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predict</title> <meta name="description" content="Search results for: predict"> <meta name="keywords" content="predict"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="predict"> <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> 2419</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predict</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2419</span> Application of Low-order Modeling Techniques and Neural-Network Based Models for System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Venkatesh%20Pulletikurthi">Venkatesh Pulletikurthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Karthik%20B.%20Ariyur"> Karthik B. Ariyur</a>, <a href="https://publications.waset.org/abstracts/search?q=Luciano%20Castillo"> Luciano Castillo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The system identification from the turbulence wakes will lead to the tactical advantage to prepare and also, to predict the trajectory of the opponents’ movements. A low-order modeling technique, POD, is used to predict the object based on the wake pattern and compared with pre-trained image recognition neural network (NN) to classify the wake patterns into objects. It is demonstrated that low-order modeling, POD, is able to predict the objects better compared to pretrained NN by ~30%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20bluff%20body%20wakes" title="the bluff body wakes">the bluff body wakes</a>, <a href="https://publications.waset.org/abstracts/search?q=low-order%20modeling" title=" low-order modeling"> low-order modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title=" system identification"> system identification</a> </p> <a href="https://publications.waset.org/abstracts/146168/application-of-low-order-modeling-techniques-and-neural-network-based-models-for-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146168.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">180</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">2418</span> Genetic and Non-Genetic Factors Affecting the Response to Clopidogrel Therapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Snezana%20Mugosa">Snezana Mugosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoran%20Todorovic"> Zoran Todorovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoran%20Bukumiric"> Zoran Bukumiric</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Radosavljevic"> Ivan Radosavljevic</a>, <a href="https://publications.waset.org/abstracts/search?q=Natasa%20Djordjevic"> Natasa Djordjevic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Various studies have shown that the frequency of clopidogrel resistance ranges from 4-40%. The aim of this study was to provide in depth analysis of genetic and non-genetic factors that influence clopidogrel resistance in cardiology patients. Methods: We have conducted a prospective study in 200 hospitalized patients hospitalized at Cardiology Centre of the Clinical Centre of Montenegro. CYP2C19 genetic testing was conducted, and the PREDICT score was calculated in 102 out of 200 patients treated with clopidogrel in order to determine the influence of genetic and non-genetic factors on outcomes of interest. Adverse cardiovascular events and adverse reactions to clopidogrel were assessed during 12 months follow up period. Results: PREDICT score and CYP2C19 enzymatic activity were found to be statistically significant predictors of expressing lack of therapeutic efficacy of clopidogrel by multivariate logistic regression, without multicollinearity or interaction between the predictors (p = 0.002 and 0.009, respectively). Conclusions: Pharmacogenetics analyses that were done in the Montenegrin population of patients for the first time suggest that these analyses can predict patient response to the certain therapy. Stepwise approach could be used in assessing the clopidogrel resistance in cardiology patients, combining the PREDICT score, platelet aggregation test, and genetic testing for CYP2C19 polymorphism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clopidogrel" title="clopidogrel">clopidogrel</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmacogenetics" title=" pharmacogenetics"> pharmacogenetics</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmacotherapy" title=" pharmacotherapy"> pharmacotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=PREDICT%20score" title=" PREDICT score"> PREDICT score</a> </p> <a href="https://publications.waset.org/abstracts/37484/genetic-and-non-genetic-factors-affecting-the-response-to-clopidogrel-therapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37484.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">349</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">2417</span> [Keynote Speech]: Simulation Studies of Pulsed Voltage Effects on Cells</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahui%20Song">Jiahui Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to predict or explain a complicated biological process, it is important first to construct mathematical models that can be used to yield analytical solutions. Through numerical simulation, mathematical model results can be used to test scenarios that might not be easily attained in a laboratory experiment, or to predict parameters or phenomena. High-intensity, nanosecond pulse electroporation has been a recent development in bioelectrics. