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modeling</title> <meta name="description" content="Search results for: predictive modeling"> <meta name="keywords" content="predictive modeling"> <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 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="predictive modeling"> <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> 4812</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predictive modeling</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4812</span> Model Predictive Control of Turbocharged Diesel Engine with Exhaust Gas Recirculation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Yavas">U. Yavas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gokasan"> M. Gokasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title="predictive control">predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20control" title=" engine control"> engine control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20modeling" title=" engine modeling"> engine modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20control" title=" PID control"> PID control</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20compensation" title=" feedforward compensation"> feedforward compensation</a> </p> <a href="https://publications.waset.org/abstracts/34455/model-predictive-control-of-turbocharged-diesel-engine-with-exhaust-gas-recirculation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34455.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">636</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">4811</span> Evaluation of Environmental, Technical, and Economic Indicators of a Fused Deposition Modeling Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Yosofi">M. Yosofi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ezeddini"> S. Ezeddini</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ollivier"> A. Ollivier</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Lavaste"> V. Lavaste</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Mayousse"> C. Mayousse</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Additive manufacturing processes have changed significantly in a wide range of industries and their application progressed from rapid prototyping to production of end-use products. However, their environmental impact is still a rather open question. In order to support the growth of this technology in the industrial sector, environmental aspects should be considered and predictive models may help monitor and reduce the environmental footprint of the processes. This work presents predictive models based on a previously developed methodology for the environmental impact evaluation combined with a technical and economical assessment. Here we applied the methodology to the Fused Deposition Modeling process. First, we present the predictive models relative to different types of machines. Then, we present a decision-making tool designed to identify the optimum manufacturing strategy regarding technical, economic, and environmental criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20manufacturing" title="additive manufacturing">additive manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-makings" title=" decision-makings"> decision-makings</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20impact" title=" environmental impact"> environmental impact</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a> </p> <a href="https://publications.waset.org/abstracts/130193/evaluation-of-environmental-technical-and-economic-indicators-of-a-fused-deposition-modeling-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130193.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">131</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">4810</span> Predictive Modeling of Student Behavior in Virtual Reality: A Machine Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gayathri%20Sadanala">Gayathri Sadanala</a>, <a href="https://publications.waset.org/abstracts/search?q=Shibam%20Pokhrel"> Shibam Pokhrel</a>, <a href="https://publications.waset.org/abstracts/search?q=Owen%20Murphy"> Owen Murphy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interaction" title="interaction">interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title=" virtual reality"> virtual reality</a> </p> <a href="https://publications.waset.org/abstracts/172739/predictive-modeling-of-student-behavior-in-virtual-reality-a-machine-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172739.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">143</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">4809</span> A Machine Learning-Based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ronit%20Chakraborty">Ronit Chakraborty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sugata%20Banerji"> Sugata Banerji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors, including socio-economic, demographic, healthcare, public policy, and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states and if they do, which factors are the most influential. The key findings of this study include (1) the confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the identification of the most influential predictive factors, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) identification of Florida as a key outlier state pointing to a potential under-diagnosis of ASD there. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism%20spectrum%20disorder" title="autism spectrum disorder">autism spectrum disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a> </p> <a href="https://publications.waset.org/abstracts/159340/a-machine-learning-based-analysis-of-autism-prevalence-rates-across-us-states-against-multiple-potential-explanatory-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159340.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">4808</span> [Keynote Speech]: Feature Selection and Predictive Modeling of Housing Data Using Random Forest</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bharatendra%20Rai">Bharatendra Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=housing%20data" title="housing data">housing data</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=Boruta%20algorithm" title=" Boruta algorithm"> Boruta algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/72464/keynote-speech-feature-selection-and-predictive-modeling-of-housing-data-using-random-forest" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72464.