytics-in-higher-education-an-assessment-of-its-status-relevance-and-future-in-the-republic-of-the-philippines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46561.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">348</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">26504</span> A Highly Accurate Computer-Aided Diagnosis: CAD System for the Diagnosis of Breast Cancer by Using Thermographic Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Bazarganigilani">Mahdi Bazarganigilani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis%20systems" title="computer-aided diagnosis systems">computer-aided diagnosis systems</a>, <a href="https://publications.waset.org/abstracts/search?q=thermographic%20analysis" title=" thermographic analysis"> thermographic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20analysis" title=" spatio-temporal analysis"> spatio-temporal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image 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/144933/a-highly-accurate-computer-aided-diagnosis-cad-system-for-the-diagnosis-of-breast-cancer-by-using-thermographic-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144933.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">210</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">26503</span> Rail Degradation Modelling Using ARMAX: A Case Study Applied to Melbourne Tram System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Karimpour">M. Karimpour</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Elkhoury"> N. Elkhoury</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Hitihamillage"> L. Hitihamillage</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Moridpour"> S. Moridpour</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Hesami"> R. Hesami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is a necessity among rail transportation authorities for a superior understanding of the rail track degradation overtime and the factors influencing rail degradation. They need an accurate technique to identify the time when rail tracks fail or need maintenance. In turn, this will help to increase the level of safety and comfort of the passengers and the vehicles as well as improve the cost effectiveness of maintenance activities. An accurate model can play a key role in prediction of the long-term behaviour of railroad tracks. An accurate model can decrease the cost of maintenance. In this research, the rail track degradation is predicted using an autoregressive moving average with exogenous input (ARMAX). An ARMAX has been implemented on Melbourne tram data to estimate the values for the tram track degradation. Gauge values and rail usage in Million Gross Tone (MGT) are the main parameters used in the model. The developed model can accurately predict the future status of the tram tracks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARMAX" title="ARMAX">ARMAX</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20systems" title=" dynamic systems"> dynamic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=MGT" title=" MGT"> MGT</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rail%20degradation" title=" rail degradation"> rail degradation</a> </p> <a href="https://publications.waset.org/abstracts/77370/rail-degradation-modelling-using-armax-a-case-study-applied-to-melbourne-tram-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77370.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">248</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">26502</span> Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Clarine">Jeff Clarine</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Shyh%20Peng"> Chang-Shyh Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20Sang"> Daisy Sang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioassay" title="bioassay">bioassay</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=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20screen" title=" virtual screen"> virtual screen</a> </p> <a href="https://publications.waset.org/abstracts/77481/optimized-preprocessing-for-accurate-and-efficient-bioassay-prediction-with-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77481.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">274</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">26501</span> Satellite LiDAR-Based Digital Terrain Model Correction using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keisuke%20Takahata">Keisuke Takahata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Suetsugu"> Hiroshi Suetsugu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20terrain%20model" title="digital terrain model">digital terrain model</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20LiDAR" title=" satellite LiDAR"> satellite LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20processes" title=" gaussian processes"> gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a> </p> <a href="https://publications.waset.org/abstracts/148360/satellite-lidar-based-digital-terrain-model-correction-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148360.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">183</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">26500</span> A Numerical Model for Simulation of Blood Flow in Vascular Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houman%20Tamaddon">Houman Tamaddon</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Behnia"> Mehrdad Behnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Masud%20Behnia"> Masud Behnia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20flow" title="blood flow">blood flow</a>, <a href="https://publications.waset.org/abstracts/search?q=morphometric%20data" title=" morphometric data"> morphometric data</a>, <a href="https://publications.waset.org/abstracts/search?q=vascular%20tree" title=" vascular tree"> vascular tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahler%20ordering%20system" title=" Strahler ordering system"> Strahler ordering system</a> </p> <a href="https://publications.waset.org/abstracts/11033/a-numerical-model-for-simulation-of-blood-flow-in-vascular-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11033.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">272</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">26499</span> Towards a Framework for Embedded Weight Comparison Algorithm with Business Intelligence in the Plantation Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Pushparani">M. Pushparani</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sagaya"> A. Sagaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=embedded%20business%20intelligence" title="embedded business intelligence">embedded business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20comparison%20algorithm" title=" weight comparison algorithm"> weight comparison algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20palm%20plantation" title=" oil palm plantation"> oil palm plantation</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20systems" title=" embedded systems"> embedded systems</a> </p> <a href="https://publications.waset.org/abstracts/53115/towards-a-framework-for-embedded-weight-comparison-algorithm-with-business-intelligence-in-the-plantation-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53115.