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models</title> <meta name="description" content="Search results for: predictive models"> <meta name="keywords" content="predictive models"> <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 models"> <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> 7413</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predictive models</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7413</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">7412</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">7411</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">7410</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">7409</span> Agriculture Yield Prediction Using Predictive Analytic Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nagini%20Sabbineni">Nagini Sabbineni</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajini%20T.%20V.%20Kanth"> Rajini T. V. Kanth</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20Kiranmayee"> B. V. Kiranmayee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture%20yield%20growth" title="agriculture yield growth">agriculture yield growth</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture%20yield%20prediction" title=" agriculture yield prediction"> agriculture yield prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=explorative%20data%20analysis" title=" explorative data analysis"> explorative data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20models" title=" regression models"> regression models</a> </p> <a href="https://publications.waset.org/abstracts/54159/agriculture-yield-prediction-using-predictive-analytic-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54159.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">314</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">7408</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">7407</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">7406</span> Online Learning for Modern Business Models: Theoretical Considerations and Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marian%20Sorin%20Ionescu">Marian Sorin Ionescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivia%20Negoita"> Olivia Negoita</a>, <a href="https://publications.waset.org/abstracts/search?q=Cosmin%20Dobrin"> Cosmin Dobrin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models. <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=business%20models" title=" business models"> business models</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20analysis" title=" convex analysis"> convex analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a> </p> <a href="https://publications.waset.org/abstracts/149342/online-learning-for-modern-business-models-theoretical-considerations-and-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149342.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">141</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">7405</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">7404</span> A-Score, Distress Prediction Model with Earning Response during the Financial Crisis: Evidence from Emerging Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Ashraf">Sumaira Ashraf</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabete%20G.S.%20F%C3%A9lix"> Elisabete G.S. Félix</a>, <a href="https://publications.waset.org/abstracts/search?q=Z%C3%A9lia%20Serrasqueiro"> Zélia Serrasqueiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20distress" title="financial distress">financial distress</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20market" title=" emerging market"> emerging market</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20models" title=" prediction models"> prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=Z-Score" title=" Z-Score"> Z-Score</a>, <a href="https://publications.waset.org/abstracts/search?q=logit%20analysis" title=" logit analysis"> logit analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=probit%20model" title=" probit model"> probit model</a> </p> <a href="https://publications.waset.org/abstracts/81316/a-score-distress-prediction-model-with-earning-response-during-the-financial-crisis-evidence-from-emerging-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81316.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">243</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">7403</span> A Predictive Machine Learning Model of the Survival of Female-led and Co-Led Small and Medium Enterprises in the UK</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mais%20Khader">Mais Khader</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingjie%20Wei"> Xingjie Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=company%20survival" title="company survival">company survival</a>, <a href="https://publications.waset.org/abstracts/search?q=entrepreneurship" title=" entrepreneurship"> entrepreneurship</a>, <a href="https://publications.waset.org/abstracts/search?q=females" title=" females"> females</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=SMEs" title=" SMEs"> SMEs</a> </p> <a href="https://publications.waset.org/abstracts/172021/a-predictive-machine-learning-model-of-the-survival-of-female-led-and-co-led-small-and-medium-enterprises-in-the-uk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172021.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">101</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">7402</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">7401</span> Artificial Intelligence Based Predictive Models for Short Term Global Horizontal Irradiation Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kudzanayi%20Chiteka">Kudzanayi Chiteka</a>, <a href="https://publications.waset.org/abstracts/search?q=Wellington%20Makondo"> Wellington Makondo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solar%20energy" title="solar energy">solar energy</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20horizontal%20irradiation" title=" global horizontal irradiation"> global horizontal irradiation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a> </p> <a href="https://publications.waset.org/abstracts/65891/artificial-intelligence-based-predictive-models-for-short-term-global-horizontal-irradiation-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65891.