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">323</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">4807</span> Predictive Modeling of Bridge Conditions Using Random Forest</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Miral%20Selim">Miral Selim</a>, <a href="https://publications.waset.org/abstracts/search?q=May%20Haggag"> May Haggag</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Abotaleb"> Ibrahim Abotaleb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aging of transportation infrastructure presents significant challenges, particularly concerning the monitoring and maintenance of bridges. This study investigates the application of Random Forest algorithms for predictive modeling of bridge conditions, utilizing data from the US National Bridge Inventory (NBI). The research is significant as it aims to improve bridge management through data-driven insights that can enhance maintenance strategies and contribute to overall safety. Random Forest is chosen for its robustness, ability to handle complex, non-linear relationships among variables, and its effectiveness in feature importance evaluation. The study begins with comprehensive data collection and cleaning, followed by the identification of key variables influencing bridge condition ratings, including age, construction materials, environmental factors, and maintenance history. Random Forest is utilized to examine the relationships between these variables and the predicted bridge conditions. The dataset is divided into training and testing subsets to evaluate the model's performance. The findings demonstrate that the Random Forest model effectively enhances the understanding of factors affecting bridge conditions. By identifying bridges at greater risk of deterioration, the model facilitates proactive maintenance strategies, which can help avoid costly repairs and minimize service disruptions. Additionally, this research underscores the value of data-driven decision-making, enabling better resource allocation to prioritize maintenance efforts where they are most necessary. In summary, this study highlights the efficiency and applicability of Random Forest in predictive modeling for bridge management. Ultimately, these findings pave the way for more resilient and proactive management of bridge systems, ensuring their longevity and reliability for future use. <p class="card-text"><strong>Keywords:</strong> <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=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20management" title=" bridge management"> bridge management</a> </p> <a href="https://publications.waset.org/abstracts/192067/predictive-modeling-of-bridge-conditions-using-random-forest" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192067.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">21</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">4806</span> Predictive Analysis for Big Data: Extension of Classification and Regression Trees Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Abdelkader">Ameur Abdelkader</a>, <a href="https://publications.waset.org/abstracts/search?q=Abed%20Bouarfa%20Hafida"> Abed Bouarfa Hafida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20analysis" title="predictive analysis">predictive analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analysis%20algorithms" title=" predictive analysis algorithms"> predictive analysis algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=CART%20algorithm" title=" CART algorithm"> CART algorithm</a> </p> <a href="https://publications.waset.org/abstracts/101647/predictive-analysis-for-big-data-extension-of-classification-and-regression-trees-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101647.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">142</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">4805</span> Feature Analysis of Predictive Maintenance Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhaoan%20Wang">Zhaoan Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20supply%20chain" title="automated supply chain">automated supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20manufacturing" title=" intelligent manufacturing"> intelligent manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance%20machine%20learning" title=" predictive maintenance machine learning"> predictive maintenance machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20engineering" title=" feature engineering"> feature engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20interpretation" title=" model interpretation"> model interpretation</a> </p> <a href="https://publications.waset.org/abstracts/129853/feature-analysis-of-predictive-maintenance-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129853.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">4804</span> Human Immunodeficiency Virus (HIV) Test Predictive Modeling and Identify Determinants of HIV Testing for People with Age above Fourteen Years in Ethiopia Using Data Mining Techniques: EDHS 2011</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Abera">S. Abera</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Gidey"> T. Gidey</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Terefe"> W. Terefe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=HIV" title=" HIV"> HIV</a>, <a href="https://publications.waset.org/abstracts/search?q=testing" title=" testing"> testing</a>, <a href="https://publications.waset.org/abstracts/search?q=ethiopia" title=" ethiopia"> ethiopia</a> </p> <a href="https://publications.waset.org/abstracts/34140/human-immunodeficiency-virus-hiv-test-predictive-modeling-and-identify-determinants-of-hiv-testing-for-people-with-age-above-fourteen-years-in-ethiopia-using-data-mining-techniques-edhs-2011" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34140.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">496</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">4803</span> Application of Fractional Model Predictive Control to Thermal System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aymen%20Rhouma">Aymen Rhouma</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Hcheichi"> Khaled Hcheichi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Hafsi"> Sami Hafsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller<em>.