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predictions</title> <meta name="description" content="Search results for: predictions"> <meta name="keywords" content="predictions"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="predictions"> <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> 639</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predictions</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">639</span> Application of Smplify-X Algorithm with Enhanced Gender Classifier in 3D Human Pose Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu">Jiahe Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Qian"> Feng Qian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SMPLX" title="SMPLX">SMPLX</a>, <a href="https://publications.waset.org/abstracts/search?q=mobileNet" title=" mobileNet"> mobileNet</a>, <a href="https://publications.waset.org/abstracts/search?q=gender%20classification" title=" gender classification"> gender classification</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20human%20reconstruction" title=" 3D human reconstruction"> 3D human reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/183520/application-of-smplify-x-algorithm-with-enhanced-gender-classifier-in-3d-human-pose-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183520.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">638</span> Multivariate Output-Associative RVM for Multi-Dimensional Affect Predictions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Achut%20Manandhar">Achut Manandhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20D.%20Morton"> Kenneth D. Morton</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20A.%20Torrione"> Peter A. Torrione</a>, <a href="https://publications.waset.org/abstracts/search?q=Leslie%20M.%20Collins"> Leslie M. Collins</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dimensional%20affect%20prediction" title="dimensional affect prediction">dimensional affect prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=output-associative%20RVM" title=" output-associative RVM"> output-associative RVM</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20regression" title=" multivariate regression"> multivariate regression</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20testing" title=" fast testing"> fast testing</a> </p> <a href="https://publications.waset.org/abstracts/39289/multivariate-output-associative-rvm-for-multi-dimensional-affect-predictions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">637</span> Metrology-Inspired Methods to Assess the Biases of Artificial Intelligence Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Laimouche">Belkacem Laimouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions. <p class="card-text"><strong>Keywords:</strong> <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=metrology" title=" metrology"> metrology</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20uncertainty" title=" measurement uncertainty"> measurement uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20error" title=" prediction error"> prediction error</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</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=probabilistic%20models" title=" probabilistic models"> probabilistic models</a>, <a href="https://publications.waset.org/abstracts/search?q=interlaboratory%20comparison" title=" interlaboratory comparison"> interlaboratory comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20reliability" title=" data reliability"> data reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20of%20bias%20impact%20on%20predictions" title=" measurement of bias impact on predictions"> measurement of bias impact on predictions</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement%20of%20model%20accuracy%20and%20reliability" title=" improvement of model accuracy and reliability"> improvement of model accuracy and reliability</a> </p> <a href="https://publications.waset.org/abstracts/167404/metrology-inspired-methods-to-assess-the-biases-of-artificial-intelligence-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167404.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">105</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">636</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">635</span> Research on the Cognition and Actual Phenomenon of School Bullying from the Perspective of Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Chun%20Wu">Chia-Chun Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Hsien%20Sung"> Yu-Hsien Sung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to examine the consistency between students’ predictions and their actual observations on the bullying prevalence rate among different types of high-risk victims, thereby clarifying the reliability of students’ reports on the identification of bullying. A total of 1,732 Taiwanese students (734 males and 998 females) participated in this study. A Rasch model was adopted for data analysis. The results showed that students with “personality or behavioral issues” are more likely to be bullied in schools, based on both students’ predictions and actual observations. Moreover, the results differed significantly between genders and between various educational levels in students’ predictions and their actual observations on the bullying prevalence rate of different types of high-risk victims. To summarize, this study not only suggests that students’ reports on the identification of bullying are accurate and could be a valuable reference in terms of recognizing a bullying incident, but it also argues that more attention should be paid to students’ gender and educational level when taking their perspectives into consideration when it comes to identifying bullying behaviors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=school%20bullying" title="school bullying">school bullying</a>, <a href="https://publications.waset.org/abstracts/search?q=student" title=" student"> student</a>, <a href="https://publications.waset.org/abstracts/search?q=bullying%20recognition" title=" bullying recognition"> bullying recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=high-risk%20victims" title=" high-risk victims"> high-risk victims</a> </p> <a href="https://publications.waset.org/abstracts/156359/research-on-the-cognition-and-actual-phenomenon-of-school-bullying-from-the-perspective-of-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156359.