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">7400</span> Currency Exchange Rate Forecasts Using Quantile Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a> </p> <a href="https://publications.waset.org/abstracts/45531/currency-exchange-rate-forecasts-using-quantile-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45531.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">256</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">7399</span> A Predictive Model of Supply and Demand in the State of Jalisco, Mexico</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Gil">M. Gil</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Montalvo"> R. Montalvo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20and%20demand" title=" supply and demand"> supply and demand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mexico" title=" Mexico"> Mexico</a> </p> <a href="https://publications.waset.org/abstracts/123714/a-predictive-model-of-supply-and-demand-in-the-state-of-jalisco-mexico" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123714.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">123</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">7398</span> Predictor Factors in Predictive Model of Soccer Talent Identification among Male Players Aged 14 to 17 Years</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhamad%20Hafiz%20Ismail">Muhamad Hafiz Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20H."> Ahmad H.</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelfianty%20M.%20R."> Nelfianty M. R.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soccer%20talent%20identification" title="soccer talent identification">soccer talent identification</a>, <a href="https://publications.waset.org/abstracts/search?q=fitness%20and%20physical%20test" title=" fitness and physical test"> fitness and physical test</a>, <a href="https://publications.waset.org/abstracts/search?q=soccer%20skills%20test" title=" soccer skills test"> soccer skills test</a>, <a href="https://publications.waset.org/abstracts/search?q=psychological%20test" title=" psychological test"> psychological test</a> </p> <a href="https://publications.waset.org/abstracts/93679/predictor-factors-in-predictive-model-of-soccer-talent-identification-among-male-players-aged-14-to-17-years" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93679.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7397</span> A Deep Learning Approach for the Predictive Quality of Directional Valves in the Hydraulic Final Test</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christian%20Neunzig">Christian Neunzig</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20Fahle"> Simon Fahle</a>, <a href="https://publications.waset.org/abstracts/search?q=J%C3%BCrgen%20Schulz"> Jürgen Schulz</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20M%C3%B6ller"> Matthias Möller</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernd%20Kuhlenk%C3%B6tter"> Bernd Kuhlenkötter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hydraulics" title=" hydraulics"> hydraulics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20quality" title=" predictive quality"> predictive quality</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/143538/a-deep-learning-approach-for-the-predictive-quality-of-directional-valves-in-the-hydraulic-final-test" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143538.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">244</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">7396</span> Predictive Maintenance of Electrical Induction Motors Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Bilal">Muhammad Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Adil%20Ahmed"> Adil Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title="predictive maintenance">predictive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=electrical%20induction%20motors" title=" electrical induction motors"> electrical induction motors</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=temperature%20signal%20methodology" title=" temperature signal methodology"> temperature signal methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20failures" title=" motor failures"> motor failures</a> </p> <a href="https://publications.waset.org/abstracts/167957/predictive-maintenance-of-electrical-induction-motors-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167957.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">117</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">7395</span> Analysis of the Predictive Performance of Value at Risk Estimations in Times of Financial Crisis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Marx">Alexander Marx</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Measuring and mitigating market risk is essential for the stability of enterprises, especially for major banking corporations and investment bank firms. To employ these risk measurement and mitigation processes, the Value at Risk (VaR) is the most commonly used risk metric by practitioners. In the past years, we have seen significant weaknesses in the predictive performance of the VaR in times of financial market crisis. To address this issue, the purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on the stock market indices of the G7 countries (Canada, France, Germany, Italy, Japan, UK, US, Europe). The study employs parametric, non-parametric, and semi-parametric VaR estimation models and is conducted during three different periods which cover the most recent financial market crisis: the overall period (2006–2022), the global financial crisis period (2008–2009), and COVID-19 period (2020–2022). Since the regulatory authorities have introduced and mandated the Conditional Value at Risk (Expected Shortfall) as an additional regulatory risk management metric, the study will analyze and compare both risk metrics on their predictive performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title="value at risk">value at risk</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20market%20risk" title=" financial market risk"> financial market risk</a>, <a href="https://publications.waset.org/abstracts/search?q=banking" title=" banking"> banking</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20risk%20management" title=" quantitative risk management"> quantitative risk management</a> </p> <a href="https://publications.waset.org/abstracts/161900/analysis-of-the-predictive-performance-of-value-at-risk-estimations-in-times-of-financial-crisis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161900.