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Martello, Rosanna. "Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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Lithium-ion batteries are the ideal choice for electric and hybrid vehicles, but the high cost and the relatively short life represent an open issue for automotive industries. For this reason, the estimation of battery Remaining Useful Life (RUL) and the State of Health (SoH) are primary goals in the automotive sector. Cloud computing provides all the resources necessary to store, process and analyze all sensor data coming from connected vehicles in order to develop Predictive Maintenance tasks. This project describes the work done during my internship at FEV Italia s.r.l. The aims were designing an architecture for managing the data coming from a vehicle fleet and developing algorithms able to predict the SoH and the RUL of Lithium-ion batteries. The designed architecture is based on three Amazon Web Services: Amazon Elastic Compute Cloud, Amazon Simple Storage Service and Amazon Relational Database Service. After data processing and the feature extraction, the RUL and SoH estimations are performed by training two Neural Networks.
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Popara, Nikola. "Využití umělé inteligence k monitorování stavu obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444960.
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This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Mohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a Long Short-Term Memory (LSTM) which is a Recurrent Neural Network is designed to predict the Remaining Useful Life (RUL) of Turbofan Engines. The dataset is taken from NASA data repository. Finally, the performance obtained by RNN is compared to the best Machine Learning algorithm for the dataset.
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Jin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.
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Daher, Alaa. "Diagnostic et pronostic des défauts pour la maintenance préventive et prédictive. Application à une colonne de distillation." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR090/document.
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Le procédé de distillation est largement utilisé dans de nombreuses applications telles que la production pétrochimique, le traitement du gaz naturel, les raffineries de pétrole, etc. Généralement, la maintenance des réacteurs chimiques est très coûteuse et perturbe la production pendant de longues périodes. Tous ces facteurs démontrent réellement la nécessité de stratégies efficaces de diagnostic et de pronostic des défauts pour pouvoir réduire et éviter le plus grand nombre de ces problèmes catastrophiques. La première partie de nos travaux vise à proposer une méthode de diagnostic fiable pouvant être utilisée dans le régime permanent d’une procédure non linéaire. De plus, nous proposons une procédure modifiée de la méthode MFCM permettant de calculer la variation en pourcentage entre deux classes. L’utilisation de MFCM a pour objectif de réduire le temps de calcul et d’accroître les performances du classifieur. Les résultats de la méthode proposée confirment la capacité de classifier entre les différentes classes de défaillances considérées. Le calcul de la durée de vie du système est extrêmement important pour éviter les pannes catastrophiques. Notre deuxième objectif est de proposer une méthode fiable de pronostic permettant d’estimer le chemin de dégradation d’une colonne de distillation et de calculer le pourcentage de durée de vie de ce système. Le travail présente une approche basée sur le système d’inférence neuro-fuzzy adaptatif (ANFIS) combiné avec (FCM) pour prédire la trajectoire future et calculer le pourcentage de durée de vie du système. Les résultats obtenus démontrent la validité de la technique proposée pour atteindre les objectifs requis avec une précision de haut niveau. Pour améliorer les performances d’ANFIS, nous proposons la distribution de Parzen comme nouvelle fonction d’appartenance de l’algorithme ANFIS. Les résultats ont démontré l’importance de la technique proposée car elle s’est avérée efficace pour réduire le temps de calcul. En outre, la distribution de Parzen présentait la plus petite erreur quadratique moyenne (RMSE). La dernière partie de cette thèse se concentrait sur la proposition d’un nouvel algorithme pouvant être appliqué pour obtenir un système de surveillance en temps réel s’appuyant sur la prédiction de défauts ; cela signifie que cette méthode permet de prédire l’état futur du système, puis de diagnostiquer quelle est la source d’erreur probable. Elle permet d’évaluer la dégradation d’une colonne de distillation et de diagnostiquer par la suite les défauts ou accidents pouvant survenir à la suite de la dégradation estimée. Cette nouvelle approche combine les avantages d’ANFIS à ceux de RNA permettant d’atteindre un haut niveau de précisionThe distillation process is largely used in many applications such a petrochemical production, natural gas processing, and petroleum refineries, etc. Usually, maintenance of the chemical reactors is very costly and it disrupts production for long periods of time. All these factors really demonstrate the fundamental need for effective fault diagnosis and prognostic strategies that they are able to reduce and avoid the greatest number of thes problems and disasters. The first part of our work aims to propose a reliable diagnostic method that can be used in the steady-state regime of a nonlinear procedure. Moreover, we propose a modified procedure of the fuzzy c-means clustering method (MFCM) where MFCM calculates the percentage variation between the two clustered classes. The purpose of using MFCM is to reduce the computing time and increase the performance of the classifier. The results of the proposed method confirm the ability to classify between normal mode and eight abnormal modes of faults. Our second goal aims to propose a prognosis reliable method used to estimate the degradation path of a distillation column and calculate the lifetime percentage of this system. The work presents an approach based on adaptive neuro-fuzzy inference system (ANFIS) combined with (FCM) to predict the future path and calculate the lifetime percentage of the system. The results obtained demonstrate the validity of the proposed technique to achieve the needed objectives with a high-level accuracy. To improve ANFIS performance we propose Parzen windows distribution as a new membership function for ANFIS algorithm. Results demonstrated the importance of the proposed technique since it proved to be highly successful in terms of reducing the time consumed. Additionally, Parzen windows had the smallest Root Mean Square Error (RMSE). The last part of this thesis was focusing on the proposing of new algorithm which can be applied to obtain real-time monitoring system which relies on the fault production module to reach the diagnosis module in contrast to the previous strategies ; this means this method predict the future state of the system then diagnosis what is the probable fault source. This proposed method has proven to be a reliable process that can evaluate the degradation of a distillation column and subsequently diagnose the possible faults or accidents that can emerge as a result of the estimated degradation. This new approach combines the benefits of ANFIS with the benefits of feedforward ANN. The results were demonstrated that the technique achieved with a high level of accuracy, the objective of prediction and diagnosis especially when applied to the data obtained from automated distillation process in the chemical industry
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Sanzani, Matteo. "La costruzione di un indicatore di salute per la manutenzione predittiva attraverso la programmazione genetica mono-obiettivo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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La manutenzione predittiva, o Prognostic Health Management (PHM), è l’innovativa politica manutentiva basata monitoraggio continuo dello stato di salute dei componenti meccanici, grazie all’acquisizione dei dati tramite sensori applicati sui componenti stessi. Questi dati non sono facilmente analizzabili direttamente: è difatti necessaria un’attività di processing, volta ad estrarre delle caratteristiche significative e sintetiche del segnale, chiamate in letteratura features. Tipicamente, alla fase di estrazione delle features, segue una fase di selezione delle features e/o costruzione di un indicatore di salute, al fine di ridurre la dimensionalità dei dati ed aumentare la performance degli algoritmi futuri che riceveranno in input tali features per la diagnostica e/o prognostica.
