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: predictive analysis</title> <meta name="description" content="Search results for: predictive analysis"> <meta name="keywords" content="predictive analysis"> <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="predictive analysis" 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="predictive analysis"> <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> 28430</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predictive analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28250</span> Role of Imaging in Predicting the Receptor Positivity Status in Lung Adenocarcinoma: A Chapter in Radiogenomics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonal%20Sethi">Sonal Sethi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukesh%20Yadav"> Mukesh Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhimanyu%20Gupta"> Abhimanyu Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The upcoming field of radiogenomics has the potential to upgrade the role of imaging in lung cancer management by noninvasive characterization of tumor histology and genetic microenvironment. Receptor positivity like epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) genotyping are critical in lung adenocarcinoma for treatment. As conventional identification of receptor positivity is an invasive procedure, we analyzed the features on non-invasive computed tomography (CT), which predicts the receptor positivity in lung adenocarcinoma. Retrospectively, we did a comprehensive study from 77 proven lung adenocarcinoma patients with CT images, EGFR and ALK receptor genotyping, and clinical information. Total 22/77 patients were receptor-positive (15 had only EGFR mutation, 6 had ALK mutation, and 1 had both EGFR and ALK mutation). Various morphological characteristics and metastatic distribution on CT were analyzed along with the clinical information. Univariate and multivariable logistic regression analyses were used. On multivariable logistic regression analysis, we found spiculated margin, lymphangitic spread, air bronchogram, pleural effusion, and distant metastasis had a significant predictive value for receptor mutation status. On univariate analysis, air bronchogram and pleural effusion had significant individual predictive value. Conclusions: Receptor positive lung cancer has characteristic imaging features compared with nonreceptor positive lung adenocarcinoma. Since CT is routinely used in lung cancer diagnosis, we can predict the receptor positivity by a noninvasive technique and would follow a more aggressive algorithm for evaluation of distant metastases as well as for the treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer" title="lung cancer">lung cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=multidisciplinary%20cancer%20care" title=" multidisciplinary cancer care"> multidisciplinary cancer care</a>, <a href="https://publications.waset.org/abstracts/search?q=oncologic%20imaging" title=" oncologic imaging"> oncologic imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=radiobiology" title=" radiobiology"> radiobiology</a> </p> <a href="https://publications.waset.org/abstracts/129528/role-of-imaging-in-predicting-the-receptor-positivity-status-in-lung-adenocarcinoma-a-chapter-in-radiogenomics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129528.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">136</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">28249</span> Privacy Concerns and Law Enforcement Data Collection to Tackle Domestic and Sexual Violence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Francesca%20Radice">Francesca Radice</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Domestic and sexual violence provokes, on average in Australia, one female death per week due to intimate violence behaviours. 83% of couples meet online, and intercepting domestic and sexual violence at this level would be beneficial. It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</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=predictive%20policing" title=" predictive policing"> predictive policing</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20manipulation" title=" virtual manipulation"> virtual manipulation</a> </p> <a href="https://publications.waset.org/abstracts/166143/privacy-concerns-and-law-enforcement-data-collection-to-tackle-domestic-and-sexual-violence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166143.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">28248</span> Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soheila%20Sadeghi">Soheila Sadeghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20impact" title="cost impact">cost impact</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=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=schedule%20impact" title=" schedule impact"> schedule impact</a>, <a href="https://publications.waset.org/abstracts/search?q=scope%20changes" title=" scope changes"> scope changes</a> </p> <a href="https://publications.waset.org/abstracts/187305/predicting-the-impact-of-scope-changes-on-project-cost-and-schedule-using-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187305.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">39</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">28247</span> Discriminant Analysis as a Function of Predictive Learning to Select Evolutionary Algorithms in Intelligent Transportation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20A.%20Ruiz-Vanoye">Jorge A. Ruiz-Vanoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ocotl%C3%A1n%20D%C3%ADaz-Parra"> Ocotlán Díaz-Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20Fuentes-Penna"> Alejandro Fuentes-Penna</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20V%C3%A9lez-D%C3%ADaz"> Daniel Vélez-Díaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Edith%20Olaco%20Garc%C3%ADa"> Edith Olaco García</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intelligent%20Transportation%20Systems" title="Intelligent Transportation Systems">Intelligent Transportation Systems</a>, <a href="https://publications.waset.org/abstracts/search?q=data-mining%20techniques" title=" data-mining techniques"> data-mining techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</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/42737/discriminant-analysis-as-a-function-of-predictive-learning-to-select-evolutionary-algorithms-in-intelligent-transportation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42737.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">472</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">28246</span> Robust Image Design Based Steganographic System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadiq%20J.%20Abou-Loukh">Sadiq J. Abou-Loukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20M.