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">95</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">7394</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</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> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</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=traffic%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">7393</span> Predictive Models of Ruin Probability in Retirement Withdrawal Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanjin%20Liu">Yuanjin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ruin%20probability" title="ruin probability">ruin probability</a>, <a href="https://publications.waset.org/abstracts/search?q=retirement%20withdrawal%20strategies" title=" retirement withdrawal strategies"> retirement withdrawal strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20model" title=" optimal model"> optimal model</a> </p> <a href="https://publications.waset.org/abstracts/147438/predictive-models-of-ruin-probability-in-retirement-withdrawal-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147438.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7392</span> Evaluating the Suitability and Performance of Dynamic Modulus Predictive Models for North Dakota’s Asphalt Mixtures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duncan%20Oteki">Duncan Oteki</a>, <a href="https://publications.waset.org/abstracts/search?q=Andebut%20Yeneneh"> Andebut Yeneneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Daba%20Gedafa"> Daba Gedafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabil%20Suleiman"> Nabil Suleiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most agencies lack the equipment required to measure the dynamic modulus (|E*|) of asphalt mixtures, necessitating the need to use predictive models. This study compared measured |E*| values for nine North Dakota asphalt mixes using the original Witczak, modified Witczak, and Hirsch models. The influence of temperature on the |E*| models was investigated, and Pavement ME simulations were conducted using measured |E*| and predictions from the most accurate |E*| model. The results revealed that the original Witczak model yielded the lowest Se/Sy and highest R² values, indicating the lowest bias and highest accuracy, while the poorest overall performance was exhibited by the Hirsch model. Using predicted |E*| as inputs in the Pavement ME generated conservative distress predictions compared to using measured |E*|. The original Witczak model was recommended for predicting |E*| for low-reliability pavements in North Dakota. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asphalt%20mixture" title="asphalt mixture">asphalt mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=binder" title=" binder"> binder</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20modulus" title=" dynamic modulus"> dynamic modulus</a>, <a href="https://publications.waset.org/abstracts/search?q=MEPDG" title=" MEPDG"> MEPDG</a>, <a href="https://publications.waset.org/abstracts/search?q=pavement%20ME" title=" pavement ME"> pavement ME</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/182251/evaluating-the-suitability-and-performance-of-dynamic-modulus-predictive-models-for-north-dakotas-asphalt-mixtures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182251.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">48</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7391</span> Predictive Analytics Algorithms: Mitigating Elementary School Drop Out Rates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bongs%20Lainjo">Bongs Lainjo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focuses on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through the education process. Such mentality is coupled with a tough curriculum that does not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study, the focus is on developing a model that can systematically be implemented by school administrations to prevent possible dropout scenarios. At the elementary level, especially the lower grades, a child's perception of education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models are installed in every educational system with a view to helping prevent an imminent school dropout just before it happens. In a competency-based curriculum that most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduce the rate of dropout. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elementary%20school" title="elementary school">elementary school</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</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=risk%20factors" title=" risk factors"> risk factors</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=dropout%20rates" title=" dropout rates"> dropout rates</a>, <a href="https://publications.waset.org/abstracts/search?q=education%20system" title=" education system"> education system</a>, <a href="https://publications.waset.org/abstracts/search?q=competency-based%20curriculum" title=" competency-based curriculum"> competency-based curriculum</a> </p> <a href="https://publications.waset.org/abstracts/143305/predictive-analytics-algorithms-mitigating-elementary-school-drop-out-rates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143305.