Questa tesi si focalizza proprio sulla costruzione di un indicatore di salute (HI) tramite programmazione genetica mono-obiettivo (algoritmo euristico basato sulla teoria della selezione naturale di Darwin, assai promettente rispetto alle tecniche tradizionali di selezione di features) a partire da un insieme di features estratte manualmente nel dominio del tempo. I segnali utilizzati provengono da un prototipo costruito all’interno del Laboratorio dell’Università di Bologna. In particolare, è stato analizzato il comportamento della cinghia, che rappresenta uno dei componenti chiave del prototipo, dalla messa in funzione in stato sano fino alla rottura (run-to-failure test). Il modello sarà costruito in ambiente MATLAB, attraverso lo sfruttamento del Genetic Programming Toolbox presente nel software stesso.
Infine, per valutare il risultato ottenuto, l’HI costruito è stato dato in pasto ad un algoritmo di fitting e di previsione della vita utile residua (RUL), allo scopo di valutare l’errore medio di previsione rispetto a quanto realmente accaduto durante il test. I risultati ottenuti sembrano positivi, ma sono necessari sviluppi futuri per valutare la robustezza dell’indicatore.
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.
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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests.
The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets.
Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction.
The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the
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improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models.
A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.Applied Science University (ASU) of Jordan
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.
Full textAbstract:
Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
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Lowy, Elliott. "The evolution of the golden rule /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9017.
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Balla, Chaitanya Kumar. "Prediction of Remaining Service Life of Pavements." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279316853.
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Campbell, William Jarrett. "Model predictive run-to-run control of chemical mechanical planarization /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
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Sasaki, Sho. "Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings." Kyoto University, 2017. http://hdl.handle.net/2433/226778.
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Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
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De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la forme de longues séquences temporelles constituées d'une succession d'événements. Le développement d'outils d'analyse automatique de ces séquences permettra d'identifier des associations significatives entre événements dans un but de prédiction d'événement signant l'apparition de défaillance grave. Cette thèse aborde la problématique de la fouille de séquences temporelles pour la prédiction d'événements rares et s'inscrit dans un contexte global de développement d'outils d'aide à la décision. Nous visons à étudier et développer diverses méthodes pour découvrir les règles d'association entre événements d'une part et à construire des modèles de classification d'autre part. Ces règles et/ou ces classifieurs peuvent ensuite être exploités pour analyser en ligne un flux d'événements entrants dans le but de prédire l'apparition d'événements cibles correspondant à des défaillances. Deux méthodologies sont considérées dans ce travail de thèse: La première est basée sur la recherche des règles d'association, qui est une approche temporelle et une approche à base de reconnaissance de formes. Les principaux défis auxquels est confronté ce travail sont principalement liés à la rareté des événements cibles à prédire, la redondance importante de certains événements et à la présence très fréquente de "bursts". Les résultats obtenus sur des données réelles recueillies par des capteurs embarqués sur une flotte de trains commerciaux permettent de mettre en évidence l'efficacité des approches proposéesIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
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Aljandal, Waleed A. "Itemset size-sensitive interestingness measures for association rule mining and link prediction." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1119.
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Wicker, Jerome. "Crystallization properties of molecular materials : prediction and rule extraction by machine learning." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:34beef4e-e499-4248-8fa6-7e8d8344f02c.
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Crystallization is an increasingly important process in a variety of applications from drug development to single crystal X-ray diffraction structure determination. However, while there is a good deal of research into prediction of molecular crystal structure, the factors that cause a molecule to be crystallizable have so far remained poorly understood. The aim of this project was to answer the seemingly straightforward question: can we predict how easily a molecule will crystallize? The Cambridge Structural Database contains almost a million examples of materials from the scientific literature that have crystallized. Models for the prediction of crystallization propensity of organic molecular materials were developed by training machine learning algorithms on carefully curated sets of molecules which are either observed or not observed to crystallize, extracted from a database of commercially available molecules. The models were validated computationally and experimentally, while feature extraction methods and high resolution powder diffraction studies were used to understand the molecular and structural features that determine the ease of crystallization. This led to the development of a new molecular descriptor which encodes information about the conformational flexibility of a molecule. The best models gave error rates of less than 5% for both cross-validation data and previously-unseen test data, demonstrating that crystallization propensity can be predicted with a high degree of accuracy. Molecular size, flexibility and nitrogen atom environments were found to be the most influential factors in determining the ease of crystallization, while microstructural features determined by powder diffraction showed almost no correlation with the model predictions. Further predictions on co-crystals show scope for extending the methodology to other relevant applications.
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Alansary, Yasser Abdo. "PREDICTION OF FATIGUE CRACK NEAR-THRESHOLD CENSORED REGRESSIONS WITH RUN-OUT DATA." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1414502102.
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Abu, Mansour Hussein Y. "Rule pruning and prediction methods for associative classification approach in data mining." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17476/.
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Recent studies in data mining revealed that Associative Classification (AC) data mining approach builds competitive classification classifiers with reference to accuracy when compared to classic classification approaches including decision tree and rule based. Nevertheless, AC algorithms suffer from a number of known defects as the generation of large number of rules which makes it hard for end-user to maintain and understand its outcome and the possible over-fitting issue caused by the confidence-based rule evaluation used by AC. This thesis attempts to deal with above problems by presenting five new pruning methods, prediction method and employs them in an AC algorithm that significantly reduces the number of generated rules without having large impact on the prediction rate of the classifiers. Particularly, the new pruning methods that discard redundant and insignificant rules during building the classifier are employed. These pruning procedures remove any rule that either has no training case coverage or covers a training case without the requirement of class similarity between the rule class and that of the training case. This enables large coverage for each rule and reduces overfitting as well as construct accurate and moderated size classifiers. Beside, a novel class assignment method based on multiple rules is proposed which employs group of rule to make the prediction decision. The integration of both the pruning and prediction procedures has been used to enhanced a known AC algorithm called Multiple-class Classification based on Association Rules (MCAR) and resulted in competent model in regard to accuracy and classifier size called " Multiple-class Classification based on Association Rules 2(MCAR2)". Experimental results against different datasets from the UCI data repository showed that the predictive power of the resulting classifiers in MCAR2 slightly increase and the resulting classifier size gets reduced comparing with other AC algorithms such as Multiple-class Classification based on Association Rules (MCAR).