%20Habbi"> Hanan M. Habbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=encryption" title="encryption">encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%0D%0Apredictive%20coding" title=" differential predictive coding"> differential predictive coding</a>, <a href="https://publications.waset.org/abstracts/search?q=four%20triangles%20operation" title=" four triangles operation "> four triangles operation </a> </p> <a href="https://publications.waset.org/abstracts/16654/robust-image-design-based-steganographic-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16654.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">493</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">28245</span> Enhance the Power of Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Zhang">Yu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20Desouza"> Pedro Desouza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=Amazon" title=" Amazon"> Amazon</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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/5977/enhance-the-power-of-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5977.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">353</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">28244</span> Molecular Topology and TLC Retention Behaviour of s-Triazines: QSRR Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lidija%20R.%20Jevri%C4%87">Lidija R. Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20O.%20Podunavac-Kuzmanovi%C4%87"> Sanja O. Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Z.%20Kova%C4%8Devi%C4%87"> Strahinja Z. Kovačević</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantitative structure-retention relationship (QSRR) analysis was used to predict the chromatographic behavior of s-triazine derivatives by using theoretical descriptors computed from the chemical structure. Fundamental basis of the reported investigation is to relate molecular topological descriptors with chromatographic behavior of s-triazine derivatives obtained by reversed-phase (RP) thin layer chromatography (TLC) on silica gel impregnated with paraffin oil and applied ethanol-water (φ = 0.5-0.8; v/v). Retention parameter (RM0) of 14 investigated s-triazine derivatives was used as dependent variable while simple connectivity index different orders were used as independent variables. The best QSRR model for predicting RM0 value was obtained with simple third order connectivity index (3χ) in the second-degree polynomial equation. Numerical values of the correlation coefficient (r=0.915), Fisher's value (F=28.34) and root mean square error (RMSE = 0.36) indicate that model is statistically significant. In order to test the predictive power of the QSRR model leave-one-out cross-validation technique has been applied. The parameters of the internal cross-validation analysis (r2CV=0.79, r2adj=0.81, PRESS=1.89) reflect the high predictive ability of the generated model and it confirms that can be used to predict RM0 value. Multivariate classification technique, hierarchical cluster analysis (HCA), has been applied in order to group molecules according to their molecular connectivity indices. HCA is a descriptive statistical method and it is the most frequently used for important area of data processing such is classification. The HCA performed on simple molecular connectivity indices obtained from the 2D structure of investigated s-triazine compounds resulted in two main clusters in which compounds molecules were grouped according to the number of atoms in the molecule. This is in agreement with the fact that these descriptors were calculated on the basis of the number of atoms in the molecule of the investigated s-triazine derivatives. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=s-triazines" title="s-triazines">s-triazines</a>, <a href="https://publications.waset.org/abstracts/search?q=QSRR" title=" QSRR"> QSRR</a>, <a href="https://publications.waset.org/abstracts/search?q=chemometrics" title=" chemometrics"> chemometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=chromatography" title=" chromatography"> chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a> </p> <a href="https://publications.waset.org/abstracts/29063/molecular-topology-and-tlc-retention-behaviour-of-s-triazines-qsrr-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29063.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">393</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">28243</span> A Comparative Study of Various Control Methods for Rendezvous of a Satellite Couple</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Basaran">Hasan Basaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Emre%20Unal"> Emre Unal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Formation flying of satellites is a mission that involves a relative position keeping of different satellites in the constellation. In this study, different control algorithms are compared with one another in terms of ΔV, velocity increment, and tracking error. Various control methods, covering continuous and impulsive approaches are implemented and tested for satellites flying in low Earth orbit. Feedback linearization, sliding mode control, and model predictive control are designed and compared with an impulsive feedback law, which is based on mean orbital elements. Feedback linearization and sliding mode control approaches have identical mathematical models that include second order Earth oblateness effects. The model predictive control, on the other hand, does not include any perturbations and assumes circular chief orbit. The comparison is done with 4 different initial errors and achieved with velocity increment, root mean square error, maximum steady state error, and settling time. It was observed that impulsive law consumed the least ΔV, while produced the highest maximum error in the steady state. The continuous control laws, however, consumed higher velocity increments and produced lower amounts of tracking errors. Finally, the inversely proportional relationship between tracking error and velocity increment was established. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chief-deputy%20satellites" title="chief-deputy satellites">chief-deputy satellites</a>, <a href="https://publications.waset.org/abstracts/search?q=feedback%20linearization" title=" feedback linearization"> feedback linearization</a>, <a href="https://publications.waset.org/abstracts/search?q=follower-leader%20satellites" title=" follower-leader satellites"> follower-leader satellites</a>, <a href="https://publications.waset.org/abstracts/search?q=formation%20flight" title=" formation flight"> formation flight</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20consumption" title=" fuel consumption"> fuel consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=rendezvous" title=" rendezvous"> rendezvous</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode" title=" sliding mode"> sliding mode</a> </p> <a href="https://publications.waset.org/abstracts/130417/a-comparative-study-of-various-control-methods-for-rendezvous-of-a-satellite-couple" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130417.