</em> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20model%20predictive%20control" title="fractional model predictive control">fractional model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order%20systems" title=" fractional order systems"> fractional order systems</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20system" title=" thermal system"> thermal system</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a> </p> <a href="https://publications.waset.org/abstracts/66187/application-of-fractional-model-predictive-control-to-thermal-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66187.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">411</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">4802</span> Tools for Analysis and Optimization of Standalone Green Microgrids</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=William%20Anderson">William Anderson</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyle%20Kobold"> Kyle Kobold</a>, <a href="https://publications.waset.org/abstracts/search?q=Oleg%20Yakimenko"> Oleg Yakimenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microgrid" title="microgrid">microgrid</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20systems" title=" complex systems"> complex systems</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/81199/tools-for-analysis-and-optimization-of-standalone-green-microgrids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81199.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">282</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">4801</span> Green Wave Control Strategy for Optimal Energy Consumption by Model Predictive Control in Electric Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Furkan%20Ozkan">Furkan Ozkan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Selcuk%20Arslan"> M. Selcuk Arslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatice%20Mercan"> Hatice Mercan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicles" title="electric vehicles">electric vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20wave%20control" title=" green wave control"> green wave 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=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/185303/green-wave-control-strategy-for-optimal-energy-consumption-by-model-predictive-control-in-electric-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185303.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">54</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">4800</span> Structural Equation Modeling Semiparametric in Modeling the Accuracy of Payment Time for Customers of Credit Bank in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adji%20Achmad%20Rinaldo%20Fernandes">Adji Achmad Rinaldo Fernandes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research was conducted to apply semiparametric SEM modeling to the timeliness of paying credit. Semiparametric SEM is structural modeling in which two combined approaches of parametric and nonparametric approaches are used. The analysis method in this research is semiparametric SEM with a nonparametric approach using a truncated spline. The data in the study were obtained through questionnaires distributed to Bank X mortgage debtors and are confidential. The study used 3 variables consisting of one exogenous variable, one intervening endogenous variable, and one endogenous variable. The results showed that (1) the effect of capacity and willingness to pay variables on timeliness of payment is significant, (2) modeling the capacity variable on willingness to pay also produces a significant estimate, (3) the effect of the capacity variable on the timeliness of payment variable is not influenced by the willingness to pay variable as an intervening variable, (4) the R^2 value of 0.763 or 76.33% indicates that the model has good predictive relevance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modeling%20semiparametric" title="structural equation modeling semiparametric">structural equation modeling semiparametric</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20bank" title=" credit bank"> credit bank</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy%20of%20payment%20time" title=" accuracy of payment time"> accuracy of payment time</a>, <a href="https://publications.waset.org/abstracts/search?q=willingness%20to%20pay" title=" willingness to pay"> willingness to pay</a> </p> <a href="https://publications.waset.org/abstracts/186761/structural-equation-modeling-semiparametric-in-modeling-the-accuracy-of-payment-time-for-customers-of-credit-bank-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186761.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">44</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">4799</span> Image Steganography Using Predictive Coding for Secure Transmission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baljit%20Singh%20Khehra">Baljit Singh Khehra</a>, <a href="https://publications.waset.org/abstracts/search?q=Jagreeti%20Kaur"> Jagreeti Kaur </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, steganographic strategy is used to hide the text file inside an image. To increase the storage limit, predictive coding is utilized to implant information. In the proposed plan, one can exchange secure information by means of predictive coding methodology. The predictive coding produces high stego-image. The pixels are utilized to insert mystery information in it. The proposed information concealing plan is powerful as contrasted with the existing methodologies. By applying this strategy, a provision helps clients to productively conceal the information. Entropy, standard deviation, mean square error and peak signal noise ratio are the parameters used to evaluate the proposed methodology. The results of proposed approach are quite promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptography" title="cryptography">cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=steganography" title=" steganography"> steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20image" title=" reversible image"> reversible image</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20coding" title=" predictive coding"> predictive coding</a> </p> <a href="https://publications.waset.org/abstracts/9850/image-steganography-using-predictive-coding-for-secure-transmission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9850.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">417</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">4798</span> Evaluating the Diagnostic Accuracy of the ctDNA Methylation for Liver Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maomao%20Cao">Maomao Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: To test the performance of ctDNA methylation for the detection of liver cancer. Methods: A total of 1233 individuals have been recruited in 2017. 