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">84</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">634</span> On Improving Breast Cancer Prediction Using GRNN-CP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=conformal%20prediction" title=" conformal prediction"> conformal prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/74483/on-improving-breast-cancer-prediction-using-grnn-cp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74483.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">291</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">633</span> Predictions for the Anisotropy in Thermal Conductivity in Polymers Subjected to Model Flows by Combination of the eXtended Pom-Pom Model and the Stress-Thermal Rule</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Nieto%20Simavilla">David Nieto Simavilla</a>, <a href="https://publications.waset.org/abstracts/search?q=Wilco%20M.%20H.%20Verbeeten"> Wilco M. H. Verbeeten</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The viscoelastic behavior of polymeric flows under isothermal conditions has been extensively researched. However, most of the processing of polymeric materials occurs under non-isothermal conditions and understanding the linkage between the thermo-physical properties and the process state variables remains a challenge. Furthermore, the cost and energy required to manufacture, recycle and dispose polymers is strongly affected by the thermo-physical properties and their dependence on state variables such as temperature and stress. Experiments show that thermal conductivity in flowing polymers is anisotropic (i.e. direction dependent). This phenomenon has been previously omitted in the study and simulation of industrially relevant flows. Our work combines experimental evidence of a universal relationship between thermal conductivity and stress tensors (i.e. the stress-thermal rule) with differential constitutive equations for the viscoelastic behavior of polymers to provide predictions for the anisotropy in thermal conductivity in uniaxial, planar, equibiaxial and shear flow in commercial polymers. A particular focus is placed on the eXtended Pom-Pom model which is able to capture the non-linear behavior in both shear and elongation flows. The predictions provided by this approach are amenable to implementation in finite elements packages, since viscoelastic and thermal behavior can be described by a single equation. Our results include predictions for flow-induced anisotropy in thermal conductivity for low and high density polyethylene as well as confirmation of our method through comparison with a number of thermoplastic systems for which measurements of anisotropy in thermal conductivity are available. Remarkably, this approach allows for universal predictions of anisotropy in thermal conductivity that can be used in simulations of complex flows in which only the most fundamental rheological behavior of the material has been previously characterized (i.e. there is no need for additional adjusting parameters other than those in the constitutive model). Accounting for polymers anisotropy in thermal conductivity in industrially relevant flows benefits the optimization of manufacturing processes as well as the mechanical and thermal performance of finalized plastic products during use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropy" title="anisotropy">anisotropy</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20constitutive%20models" title=" differential constitutive models"> differential constitutive models</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20simulations%20in%20polymers" title=" flow simulations in polymers"> flow simulations in polymers</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20conductivity" title=" thermal conductivity"> thermal conductivity</a> </p> <a href="https://publications.waset.org/abstracts/76961/predictions-for-the-anisotropy-in-thermal-conductivity-in-polymers-subjected-to-model-flows-by-combination-of-the-extended-pom-pom-model-and-the-stress-thermal-rule" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76961.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">182</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">632</span> Predictions of Values in a Causticizing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Andreola">R. Andreola</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20A.%20A.%20Santos"> O. A. A. Santos</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20M.%20Jorge"> L. M. M. Jorge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An industrial system for the production of white liquor of a paper industry, Klabin Paraná Papé is, formed by ten reactors was modeled, simulated, and analyzed. The developed model considered possible water losses by evaporation and reaction, in addition to variations in volumetric flow of lime mud across the reactors due to composition variations. The model predictions agreed well with the process measurements at the plant and the results showed that the slaking reaction is nearly complete at the third causticizing reactor, while causticizing ends by the seventh reactor. Water loss due to slaking reaction and evaporation occurs more pronouncedly in the slaking reaction than in the final causticizing reactors; nevertheless, the lime mud flow remains nearly constant across the reactors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=causticizing" title="causticizing">causticizing</a>, <a href="https://publications.waset.org/abstracts/search?q=lime" title=" lime"> lime</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=process" title=" process"> process</a> </p> <a href="https://publications.waset.org/abstracts/24627/predictions-of-values-in-a-causticizing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24627.