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">175</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">7390</span> Estimation of the Acute Toxicity of Halogenated Phenols Using Quantum Chemistry Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadidja%20Bellifa">Khadidja Bellifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Mohamed%20Mekelleche"> Sidi Mohamed Mekelleche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=halogenated%20phenols" title="halogenated phenols">halogenated phenols</a>, <a href="https://publications.waset.org/abstracts/search?q=toxicity%20mechanism" title=" toxicity mechanism"> toxicity mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophobicity" title=" hydrophobicity"> hydrophobicity</a>, <a href="https://publications.waset.org/abstracts/search?q=electrophilicity%20index" title=" electrophilicity index"> electrophilicity index</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20stucture-toxicity%20relationships" title=" quantitative stucture-toxicity relationships"> quantitative stucture-toxicity relationships</a> </p> <a href="https://publications.waset.org/abstracts/45757/estimation-of-the-acute-toxicity-of-halogenated-phenols-using-quantum-chemistry-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45757.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</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">7389</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">7388</span> A Predictive MOC Solver for Water Hammer Waves Distribution in Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Bayle">A. Bayle</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Plourabou%C3%A9"> F. Plouraboué</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water Distribution Network (WDN) still suffers from a lack of knowledge about fast pressure transient events prediction, although the latter may considerably impact their durability. Accidental or planned operating activities indeed give rise to complex pressure interactions and may drastically modified the local pressure value generating leaks and, in rare cases, pipe’s break. In this context, a numerical predictive analysis is conducted to prevent such event and optimize network management. A couple of Python/FORTRAN 90, home-made software, has been developed using Method Of Characteristic (MOC) solving for water-hammer equations. The solver is validated by direct comparison with theoretical and experimental measurement in simple configurations whilst afterward extended to network analysis. The algorithm's most costly steps are designed for parallel computation. A various set of boundary conditions and energetic losses models are considered for the network simulations. The results are analyzed in both real and frequencies domain and provide crucial information on the pressure distribution behavior within the network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energetic%20losses%20models" title="energetic losses models">energetic losses models</a>, <a href="https://publications.waset.org/abstracts/search?q=method%20of%20characteristic" title=" method of characteristic"> method of characteristic</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20predictive%20analysis" title=" numerical predictive analysis"> numerical predictive analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20distribution%20network" title=" water distribution network"> water distribution network</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20hammer" title=" water hammer"> water hammer</a> </p> <a href="https://publications.waset.org/abstracts/141429/a-predictive-moc-solver-for-water-hammer-waves-distribution-in-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141429.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">232</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">7387</span> Lessons Learned from Interlaboratory Noise Modelling in Scope of Environmental Impact Assessments in Slovenia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Cencek">S. Cencek</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Markun"> A. Markun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Noise assessment methods are regularly used in scope of Environmental Impact Assessments for planned projects to assess (predict) the expected noise emissions of these projects. Different noise assessment methods could be used. In recent years, we had an opportunity to collaborate in some noise assessment procedures where noise assessments of different laboratories have been performed simultaneously. We identified some significant differences in noise assessment results between laboratories in Slovenia. We estimate that despite good input Georeferenced Data to set up acoustic model exists in Slovenia; there is no clear consensus on methods for predictive noise methods for planned projects. We analyzed input data, methods and results of predictive noise methods for two planned industrial projects, both were done independently by two laboratories. We also analyzed the data, methods and results of two interlaboratory collaborative noise models for two existing noise sources (railway and motorway). In cases of predictive noise modelling, the validations of acoustic models were performed by noise measurements of surrounding existing noise sources, but in varying durations. The acoustic characteristics of existing buildings were also not described identically. The planned noise sources were described and digitized differently. Differences in noise assessment results between different laboratories have ranged up to 10 dBA, which considerably exceeds the acceptable uncertainty ranged between 3 to 6 dBA. Contrary to predictive noise modelling, in cases of collaborative noise modelling for two existing noise sources the possibility to perform the validation noise measurements of existing noise sources greatly increased the comparability of noise modelling results. In both