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">26498</span> Jordan Water District Interactive Billing and Accounting Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20J.%20Forca">Adrian J. Forca</a>, <a href="https://publications.waset.org/abstracts/search?q=Simeon%20J.%20Cainday%20III"> Simeon J. Cainday III</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Jordan Water District Interactive Billing and Accounting Information Systems is designed for Jordan Water District to uplift the efficiency and effectiveness of its services to its customers. It is designed to process computations of water bills in accurate and fast way through automating the manual process and ensures that correct rates and fees are applied. In addition to billing process, a mobile app will be integrated into it to support rapid and accurate water bill generation. An interactive feature will be incorporated to support electronic billing to customers who wish to receive water bills through the use of electronic mail. The system will also improve, organize and avoid data inaccuracy in accounting processes because data will be stored in a database which is designed logically correct through normalization. Furthermore, strict programming constraints will be plunged to validate account access privilege based on job function and data being stored and retrieved to ensure data security, reliability, and accuracy. The system will be able to cater the billing and accounting services of Jordan Water District resulting in setting forth the manual process and adapt to the modern technological innovations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accounting" title="accounting">accounting</a>, <a href="https://publications.waset.org/abstracts/search?q=bill" title=" bill"> bill</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20system" title=" information system"> information system</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive" title=" interactive"> interactive</a> </p> <a href="https://publications.waset.org/abstracts/95441/jordan-water-district-interactive-billing-and-accounting-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95441.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">251</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">26497</span> An Accurate Method for Phylogeny Tree Reconstruction Based on a Modified Wild Dog Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study solves a phylogeny problem by using modified wild dog pack optimization. The least squares error is considered as a cost function that needs to be minimized. Therefore, in each iteration, new distance matrices based on the constructed trees are calculated and used to select the alpha dog. To test the suggested algorithm, ten homologous genes are selected and collected from National Center for Biotechnology Information (NCBI) databanks (i.e., 16S, 18S, 28S, Cox 1, ITS1, ITS2, ETS, ATPB, Hsp90, and STN). The data are divided into three categories: 50 taxa, 100 taxa and 500 taxa. The empirical results show that the proposed algorithm is more reliable and accurate than other implemented methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20square" title="least square">least square</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20joining" title=" neighbor joining"> neighbor joining</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20tree" title=" phylogenetic tree"> phylogenetic tree</a>, <a href="https://publications.waset.org/abstracts/search?q=wild%20dog%20pack" title=" wild dog pack"> wild dog pack</a> </p> <a href="https://publications.waset.org/abstracts/42453/an-accurate-method-for-phylogeny-tree-reconstruction-based-on-a-modified-wild-dog-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42453.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">320</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">26496</span> Using Open Source Data and GIS Techniques to Overcome Data Deficiency and Accuracy Issues in the Construction and Validation of Transportation Network: Case of Kinshasa City</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christian%20Kapuku">Christian Kapuku</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> An accurate representation of the transportation system serving the region is one of the important aspects of transportation modeling. Such representation often requires developing an abstract model of the system elements, which also requires important amount of data, surveys and time. However, in some cases such as in developing countries, data deficiencies, time and budget constraints do not always allow such accurate representation, leaving opportunities to assumptions that may negatively affect the quality of the analysis. With the emergence of Internet open source data especially in the mapping technologies as well as the advances in Geography Information System, opportunities to tackle these issues have raised. Therefore, the objective of this paper is to demonstrate such application through a practical case of the development of the transportation network for the city of Kinshasa. The GIS geo-referencing was used to construct the digitized map of Transportation Analysis Zones using available scanned images. Centroids were then dynamically placed at the center of activities using an activities density map. Next, the road network with its characteristics was built using OpenStreet data and other official road inventory data by intersecting their layers and cleaning up unnecessary links such as residential streets. The accuracy of the final network was then checked, comparing it with satellite images from Google and Bing. For the validation, the final network was exported into Emme3 to check for potential network coding issues. Results show a high accuracy between the built network and satellite images, which can mostly be attributed to the use of open source data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system%20%28GIS%29" title="geographic information system (GIS)">geographic information system (GIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20construction" title=" network construction"> network construction</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation%20database" title=" transportation database"> transportation database</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source%20data" title=" open source data"> open source data</a> </p> <a href="https://publications.waset.org/abstracts/80526/using-open-source-data-and-gis-techniques-to-overcome-data-deficiency-and-accuracy-issues-in-the-construction-and-validation-of-transportation-network-case-of-kinshasa-city" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80526.