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Islam, Raihan Ul. "Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66415.
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Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.
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Gao, Xiaoxu. "Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650.
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En kunskapsgraf lagrar information från webben i form av relationer mellan olika entiteter. En kunskapsgrafs kvalité bestäms av hur komplett den är och dess noggrannhet. Dessvärre har många nuvarande kunskapsgrafer brister i form av saknad fakta och inkorrekt information. Nuvarande lösningar av länkförutsägelser mellan entiteter har problem med skalbarhet och hög arbetskostnad. Denna uppsats föreslår ett deklarativt regelbaserat probabilistiskt ramverk för att utföra länkförutsägelse. Systemet involverar en regelutvinnande modell till ett “hinge-loss Markov random fields” för att föreslå länkar. Vidare utvecklades tre strategier för regeloptimering för att förbättra reglernas kvalité. Jämfört med tidigare lösningar så bidrar detta arbete till att drastiskt reducera arbetskostnader och en mer spårbar modell. Varje metod har utvärderas med precision och F-värde på NELL och Freebase15k. Det visar sig att strategin för regeloptimering presterade bäst. MAP-uppskattningen för den bästa modellen på NELL är 0.754, vilket är bättre än en nuvarande spjutspetsteknologi graphical model(0.306). F-värdet för den bästa modellen på Freebase15k är 0.709.The knowledge graph stores factual information from the web in form of relationships between entities. The quality of a knowledge graph is determined by its completeness and accuracy. However, most current knowledge graphs often miss facts or have incorrect information. Current link prediction solutions have problems of scalability and high labor costs. This thesis proposed a declarative rule-based probabilistic framework to perform link prediction. The system incorporates a rule-mining model into a hingeloss Markov random fields to infer links. Moreover, three rule optimization strategies were developed to improve the quality of rules. Compared with previous solutions, this work dramatically reduces manual costs and provides a more tractable model. Each proposed method has been evaluated with Average Precision or F-score on NELL and Freebase15k. It turns out that the rule optimization strategy performs the best. The MAP of the best model on NELL is 0.754, better than a state-of-the-art graphical model (0.306). The F-score of the best model on Freebase15k is 0.709.
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MacDonald, Calum Angus. "The development of an objective methodology for the prediction of helicopter pilot workload." Thesis, Glasgow Caledonian University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340607.
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Conroy, Sean F. "Nonproliferation Regime Compliance: Prediction and Measure Using UNSCR 1540." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2308.
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This dissertation investigates factors that predict compliance with international regimes, specifically the Non-Proliferation Regime. Generally accepted in international relations literature, is Krasner’s (1983) definition that regimes are “sets of implicit or explicit principles, norms, rules, and decision-making procedures around which actor expectations converge in a given [issue] area of international relations.” Using institutionalization as a framework, I hypothesize that compliance is a function of the respect for which a nation has for the rule of law. I investigate the NP regime through the lens of United Nations Security Council Resolution 1540, a mandate for member nations to enact domestic legislation criminalizing the proliferation of Weapons of Mass Destruction. Using NP regime compliance and implementation of UNSCR 1540’s mandates as dependent variables, I test the hypotheses with the following independent variables: rule of law, political competition, and regional compliance. I also present qualitative case studies on Argentina, South Africa, and Malaysia. The quantitative results of these analyses indicated a strong relationship between rule of law and regional compliance and a nation’s compliance with the overall NP regime and implementation of UNSCR 1540. These results indicate a nation will institutionalize the NP norms, and comply with the specifics of implementation. The results of in-depth analysis of Argentina, South Africa, and Malaysia showed that predicting an individual nation’s compliance is more complex than descriptions of government capacity or geography. Argentina and South Africa, expected by the hypotheses to exhibit low to medium compliance and implementation, scored high and well above their region for both measures. Malaysia, expected to score high in compliance, scored low. Findings thus reveal that rule of law is probably less influential on individual cases and regional compliance and cooperation better predictors of a nation’s compliance with a security regime.
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Solari, Lely, Alonso Soto, and der Stuyft Patrick Van. "Development of a clinical prediction rule for the diagnosis of pleural tuberculosis in Peru." Elsevier B.V, 2018. http://hdl.handle.net/10757/623065.
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Objectives: To develop a clinical prediction rule (CPR) for the diagnosis of pleural tuberculosis (PT) in patients with pleural exudates in Peru. Methods: Clinical and laboratory information was collected from patients with exudative pleural effusion attending two reference hospitals in Lima, Peru. Predictive findings associated with PT in a multiple logistic regression model were used to develop the CPR. A definite diagnosis of PT was based on a composite reference standard including bacteriological and/or histological analysis of pleural fluid and pleural biopsy specimens. Results: A total of 238 patients were included in the analysis, of whom 176 had PT. Age, sex, previous contact with a TB patient, presence of lymphadenopathy, and pleural adenosine deaminase (ADA) levels were found to be independently associated with PT. These predictive findings were used to construct a CPR, for which the area under the receiver operating characteristics curve (AUC) was 0.92. The single best cut-off point was a score of ≥60 points, which had a sensitivity of 88%, specificity of 92%, a positive likelihood ratio of 10.9, and a negative likelihood ratio of 0.13. Conclusions: The CPR is accurate for the diagnosis of PT and could be useful for treatment initiation while avoiding pleural biopsy. A prospective evaluation is needed before its implementation in different settings.
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Grigor, Emma. "A Prediction Rule to Screen Patients with Moderate-To-Severe Obstructive Sleep Apnea." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38023.