15 male and 15 female samples (including 10 cases of liver cancer) were randomly selected in the present study. CfDNA was extracted by MagPure Circulating DNA Maxi Kit. The concentration of cfDNA was obtained by Qubit™ dsDNA HS Assay Kit. A pre-constructed predictive model was used to analyze methylation data and to give a predictive score for each cfDNA sample. Individuals with a predictive score greater than or equal to 80 were classified as having liver cancer. CT tests were considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the diagnosis of liver cancer were calculated. Results: 9 patients were diagnosed with liver cancer according to the prediction model (with high sensitivity and threshold of 80 points), with scores of 99.2, 91.9, 96.6, 92.4, 91.3, 92.5, 96.8, 91.1, and 92.2, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of ctDNA methylation for the diagnosis of liver cancer were 0.70, 0.90, 0.78, and 0.86, respectively. Conclusions: ctDNA methylation could be an acceptable diagnostic modality for the detection of liver cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liver%20cancer" title="liver cancer">liver cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=ctDNA%20methylation" title=" ctDNA methylation"> ctDNA methylation</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20performance" title=" diagnostic performance"> diagnostic performance</a> </p> <a href="https://publications.waset.org/abstracts/146512/evaluating-the-diagnostic-accuracy-of-the-ctdna-methylation-for-liver-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146512.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">150</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">4797</span> Temperature Control Improvement of Membrane Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pornsiri%20Kaewpradit">Pornsiri Kaewpradit</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalisa%20Pourneaw"> Chalisa Pourneaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Temperature control improvement of a membrane reactor with exothermic and reversible esterification reaction is studied in this work. It is well known that a batch membrane reactor requires different control strategies from a continuous one due to the fact that it is operated dynamically. Due to the effect of the operating temperature, the suitable control scheme has to be designed based reliable predictive model to achieve a desired objective. In the study, the optimization framework has been preliminary formulated in order to determine an optimal temperature trajectory for maximizing a desired product. In model predictive control scheme, a set of predictive models have been initially developed corresponding to the possible operating points of the system. The multiple predictive control moves have been further calculated on-line using the developed models corresponding to current operating point. It is obviously seen in the simulation results that the temperature control has been improved compared to the performance obtained by the conventional predictive controller. Further robustness tests have also been investigated in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=batch%20reactor" title=" batch reactor"> batch reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20control" title=" temperature control"> temperature control</a>, <a href="https://publications.waset.org/abstracts/search?q=membrane%20reactor" title=" membrane reactor "> membrane reactor </a> </p> <a href="https://publications.waset.org/abstracts/17487/temperature-control-improvement-of-membrane-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17487.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">468</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">4796</span> Modeling of Tool Flank Wear in Finish Hard Turning of AISI D2 Using Genetic Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Pourmostaghimi">V. Pourmostaghimi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zadshakoyan"> M. Zadshakoyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Efficiency and productivity of the finish hard turning can be enhanced impressively by utilizing accurate predictive models for cutting tool wear. However, the ability of genetic programming in presenting an accurate analytical model is a notable characteristic which makes it more applicable than other predictive modeling methods. In this paper, the genetic equation for modeling of tool flank wear is developed with the use of the experimentally measured flank wear values and genetic programming during finish turning of hardened AISI D2. Series of tests were conducted over a range of cutting parameters and the values of tool flank wear were measured. On the basis of obtained results, genetic model presenting connection between cutting parameters and tool flank wear were extracted. The accuracy of the genetically obtained model was assessed by using two statistical measures, which were root mean square error (RMSE) and coefficient of determination (R²). Evaluation results revealed that presented genetic model predicted flank wear over the study area accurately (R² = 0.9902 and RMSE = 0.0102). These results allow concluding that the proposed genetic equation corresponds well with experimental data and can be implemented in real industrial applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutting%20parameters" title="cutting parameters">cutting parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=flank%20wear" title=" flank wear"> flank wear</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20programming" title=" genetic programming"> genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20turning" title=" hard turning"> hard turning</a> </p> <a href="https://publications.waset.org/abstracts/96967/modeling-of-tool-flank-wear-in-finish-hard-turning-of-aisi-d2-using-genetic-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96967.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">178</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">4795</span> Metabolic Predictive Model for PMV Control Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eunji%20Choi">Eunji Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Borang%20Park"> Borang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Choi"> Youngjae Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwoo%20Moon"> Jinwoo Moon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model. <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=indoor%20quality" title=" indoor quality"> indoor quality</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolism" title=" metabolism"> metabolism</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a> </p> <a href="https://publications.waset.org/abstracts/93271/metabolic-predictive-model-for-pmv-control-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93271.