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">104</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">28242</span> Role of von Willebrand Factor Antigen as Non-Invasive Biomarker for the Prediction of Portal Hypertensive Gastropathy in Patients with Liver Cirrhosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Horri">Mohamed El Horri</a>, <a href="https://publications.waset.org/abstracts/search?q=Amine%20Mouden"> Amine Mouden</a>, <a href="https://publications.waset.org/abstracts/search?q=Reda%20Messaoudi"> Reda Messaoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Chekkal"> Mohamed Chekkal</a>, <a href="https://publications.waset.org/abstracts/search?q=Driss%20Benlaldj"> Driss Benlaldj</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Baghdadi"> Malika Baghdadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcene%20Benmahdi"> Lahcene Benmahdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Seghier"> Fatima Seghier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background/aim: Recently, the Von Willebrand factor antigen (vWF-Ag)has been identified as a new marker of portal hypertension (PH) and its complications. Few studies talked about its role in the prediction of esophageal varices. VWF-Ag is considered a non-invasive approach, In order to avoid the endoscopic burden, cost, drawbacks, unpleasant and repeated examinations to the patients. In our study, we aimed to evaluate the ability of this marker in the prediction of another complication of portal hypertension, which is portal hypertensive gastropathy (PHG), the one that is diagnosed also by endoscopic tools. Patients and methods: It is about a prospective study, which include 124 cirrhotic patients with no history of bleeding who underwent screening endoscopy for PH-related complications like esophageal varices (EVs) and PHG. Routine biological tests were performed as well as the VWF-Ag testing by both ELFA and Immunoturbidimetric techniques. The diagnostic performance of our marker was assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curves. Results: 124 patients were enrolled in this study, with a mean age of 58 years [CI: 55 – 60 years] and a sex ratio of 1.17. Viral etiologies were found in 50% of patients. Screening endoscopy revealed the presence of PHG in 20.2% of cases, while for EVsthey were found in 83.1% of cases. VWF-Ag levels, were significantly increased in patients with PHG compared to those who have not: 441% [CI: 375 – 506], versus 279% [CI: 253 – 304], respectively (p <0.0001). Using the area under the receiver operating characteristic curve (AUC), vWF-Ag was a good predictor for the presence of PHG. With a value higher than 320% and an AUC of 0.824, VWF-Ag had an 84% sensitivity, 74% specificity, 44.7% positive predictive value, 94.8% negative predictive value, and 75.8% diagnostic accuracy. Conclusion: VWF-Ag is a good non-invasive low coast marker for excluding the presence of PHG in patients with liver cirrhosis. Using this marker as part of a selective screening strategy might reduce the need for endoscopic screening and the coast of the management of these kinds of patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=von%20willebrand%20factor" title="von willebrand factor">von willebrand factor</a>, <a href="https://publications.waset.org/abstracts/search?q=portal%20hypertensive%20gastropathy" title=" portal hypertensive gastropathy"> portal hypertensive gastropathy</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20cirrhosis" title=" liver cirrhosis"> liver cirrhosis</a> </p> <a href="https://publications.waset.org/abstracts/143425/role-of-von-willebrand-factor-antigen-as-non-invasive-biomarker-for-the-prediction-of-portal-hypertensive-gastropathy-in-patients-with-liver-cirrhosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143425.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">205</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">28241</span> Nonlinear Model Predictive Control for Biodiesel Production via Transesterification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juliette%20Harper">Juliette Harper</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Yang"> Yu Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biofuels have gained significant attention recently due to the new regulations and agreements regarding fossil fuels and greenhouse gases being made by countries around the globe. One of the most common types of biofuels is biodiesel, primarily made via the transesterification reaction. We model this nonlinear process in MATLAB using the standard kinetic equations. Then, a nonlinear Model predictive control (NMPC) was developed to regulate this process due to its capability to handle process constraints. The feeding flow uncertainty and kinetic disturbances are further incorporated in the model to capture the real-world operating conditions. The simulation results will show that the proposed NMPC can guarantee the final composition of fatty acid methyl esters (FAME) above the target threshold with a high chance by adjusting the process temperature and flowrate. This research will allow further understanding of NMPC under uncertainties and how to design the computational strategy for larger process with more variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NMPC" title="NMPC">NMPC</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title=" biodiesel"> biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainties" title=" uncertainties"> uncertainties</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear" title=" nonlinear"> nonlinear</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/172002/nonlinear-model-predictive-control-for-biodiesel-production-via-transesterification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172002.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">97</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">28240</span> Energy Efficiency and Sustainability Analytics for Reducing Carbon Emissions in Oil Refineries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaurav%20Kumar%20Sinha">Gaurav Kumar Sinha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability%20analytics" title=" sustainability analytics"> sustainability analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20emissions" title=" carbon emissions"> carbon emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20refineries" title=" oil refineries"> oil refineries</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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse%20gas%20reduction" title=" greenhouse gas reduction"> greenhouse gas reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20performance" title=" environmental performance"> environmental performance</a> </p> <a href="https://publications.waset.org/abstracts/187014/energy-efficiency-and-sustainability-analytics-for-reducing-carbon-emissions-in-oil-refineries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187014.