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Introduction: Obstructive sleep apnea (OSA) is a common breathing disorder with numerous health consequences, including greater risk of complications perioperatively. Undiagnosed OSA is known to place surgical patients at a higher risk of serious adverse events, including stroke and death. Polysomnography (PSG) assessment is the current gold standard test for diagnosing OSA. However, due to the significant time commitment and cost associated with PSG, a substantial number of OSA patients go undiagnosed before the perioperative period. Although the STOP-Bang questionnaire screening tool is currently used to help detect OSA patients, the low specificity to screen people without the disease is considered a major limitation. There is a clear need to develop a quick and effective prediction rule with higher overall accuracy to help streamline OSA diagnosis. Tracheal breathing sound analysis in awake patients at the bedside has shown potential to screen OSA patients with higher specificity compared to the STOP-Bang questionnaire. To date, no screening tools exist to detect OSA patients that combine the results of breathing sound analysis and STOP-Bang.
Objectives: The present study aimed to develop a prediction rule, using both breathing sound analysis and variables in the STOP-Bang questionnaire, to better streamline the diagnosis of OSA.
Methods: This prospective cohort study recruited patients referred for PSG at the Ottawa Hospital Sleep Centre from November 2016 to May 2017. The study conduct was approved by the Ottawa Health Science Network Research Ethics Board (#20160494-01H). After obtaining informed consent, anthropomorphic, breathing sound recordings, and STOP-Bang questionnaire data was collected from over 400 consenting patients. All patients that met the eligibility criteria were included. The breathing sound analysis and STOP-Bang results were utilized to design a prediction rule using logistic regression. Sensitivity, specificity, and likelihood ratio were used to compare the diagnostic performance of the final model.
Results: Of the 439 consenting study participants, 280 study participants data were eligible for inclusion in the logistic regression analysis. Physician sleep specialists diagnosed 114 participants (41%) with moderate-to-severe OSA and 166 participants (59%) with normal-to-mild OSA. At a predicted probability of moderate-to-severe OSA greater than or equal to 0.5, breathing sound analysis had a similar sensitivity of 75.9 (95%CI; 65.4, 82.0) and higher specificity of 74.5% (95%CI; 68.5, 82.0) when compared to STOP-Bang with a sensitivity and specificity of 68.4% (95%CI; 58.9, 76.6) and 63.2% (95%CI: 55.0, 70.1), respectively. The sensitivity and specificity for the Safe-OSA rule, obtained by combining breathing sound analysis and STOP-Bang variables, were determined to be 75.4% (95%CI; 65.4, 82.0) and 74.5% (95%CI; 68.5, 82.0), respectively. A sensitivity analysis using a likelihood ratio test showed that breathing sound analysis contributed significantly to the performance of the Safe-OSA rule. The Safe-OSA rule was determined to be reasonably discriminative and well calibrated. The five-fold cross-validation showed similar results for the final model in the derivation and testing subsamples, which provides support for the internal validity of the Safe-OSA rule in our study population.
Conclusion: The present study lends further support for the future testing of tracheal breathing sound analysis as a potential method to screen for moderate-to-severe OSA to help streamline patient care in the perioperative setting.
Trial registration: ClinicalTrials.gov identifier NCT02987283.
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Kao, Hung-An. "Quality Prediction Modeling for Multistage Manufacturing using Classification and Association Rule Mining Techniques." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535382878246765.
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Variankaval, Narayan. "Structure and thermodynamics of associating solutions : prediction of phase equilibria." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8304.
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Mclennan, Jacqueline. "Derivation and validation of a clinical prediction rule to predict the likelihood of massive transfusion in military major trauma." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/derivation-and-validation-of-a-clinical-prediction-rule-to-predict-the-likelihood-of-massive-transfusion-in-military-major-trauma(ce3c366d-8679-4d1f-a205-17304460dc66).html.
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Introduction: Coagulopathy in trauma increases the likelihood of death. Various approaches to correcting this coagulopathy are being investigated but the decision to use them is often haphazard. The guidelines on their use are limited. This project aims to produce a decision rule to rule in the need for massive transfusion in military trauma, and therefore to rule in the use of methods for treating the coagulopathy of trauma.Methods: A Delphi Study was performed to ascertain which variables a panel of experts felt were most predictive of the need for massive transfusion. This encompassed experts for the fields of emergency medicine, critical care, trauma surgery, pre-hospital care and haematology. Data from the British Defence Joint Theatre Trauma Registry, from January 2007 –July 2010 were obtained and divided into two groups to provide a derivation and validation dataset. Blood timings for people receiving 5 or more units were obtained from clinical records. Regression analysis of potential predictive factors either indicated by the Delphi panel or by literature review were analysed to confirm their value in prediction of Massive Blood Transfusion. A range of clinical prediction rules were produced using parameters deemed appropriate by the Delphi panel and shown to be predictive through regression analysis. Three of these models were then tested in the validation dataset.Results: Ethical approval has been found not to be required following decisions by the relevant military and civilian ethical boards. The Delphi panel was conducted over 3 rounds with a panel of 33 members. Response rates of 94%, 80% and 77% were achieved in rounds 1, 2, and 3 respectively. 195 statements were produced; agreement was achieved at the 80% level in 97 (49.7%) of statements.Regression analysis produced multiple factors that were highly predictive of Massive Blood Transfusion. These were formulated into 22 potential rules combining evidence of injury, clinical observations and pre-hospital care received to produce rules with high sensitivity and specificity. Overall 3 rules were deemed to provide the best balance of sensitivity and specificity, while remaining clinically valid. These were then validated, in a second dataset. The simplest of these rules has a sensitivity of 83.3% and a specificity of 85.5% with an AUROC of 0.907 in the derivation dataset. In the validation dataset sensitivity improved to 87.65% with a specificity of 80.45% with an AUROC curve of 0.91.Discussion: A clinical decision tool which ruled in the use of a massive transfusion protocol allowing early and aggressive resuscitation, and early provision of blood products, would result in better care for severely injured military trauma patients. Although several prediction models are available, they all require either weighted parameters or blood tests so limiting their utility. Further, sensitivity and specificity is poor. This project, through the use of expert opinion, and the production of a validated decision rule, has provided such a tool with improved sensitivity over the currently available prediction models. By using consensus from a Delphi panel, it is hoped that any tool will be acceptable to clinicians therefore improving quality of care. This rule now needs to be assessed prospectively for validity, ease of use and clinical acceptance.
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Lee, Hubert. "Clinical Prediction of Symptomatic Vasospasm in Aneurysmal Subarachnoid Hemorrhage." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35673.