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">257</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">4794</span> Use of Predictive Food Microbiology to Determine the Shelf-Life of Foods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatih%20Tarlak">Fatih Tarlak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shelf-life" title="shelf-life">shelf-life</a>, <a href="https://publications.waset.org/abstracts/search?q=growth%20model" title=" growth model"> growth model</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20microbiology" title=" predictive microbiology"> predictive microbiology</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/133723/use-of-predictive-food-microbiology-to-determine-the-shelf-life-of-foods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133723.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">211</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">4793</span> Improved Predictive Models for the IRMA Network Using Nonlinear Optimisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwesh%20Kulkarni">Vishwesh Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Bellarykar"> Nikhil Bellarykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20gene%20network" title="synthetic gene network">synthetic gene network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20identification" title=" network identification"> network identification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20modeling" title=" nonlinear modeling"> nonlinear modeling</a> </p> <a href="https://publications.waset.org/abstracts/94037/improved-predictive-models-for-the-irma-network-using-nonlinear-optimisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94037.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">4792</span> Computational Simulations on Stability of Model Predictive Control for Linear Discrete-Time Stochastic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20simulations" title="computational simulations">computational simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a> </p> <a href="https://publications.waset.org/abstracts/35462/computational-simulations-on-stability-of-model-predictive-control-for-linear-discrete-time-stochastic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35462.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">432</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">4791</span> Multiscale Modeling of Damage in Textile Composites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaan-Willem%20Simon">Jaan-Willem Simon</a>, <a href="https://publications.waset.org/abstracts/search?q=Bertram%20Stier"> Bertram Stier</a>, <a href="https://publications.waset.org/abstracts/search?q=Brett%20Bednarcyk"> Brett Bednarcyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Evan%20Pineda"> Evan Pineda</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefanie%20Reese"> Stefanie Reese</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Textile composites, in which the reinforcing fibers are woven or braided, have become very popular in numerous applications in aerospace, automotive, and maritime industry. These textile composites are advantageous due to their ease of manufacture, damage tolerance, and relatively low cost. However, physics-based modeling of the mechanical behavior of textile composites is challenging. Compared to their unidirectional counterparts, textile composites introduce additional geometric complexities, which cause significant local stress and strain concentrations. Since these internal concentrations are primary drivers of nonlinearity, damage, and failure within textile composites, they must be taken into account in order for the models to be predictive. The macro-scale approach to modeling textile-reinforced composites treats the whole composite as an effective, homogenized material. This approach is very computationally efficient, but it cannot be considered predictive beyond the elastic regime because the complex microstructural geometry is not considered. Further, this approach can, at best, offer a phenomenological treatment of nonlinear deformation and failure. In contrast, the mesoscale approach to modeling textile composites explicitly considers the internal geometry of the reinforcing tows, and thus, their interaction, and the effects of their curved paths can be modeled. The tows are treated as effective (homogenized) materials, requiring the use of anisotropic material models to capture their behavior. Finally, the micro-scale approach goes one level lower, modeling the individual filaments that constitute the tows. This paper will compare meso- and micro-scale approaches to modeling the deformation, damage, and failure of textile-reinforced polymer matrix composites. For the mesoscale approach, the woven composite architecture will be modeled using the finite element method, and an anisotropic damage model for the tows will be employed to capture the local nonlinear behavior. For the micro-scale, two different models will be used, the one being based on the finite element method, whereas the other one makes use of an embedded semi-analytical approach. The goal will be the comparison and evaluation of these approaches to modeling textile-reinforced composites in terms of accuracy, efficiency, and utility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiscale%20modeling" title="multiscale modeling">multiscale modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=continuum%20damage%20model" title=" continuum damage model"> continuum damage model</a>, <a href="https://publications.waset.org/abstracts/search?q=damage%20interaction" title=" damage interaction"> damage interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=textile%20composites" title=" textile composites"> textile composites</a> </p> <a href="https://publications.waset.org/abstracts/32218/multiscale-modeling-of-damage-in-textile-composites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32218.