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">31</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">28239</span> Modeling and Control Design of a Centralized Adaptive Cruise Control System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markus%20Mazzola">Markus Mazzola</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunther%20Schaaf"> Gunther Schaaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A vehicle driving with an Adaptive Cruise Control System (ACC) is usually controlled decentrally, based on the information of radar systems and in some publications based on C2X-Communication (CACC) to guarantee stable platoons. In this paper, we present a Model Predictive Control (MPC) design of a centralized, server-based ACC-System, whereby the vehicular platoon is modeled and controlled as a whole. It is then proven that the proposed MPC design guarantees asymptotic stability and hence string stability of the platoon. The Networked MPC design is chosen to be able to integrate system constraints optimally as well as to reduce the effects of communication delay and packet loss. The performance of the proposed controller is then simulated and analyzed in an LTE communication scenario using the LTE/EPC Network Simulator LENA, which is based on the ns-3 network simulator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20cruise%20control" title="adaptive cruise control">adaptive cruise control</a>, <a href="https://publications.waset.org/abstracts/search?q=centralized%20server" title=" centralized server"> centralized server</a>, <a href="https://publications.waset.org/abstracts/search?q=networked%0D%0Amodel%20predictive%20control" title=" networked model predictive control"> networked model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=string%20stability" title=" string stability"> string stability</a> </p> <a href="https://publications.waset.org/abstracts/6450/modeling-and-control-design-of-a-centralized-adaptive-cruise-control-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6450.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">514</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">28238</span> Modelling of Multi-Agent Systems for the Scheduling of Multi-EV Charging from Power Limited Sources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manan%E2%80%99Iarivo%20Rasolonjanahary">Manan’Iarivo Rasolonjanahary</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Bingham"> Chris Bingham</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigel%20Schofield"> Nigel Schofield</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Bazargan"> Masoud Bazargan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=non-scheduled" title=" non-scheduled"> non-scheduled</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20limited%20sources" title=" power limited sources"> power limited sources</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduled%20and%20stop-start%20battery%20charging" title=" scheduled and stop-start battery charging"> scheduled and stop-start battery charging</a> </p> <a href="https://publications.waset.org/abstracts/134020/modelling-of-multi-agent-systems-for-the-scheduling-of-multi-ev-charging-from-power-limited-sources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134020.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">157</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">28237</span> Estimation of the Acute Toxicity of Halogenated Phenols Using Quantum Chemistry Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadidja%20Bellifa">Khadidja Bellifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Mohamed%20Mekelleche"> Sidi Mohamed Mekelleche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=halogenated%20phenols" title="halogenated phenols">halogenated phenols</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity%20mechanism" title=" toxicity mechanism"> toxicity mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophobicity" title=" hydrophobicity"> hydrophobicity</a>, <a href="https://publications.waset.org/abstracts/search?q=electrophilicity%20index" title=" electrophilicity index"> electrophilicity index</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20stucture-toxicity%20relationships" title=" quantitative stucture-toxicity relationships"> quantitative stucture-toxicity relationships</a> </p> <a href="https://publications.waset.org/abstracts/45757/estimation-of-the-acute-toxicity-of-halogenated-phenols-using-quantum-chemistry-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45757.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">300</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">28236</span> Data-Driven Crop Advisory – A Use Case on Grapes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shailaja%20Grover">Shailaja Grover</a>, <a href="https://publications.waset.org/abstracts/search?q=Purvi%20Tiwari"> Purvi Tiwari</a>, <a href="https://publications.waset.org/abstracts/search?q=Vigneshwaran%20S.%20R."> Vigneshwaran S. R.</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20Dinesh%20Kumar"> U. Dinesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In India, grapes are one of the most important horticulture crops. Grapes are most vulnerable to downy mildew, which is one of the most devasting diseases. In the absence of a precise weather-based advisory system, farmers spray pesticides on their crops extensively. There are two main challenges associated with using these pesticides. Firstly, most of these sprays were panic sprays, which could have been avoided. Second, farmers use more expensive "Preventive and Eradicate" chemicals than "Systemic, Curative and Anti-sporulate" chemicals. When these chemicals are used indiscriminately, they can enter the fruit and cause health problems such as cancer. This paper utilizes decision trees and predictive modeling techniques to provide grape farmers with customized advice on grape disease management. This model is expected to reduce the overall use of chemicals by approximately 50% and the cost by around 70%. Most of the grapes produced will have relatively low residue levels of pesticides, i.e., below the permissible level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytics%20in%20agriculture" title="analytics in agriculture">analytics in agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=downy%20mildew" title=" downy mildew"> downy mildew</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20based%20advisory" title=" weather based advisory"> weather based advisory</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modelling" title=" predictive modelling"> predictive modelling</a> </p> <a href="https://publications.waset.org/abstracts/171370/data-driven-crop-advisory-a-use-case-on-grapes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28235</span> The Dark Triad’s Moral Labyrinth: Differentiating Cognitive Processes Involved in Machiavellianism and Psychopathy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Megan%20E.