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Objective: This study aims to derive a clinically-applicable decision rule to predict the risk of symptomatic vasospasm, a neurological deficit primarily due to abnormal narrowing of cerebral arteries supplying an attributable territory, in aneurysmal subarachnoid hemorrhage (SAH).
Methods: SAH patients presenting from 2002 to 2011 were analyzed using logistic regression and recursive partitioning to identify clinical, radiological, and laboratory features that predict the occurrence of symptomatic vasospasm.
Results: The incidence of symptomatic vasospasm was 21.0%. On multivariate logistic regression analysis, significant predictors of symptomatic vasospasm included age 40-59 years, high Modified Fisher Grade (Grades 3 and 4), and anterior circulation aneurysms.
Conclusion: Development of symptomatic vasospasm can be reliably predicted using a clinical decision rule created by logistic regression. It exhibits increased accuracy over the Modified Fisher Grade alone and may serve as a useful clinical tool to individualize vasospasm risk once prospectively validated in other neurosurgical centres.
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Moseley, Charles Warren. "A Timescale Estimating Model for Rule-Based Systems." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc332089/.
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The purpose of this study was to explore the subject of timescale estimating for rule-based systems. A model for estimating the timescale necessary to build rule-based systems was built and then tested in a controlled environment.
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Rodger, Marc. "A study to develop and validate a clinical prediction rule to exclude pulmonary embolism." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0019/MQ48177.pdf.
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Solari, L., der Stuyft P. Van, and Alonso Soto. "Development of a clinical prediction rule for tuberculous meningitis in adults in Lima, Peru." Blackwell Publishing Ltd, 2018. http://hdl.handle.net/10757/624693.
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.Objectives: Diagnosis of tuberculous meningitis (TM) is a challenge in countries with a high burden of the disease and constrained resources and clinical prediction rules (CPRs) could be of assistance. We aimed at developing a CPR for diagnosis of TM in a Latin American setting with high tuberculosis incidence and a concentrated HIV epidemic. Methods: We enrolled adult patients with clinical suspicion of TM attending two hospitals in Lima, Peru. We obtained information on potential anamnestic, clinical and laboratory predictive findings that are easy to collect and promptly available. We independently diagnosed TM according to a composite reference standard that included a series of microbiological tests. We performed bivariate analysis and constructed a logistic regression model to select the predictive findings associated with TM. With the selected predictors included in the model, we developed a score-based CPR. We assessed its internal validity and diagnostic performance. Results: Of 155 analysed patients, 59 (38%) had TM. The CPR we derived includes three predictors: cough for 14 days or more, 10–500 cells in CSF and adenosine deaminase ≥ 6 U/l in CSF. It classifies patients into high-, moderate- or low-score groups and has an overall area under the ROC curve of 0.87. 59% of patients were assigned to either the high- or the low-score group, permitting prompt decision-making. In patients in the high-score group, it attains a positive likelihood ratio for TM of 10.6 and in patients with low scores, a negative likelihood ratio of 0.10. Bootstrap analysis indicated high internal validity. Conclusion: This CPR could support decision-making in patients with clinical suspicion of TM. External validation and further assessment of its clinical impact are necessary before application in other settings.
Revisión por pares
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Prakash, Atma. "Prediction of NOX emissions for an RQL combustor using a stirred reactor modelling approach." Thesis, Cranfield University, 2015. http://dspace.lib.cranfield.ac.uk/handle/1826/10010.
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In an effort to reduce NOX emissions both in the landing and take-off (LTO) cycle as well as in cruise, significant research has been conducted on novel aero-engine low emissions combustor design concepts. Preliminary combustor design and emissions prediction software tools are becoming increasingly important during the conceptual design phase of aero-engine combustors. They allow a large number of designs to be explored, in a relatively short amount of time, thereby identifying the most promising designs to consider for further development. There are three methods for NOX emission prediction; correlations, stirred reactor models and CFD models. Correlation methods are derived from experimental results and are therefore only applicable for combustors for which data is available. The stirred reactor modelling approach provides a reasonably good compromise with respect to computational time and robustness relative to correlation and CFD based methods. The stirred reactor method assumes finite rate chemistry inside the combustor using simplified chemical kinetic models. The basic concept of the reactor-based method is to split the combustor into a number of reactors (perfectly or partially stirred) to compute the overall emissions. The primary objective of this doctoral research was to assess the suitability and limitations of the stirred reactor modelling approach to predict NOX emissions of a Rich-Burn Quick-Quench and Lean-Burn (RQL) combustor concept. The geometry of the RQL combustor and the model constraints were assumed from a NASA test rig experiment. The stirred reactor emission prediction model developed was verified using this test data. The results suggest that, based on the modelling assumptions made, the stirred reactor modelling approach is able to capture the trends of emissions (with changing boundary conditions) even though there are discrepancies in the absolute values. This suggests that the stirred reactor model is a useful tool during the preliminary design phase to quantify the impact of changes in boundary conditions/design parameters on changes in NOX emissions.
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Tran, Diem. "Clinical Prediction Rule for the Development of New Onset Postoperative Atrial Fibrillation After Cardiac Surgery." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24400.
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This project set out to derive a prediction rule based on preoperative clinical variables to identify patients with high risk of developing atrial fibrillation following cardiac surgery. Methods: Prospectively collected data from a perioperative database was corroborated with chart review to identify eligible patients who had non-emergent surgery in 2010. Details on 28 preoperative variables were collected and significant predictors (p<0.2) were inserted into multivariable logistic regression and recursive partitioning. Results: 305 (30.5%) of 999 patients developed new onset postoperative atrial fibrillation. Eleven variables were significantly associated with atrial fibrillation, however, both final models included only three: left atrial dilatation, mitral valve disease and age. Bootstrapping with 5000 samples confirmed that both final models provide consistent predictions. Coefficients from the logistic regression model were converted into a simple seven point predictive score. Conclusions: This simple risk score can identify patients at higher risk of developing atrial fibrillation after cardiac surgery.
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Sukhadia, Tejas. "Prediction of phase equilibria in solutions : an associative reformulation of thermodynamic theories of solutions." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/11886.
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Alsadoon, Abdulaziz. "Clinical Prediction Rule for Treatment Change Based on Echocardiogram Findings in Transient Ischemic Attack and Non-Disabling Stroke." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32406.
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The goal of this study was to derive a clinical prediction rule for transient ischemic attack (TIA) and non-disabling stroke to predict a treatment change based on echocardiogram.