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">354</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">631</span> Energy Performance Gaps in Residences: An Analysis of the Variables That Cause Energy Gaps and Their Impact</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amrutha%20Kishor">Amrutha Kishor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, with the rising global warming and depletion of resources every industry is moving toward sustainability and energy efficiency. As part of this movement, it is nowadays obligatory for architects to play their part by creating energy predictions for their designs. But in a lot of cases, these predictions do not reflect the real quantities of energy in newly built buildings when operating. These can be described as ‘Energy Performance Gaps’. This study aims to determine the underlying reasons for these gaps. Seven houses designed by Allan Joyce Architects, UK from 1998 until 2019 were considered for this study. The data from the residents’ energy bills were cross-referenced with the predictions made with the software SefairaPro and from energy reports. Results indicated that the predictions did not match the actual energy usage. An account of how energy was used in these seven houses was made by means of personal interviews. The main factors considered in the study were occupancy patterns, heating systems and usage, lighting profile and usage, and appliances’ profile and usage. The study found that the main reasons for the creation of energy gaps were the discrepancies in occupant usage and patterns of energy consumption that are predicted as opposed to the actual ones. This study is particularly useful for energy-conscious architectural firms to fine-tune the approach to designing houses and analysing their energy performance. As the findings reveal that energy usage in homes varies based on the way residents use the space, it helps deduce the most efficient technological combinations. This information can be used to set guidelines for future policies and regulations related to energy consumption in homes. This study can also be used by the developers of simulation software to understand how architects use their product and drive improvements in its future versions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architectural%20simulation" title="architectural simulation">architectural simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficient%20design" title=" energy efficient design"> energy efficient design</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20performance%20gaps" title=" energy performance gaps"> energy performance gaps</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20design" title=" environmental design"> environmental design</a> </p> <a href="https://publications.waset.org/abstracts/122200/energy-performance-gaps-in-residences-an-analysis-of-the-variables-that-cause-energy-gaps-and-their-impact" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122200.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">118</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">630</span> Visualization-Based Feature Extraction for Classification in Real-Time Interaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C3%81goston%20Nagy">Ágoston Nagy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</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=real-time%20interaction" title=" real-time interaction"> real-time interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/68382/visualization-based-feature-extraction-for-classification-in-real-time-interaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68382.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">629</span> Predicting Relative Performance of Sector Exchange Traded Funds Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Wang">Jun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ge%20Zhang"> Ge Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample. <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=ETF%20prediction" title=" ETF prediction"> ETF prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20trading" title=" dynamic trading"> dynamic trading</a>, <a href="https://publications.waset.org/abstracts/search?q=asset%20allocation" title=" asset allocation"> asset allocation</a> </p> <a href="https://publications.waset.org/abstracts/160995/predicting-relative-performance-of-sector-exchange-traded-funds-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160995.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">98</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">628</span> Review on Rainfall Prediction Using Machine Learning Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prachi%20Desai">Prachi Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ankita%20Gandhi"> Ankita Gandhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mitali%20Acharya"> Mitali Acharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/146605/review-on-rainfall-prediction-using-machine-learning-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146605.