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">4790</span> Energy Efficiency and Sustainability Analytics for Reducing Carbon Emissions in Oil Refineries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaurav%20Kumar%20Sinha">Gaurav Kumar Sinha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability%20analytics" title=" sustainability analytics"> sustainability analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20emissions" title=" carbon emissions"> carbon emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20refineries" title=" oil refineries"> oil refineries</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse%20gas%20reduction" title=" greenhouse gas reduction"> greenhouse gas reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20performance" title=" environmental performance"> environmental performance</a> </p> <a href="https://publications.waset.org/abstracts/187014/energy-efficiency-and-sustainability-analytics-for-reducing-carbon-emissions-in-oil-refineries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">31</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4789</span> Sampled-Data Model Predictive Tracking Control for Mobile Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wookyong%20Kwon">Wookyong Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangmoon%20Lee"> Sangmoon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=sampled-data%20control" title=" sampled-data control"> sampled-data control</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter%20varying%20systems" title=" linear parameter varying systems"> linear parameter varying systems</a>, <a href="https://publications.waset.org/abstracts/search?q=LPV" title=" LPV"> LPV</a> </p> <a href="https://publications.waset.org/abstracts/71683/sampled-data-model-predictive-tracking-control-for-mobile-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71683.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">309</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">4788</span> Navigating Uncertainties in Project Control: A Predictive Tracking Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byung%20Cheol%20Kim">Byung Cheol Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores a method for the signal-noise separation challenge in project control, focusing on the limitations of traditional deterministic approaches that use single-point performance metrics to predict project outcomes. We detail how traditional methods often overlook future uncertainties, resulting in tracking biases when reliance is placed solely on immediate data without adjustments for predictive accuracy. Our investigation led to the development of the Predictive Tracking Project Control (PTPC) framework, which incorporates network simulation and Bayesian control models to adapt more effectively to project dynamics. The PTPC introduces controlled disturbances to better identify and separate tracking biases from useful predictive signals. We will demonstrate the efficacy of the PTPC with examples, highlighting its potential to enhance real-time project monitoring and decision-making, marking a significant shift towards more accurate project management practices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20tracking" title="predictive tracking">predictive tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20control" title=" project control"> project control</a>, <a href="https://publications.waset.org/abstracts/search?q=signal-noise%20separation" title=" signal-noise separation"> signal-noise separation</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20inference" title=" Bayesian inference"> Bayesian inference</a> </p> <a href="https://publications.waset.org/abstracts/192188/navigating-uncertainties-in-project-control-a-predictive-tracking-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192188.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">18</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">4787</span> A Predictive Analytics Approach to Project Management: Reducing Project Failures in Web and Software Development Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tazeen%20Fatima">Tazeen Fatima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Use of project management in web & software development projects is very significant. It has been observed that even with the application of effective project management, projects usually do not complete their lifecycle and fail. To minimize these failures, key performance indicators have been introduced in previous studies to counter project failures. However, there are always gaps and problems in the KPIs identified. Despite of incessant efforts at technical and managerial levels, projects still fail. There is no substantial approach to identify and avoid these failures in the very beginning of the project lifecycle. In this study, we aim to answer these research problems by analyzing the concept of predictive analytics which is a specialized technology and is very easy to use in this era of computation. Project organizations can use data gathering, compute power, and modern tools to render efficient Predictions. The research aims to identify such a predictive analytics approach. The core objective of the study was to reduce failures and introduce effective implementation of project management principles. Existing predictive analytics methodologies, tools and solution providers were also analyzed. Relevant data was gathered from projects and was analyzed via predictive techniques to make predictions well advance in time to render effective project management in web & software development industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20management" title="project management">project management</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics%20methodology" title=" predictive analytics methodology"> predictive analytics methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20failures" title=" project failures"> project failures</a> </p> <a href="https://publications.waset.org/abstracts/69625/a-predictive-analytics-approach-to-project-management-reducing-project-failures-in-web-and-software-development-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69625.