Methods: We conducted a cohort sub-study for TIA and non-disabling stroke patients collected over five years from 8 Emergency Departments. We compiled a list of 27 potential predictors to look for treatment change based on echocardiogram findings. We used a univariate, logistic regression and recursive partitioning analysis to develop the final prediction model.
Results: The frequency of treatment change was seen in 87 (3.1%) of 2804 cases. The final model contains six predictors: age less than 50 years old, coronary artery disease history, history of heart failure, any language deficit, posterior circulation infarct and middle cerebral artery infarct on neuroimaging.
Conclusions: We have developed a highly sensitive clinic prediction rule to guide in the use of echocardiogram in TIA and non-disabling stroke.
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Arroyo, Cesari Gabriel José, Carranza Christian Arturo Aznarán, and Risco José Carlos Ubarnes. "La búsqueda de Feedback como predictor del desempeño laboral : el rol mediador de la claridad de rol." Master's thesis, Universidad del Pacífico, 2017. http://hdl.handle.net/11354/1710.
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Esta investigación le otorgará a la organización estudiada, la posibilidad de revisar las estrategias de gestión y desarrollo humano asociadas al desempeño laboral, proporcionando información que permita validar la efectividad de los métodos de búsqueda de feedback orientados al logro instrumental de metas y la disminución de la ambigüedad de la tarea. Producto del presente estudio, la compañía podrá ejecutar acciones puntuales como implementar sesiones trimestrales de feedback, y un instructivo de feedback que permitan clarificar el rol de los colaboradores, además de reforzar la apertura de los líderes para ser buscados con el fin de brindar retroalimentación; todo esto bajo el contexto de reformular el proceso de evaluación de desempeño fomentando una cultura de feedback constante y facilitando la toma de decisiones. Lo mencionado podría apoyarse con capacitaciones a los líderes, con el fin de reforzar la capacidad de estructurar el proceso de feedback, en busca de mejorar la calidad de las sesiones de feedback, evitar que el proceso sea percibido como una mera puntuación por los colaboradores y, como fin último, el desempeño laboral individual.
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Sebastián, Soto Niflin Roger. "Aplicación de la termografía en el mantenimiento predictivo - DOE RUN PERÚ." Universidad Nacional de Ingeniería. Programa Cybertesis PERÚ, 2006. http://cybertesis.uni.edu.pe/uni/2006/sebastian_sn/html/index-frames.html.
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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.
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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.
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Davies, Jerome Edward. "Predicting the Bull Run: scientific evidence for turning points of markets." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/10323.
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Includes bibliographical references.This study investigates predictability in financial markets, specifically the South African financial market, proxied by the Johannesburg Stock Exchange (JSE) All Share Index (ALSI). It provides scientific evidence of past research of turning points in markets, focusing on bull markets as evidence suggests that predictability of bull markets leads to superior returns for an asset manager. In addition, this study provides an analysis of macroeconomic variables that can be used for predictability in the South Africa financial market. We found that certain macroeconomic variables do contain an element of predictability with the yield spread and short term interest rates being the best indicators. In addition we found that predicting the Bull Run in its earliest phase provides superior returns to an asset manager.
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Visvanatha, Sanjeev K. "A study on the use of Neuber's rule in fatigue crack initiation predictions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0012/MQ36898.pdf.
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SOUZA, LUCIENE GOMES DE. "COMPARISON OF METHODS OF MICRO-DATA AND RUN-OFF TRIANGLE FOR PREDICTION AMOUNT OF IBNR." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=22979@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A reserva IBNR é uma reserva de suma importância para as seguradoras. Seu cálculo tem sido realizado por métodos, em sua grande maioria, determinísticos, tradicionalmente aplicados a informações de sinistros agrupadas num formato particular intitulado triangulo de run-off. Esta forma de cálculo foi muito usada por décadas por sua simplicidade e pela limitação da capacidade de processamento computacional existente. Hoje, com o grande avanço dessa capacidade, não haveria necessidade de deixar de investigar informações relevantes que podem ser perdidas com agrupamento dos dados. Muitas são as deficiências dos métodos tradicionais apontadas na literatura e o uso de informação detalhada tem sido apontado por alguns artigos como a fonte para superação dessas deficiências. Outra busca constante nas metodologias propostas para cálculo da IBNR é pela obtenção de boas medidas de precisão das estimativas obtidas por eles. Neste ponto, sobre o uso de dados detalhados, há a expectativa de obtenção de medidas de precisão mais justas, já que se tem mais dados. Inspirada em alguns artigos já divulgados com propostas para modelagem desses dados não agrupados esta dissertação propõe um novo modelo, avaliando sua capacidade de predição e ganho de conhecimento a respeito do processo de ocorrência e aviso de sinistros frente ao que se pode obter a partir dos métodos tradicionais aplicados à dados de quantidade para obtenção da quantidade de sinistros IBNR e sua distribuição.
The IBNR reserve is a reserve of paramount importance for insurers. Its calculation has been accomplished by methods, mostly, deterministic, traditionally applied to claims grouped information in a particular format called run-off triangle . This method of calculation was very adequate for decades because of its simplicity and the limited computational processing capacity existing in the past. Today, with the breakthrough of this capacity, no waiver to investigating relevant information that may be lost with grouping data would be need. Many flaws of the traditional methods has been mentioned in the literature and the use of detailed information has been pointed as a form of overcoming these deficiencies. Another frequent aim in methodologies proposed for the calculation of IBNR is get a good measure of the accuracy of the estimates obtained by them and that is another expectation about the use of detailed data, since if you got more data you could get better measures. Inspired by some articles already published with proposals for modeling such not grouped data, this dissertation proposes a new model and evaluate its predictive ability and gain of knowledge about the process of occurrence and notice of the claim against that one can get from the traditional methods applied to data of amount of claims for obtain the amount of IBNR claims and their distribution.
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Postovskaya, Anna. "Rule-based machine learning for prediction of Macaca mulatta SIV-vaccination outcome using transcriptome profiles." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440182.