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">201</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">627</span> D-Wave Quantum Computing Ising Model: A Case Study for Forecasting of Heat Waves</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dmytro%20Zubov">Dmytro Zubov</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesco%20Volponi"> Francesco Volponi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, D-Wave quantum computing Ising model is used for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model’s real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places around the world, as well as sea level (Aburatsu, Japan). In comparison with current methods, this approach is better suited to predict heat wave values because it does not require the estimation of a probability distribution from scarce observations. Proposed forecast quantum computing algorithm is simulated based on traditional computer architecture and combinatorial optimization of Ising model parameters for the Ronald Reagan Washington National Airport dataset with 1-day lead-time on learning sample (1975-2010 yr). Analysis of the forecast accuracy (ratio of successful predictions to total number of predictions) on the validation sample (2011-2014 yr) shows that Ising model with three qubits has 100 % accuracy, which is quite significant as compared to other methods. However, number of identified heat waves is small (only one out of nineteen in this case). Other models with 2, 4, and 5 qubits have 20 %, 3.8 %, and 3.8 % accuracy respectively. Presented three-qubit forecast model is applied for prediction of heat waves at other five locations: Aurel Vlaicu, Romania – accuracy is 28.6 %; Bratislava, Slovakia – accuracy is 21.7 %; Brussels, Belgium – accuracy is 33.3 %; Sofia, Bulgaria – accuracy is 50 %; Akhisar, Turkey – accuracy is 21.4 %. These predictions are not ideal, but not zeros. They can be used independently or together with other predictions generated by different method(s). The loss of human life, as well as environmental, economic, and material damage, from extreme air temperatures could be reduced if some of heat waves are predicted. Even a small success rate implies a large socio-economic benefit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20wave" title="heat wave">heat wave</a>, <a href="https://publications.waset.org/abstracts/search?q=D-wave" title=" D-wave"> D-wave</a>, <a href="https://publications.waset.org/abstracts/search?q=forecast" title=" forecast"> forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=Ising%20model" title=" Ising model"> Ising model</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20computing" title=" quantum computing"> quantum computing</a> </p> <a href="https://publications.waset.org/abstracts/34119/d-wave-quantum-computing-ising-model-a-case-study-for-forecasting-of-heat-waves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34119.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">500</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">626</span> Exploring the Impact of Input Sequence Lengths on Long Short-Term Memory-Based Streamflow Prediction in Flashy Catchments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Hosseini%20Hossein%20Abadi">Farzad Hosseini Hossein Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Prieto%20Sierra"> Cristina Prieto Sierra</a>, <a href="https://publications.waset.org/abstracts/search?q=Cesar%20%C3%81lvarez%20D%C3%ADaz"> Cesar Álvarez Díaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTMs" title="LSTMs">LSTMs</a>, <a href="https://publications.waset.org/abstracts/search?q=streamflow" title=" streamflow"> streamflow</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters" title=" hyperparameters"> hyperparameters</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrology" title=" hydrology"> hydrology</a> </p> <a href="https://publications.waset.org/abstracts/184629/exploring-the-impact-of-input-sequence-lengths-on-long-short-term-memory-based-streamflow-prediction-in-flashy-catchments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184629.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">625</span> A Study for Area-level Mosquito Abundance Prediction by Using Supervised Machine Learning Point-level Predictor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theoktisti%20Makridou">Theoktisti Makridou</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Tsaprailis"> Konstantinos Tsaprailis</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Arvanitakis"> George Arvanitakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Charalampos%20Kontoes"> Charalampos Kontoes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mosquito%20abundance" title="mosquito abundance">mosquito abundance</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=culex%20pipiens" title=" culex pipiens"> culex pipiens</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20sampling" title=" spatial sampling"> spatial sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=west%20nile%20virus" title=" west nile virus"> west nile virus</a>, <a href="https://publications.waset.org/abstracts/search?q=earth%20observation%20data" title=" earth observation data"> earth observation data</a> </p> <a href="https://publications.waset.org/abstracts/154245/a-study-for-area-level-mosquito-abundance-prediction-by-using-supervised-machine-learning-point-level-predictor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154245.