%20Davies">Megan E. Davies</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the intention of identifying cognitive processes uniquely involved in the dark triad personality traits of psychopathy, Machiavellianism, and narcissism, this study aimed to determine further potential differences and parameters of individual traits by explaining a statistically significant amount of variance between the constructs of manipulativeness, impulsiveness, grit, and need for cognition within the dark triad. Applying a cross-sectional design, N = 96 participants self-reported using the MACH-IV, SRP-III, NFC-S, and Grit Scale for Perseverance and Passion for Long-Term Goals. Hierarchical regression analyses showed that only manipulativeness predicted Machiavellianism, whereas manipulativeness and impulsiveness were found to have predictive qualities for psychopathy. Overall, these results found areas of discrepancy and overlap between manipulation and impulsivity regarding psychopathy and Machiavellianism. Additionally, this study serves to preliminarily eliminate the Need for Cognition and grit as predictive variables for Machiavellianism and psychopathy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Machiavellianism" title="Machiavellianism">Machiavellianism</a>, <a href="https://publications.waset.org/abstracts/search?q=psychopathy" title=" psychopathy"> psychopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=manipulation" title=" manipulation"> manipulation</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsiveness" title=" impulsiveness"> impulsiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=need%20for%20cognition" title=" need for cognition"> need for cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=grit" title=" grit"> grit</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20triad" title=" dark triad"> dark triad</a> </p> <a href="https://publications.waset.org/abstracts/158474/the-dark-triads-moral-labyrinth-differentiating-cognitive-processes-involved-in-machiavellianism-and-psychopathy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158474.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">109</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">28234</span> Associations between Parental Divorce Process Variables and Parent-Child Relationships Quality in Young Adulthood</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Klara%20Smith-Etxeberria">Klara Smith-Etxeberria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> main goal of this study was to analyze the predictive ability of some variables associated with the parental divorce process alongside attachment history with parents on both, mother-child and father-child relationship quality. Our sample consisted of 173 undergraduate and vocational school students from the Autonomous Community of the Basque Country. All of them belonged to a divorced family. Results showed that adequate maternal strategies during the divorce process (e.g.: stable, continuous and positive role as a mother) was the variable with greater predictive ability on mother-child relationships quality. In addition, secure attachment history with mother also predicted positive mother-child relationships. On the other hand, father-child relationship quality was predicted by adequate paternal strategies during the divorce process, such as his stable, continuous and positive role as a father, along with not badmouthing the mother and promoting good mother-child relationships. Furthermore, paternal negative emotional state due to divorce was positively associated with father-child relationships quality, and both, history of attachment with mother and with father predicted father-child relationships quality. In conclusion, our data indicate that both, paternal and maternal strategies for children´s adequate adjustment during the divorce process influence on mother-child and father-child relationships quality. However, these results suggest that paternal strategies during the divorce process have a greater predictive ability on father-child relationships quality, whereas maternal positive strategies during divorce determine positive mother-child relationships among young adults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=father-child%20relationships%20quality" title="father-child relationships quality">father-child relationships quality</a>, <a href="https://publications.waset.org/abstracts/search?q=mother-child%20relationships%20quality" title=" mother-child relationships quality"> mother-child relationships quality</a>, <a href="https://publications.waset.org/abstracts/search?q=parental%20divorce%20process" title=" parental divorce process"> parental divorce process</a>, <a href="https://publications.waset.org/abstracts/search?q=young%20adulthood" title=" young adulthood"> young adulthood</a> </p> <a href="https://publications.waset.org/abstracts/72529/associations-between-parental-divorce-process-variables-and-parent-child-relationships-quality-in-young-adulthood" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72529.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">28233</span> Predicting Football Player Performance: Integrating Data Visualization and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saahith%20M.%20S.">Saahith M. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sivakami%20R."> Sivakami R.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=football%20analytics" title="football analytics">football analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=player%20performance%20prediction" title=" player performance prediction"> player performance prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title=" data visualization"> data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20evaluation" title=" model evaluation"> model evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=top%20player%20analysis" title=" top player analysis"> top player analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=nationality%20analysis" title=" nationality analysis"> nationality analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=positional%20analysis" title=" positional analysis"> positional analysis</a> </p> <a href="https://publications.waset.org/abstracts/185376/predicting-football-player-performance-integrating-data-visualization-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185376.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">38</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">28232</span> A Systematic Review Investigating the Use of EEG Measures in Neuromarketing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Byrne">A. M. Byrne</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Bonfiglio"> E. Bonfiglio</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Rigby"> C. Rigby</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Edelstyn"> N. Edelstyn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ERP" title=" ERP"> ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=neuromarketing" title=" neuromarketing"> neuromarketing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine-learning" title=" machine-learning"> machine-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20review" title=" systematic review"> systematic review</a>, <a href="https://publications.waset.org/abstracts/search?q=time-frequency" title=" time-frequency"> time-frequency</a> </p> <a href="https://publications.waset.org/abstracts/151183/a-systematic-review-investigating-the-use-of-eeg-measures-in-neuromarketing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151183.