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into pore formation energy equation to analyze and predict such electroporation effects. For greater accuracy, with inclusion of atomistic details, molecular dynamics (MD) simulations were also carried out during this study. Besides inducing pores in cells, external voltages could also be used in principle to modulate action potential generation in nerves. This could have an application in electrically controlled ‘pain management’. Also a simple model-based rate equation treatment of the various cellular bio-chemical processes has been used to predict the pulse number dependent cell survival trends. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model" title="model">model</a>, <a href="https://publications.waset.org/abstracts/search?q=high-intensity" title=" high-intensity"> high-intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=nanosecond" title=" nanosecond"> nanosecond</a>, <a href="https://publications.waset.org/abstracts/search?q=bioelectrics" title=" bioelectrics"> bioelectrics</a> </p> <a href="https://publications.waset.org/abstracts/72467/keynote-speech-simulation-studies-of-pulsed-voltage-effects-on-cells" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72467.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">225</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">2416</span> An Empirical Study to Predict Myocardial Infarction Using K-Means and Hierarchical Clustering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Minhazul%20%20%20Islam">Md. Minhazul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Shah%20Ashisul%20Abed%20%20Nipun"> Shah Ashisul Abed Nipun</a>, <a href="https://publications.waset.org/abstracts/search?q=Majharul%20%20Islam"> Majharul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Abdur%20Rakib%20Rahat"> Md. Abdur Rakib Rahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonayet%20Miah"> Jonayet Miah</a>, <a href="https://publications.waset.org/abstracts/search?q=Salsavil%20Kayyum"> Salsavil Kayyum</a>, <a href="https://publications.waset.org/abstracts/search?q=Anwar%20Shadaab"> Anwar Shadaab</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiz%20Al%20Faisal"> Faiz Al Faisal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques. <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=K-means" title=" K-means"> K-means</a>, <a href="https://publications.waset.org/abstracts/search?q=Hierarchical%20Clustering" title=" Hierarchical Clustering"> Hierarchical Clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Myocardial%20Infarction" title=" Myocardial Infarction"> Myocardial Infarction</a>, <a href="https://publications.waset.org/abstracts/search?q=Heart%20Disease" title=" Heart Disease"> Heart Disease</a> </p> <a href="https://publications.waset.org/abstracts/121240/an-empirical-study-to-predict-myocardial-infarction-using-k-means-and-hierarchical-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121240.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">203</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">2415</span> Development of Interaction Factors Charts for Piled Raft Foundation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelazim%20Makki%20Ibrahim">Abdelazim Makki Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Esamaldeen%20Ali"> Esamaldeen Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims at analysing the load settlement behavior and predict the bearing capacity of piled raft foundation a series of finite element models with different foundation configurations and stiffness were established. Numerical modeling is used to study the behavior of the piled raft foundation due to the complexity of piles, raft, and soil interaction and also due to the lack of reliable analytical method that can predict the behavior of the piled raft foundation system. Simple analytical models are developed to predict the average settlement and the load sharing between the piles and the raft in piled raft foundation system. A simple example to demonstrate the applications of these charts is included. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finite%20element" title="finite element">finite element</a>, <a href="https://publications.waset.org/abstracts/search?q=pile-raft%20foundation" title=" pile-raft foundation"> pile-raft foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=method" title=" method"> method</a>, <a href="https://publications.waset.org/abstracts/search?q=PLAXIS%20software" title=" PLAXIS software"> PLAXIS software</a>, <a href="https://publications.waset.org/abstracts/search?q=settlement" title=" settlement"> settlement</a> </p> <a href="https://publications.waset.org/abstracts/35331/development-of-interaction-factors-charts-for-piled-raft-foundation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35331.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">557</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">2414</span> The Reliability of Management Earnings Forecasts in IPO Prospectuses: A Study of Managers’ Forecasting Preferences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maha%20Hammami">Maha Hammami</a>, <a href="https://publications.waset.org/abstracts/search?q=Olfa%20Benouda%20Sioud"> Olfa Benouda Sioud </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intentional%20bias" title="intentional bias">intentional bias</a>, <a href="https://publications.waset.org/abstracts/search?q=management%20earnings%20forecasts" title=" management earnings forecasts"> management earnings forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=neutrality" title=" neutrality"> neutrality</a>, <a href="https://publications.waset.org/abstracts/search?q=verifiability" title=" verifiability"> verifiability</a> </p> <a href="https://publications.waset.org/abstracts/6243/the-reliability-of-management-earnings-forecasts-in-ipo-prospectuses-a-study-of-managers-forecasting-preferences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6243.