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One of the reasons, why the development of an effective HIV vaccine remains challenging, is the lack of understanding of potential vaccination-induced protection mechanisms. In the present study, Rhesus Macaques (Macaca mulatta) gene expression profiles obtained during vaccination with promising candidate vaccines against Simian Immunodeficiency Virus (SIV) were processed with a rule-based supervised machine learning approach to analyze the effects of vaccine combination treatment. The findings from constructed rule-based classifiers suggest that the immune response against SIV builds up throughout the immunization procedure. The upregulation of three genes (NHEJ1, GBP7, LAMB1), known to contribute to immune system development and functioning, cellular signalling, and DNA reparation, during or after vaccination boost appears to play an important role in the development of protection against SIV. What is more, the data suggest that the mechanisms of protection development might be dependent on the vaccine type providing a plausible explanation for the difference in effect between vaccines. Further studies are necessary to confirm or disprove our preliminary understanding of the vaccination-induced protection mechanisms against SIV and to use this information for rational vaccine design.
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Braida, Giacomo, and Roberto Tomasetig. "Preliminary analysis of the potential energy saving achievable with a predictive control strategy of a heat pump for a single family house." Thesis, KTH, Tillämpad termodynamik och kylteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240067.
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The present work reports a study related to the potential improvement of the energy performances of a heat pump based heating system for a Swedish single-family house. The analysis is focused on the design of new rule-based control strategies which employ perfect predictions of weather forecast and human behaviour information. In particular, the considered signals are the outdoor temperature, the solar radiation, the internal gain due to inhabitants’ activities and the Domestic Hot Water (DHW) consumption. The study is performed by means of the TRNSYS® simulation software in which the model of the heating system is implemented. More specifically, it is composed by a Ground Source Heat Pump (GSHP) unit, a stratified storage tank of three hundred litres and the building element. The performances of the developed control logics are evaluated using a degree-minute on/off controller as reference case. The results show that the improved control logics yield to an increase of the energy efficiency of the system as well as an enhancement of the indoor and DHW temperatures stability.EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems
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Monteggia, Mattia. "Weather data for heat pump system control improvement: analysis of instantaneous and forecasted measurements and evaluation of potential energy savings." Thesis, KTH, Tillämpad termodynamik och kylteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240069.
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The present work deals with a study related to the analysis of weather data for heat pump system control improvement based on both instantaneous and forecasted measurements. In particular, the analysis is firstly focused on the comparison of multiple weather sources for the assessment of weather forecast uncertainties, based on the evaluation of errors in prediction with respect to measured values. Afterwards, the results are compared with the ones related to persistent predictions methods that assumes the state of the atmosphere to be stationary over the considered time interval. The development and testing of a new preliminary “predictive” control logic is also performed, thanks to TRNSYS numerical simulations, considering a typical Swedish single-family house located in Stockholm, with the aim of optimizing the operation of a heat pump heating system based on solar radiation prediction to yield energy and cost savings. With the crucial points of accuracy and precision by which the local weather processes can be predicted, the same TRNSYS model is run accounting for perfect predictions and solar radiation forecasted values. From this perspective, given the fact that forecast of solar radiation are usually absent within most of the weather forecast datasets, a deep analysis is also performed on hourly measurements of solar radiation to define a simple and effective methods to calculate hourly solar radiation predictions. The results show that, when a short-time horizon is considered, persistent predictions allow to provide forecasts with a sufficient accuracy, whereas, when longer horizon time are considered, significantly higher errors are calculated when persistent prediction techniques are adopted. Independently of the uncertainties considered for weather forecasts, the improved control logics demonstrated a potential for energy savings and improvements in indoor temperature stability when compared with a reference case of variable speed compressor with PID controller.EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems
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Ball, Ian. "Predicting Outcomes in Critically Ill Canadian Octogenarians." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34211.
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Background: Based on survey data from both Canada and abroad, most people would prefer to be cared for and to die in their own homes. Although 70% of elderly patients state a preference for comfort care over high technology life prolonging treatment in an inpatient setting, 54% are still admitted to intensive care units (ICUs). Understanding their wishes regarding end-of-life care, and being able to engage in evidence informed end-of-life discussions has never been so important, in order to empower patients, and to optimize scarce resource management. For the purpose of this thesis, “very old” patients will be defined as those eighty years of age and older.
All three manuscripts will be based on data from the Realistic 80 study, a prospective cohort trial of 1671 critically ill very old patients admitted to 22 Canadian ICUs.
Objectives:
Manuscript 1: To describe the hospital outcomes of the entire cohort of Realistic 80 patients, including their ICU mortality and length of stay, their hospital mortality and length of stay, and their ultimate dispositions.
Manuscript 2: To derive a clinical prediction rule for hospital mortality in the medical patient cohort.
Manuscript 3: To derive a clinical prediction rule for hospital mortality in the emergency surgical patient cohort.
Data Source: A prospective, multicenter cohort study of very elderly medical and surgical patients admitted to 22 Canadian academic and non-academic ICUs.
Methods: Clinical decision rule methodology was used to analyze the data set and to create two separate clinical prediction tools, one for critically ill elderly medical patients, and one for critically ill surgical emergency patients. A third manuscript describing general clinical outcomes was also produced.
Results of Manuscript 1: A total of 1671 patients were included in this section of the “Realities, Expectations and Attitudes to Life Support Technologies in Intensive Care for Octogenarians: The Realistic 80 Study (a prospective cohort of nearly 2000 critically ill Canadian patients over eighty years old enrolled from 22 ICUs across Canada) that will provide the data for this thesis.
The Realistic 80 cohort had a mean age of 84.5, a baseline Apache II score of 22.4, a baseline SOFA score of 5.3, an overall ICU mortality of 21.8%, and an overall hospital mortality of 35%. The cohort had a median ICU length of stay of 3.7 days, and an overall median hospital length of stay of 16.6 days. Only 46.4% of the survivors were able to return home to live.
Results of Manuscript 2: Age, renal function, level of consciousness, and serum pH were the important predictors of hospital mortality in critically ill elderly medical patients. Our clinical prediction tool is very good, particularly at the all-important extremes of prognosis, and ready for external validation.
Results of Manuscript 3: Renal function and serum pH were the important predictors of hospital mortality in critically ill elderly surgical patients. Our model’s performance is very good, and will serve to inform clinical practice once validated.
Conclusions: Very old medical patients have longer ICU stays and higher mortality than their surgical counterparts. Premorbid health status and severity of illness are associated with mortality. Our medical patient clinical prediction tool is very good and ready for external validation. Our surgical emergency clinical prediction tool shows promise, but will require the incorporation of more patients and a repeat derivation phase prior to external validation or clinical implementation.