cases of collaborative noise modelling for existing motorway and railway, the modelling results of different laboratories were comparable. Differences in noise modeling results between different laboratories were below 5 dBA, which was acceptable uncertainty set up by interlaboratory noise modelling organizer. The lessons learned from the study were: 1) Predictive noise calculation using formulae from International standard SIST ISO 9613-2: 1997 is not an appropriate method to predict noise emissions of planned projects since due to complexity of procedure they are not used strictly, 2) The noise measurements are important tools to minimize noise assessment errors of planned projects and should be in cases of predictive noise modelling performed at least for validation of acoustic model, 3) National guidelines should be made on the appropriate data, methods, noise source digitalization, validation of acoustic model etc. in order to unify the predictive noise models and their results in scope of Environmental Impact Assessments for planned projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20noise%20assessment" title="environmental noise assessment">environmental noise assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20noise%20modelling" title=" predictive noise modelling"> predictive noise modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20planning" title=" spatial planning"> spatial planning</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20measurements" title=" noise measurements"> noise measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=national%20guidelines" title=" national guidelines"> national guidelines</a> </p> <a href="https://publications.waset.org/abstracts/71096/lessons-learned-from-interlaboratory-noise-modelling-in-scope-of-environmental-impact-assessments-in-slovenia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71096.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">234</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">7386</span> Transfer Function Model-Based Predictive Control for Nuclear Core Power Control in PUSPATI TRIGA Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Sabri%20Minhat">Mohd Sabri Minhat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Adilla%20Mohd%20Subha"> Nurul Adilla Mohd Subha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 1MWth PUSPATI TRIGA Reactor (RTP) in Malaysia Nuclear Agency has been operating more than 35 years. The existing core power control is using conventional controller known as Feedback Control Algorithm (FCA). It is technically challenging to keep the core power output always stable and operating within acceptable error bands for the safety demand of the RTP. Currently, the system could be considered unsatisfactory with power tracking performance, yet there is still significant room for improvement. Hence, a new design core power control is very important to improve the current performance in tracking and regulating reactor power by controlling the movement of control rods that suit the demand of highly sensitive of nuclear reactor power control. In this paper, the proposed Model Predictive Control (MPC) law was applied to control the core power. The model for core power control was based on mathematical models of the reactor core, MPC, and control rods selection algorithm. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The proposed MPC was presented in a transfer function model of the reactor core according to perturbations theory. The transfer function model-based predictive control (TFMPC) was developed to design the core power control with predictions based on a T-filter towards the real-time implementation of MPC on hardware. This paper introduces the sensitivity functions for TFMPC feedback loop to reduce the impact on the input actuation signal and demonstrates the behaviour of TFMPC in term of disturbance and noise rejections. The comparisons of both tracking and regulating performance between the conventional controller and TFMPC were made using MATLAB and analysed. In conclusion, the proposed TFMPC has satisfactory performance in tracking and regulating core power for controlling nuclear reactor with high reliability and safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=core%20power%20control" title="core power control">core power 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=PUSPATI%20TRIGA%20reactor" title=" PUSPATI TRIGA reactor"> PUSPATI TRIGA reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=TFMPC" title=" TFMPC"> TFMPC</a> </p> <a href="https://publications.waset.org/abstracts/93326/transfer-function-model-based-predictive-control-for-nuclear-core-power-control-in-puspati-triga-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93326.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">241</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">7385</span> Aggregate Angularity on the Permanent Deformation Zones of Hot Mix Asphalt </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lee%20P.%20Leon">Lee P. Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Charles"> Raymond Charles</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aggregate%20angularity" title="aggregate angularity">aggregate angularity</a>, <a href="https://publications.waset.org/abstracts/search?q=asphalt%20concrete" title=" asphalt concrete"> asphalt concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=permanent%20deformation" title=" permanent deformation"> permanent deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=rutting%20prediction" title=" rutting prediction "> rutting prediction </a> </p> <a href="https://publications.waset.org/abstracts/27233/aggregate-angularity-on-the-permanent-deformation-zones-of-hot-mix-asphalt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27233.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">405</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">7384</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> 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