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2413</span> A Multi-Agent Simulation of Serious Games to Predict Their Impact on E-Learning Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibtissem%20Daoudi">Ibtissem Daoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Raoudha%20Chebil"> Raoudha Chebil</a>, <a href="https://publications.waset.org/abstracts/search?q=Wided%20Lejouad%20Chaari"> Wided Lejouad Chaari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Serious games constitute actually a recent and attractive way supposed to replace the classical boring courses. However, the choice of the adapted serious game to a specific learning environment remains a challenging task that makes teachers unwilling to adopt this concept. To fill this gap, we present, in this paper, a multi-agent-based simulator allowing to predict the impact of a serious game integration in a learning environment given several game and players characteristics. As results, the presented tool gives intensities of several emotional aspects characterizing learners reactions to the serious game adoption. The presented simulator is tested to predict the effect of basing a coding course on the serious game ”CodeCombat”. The obtained results are compared with feedbacks of using the same serious game in a real learning process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion" title="emotion">emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20process" title=" learning process"> learning process</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20simulation" title=" multi-agent simulation"> multi-agent simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=serious%20games" title=" serious games"> serious games</a> </p> <a href="https://publications.waset.org/abstracts/61776/a-multi-agent-simulation-of-serious-games-to-predict-their-impact-on-e-learning-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61776.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">398</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">2412</span> Machine Learning Techniques to Predict Cyberbullying and Improve Social Work Interventions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oscar%20E.%20Cariceo">Oscar E. Cariceo</a>, <a href="https://publications.waset.org/abstracts/search?q=Claudia%20V.%20Casal"> Claudia V. Casal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyberbullying" title="cyberbullying">cyberbullying</a>, <a href="https://publications.waset.org/abstracts/search?q=evidence%20based%20practice" title=" evidence based practice"> evidence based practice</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=social%20work%20research" title=" social work research"> social work research</a> </p> <a href="https://publications.waset.org/abstracts/101259/machine-learning-techniques-to-predict-cyberbullying-and-improve-social-work-interventions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101259.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">168</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">2411</span> Data-Driven Approach to Predict Inpatient's Estimated Discharge Date</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayliana%20Dharmawan">Ayliana Dharmawan</a>, <a href="https://publications.waset.org/abstracts/search?q=Heng%20Yong%20Sheng"> Heng Yong Sheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Xiaojin"> Zhang Xiaojin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Thai%20Lian"> Tan Thai Lian </a> </p> <p class="card-text"><strong>Abstract:</strong></p> To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inpatient" title="inpatient">inpatient</a>, <a href="https://publications.waset.org/abstracts/search?q=estimated%20discharge%20date" title=" estimated discharge date"> estimated discharge date</a>, <a href="https://publications.waset.org/abstracts/search?q=EDD" title=" EDD"> EDD</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven" title=" data-driven"> data-driven</a> </p> <a href="https://publications.waset.org/abstracts/91693/data-driven-approach-to-predict-inpatients-estimated-discharge-date" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91693.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">174</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">2410</span> A Study of Learning Achievement for Heat Transfer by Using Experimental Sets of Convection with the Predict-Observe-Explain Teaching Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanlapa%20Boonsod">Wanlapa Boonsod</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisachon%20Yangprasong"> Nisachon Yangprasong</a>, <a href="https://publications.waset.org/abstracts/search?q=Udomsak%20Kitthawee"> Udomsak Kitthawee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thermal physics education is a complicated and challenging topic to discuss in any classroom. As a result, most students tend to be uninterested in learning this topic. In the current study, a convection experiment set was devised to show how heat can be transferred by a convection system to a thermoelectric plate until a LED flashes. This research aimed to 1) create a natural convection experimental set, 2) study learning achievement on the convection experimental set with the predict-observe-explain (POE) technique, and 3) study satisfaction for the convection experimental set with the predict-observe-explain (POE) technique. The samples were chosen by purposive sampling and comprised 28 students in grade 11 at Patumkongka School in Bangkok, Thailand. The primary research instrument was the plan for predict-observe-explain (POE) technique on heat transfer using a convection experimental set. Heat transfer experimental