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">147</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">624</span> Partially-Averaged Navier-Stokes for Computations of Flow Around Three-Dimensional Ahmed Bodies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Mirzaei">Maryam Mirzaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinisa%20Krajnovic%C2%B4"> Sinisa Krajnovic´ </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper reports a study about the prediction of flows around simplified vehicles using Partially-Averaged Navier-Stokes (PANS). Numerical simulations are performed for two simplified vehicles: A slanted-back Ahmed body at Re=30 000 and a square back Ahmed body at Re=300 000. A comparison of the resolved and modeled physical flow scales is made with corresponding LES and experimental data for a better understanding of the performance of the PANS model. The PANS model is compared for coarse and fine grid resolutions and it is indicated that even a coarse-grid PANS simulation is able to produce fairly close flow predictions to those from a well-resolved LES simulation. The results indicate the possibility of improvement of the predictions by employing a finer grid resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially-averaged%20Navier-Stokes" title="partially-averaged Navier-Stokes">partially-averaged Navier-Stokes</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20eddy%20simulation" title=" large eddy simulation"> large eddy simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=PANS" title=" PANS"> PANS</a>, <a href="https://publications.waset.org/abstracts/search?q=LES" title=" LES"> LES</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20body" title=" Ahmed body"> Ahmed body</a> </p> <a href="https://publications.waset.org/abstracts/17810/partially-averaged-navier-stokes-for-computations-of-flow-around-three-dimensional-ahmed-bodies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17810.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">600</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">623</span> Patient-Specific Modeling Algorithm for Medical Data Based on AUC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guilherme%20Ribeiro">Guilherme Ribeiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Oliveira"> Alexandre Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Ferreira"> Antonio Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyam%20Visweswaran"> Shyam Visweswaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Cooper"> Gregory Cooper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approach%20instance-based" title="approach instance-based">approach instance-based</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20under%20the%20ROC%20curve" title=" area under the ROC curve"> area under the ROC curve</a>, <a href="https://publications.waset.org/abstracts/search?q=patient-specific%20decision%20path" title=" patient-specific decision path"> patient-specific decision path</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20predictions" title=" clinical predictions"> clinical predictions</a> </p> <a href="https://publications.waset.org/abstracts/35519/patient-specific-modeling-algorithm-for-medical-data-based-on-auc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35519.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">479</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">622</span> A Non-Linear Eddy Viscosity Model for Turbulent Natural Convection in Geophysical Flows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20P.%20Panda">J. P. Panda</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sasmal"> K. Sasmal</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20V.%20Warrior"> H. V. Warrior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Eddy viscosity models in turbulence modeling can be mainly classified as linear and nonlinear models. Linear formulations are simple and require less computational resources but have the disadvantage that they cannot predict actual flow pattern in complex geophysical flows where streamline curvature and swirling motion are predominant. A constitutive equation of Reynolds stress anisotropy is adopted for the formulation of eddy viscosity including all the possible higher order terms quadratic in the mean velocity gradients, and a simplified model is developed for actual oceanic flows where only the vertical velocity gradients are important. The new model is incorporated into the one dimensional General Ocean Turbulence Model (GOTM). Two realistic oceanic test cases (OWS Papa and FLEX' 76) have been investigated. The new model predictions match well with the observational data and are better in comparison to the predictions of the two equation k-epsilon model. The proposed model can be easily incorporated in the three dimensional Princeton Ocean Model (POM) to simulate a wide range of oceanic processes. Practically, this model can be implemented in the coastal regions where trasverse shear induces higher vorticity, and for prediction of flow in estuaries and lakes, where depth is comparatively less. The model predictions of marine turbulence and other related data (e.g. Sea surface temperature, Surface heat flux and vertical temperature profile) can be utilized in short term ocean and climate forecasting and warning systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eddy%20viscosity" title="Eddy viscosity">Eddy viscosity</a>, <a href="https://publications.waset.org/abstracts/search?q=turbulence%20modeling" title=" turbulence modeling"> turbulence modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=GOTM" title=" GOTM"> GOTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CFD" title=" CFD"> CFD</a> </p> <a href="https://publications.waset.org/abstracts/84098/a-non-linear-eddy-viscosity-model-for-turbulent-natural-convection-in-geophysical-flows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84098.