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König, Rikard. "Enhancing genetic programming for predictive modeling." Doctoral thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3689.
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Avhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.
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Ma, Qi. "Model based control and efficient calibration for crank-to-run transition in SI engines." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1125429289.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xiii, 160 p.; also includes graphics (some col.). Includes bibliographical references (p. 156-160). Available online via OhioLINK's ETD Center
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Nallendran, Vignesh Raja. "Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102501.
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The integration of Fog-Cloud computing in manufacturing has given rise to a new paradigm called Fog manufacturing. Fog manufacturing is a form of distributed computing platform that integrates Fog-Cloud collaborative computing strategy to facilitate responsive, scalable, and reliable data analysis in manufacturing networks. The computation services provided by Fog-Cloud computing can effectively support quality prediction, process monitoring, and diagnosis efforts in a timely manner for manufacturing processes. However, the communication and computation resources for Fog-Cloud computing are limited in Fog manufacturing. Therefore, it is significant to effectively utilize the computation services based on the optimal computation task offloading, scheduling, and hardware autoscaling strategies to finish the computation tasks on time without compromising on the quality of the computation service. A prerequisite for adapting such optimal strategies is to accurately predict the run-time metrics (e.g., Time-latency) of the Fog nodes by capturing their inherent stochastic nature in real-time. It is because these run-time metrics are directly related to the performance of the computation service in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The run-time metrics that reflect the performance in the Fog nodes are heterogenous in nature and the performance cannot be effectively modeled through traditional predictive analysis. In this thesis, a multi-task learning methodology is adopted to predict the run-time metrics that reflect performance in Fog manufacturing by addressing the heterogeneities among the Fog nodes. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model
and benchmark models. The proposed model can be further extended in computation tasks offloading and architecture optimization in Fog manufacturing to minimize the time-latency and improve the robustness of the system.Master of Science
Smart manufacturing aims at utilizing Internet of things (IoT), data analytics, cloud computing, etc. to handle varying market demand without compromising the productivity or quality in a manufacturing plant. To support these efforts, Fog manufacturing has been identified as a suitable computing architecture to handle the surge of data generated from the IoT devices. In Fog manufacturing computational tasks are completed locally through the means of interconnected computing devices called Fog nodes. However, the communication and computation resources in Fog manufacturing are limited. Therefore, its effective utilization requires optimal strategies to schedule the computational tasks and assign the computational tasks to the Fog nodes. A prerequisite for adapting such strategies is to accurately predict the performance of the Fog nodes. In this thesis, a multi-task learning methodology is adopted to predict the performance in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The metrics that reflect the performance in the Fog nodes are heterogenous in nature and cannot be effectively modeled through conventional predictive analysis. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model and benchmark models. The results show that the multi-task learning model has better prediction accuracy than the benchmarks and that it can model the heterogeneities among the Fog nodes. The proposed model can further be incorporated in scheduling and assignment strategies to effectively utilize Fog manufacturing's computational services.
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Plint, Amy Catherine. "Feasibility study to derive a clinical decision rule for predicting severe bronchiolitis." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27285.
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Statement of problem. Bronchiolitis is a common childhood lower respiratory tract infection. Determining risk of developing severe disease is important.
Methods. 312 children, aged ≤ 12 months, with bronchiolitis were prospectively enrolled over 5 months at 2 emergency departments. We assessed 22 standardized variables and clinical outcome. A decision rule predicting severe bronchiolitis (apnea, intubation, pediatric ICU admission, death) was derived using chi2 recursive partitioning techniques.
Results. 8 children (2.6 %) developed severe bronchiolitis. Age, length of breastfeeding, heart rate (HR) and systolic blood pressure outside normal, respiratory exam score, oxygen saturation, and lung atelectasis were associated (p < 0.05) with severe bronchiolitis. Preliminary decision rule included (1) heart rate outside normal range and (2) oxygen saturation < 88%. Rule sensitivity was 100% (95% CI 68,100) and specificity was 93% (95% CI 90,96).
Conclusion. Small sample size resulted in the rule's large CIs. Successful model development suggests a decision rule is feasible.
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Louzada, Martha. "Evaluating Risk of Recurrent Venous Thromboembolism During the Anticoagulation Period in Patients with Malignancy." Thesis, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19827.
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Background - Current guidelines suggest that all cancer patients with venous thrombosis be treated with long-term low molecular weight heparin. Whether treatment strategies should vary according to clinical characteristics remains unknown. // Systematic review -
A systematic review was performed to determine current understanding of the association between malignancy characteristics in patients with cancer-associated VTE and the risk of VTE recurrence. Four retrospective and 6 prospective studies were included. They suggest that lung cancer, metastases, and adenocarcinomas confer an increased the risk of recurrence and breast cancer a low risk. // Survey - I performed survey to evaluate thrombosis experts’ opinion about the low risk of VTE recurrence they would consider acceptable for patients with cancer- associated thrombosis 103 specialists participated. 80% of respondents agreed that a risk of recurrent VTE during anticoagulation below 7% is low enough. 92% agreed that a CPR that categorizes risk of recurrence is relevant. // Retrospective Study - I performed a single retrospective cohort study to assess the feasibility of derivation of a CPR that stratifies VTE recurrence risk in patients with cancer–associated thrombosis. The study included 543 patients. A multivariate analysis selected female, lung cancer and prior history of VTE as high risk predictors and breast cancer and stage I disease as low risk. // Conclusion - Patients with cancer-associated thrombosis do have varying risks of recurrent VTE depending on clinical characteristics.
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Hu, Hae-Jin. "Design of Comprehensible Learning Machine Systems for Protein Structure Prediction." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/22.
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With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing classifier, CPAR to handle the sequential data. The performance of the PCPAR was improved more by designing the following two hybrid schemes. The PCPAR/SVM method is a parallel combination of the PCPAR and the SVM and the PCPAR_SVM method is a sequential combination of the PCPAR and the SVM. To understand the SVM prediction, the SVM_PCPAR scheme was developed. The experimental result presents that the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method. The PCPAR/SVM scheme presents better performance than the PCPAR, PCPAR_SVM or the SVM_PCPAR and almost equal performance to the SVM. The generated patterns are easily understandable and biologically meaningful. The system sturdiness evaluation and the ROC curve analysis proved that this new scheme is robust and competent.