set by convection. The instruments used to collect data included a heat transfer achievement model by convection, a Satisfaction Questionnaire after the learning activity, and the predict-observe-explain (POE) technique for heat transfer using a convection experimental set. The research format comprised a one-group pretest-posttest design. The data was analyzed by GeoGebra program. The statistics used in the research were mean, standard deviation and t-test for dependent samples. The results of the research showed that achievement on heat transfer using convection experimental set was composed of thermo-electrics on the top side attached to the heat sink and another side attached to a stainless plate. Electrical current was displayed by the flashing of a 5v LED. The entire set of thermo-electrics was set up on the top of the box and heated by an alcohol burner. The achievement of learning was measured with the predict-observe-explain (POE) technique, with the natural convection experimental set statistically higher than before learning at a 0.01 level. Satisfaction with POE for physics learning of heat transfer by using convection experimental set was at a high level (4.83 from 5.00). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convection" title="convection">convection</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=physics%20education" title=" physics education"> physics education</a>, <a href="https://publications.waset.org/abstracts/search?q=POE" title=" POE"> POE</a> </p> <a href="https://publications.waset.org/abstracts/93014/a-study-of-learning-achievement-for-heat-transfer-by-using-experimental-sets-of-convection-with-the-predict-observe-explain-teaching-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93014.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">218</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">2409</span> Predicting National Football League (NFL) Match with Score-Based System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcho%20Setiawan%20Handok">Marcho Setiawan Handok</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20S.%20Lemma"> Samuel S. Lemma</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdoulaye%20Fofana"> Abdoulaye Fofana</a>, <a href="https://publications.waset.org/abstracts/search?q=Naseef%20Mansoor"> Naseef Mansoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=game%20prediction" title="game prediction">game prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=NFL" title=" NFL"> NFL</a>, <a href="https://publications.waset.org/abstracts/search?q=football" title=" football"> football</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/160270/predicting-national-football-league-nfl-match-with-score-based-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160270.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">2408</span> College Students’ Multitasking and Its Causes </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huey-Wen%20Chou">Huey-Wen Chou</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuo-Heng%20Liang"> Shuo-Heng Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on studying college students’ multitasking with cellphones/laptops during lectures. In-class multitasking behavior is defined as the activities students engaged that are irrelevant to learning. This study aims to understand if students' learning engagement affects students' multitasking as well as to investigate the causes or motivations that contribute to the occurrence of multitasking behavior. Survey data were collected and analyzed by PLS method and multiple regression to test the research model and hypothesis. Major results include: 1. Students' multitasking motivation positively predicts students’ in-class multitasking. 2. Factors affecting multitasking in class, including efficiency, entertainment and social needs, significantly impact on multitasking. 3. Polychronic personality traits will positively predict students’ multitasking. 4. Students' classroom learning engagement negatively predicts multitasking. 5. Course attributes negatively predict student learning engagement and positively predict student multitasking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=engagement" title="engagement">engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=monochronic%20personality" title=" monochronic personality"> monochronic personality</a>, <a href="https://publications.waset.org/abstracts/search?q=multitasking" title=" multitasking"> multitasking</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=personality%20traits" title=" personality traits"> personality traits</a> </p> <a href="https://publications.waset.org/abstracts/123421/college-students-multitasking-and-its-causes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123421.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">133</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">2407</span> Prediction of the Thermodynamic Properties of Hydrocarbons Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Alhazmi">N. Alhazmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermodynamic" title="thermodynamic">thermodynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrocarbons" title=" hydrocarbons"> hydrocarbons</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</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=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=enthalpy" title=" enthalpy"> enthalpy</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20capacity" title=" heat capacity"> heat capacity</a> </p> <a href="https://publications.waset.org/abstracts/145010/prediction-of-the-thermodynamic-properties-of-hydrocarbons-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145010.