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">202</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">621</span> Hourly Solar Radiations Predictions for Anticipatory Control of Electrically Heated Floor: Use of Online Weather Conditions Forecast</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helene%20Thieblemont">Helene Thieblemont</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Haghighat"> Fariborz Haghighat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anticipatory%20control" title="anticipatory control">anticipatory control</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=solar%20radiation%20forecast" title=" solar radiation forecast"> solar radiation forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20storage" title=" thermal storage"> thermal storage</a> </p> <a href="https://publications.waset.org/abstracts/61503/hourly-solar-radiations-predictions-for-anticipatory-control-of-electrically-heated-floor-use-of-online-weather-conditions-forecast" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61503.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">271</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">620</span> Stacking Ensemble Approach for Combining Different Methods in Real Estate Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sol%20Girouard">Sol Girouard</a>, <a href="https://publications.waset.org/abstracts/search?q=Zona%20Kostic"> Zona Kostic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A home is often the largest and most expensive purchase a person makes. Whether the decision leads to a successful outcome will be determined by a combination of critical factors. In this paper, we propose a method that efficiently handles all the factors in residential real estate and performs predictions given a feature space with high dimensionality while controlling for overfitting. The proposed method was built on gradient descent and boosting algorithms and uses a mixed optimizing technique to improve the prediction power. Usually, a single model cannot handle all the cases thus our approach builds multiple models based on different subsets of the predictors. The algorithm was tested on 3 million homes across the U.S., and the experimental results demonstrate the efficiency of this approach by outperforming techniques currently used in forecasting prices. With everyday changes on the real estate market, our proposed algorithm capitalizes from new events allowing more efficient predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real%20estate%20prediction" title="real estate prediction">real estate prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a>, <a href="https://publications.waset.org/abstracts/search?q=boosting" title=" boosting"> boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20methods" title=" ensemble methods"> ensemble methods</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title=" active learning"> active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=training" title=" training"> training</a> </p> <a href="https://publications.waset.org/abstracts/90597/stacking-ensemble-approach-for-combining-different-methods-in-real-estate-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90597.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">277</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">619</span> Improving Predictions of Coastal Benthic Invertebrate Occurrence and Density Using a Multi-Scalar Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephanie%20Watson">Stephanie Watson</a>, <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Stephenson"> Fabrice Stephenson</a>, <a href="https://publications.waset.org/abstracts/search?q=Conrad%20Pilditch"> Conrad Pilditch</a>, <a href="https://publications.waset.org/abstracts/search?q=Carolyn%20Lundquist"> Carolyn Lundquist</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benthic%20ecology" title="Benthic ecology">Benthic ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20modelling" title=" spatial modelling"> spatial modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scalar%20modelling" title=" multi-scalar modelling"> multi-scalar modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=marine%20conservation." title=" marine conservation."> marine conservation.</a> </p> <a href="https://publications.waset.org/abstracts/156434/improving-predictions-of-coastal-benthic-invertebrate-occurrence-and-density-using-a-multi-scalar-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156434.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">77</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">618</span> Road Accidents Bigdata Mining and Visualization Using Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usha%20Lokala">Usha Lokala</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Nowduri"> Srinivas Nowduri</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabhakar%20K.%20Sharma"> Prabhakar K. Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20mechanism%20%28SVM%29" title="support vector mechanism (SVM)">support vector mechanism (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20%28ML%29" title=" machine learning (ML)"> machine learning (ML)</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=department%20of%20transportation%20%28DFT%29" title=" department of transportation (DFT)"> department of transportation (DFT)</a> </p> <a href="https://publications.waset.org/abstracts/70645/road-accidents-bigdata-mining-and-visualization-using-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70645.