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">111</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">28231</span> GIS Based Spatial Modeling for Selecting New Hospital Sites Using APH, Entropy-MAUT and CRITIC-MAUT: A Study in Rural West Bengal, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alokananda%20Ghosh">Alokananda Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shraban%20Sarkar"> Shraban Sarkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hospital%20site%20suitability" title="hospital site suitability">hospital site suitability</a>, <a href="https://publications.waset.org/abstracts/search?q=analytic%20hierarchy%20process" title=" analytic hierarchy process"> analytic hierarchy process</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-attribute%20utility%20theory" title=" multi-attribute utility theory"> multi-attribute utility theory</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=criteria%20importance%20through%20interaction%20correlation" title=" criteria importance through interaction correlation"> criteria importance through interaction correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-criteria%20decision%20analysis" title=" multi-criteria decision analysis"> multi-criteria decision analysis</a> </p> <a href="https://publications.waset.org/abstracts/175876/gis-based-spatial-modeling-for-selecting-new-hospital-sites-using-aph-entropy-maut-and-critic-maut-a-study-in-rural-west-bengal-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175876.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">66</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">28230</span> Development of Programmed Cell Death Protein 1 Pathway-Associated Prognostic Biomarkers for Bladder Cancer Using Transcriptomic Databases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shu-Pin%20Huang">Shu-Pin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pai-Chi%20Teng"> Pai-Chi Teng</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao-Han%20Chang"> Hao-Han Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Hsin%20Liu"> Chia-Hsin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yung-Lun%20Lin"> Yung-Lun Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shu-Chi%20Wang"> Shu-Chi Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsin-Chih%20Yeh"> Hsin-Chih Yeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Pin%20Chuu"> Chih-Pin Chuu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiun-Hung%20Geng"> Jiun-Hung Geng</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Hsin%20Chang"> Li-Hsin Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Chung%20Cheng"> Wei-Chung Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Yang%20Li"> Chia-Yang Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emergence of immune checkpoint inhibitors (ICIs) targeting proteins like PD-1 and PD-L1 has changed the treatment paradigm of bladder cancer. However, not all patients benefit from ICIs, with some experiencing early death. There's a significant need for biomarkers associated with the PD-1 pathway in bladder cancer. Current biomarkers focus on tumor PD-L1 expression, but a more comprehensive understanding of PD-1-related biology is needed. Our study has developed a seven-gene risk score panel, employing a comprehensive bioinformatics strategy, which could serve as a potential prognostic and predictive biomarker for bladder cancer. This panel incorporates the FYN, GRAP2, TRIB3, MAP3K8, AKT3, CD274, and CD80 genes. Additionally, we examined the relationship between this panel and immune cell function, utilizing validated tools such as ESTIMATE, TIDE, and CIBERSORT. Our seven-genes panel has been found to be significantly associated with bladder cancer survival in two independent cohorts. The panel was also significantly correlated with tumor infiltration lymphocytes, immune scores, and tumor purity. These factors have been previously reported to have clinical implications on ICIs. The findings suggest the potential of a PD-1 pathway-based transcriptomic panel as a prognostic and predictive biomarker in bladder cancer, which could help optimize treatment strategies and improve patient outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bladder%20cancer" title="bladder cancer">bladder cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=programmed%20cell%20death%20protein%201" title=" programmed cell death protein 1"> programmed cell death protein 1</a>, <a href="https://publications.waset.org/abstracts/search?q=prognostic%20biomarker" title=" prognostic biomarker"> prognostic biomarker</a>, <a href="https://publications.waset.org/abstracts/search?q=immune%20checkpoint%20inhibitors" title=" immune checkpoint inhibitors"> immune checkpoint inhibitors</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20biomarker" title=" predictive biomarker"> predictive biomarker</a> </p> <a href="https://publications.waset.org/abstracts/173666/development-of-programmed-cell-death-protein-1-pathway-associated-prognostic-biomarkers-for-bladder-cancer-using-transcriptomic-databases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173666.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">28229</span> Fuzzy Logic Based Fault Tolerant Model Predictive MLI Topology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhimanyu%20Kumar">Abhimanyu Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Chirag%20Gupta"> Chirag Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a comprehensive study on the employment of Model Predictive Control (MPC) for a three-phase voltage-source inverter to regulate the output voltage efficiently. The inverter is modeled via the Clarke Transformation, considering a scenario where the load is unknown. An LC filter model is developed, demonstrating its efficacy in Total Harmonic Distortion (THD) reduction. The system, when implemented with fault-tolerant multilevel inverter topologies, ensures reliable operation even under fault conditions, a requirement that is paramount with the increasing dependence on renewable energy sources. The research also integrates a Fuzzy Logic based fault tolerance system which identifies and manages faults, ensuring consistent inverter performance. The efficacy of the proposed methodology is substantiated through rigorous simulations and comparative results, shedding light on the voltage prediction efficiency and the robustness of the model even under fault conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=total%20harmonic%20distortion" title="total harmonic distortion">total harmonic distortion</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=renewable%20energy%20sources" title=" renewable energy sources"> renewable energy sources</a>, <a href="https://publications.waset.org/abstracts/search?q=MLI" title=" MLI"> MLI</a> </p> <a href="https://publications.waset.org/abstracts/172530/fuzzy-logic-based-fault-tolerant-model-predictive-mli-topology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172530.