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">222</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">2406</span> Resilience Grit and Intrinsic Motivation Are Predictors of Better Studying Results among First-year Cadets in the Cadet Basic Training Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rosita%20Kanapeckaite">Rosita Kanapeckaite</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every year, some candidates who enroll in Generolas Jonas Zemaitis Military Academy of Lithuania do not complete a basic seven weeks cadet course and leave the Academy. Experience in other countries shows that psychological resilience grit and intrinsic motivation can lead to better course completion results. We examined the psychological resilience grit and intrinsic motivation as predictors of better results among newcomers who participate in the Cadet Basic Training (CBT) course. Based on past research and theory of other countries' military academies, we expected that resilience grit, and intrinsic motivation would predict performance in the Cadet Basic Training course. Results of regression analyses revealed that resilience and grit can predict better course results, but intrinsic motivation can not predict retention. These findings suggest that resilience and grit assessment and training may prove valuable in enhancing performance and retention within military training environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=military" title="military">military</a>, <a href="https://publications.waset.org/abstracts/search?q=intrinsic%20motivation" title=" intrinsic motivation"> intrinsic motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=grit" title=" grit"> grit</a>, <a href="https://publications.waset.org/abstracts/search?q=resilience" title=" resilience"> resilience</a> </p> <a href="https://publications.waset.org/abstracts/143469/resilience-grit-and-intrinsic-motivation-are-predictors-of-better-studying-results-among-first-year-cadets-in-the-cadet-basic-training-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">208</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2405</span> Time Series Forecasting (TSF) Using Various Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jimeng%20Shi">Jimeng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahek%20Jain"> Mahek Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Giri%20Narasimhan"> Giri Narasimhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality%20prediction" title="air quality prediction">air quality prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20algorithms" title=" deep learning algorithms"> deep learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=look-back%20window" title=" look-back window"> look-back window</a> </p> <a href="https://publications.waset.org/abstracts/146879/time-series-forecasting-tsf-using-various-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146879.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">153</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">2404</span> Using Machine Learning to Predict Answers to Big-Five Personality Questions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aadityaa%20Singla">Aadityaa Singla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality. <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=personally" title=" personally"> personally</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20five%20personality%20traits" title=" big five personality traits"> big five personality traits</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20science" title=" cognitive science"> cognitive science</a> </p> <a href="https://publications.waset.org/abstracts/157911/using-machine-learning-to-predict-answers-to-big-five-personality-questions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157911.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">145</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">2403</span> A Study on Performance Prediction in Early Design Stage of Apartment Housing Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seongjun%20Kim">Seongjun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanghoon%20Shim"> Sanghoon Shim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwooung%20Kim"> Jinwooung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaehwan%20Jung"> Jaehwan Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Ah%20Kim"> Sung-Ah Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=apartment%20housing" title="apartment housing">apartment housing</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=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20prediction" title=" performance prediction"> performance prediction</a> </p> <a href="https://publications.waset.org/abstracts/80644/a-study-on-performance-prediction-in-early-design-stage-of-apartment-housing-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2402</span> Predicting Options Prices Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishang%20Surapaneni">Krishang Surapaneni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42% <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression%20model" title=" linear regression model"> linear regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20model" title=" machine learning model"> machine learning model</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20price" title=" stock price"> stock price</a> </p> <a href="https://publications.waset.org/abstracts/160197/predicting-options-prices-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160197.