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">617</span> Evaluating the Suitability and Performance of Dynamic Modulus Predictive Models for North Dakota’s Asphalt Mixtures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duncan%20Oteki">Duncan Oteki</a>, <a href="https://publications.waset.org/abstracts/search?q=Andebut%20Yeneneh"> Andebut Yeneneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Daba%20Gedafa"> Daba Gedafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabil%20Suleiman"> Nabil Suleiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most agencies lack the equipment required to measure the dynamic modulus (|E*|) of asphalt mixtures, necessitating the need to use predictive models. This study compared measured |E*| values for nine North Dakota asphalt mixes using the original Witczak, modified Witczak, and Hirsch models. The influence of temperature on the |E*| models was investigated, and Pavement ME simulations were conducted using measured |E*| and predictions from the most accurate |E*| model. The results revealed that the original Witczak model yielded the lowest Se/Sy and highest R² values, indicating the lowest bias and highest accuracy, while the poorest overall performance was exhibited by the Hirsch model. Using predicted |E*| as inputs in the Pavement ME generated conservative distress predictions compared to using measured |E*|. The original Witczak model was recommended for predicting |E*| for low-reliability pavements in North Dakota. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asphalt%20mixture" title="asphalt mixture">asphalt mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=binder" title=" binder"> binder</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20modulus" title=" dynamic modulus"> dynamic modulus</a>, <a href="https://publications.waset.org/abstracts/search?q=MEPDG" title=" MEPDG"> MEPDG</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20ME" title=" pavement ME"> pavement ME</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/182251/evaluating-the-suitability-and-performance-of-dynamic-modulus-predictive-models-for-north-dakotas-asphalt-mixtures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182251.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">48</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">616</span> Identification of CLV for Online Shoppers Using RFM Matrix: A Case Based on Features of B2C Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riktesh%20Srivastava">Riktesh Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online Shopping have established an astonishing evolution in the last few years. And it is now apparent that B2C architecture is becoming progressively imperative channel for even traditional brick and mortar type traders as well. In this completion knowing customers and predicting behavior are extremely important. More important, when any customer logs onto the B2C architecture, the traces of their buying patterns can be stored and used for future predictions. Such a prediction is called Customer Lifetime Value (CLV). Earlier, we used Net Present Value to do so, however, it ignores two important aspects of B2C architecture, “market risks” and “big amount of customer data”. Now, we use RFM- Recency, Frequency and Monetary Value to estimate the CLV, and as the term exemplifies, market risks, is well sheltered. Big Data Analysis is also roofed in RFM, which gives real exploration of the Big Data and lead to a better estimation for future cash flow from customers. In the present paper, 6 factors (collected from varied sources) are used to determine as to what attracts the customers to the B2C architecture. For these 6 factors, RFM is computed for 3 years (2013, 2014 and 2015) respectively. CLV and Revenue are the two parameters defined using RFM analysis, which gives the clear picture of the future predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CLV" title="CLV">CLV</a>, <a href="https://publications.waset.org/abstracts/search?q=RFM" title=" RFM"> RFM</a>, <a href="https://publications.waset.org/abstracts/search?q=revenue" title=" revenue"> revenue</a>, <a href="https://publications.waset.org/abstracts/search?q=recency" title=" recency"> recency</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency" title=" frequency"> frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=monetary%20value" title=" monetary value"> monetary value</a> </p> <a href="https://publications.waset.org/abstracts/43479/identification-of-clv-for-online-shoppers-using-rfm-matrix-a-case-based-on-features-of-b2c-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43479.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">220</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">615</span> Oil Reservoir Asphalting Precipitation Estimating during CO2 Injection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Alhajri">I. Alhajri</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Zahedi"> G. Zahedi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Alazmi"> R. Alazmi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Akbari"> A. Akbari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=asphaltene" title=" asphaltene"> asphaltene</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20injection" title=" CO2 injection"> CO2 injection</a>, <a href="https://publications.waset.org/abstracts/search?q=Hirschberg%20model" title=" Hirschberg model"> Hirschberg model</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20reservoirs" title=" oil reservoirs"> oil reservoirs</a> </p> <a href="https://publications.waset.org/abstracts/6156/oil-reservoir-asphalting-precipitation-estimating-during-co2-injection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6156.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">364</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">614</span> Transfer Learning for Protein Structure Classification at Low Resolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Hudson">Alexander Hudson</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaogang%20Gong"> Shaogang Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution. <p class="card-text"><strong>Keywords:</strong> <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=protein%20distance%20maps" title=" protein distance maps"> protein distance maps</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20classification" title=" protein structure classification"> protein structure classification</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/129704/transfer-learning-for-protein-structure-classification-at-low-resolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129704.