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">167</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">26495</span> Real Time Lidar and Radar High-Level Fusion for Obstacle Detection and Tracking with Evaluation on a Ground Truth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hatem%20Hajri">Hatem Hajri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed-Cherif%20Rahal"> Mohamed-Cherif Rahal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ground%20truth" title="ground truth">ground truth</a>, <a href="https://publications.waset.org/abstracts/search?q=Hungarian%20algorithm" title=" Hungarian algorithm"> Hungarian algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=lidar%20Radar%20data%20fusion" title=" lidar Radar data fusion"> lidar Radar data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20nearest%20neighbor%20filter" title=" global nearest neighbor filter"> global nearest neighbor filter</a> </p> <a href="https://publications.waset.org/abstracts/95451/real-time-lidar-and-radar-high-level-fusion-for-obstacle-detection-and-tracking-with-evaluation-on-a-ground-truth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95451.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">171</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">26494</span> Identity Verification Using k-NN Classifiers and Autistic Genetic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN).  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <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=identity%20verification" title=" identity verification"> identity verification</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor" title=" k nearest neighbor"> k nearest neighbor</a> </p> <a href="https://publications.waset.org/abstracts/75552/identity-verification-using-k-nn-classifiers-and-autistic-genetic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75552.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">258</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">26493</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">26492</span> Unsupervised Feature Learning by Pre-Route Simulation of Auto-Encoder Behavior Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Jin">Youngjae Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Daeshik%20Kim"> Daeshik Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-encoder" title="auto-encoder">auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior%20model%20simulation" title=" behavior model simulation"> behavior model simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20hardware%20design" title=" digital hardware design"> digital hardware design</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-route%20simulation" title=" pre-route simulation"> pre-route simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Unsupervised%20feature%20learning" title=" Unsupervised feature learning"> Unsupervised feature learning</a> </p> <a href="https://publications.waset.org/abstracts/4980/unsupervised-feature-learning-by-pre-route-simulation-of-auto-encoder-behavior-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4980.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">446</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">26491</span> User Experience Measurement of User Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hashemi">Mohammad Hashemi</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Herbert"> John Herbert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantifying and measuring Quality of Experience (QoE) are important and difficult concerns in Human Computer Interaction (HCI). Quality of Service (QoS) and the actual User Interface (UI) of the application are both important contributors to the QoE of a user. This paper describes a framework that measures accurately the way a user uses the UI in order to model users' behaviours and profiles. It monitors the use of the mouse and use of UI elements with accurate time measurement. It does this in real-time and does so unobtrusively and efficiently allowing the user to work as normal with the application. This real-time accurate measurement of the user's interaction provides valuable data and insight into the use of the UI, and is also the basis for analysis of the user's QoE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=user%20modelling" title="user modelling">user modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20interface%20experience" title=" user interface experience"> user interface experience</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20experience" title=" quality of experience"> quality of experience</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20experience" title=" user experience"> user experience</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20and%20computer%20interaction" title=" human and computer interaction"> human and computer interaction</a> </p> <a href="https://publications.waset.org/abstracts/3652/user-experience-measurement-of-user-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3652.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">503</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">26490</span> Data Refinement Enhances The Accuracy of Short-Term Traffic Latency Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Man%20Fung%20Ho">Man Fung Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=Lap%20So"> Lap So</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Zhang"> Jiaqi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuheng%20Zhao"> Yuheng Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Huiyang%20Lu"> Huiyang Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tat%20Shing%20Choi"> Tat Shing Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Y.%20Michael%20Wong"> K. Y. Michael Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20refinement" title="data refinement">data refinement</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=mutual%20information" title=" mutual information"> mutual information</a>, <a href="https://publications.waset.org/abstracts/search?q=short-term%20latency%20prediction" title=" short-term latency prediction"> short-term latency prediction</a> </p> <a href="https://publications.waset.org/abstracts/139538/data-refinement-enhances-the-accuracy-of-short-term-traffic-latency-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139538.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">169</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=accurate%20data&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accurate%20data&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accurate%20data&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=accurate%20data&page=5">5</a></li> <li class="page-item"><a class="page-link" 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