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">75</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">2401</span> Application of Nonlinear Model to Optimize the Coagulant Dose in Drinking Water Treatment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Derraz">M. Derraz</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Farhaoui"> M.Farhaoui </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the water treatment processes, the determination of the optimal dose of the coagulant is an issue of particular concern. Coagulant dosing is correlated to raw water quality which depends on some parameters (turbidity, ph, temperature, conductivity…). The objective of this study is to provide water treatment operators with a tool that enables to predict and replace, sometimes, the manual method (jar testing) used in this plant to predict the optimum coagulant dose. The model is constructed using actual process data for a water treatment plant located in the middle of Morocco (Meknes). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coagulation%20process" title="coagulation process">coagulation process</a>, <a href="https://publications.waset.org/abstracts/search?q=aluminum%20sulfate" title=" aluminum sulfate"> aluminum sulfate</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=coagulant%20dose" title=" coagulant dose"> coagulant dose</a> </p> <a href="https://publications.waset.org/abstracts/45249/application-of-nonlinear-model-to-optimize-the-coagulant-dose-in-drinking-water-treatment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45249.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">2400</span> An Integrated Approach to Find the Effect of Strain Rate on Ultimate Tensile Strength of Randomly Oriented Short Glass Fiber Composite in Combination with Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharad%20Shrivastava">Sharad Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Arun%20Jalan"> Arun Jalan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study tensile testing was performed on randomly oriented short glass fiber/epoxy resin composite specimens which were prepared using hand lay-up method. Samples were tested over a wide range of strain rate/loading rate from 2mm/min to 40mm/min to see the effect on ultimate tensile strength of the composite. A multi layered 'back propagation artificial neural network of supervised learning type' was used to analyze and predict the tensile properties with strain rate and temperature as given input and output as UTS to predict. Various network structures were designed and investigated with varying parameters and network sizes, and an optimized network structure was proposed to predict the UTS of short glass fiber/epoxy resin composite specimens with reasonably good accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=glass%20fiber%20composite" title="glass fiber composite">glass fiber composite</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20properties" title=" mechanical properties"> mechanical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=strain%20rate" title=" strain rate"> strain rate</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/18900/an-integrated-approach-to-find-the-effect-of-strain-rate-on-ultimate-tensile-strength-of-randomly-oriented-short-glass-fiber-composite-in-combination-with-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18900.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">437</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">2399</span> Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Lindsay">L. Lindsay</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Coleman"> S. A. Coleman</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Kerr"> D. Kerr</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Taylor"> B. J. Taylor</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Moorhead"> A. Moorhead</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=falls" title=" falls"> falls</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20risk%20factors" title=" health risk factors"> health risk factors</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=older%20adults" title=" older adults"> older adults</a> </p> <a href="https://publications.waset.org/abstracts/104420/classification-of-health-risk-factors-to-predict-the-risk-of-falling-in-older-adults" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104420.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">2398</span> Are the Organizations Prepared for Potential Crises? A Research Intended to Measure the Proactivity Level of Industrial Organizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Tahir%20Demirsel">M. Tahir Demirsel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Atsan"> Mustafa Atsan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many elements of the environment in which businesses operate today leave them faced with unexpected threats and opportunities. One of the major threats is business crisis. The crisis is a state of affairs in a business wherein the executives must take urgent and unprecedented action to try to save the business from failure. In order to survive in the business environment, organizations should be prepared for the potential crises. Technological developments, uncertainty in the market and the intense competition increase the probability of encountering a crisis for organizations. Therefore, by acting proactively to predict crisis, to detect signals of crisis and be prepared for a crisis by taking necessary precautions accordingly, is of great importance for businesses. In this context, the objective of this study is to reveal that how much organizations are proactive and can predict the future crises and investigate whether they are prepared for possible crises or not. The research was conducted on 222 business executives in one of the major industrial zones of Turkey, Konya Organized Industrial Zone (KOS). The findings are analyzed through descriptive statistics and multiple regression analysis. According to the results, it has been observed that organizations cannot predict the crisis signals and are not prepared for potential crises. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crisis%20preparedness" title="crisis preparedness">crisis preparedness</a>, <a href="https://publications.waset.org/abstracts/search?q=crisis%20signals" title=" crisis signals"> crisis signals</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20organizations" title=" industrial organizations"> industrial organizations</a>, <a href="https://publications.waset.org/abstracts/search?q=proactivity" title=" proactivity"> proactivity</a> </p> <a href="https://publications.waset.org/abstracts/29479/are-the-organizations-prepared-for-potential-crises-a-research-intended-to-measure-the-proactivity-level-of-industrial-organizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29479.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">516</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">2397</span> Ending Wars Over Water: Evaluating the Extent to Which Artificial Intelligence Can Be Used to Predict and Prevent Transboundary Water Conflicts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akhila%20Potluru">Akhila Potluru</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Worldwide, more than 250 bodies of water are transboundary, meaning they cross the political boundaries of multiple countries. This creates a system of hydrological, economic, and social interdependence between communities reliant on these water sources. Transboundary water conflicts can occur as a result of this intense interdependence. Many factors contribute to the sparking of transboundary water conflicts, ranging from natural hydrological factors to hydro-political interactions. Previous attempts to predict transboundary water conflicts by analysing changes or trends in the contributing factors have typically failed because patterns in the data are hard to identify. However, there is potential for artificial intelligence and machine learning to fill this gap and identify future ‘hotspots’ up to a year in advance by identifying patterns in data where humans can’t. This research determines the extent to which AI can be used to predict and prevent transboundary water conflicts. This is done via a critical literature review of previous case studies and datasets where AI was deployed to predict water conflict. This research not only delivered a more nuanced understanding of previously undervalued factors that contribute toward transboundary water conflicts (in particular, culture and disinformation) but also by detecting conflict early, governance bodies can engage in processes to de-escalate conflict by providing pre-emptive solutions. Looking forward, this gives rise to significant policy implications and water-sharing agreements, which may be able to prevent water conflicts from developing into wide-scale disasters. Additionally, AI can be used to gain a fuller picture of water-based conflicts in areas where security concerns mean it is not possible to have staff on the ground. Therefore, AI enhances not only the depth of our knowledge about transboundary water conflicts but also the breadth of our knowledge. With demand for water constantly growing, competition between countries over shared water will increasingly lead to water conflict. There has never been a more significant time for us to be able to accurately predict and take precautions to prevent global water conflicts. <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transboundary%20water%20conflict" title=" transboundary water conflict"> transboundary water conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20management" title=" water management"> water management</a> </p> <a href="https://publications.waset.org/abstracts/155005/ending-wars-over-water-evaluating-the-extent-to-which-artificial-intelligence-can-be-used-to-predict-and-prevent-transboundary-water-conflicts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155005.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">2396</span> Predicting Radioactive Waste Glass Viscosity, Density and Dissolution with Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Lillington">Joseph Lillington</a>, <a href="https://publications.waset.org/abstracts/search?q=Tom%20Gout"> Tom Gout</a>, <a href="https://publications.waset.org/abstracts/search?q=Mike%20Harrison"> Mike Harrison</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20Farnan"> Ian Farnan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data. <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=predictive%20modelling" title=" predictive modelling"> predictive modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=radioactive%20waste%20glass" title=" radioactive waste glass "> radioactive waste glass </a> </p> <a href="https://publications.waset.org/abstracts/115477/predicting-radioactive-waste-glass-viscosity-density-and-dissolution-with-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115477.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">115</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">2395</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.