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">613</span> Verification and Application of Finite Element Model Developed for Flood Routing in Rivers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20L.%20Qureshi">A. L. Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Mahessar"> A. A. Mahessar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Baloch"> A. Baloch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Flood wave propagation in river channel flow can be enunciated by nonlinear equations of motion for unsteady flow. However, it is difficult to find analytical solution of these complex non-linear equations. Hence, verification of the numerical model should be carried out against field data and numerical predictions. This paper presents the verification of developed finite element model applying for unsteady flow in the open channels. The results of a proposed model indicate a good matching with both Preissmann scheme and HEC-RAS model for a river reach of 29 km at both sites (15 km from upstream and at downstream end) for discharge hydrographs. It also has an agreeable comparison with the Preissemann scheme for the flow depth (stage) hydrographs. The proposed model has also been applying to forecast daily discharges at 400 km downstream from Sukkur barrage, which demonstrates accurate model predictions with observed daily discharges. Hence, this model may be utilized for predicting and issuing flood warnings about flood hazardous in advance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title="finite element method">finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=Preissmann%20scheme" title=" Preissmann scheme"> Preissmann scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-RAS" title=" HEC-RAS"> HEC-RAS</a>, <a href="https://publications.waset.org/abstracts/search?q=flood%20forecasting" title=" flood forecasting"> flood forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Indus%20river" title=" Indus river"> Indus river</a> </p> <a href="https://publications.waset.org/abstracts/2616/verification-and-application-of-finite-element-model-developed-for-flood-routing-in-rivers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2616.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">504</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">612</span> Predicting Match Outcomes in Team Sport via Machine Learning: Evidence from National Basketball Association</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jacky%20Liu">Jacky Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets. <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=team%20sports" title=" team sports"> team sports</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20outcome%20prediction" title=" game outcome prediction"> game outcome prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20betting" title=" sports betting"> sports betting</a>, <a href="https://publications.waset.org/abstracts/search?q=profits%20simulation" title=" profits simulation"> profits simulation</a> </p> <a href="https://publications.waset.org/abstracts/169745/predicting-match-outcomes-in-team-sport-via-machine-learning-evidence-from-national-basketball-association" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">611</span> A Trend Based Forecasting Framework of the ATA Method and Its Performance on the M3-Competition Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Taylan%20Selamlar">H. Taylan Selamlar</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Yavuz"> I. Yavuz</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Yapar"> G. Yapar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing" title=" exponential smoothing"> exponential smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=initial%20value" title=" initial value"> initial value</a> </p> <a href="https://publications.waset.org/abstracts/83013/a-trend-based-forecasting-framework-of-the-ata-method-and-its-performance-on-the-m3-competition-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83013.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">177</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">610</span> Evaluation Methods for Question Decomposition Formalism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aviv%20Yaniv">Aviv Yaniv</a>, <a href="https://publications.waset.org/abstracts/search?q=Ron%20Ben%20Arosh"> Ron Ben Arosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadav%20Gasner"> Nadav Gasner</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Konviser"> Michael Konviser</a>, <a href="https://publications.waset.org/abstracts/search?q=Arbel%20Yaniv"> Arbel Yaniv</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP" title="NLP">NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=question%20answering" title=" question answering"> question answering</a>, <a href="https://publications.waset.org/abstracts/search?q=question%20decomposition%20meaning%20representation" title=" question decomposition meaning representation"> question decomposition meaning representation</a>, <a href="https://publications.waset.org/abstracts/search?q=QDMR%20evaluation%20metrics" title=" QDMR evaluation metrics"> QDMR evaluation metrics</a> </p> <a href="https://publications.waset.org/abstracts/173593/evaluation-methods-for-question-decomposition-formalism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173593.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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictions&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictions&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictions&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predictions&page=5">5</a></li> <li class="page-item"><a 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