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">130</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">28228</span> A High Content Screening Platform for the Accurate Prediction of Nephrotoxicity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sijing%20Xiong">Sijing Xiong</a>, <a href="https://publications.waset.org/abstracts/search?q=Ran%20Su"> Ran Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Lit-Hsin%20Loo"> Lit-Hsin Loo</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniele%20Zink"> Daniele Zink</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The kidney is a major target for toxic effects of drugs, industrial and environmental chemicals and other compounds. Typically, nephrotoxicity is detected late during drug development, and regulatory animal models could not solve this problem. Validated or accepted in silico or in vitro methods for the prediction of nephrotoxicity are not available. We have established the first and currently only pre-validated in vitro models for the accurate prediction of nephrotoxicity in humans and the first predictive platforms based on renal cells derived from human pluripotent stem cells. In order to further improve the efficiency of our predictive models, we recently developed a high content screening (HCS) platform. This platform employed automated imaging in combination with automated quantitative phenotypic profiling and machine learning methods. 129 image-based phenotypic features were analyzed with respect to their predictive performance in combination with 44 compounds with different chemical structures that included drugs, environmental and industrial chemicals and herbal and fungal compounds. The nephrotoxicity of these compounds in humans is well characterized. A combination of chromatin and cytoskeletal features resulted in high predictivity with respect to nephrotoxicity in humans. Test balanced accuracies of 82% or 89% were obtained with human primary or immortalized renal proximal tubular cells, respectively. Furthermore, our results revealed that a DNA damage response is commonly induced by different PTC-toxicants with diverse chemical structures and injury mechanisms. Together, the results show that the automated HCS platform allows efficient and accurate nephrotoxicity prediction for compounds with diverse chemical structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20content%20screening" title="high content screening">high content screening</a>, <a href="https://publications.waset.org/abstracts/search?q=in%20vitro%20models" title=" in vitro models"> in vitro models</a>, <a href="https://publications.waset.org/abstracts/search?q=nephrotoxicity" title=" nephrotoxicity"> nephrotoxicity</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity%20prediction" title=" toxicity prediction"> toxicity prediction</a> </p> <a href="https://publications.waset.org/abstracts/42832/a-high-content-screening-platform-for-the-accurate-prediction-of-nephrotoxicity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42832.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">313</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">28227</span> Uncertainty Estimation in Neural Networks through Transfer Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20James">Ashish James</a>, <a href="https://publications.waset.org/abstracts/search?q=Anusha%20James"> Anusha James</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20estimation" title="uncertainty estimation">uncertainty estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/153501/uncertainty-estimation-in-neural-networks-through-transfer-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153501.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">135</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">28226</span> Breast Cancer Mortality and Comorbidities in Portugal: A Predictive Model Built with Real World Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cec%C3%ADlia%20M.%20Ant%C3%A3o">Cecília M. Antão</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Jorge%20Nogueira"> Paulo Jorge Nogueira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=comorbidities" title=" comorbidities"> comorbidities</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=adjusted%20odds%20ratio" title=" adjusted odds ratio"> adjusted odds ratio</a> </p> <a href="https://publications.waset.org/abstracts/143667/breast-cancer-mortality-and-comorbidities-in-portugal-a-predictive-model-built-with-real-world-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143667.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">87</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">28225</span> Computer-Assisted Management of Building Climate and Microgrid with Model Predictive Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinko%20Le%C5%A1i%C4%87">Vinko Lešić</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Va%C5%A1ak"> Mario Vašak</a>, <a href="https://publications.waset.org/abstracts/search?q=Anita%20Martin%C4%8Devi%C4%87"> Anita Martinčević</a>, <a href="https://publications.waset.org/abstracts/search?q=Marko%20Gulin"> Marko Gulin</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Star%C4%8Di%C4%87"> Antonio Starčić</a>, <a href="https://publications.waset.org/abstracts/search?q=Hrvoje%20Novak"> Hrvoje Novak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With 40% of total world energy consumption, building systems are developing into technically complex large energy consumers suitable for application of sophisticated power management approaches to largely increase the energy efficiency and even make them active energy market participants. Centralized control system of building heating and cooling managed by economically-optimal model predictive control shows promising results with estimated 30% of energy efficiency increase. The research is focused on implementation of such a method on a case study performed on two floors of our faculty building with corresponding sensors wireless data acquisition, remote heating/cooling units and central climate controller. Building walls are mathematically modeled with corresponding material types, surface shapes and sizes. Models are then exploited to predict thermal characteristics and changes in different building zones. Exterior influences such as environmental conditions and weather forecast, people behavior and comfort demands are all taken into account for deriving price-optimal climate control. Finally, a DC microgrid with photovoltaics, wind turbine, supercapacitor, batteries and fuel cell stacks is added to make the building a unit capable of active participation in a price-varying energy market. Computational burden of applying model predictive control on such a complex system is relaxed through a hierarchical decomposition of the microgrid and climate control, where the former is designed as higher hierarchical level with pre-calculated price-optimal power flows control, and latter is designed as lower level control responsible to ensure thermal comfort and exploit the optimal supply conditions enabled by microgrid energy flows management. Such an approach is expected to enable the inclusion of more complex building subsystems into consideration in order to further increase the energy efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=price-optimal%20building%20climate%20control" title="price-optimal building climate control">price-optimal building climate control</a>, <a href="https://publications.waset.org/abstracts/search?q=Microgrid%20power%20flow%20optimisation" title=" Microgrid power flow optimisation"> Microgrid power flow optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20model%20predictive%20control" title=" hierarchical model predictive control"> hierarchical model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficient%20buildings" title=" energy efficient buildings"> energy efficient buildings</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20market%20participation" title=" energy market participation"> energy market participation</a> </p> <a href="https://publications.waset.org/abstracts/30873/computer-assisted-management-of-building-climate-and-microgrid-with-model-predictive-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30873.