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">200</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">2394</span> Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Mohsenifar">Najmeh Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Narjes%20Mohsenifar"> Narjes Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Kargar"> Abbas Kargar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20artificial%20neural%20network" title=" RBF artificial neural network"> RBF artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/33466/selecting-the-best-rbf-neural-network-using-pso-algorithm-for-ecg-signal-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33466.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">626</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">2393</span> Study of Inhibition of the End Effect Based on AR Model Predict of Combined Data Extension and Window Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pan%20Hongxia">Pan Hongxia</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhenhua"> Wang Zhenhua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the EMD decomposition in the process of endpoint effect adopted data based on AR model to predict the continuation and window function method of combining the two effective inhibition. Proven by simulation of the simulation signal obtained the ideal effect, then, apply this method to the gearbox test data is also achieved good effect in the process, for the analysis of the subsequent data processing to improve the calculation accuracy. In the end, under various working conditions for the gearbox fault diagnosis laid a good foundation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gearbox" title="gearbox">gearbox</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=ar%20model" title=" ar model"> ar model</a>, <a href="https://publications.waset.org/abstracts/search?q=end%20effect" title=" end effect"> end effect</a> </p> <a href="https://publications.waset.org/abstracts/30984/study-of-inhibition-of-the-end-effect-based-on-ar-model-predict-of-combined-data-extension-and-window-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30984.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">366</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">2392</span> Market Index Trend Prediction using Deep Learning and Risk Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shervin%20Alaei">Shervin Alaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Moradi"> Reza Moradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=trend%20prediction" title=" trend prediction"> trend prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a> </p> <a href="https://publications.waset.org/abstracts/148318/market-index-trend-prediction-using-deep-learning-and-risk-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148318.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">156</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">2391</span> Project Time Prediction Model: A Case Study of Construction Projects in Sindh, Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tauha%20Hussain%20Ali">Tauha Hussain Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Shabir%20Hussain%20Khahro"> Shabir Hussain Khahro</a>, <a href="https://publications.waset.org/abstracts/search?q=Nafees%20Ahmed%20Memon"> Nafees Ahmed Memon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BTC%20Model" title="BTC Model">BTC Model</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20time" title=" project time"> project time</a>, <a href="https://publications.waset.org/abstracts/search?q=relationship%20of%20time%20cost" title=" relationship of time cost"> relationship of time cost</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/50762/project-time-prediction-model-a-case-study-of-construction-projects-in-sindh-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50762.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">382</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">2390</span> Prediction of the Heat Transfer Characteristics of Tunnel Concrete</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung%20Cho%20Yang">Seung Cho Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jae%20Sung%20Lee"> Jae Sung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Hee%20Park"> Se Hee Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study suggests the analysis method to predict the damages of tunnel concrete caused by fires. The result obtained from the analyses of concrete temperatures at a fire in a tunnel using ABAQUS was compared with the test result. After the reliability of the analysis method was verified, the temperatures of a tunnel at a real fire and those of concrete during the fire were estimated to predict fire damages. The temperatures inside the tunnel were estimated by FDS, a CFD model. It was deduced that the fire performance of tunnel lining and the fire damages of the structure at an actual fire could be estimated by the analysis method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fire%20resistance" title="fire resistance">fire resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20analysis" title=" numerical analysis"> numerical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=tunnel%20fire" title=" tunnel fire"> tunnel fire</a> </p> <a href="https://publications.waset.org/abstracts/50411/prediction-of-the-heat-transfer-characteristics-of-tunnel-concrete" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50411.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">436</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=predict&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predict&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predict&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predict&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=predict&page=6">6</a></li> <li class="page-item"><a class="page-link" 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