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">347</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">4786</span> Data Mining in Healthcare for Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruzanna%20Muradyan">Ruzanna Muradyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20care" title=" patient care"> patient care</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20medicine" title=" precision medicine"> precision medicine</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20health%20records" title=" electronic health records"> electronic health records</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20prognosis" title=" disease prognosis"> disease prognosis</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20stratification" title=" risk stratification"> risk stratification</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment%20efficacy" title=" treatment efficacy"> treatment efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20profiles" title=" genetic profiles"> genetic profiles</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20health" title=" precision health"> precision health</a> </p> <a href="https://publications.waset.org/abstracts/178634/data-mining-in-healthcare-for-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178634.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">62</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">4785</span> Predictive Analytics for Theory Building</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ho-Won%20Jung">Ho-Won Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Donghun%20Lee"> Donghun Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyung-Jin%20Kim"> Hyung-Jin Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predictive analytics (data analysis) uses a subset of measurements (the features, predictor, or independent variable) to predict another measurement (the outcome, target, or dependent variable) on a single person or unit. It applies empirical methods in statistics, operations research, and machine learning to predict the future, or otherwise unknown events or outcome on a single or person or unit, based on patterns in data. Most analyses of metabolic syndrome are not predictive analytics but statistical explanatory studies that build a proposed model (theory building) and then validate metabolic syndrome predictors hypothesized (theory testing). A proposed theoretical model forms with causal hypotheses that specify how and why certain empirical phenomena occur. Predictive analytics and explanatory modeling have their own territories in analysis. However, predictive analytics can perform vital roles in explanatory studies, i.e., scientific activities such as theory building, theory testing, and relevance assessment. In the context, this study is to demonstrate how to use our predictive analytics to support theory building (i.e., hypothesis generation). For the purpose, this study utilized a big data predictive analytics platform TM based on a co-occurrence graph. The co-occurrence graph is depicted with nodes (e.g., items in a basket) and arcs (direct connections between two nodes), where items in a basket are fully connected. A cluster is a collection of fully connected items, where the specific group of items has co-occurred in several rows in a data set. Clusters can be ranked using importance metrics, such as node size (number of items), frequency, surprise (observed frequency vs. expected), among others. The size of a graph can be represented by the numbers of nodes and arcs. Since the size of a co-occurrence graph does not depend directly on the number of observations (transactions), huge amounts of transactions can be represented and processed efficiently. For a demonstration, a total of 13,254 metabolic syndrome training data is plugged into the analytics platform to generate rules (potential hypotheses). Each observation includes 31 predictors, for example, associated with sociodemographic, habits, and activities. Some are intentionally included to get predictive analytics insights on variable selection such as cancer examination, house type, and vaccination. The platform automatically generates plausible hypotheses (rules) without statistical modeling. Then the rules are validated with an external testing dataset including 4,090 observations. Results as a kind of inductive reasoning show potential hypotheses extracted as a set of association rules. Most statistical models generate just one estimated equation. On the other hand, a set of rules (many estimated equations from a statistical perspective) in this study may imply heterogeneity in a population (i.e., different subpopulations with unique features are aggregated). Next step of theory development, i.e., theory testing, statistically tests whether a proposed theoretical model is a plausible explanation of a phenomenon interested in. If hypotheses generated are tested statistically with several thousand observations, most of the variables will become significant as the p-values approach zero. Thus, theory validation needs statistical methods utilizing a part of observations such as bootstrap resampling with an appropriate sample size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=explanatory%20modeling" title="explanatory modeling">explanatory modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20syndrome" title=" metabolic syndrome"> metabolic syndrome</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=theory%20building" title=" theory building"> theory building</a> </p> <a href="https://publications.waset.org/abstracts/44792/predictive-analytics-for-theory-building" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44792.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">276</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">4784</span> Learning Predictive Models for Efficient Energy Management of Exhibition Hall</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim">Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eunju%20Lee"> Eunju Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title="predictive control">predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management" title=" energy management"> energy management</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=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/59405/learning-predictive-models-for-efficient-energy-management-of-exhibition-hall" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59405.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">4783</span> Model Predictive Control Using Thermal Inputs for Crystal Growth Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a>, <a href="https://publications.waset.org/abstracts/search?q=crystal%20growth" title=" crystal growth"> crystal growth</a> </p> <a href="https://publications.waset.org/abstracts/88644/model-predictive-control-using-thermal-inputs-for-crystal-growth-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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