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">465</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28224</span> Indian Premier League (IPL) Score Prediction: Comparative Analysis of Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohini%20Hariharan">Rohini Hariharan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yazhini%20R"> Yazhini R</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhamidipati%20Naga%20Shrikarti"> Bhamidipati Naga Shrikarti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indian%20premier%20league%20%28IPL%29" title="indian premier league (IPL)">indian premier league (IPL)</a>, <a href="https://publications.waset.org/abstracts/search?q=cricket" title=" cricket"> cricket</a>, <a href="https://publications.waset.org/abstracts/search?q=score%20prediction" title=" score prediction"> score prediction</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=support%20vector%20machines%20%28SVM%29" title=" support vector machines (SVM)"> support vector machines (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=xgboost" title=" xgboost"> xgboost</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20regression" title=" multiple regression"> multiple regression</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20%28KNN%29" title=" k-nearest neighbors (KNN)"> k-nearest neighbors (KNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20analytics" title=" sports analytics"> sports analytics</a> </p> <a href="https://publications.waset.org/abstracts/185364/indian-premier-league-ipl-score-prediction-comparative-analysis-of-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185364.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">53</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">28223</span> A Comprehensive Review of Artificial Intelligence Applications in Sustainable Building</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yazan%20Al-Kofahi">Yazan Al-Kofahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamal%20Alqawasmi."> Jamal Alqawasmi.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model. <p class="card-text"><strong>Keywords:</strong> <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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20building" title=" sustainable building"> sustainable building</a> </p> <a href="https://publications.waset.org/abstracts/183739/a-comprehensive-review-of-artificial-intelligence-applications-in-sustainable-building" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183739.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">67</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">28222</span> Predicting Machine-Down of Woodworking Industrial Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matteo%20Calabrese">Matteo Calabrese</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Cimmino"> Martin Cimmino</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimos%20Kapetis"> Dimos Kapetis</a>, <a href="https://publications.waset.org/abstracts/search?q=Martina%20Manfrin"> Martina Manfrin</a>, <a href="https://publications.waset.org/abstracts/search?q=Donato%20Concilio"> Donato Concilio</a>, <a href="https://publications.waset.org/abstracts/search?q=Giuseppe%20Toscano"> Giuseppe Toscano</a>, <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Ciandrini"> Giovanni Ciandrini</a>, <a href="https://publications.waset.org/abstracts/search?q=Giancarlo%20Paccapeli"> Giancarlo Paccapeli</a>, <a href="https://publications.waset.org/abstracts/search?q=Gianluca%20Giarratana"> Gianluca Giarratana</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20Siciliano"> Marco Siciliano</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrea%20Forlani"> Andrea Forlani</a>, <a href="https://publications.waset.org/abstracts/search?q=Alberto%20Carrotta"> Alberto Carrotta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title="predictive maintenance">predictive maintenance</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=connected%20machines" title=" connected machines"> connected machines</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/98461/predicting-machine-down-of-woodworking-industrial-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98461.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">226</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">28221</span> Development of a Practical Screening Measure for the Prediction of Low Birth Weight and Neonatal Mortality in Upper Egypt</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prof.%20Ammal%20Mokhtar%20Metwally">Prof. Ammal Mokhtar Metwally</a>, <a href="https://publications.waset.org/abstracts/search?q=Samia%20M.%20Sami"> Samia M. Sami</a>, <a href="https://publications.waset.org/abstracts/search?q=Nihad%20A.%20Ibrahim"> Nihad A. Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatma%20A.%20Shaaban"> Fatma A. Shaaban</a>, <a href="https://publications.waset.org/abstracts/search?q=Iman%20I.%20Salama"> Iman I. Salama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20birth%20weight" title="low birth weight">low birth weight</a>, <a href="https://publications.waset.org/abstracts/search?q=neonatal%20mortality" title=" neonatal mortality"> neonatal mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=anthropometric%20measures" title=" anthropometric measures"> anthropometric measures</a>, <a href="https://publications.waset.org/abstracts/search?q=practical%20screening" title=" practical screening"> practical screening</a> </p> <a href="https://publications.waset.org/abstracts/162775/development-of-a-practical-screening-measure-for-the-prediction-of-low-birth-weight-and-neonatal-mortality-in-upper-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162775.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">99</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=6" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=6">6</a></li> <li class="page-item active"><span class="page-link">7</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=947">947</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=948">948</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictive%20analysis&page=8" 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>