Thesis (M.S.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (M.Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on July 30, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M.Sc. (Statistics)) - Rhodes University, 2009. / A thesis submitted to Rhodes University in partial fulfillment of the requirements for the degree of Master of Science in Mathematical Statistics.

Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information.

Strength of concrete is the major parameter in the design of structures and is represented by the 28-day compressive strength of concrete. Many earlier studies proved that the compressive strength of concrete is not only related to w/c ratio but also rely on proportion of other constituent materials. Application of recently developed new generation admixtures for the production of high performance concrete, has made the concrete strength prediction complex and highly nonlinear challenging the research engineers and data scientists. Development of early accurate prediction model for concrete strength provides the mix designer a tentative idea to proportionate the mix ingredients accordingly reducing the number of trial mixes ultimately saving a lot of cost and time associated with it. In this study, we have proposed SVM regression tool to create the model for the prediction of concrete strength. Support vector machine (SVM) is a supervised machine learning technique based on statistical learning theory developed by Vapnik in 1995. SVM employs a kernel function to transform the data into high dimensional feature space and linear modeling is performed in the feature space to overcome the complexity related to highly nonlinear datasets. A dataset containing 425 observations of high performance concrete mix design with nine attribute variables from University of California, Irvine Repository are considered for this study. 395 datasets were used to train the model and 30 samples were taken as a test set by random sub sampling to test the model. Five-fold cross-validation technique was used to select the parameters of SVM. The metaparameter values ε = 0.001, C = 29.47 and γ = 10 are selected for creating the model. The model performance measures correlation coefficient (R), root mean square error (RMSE) values and residual plots suggest that the proposed SVM model is competent enough to predict the strength of concrete. The performance measures of proposed SVM model was compared with RVM model.

Made available in DSpace on 2016-08-29T15:33:12Z (GMT). No. of bitstreams: 1 tese_3521_.pdf: 2782962 bytes, checksum: d4b2294e5ee9ab86b7a35aec083af692 (MD5) Previous issue date: 2010-02-12 / Os Modelos de Predição implementados pelos algoritmos de Aprendizagem de Máquina advindos como linha de pesquisa da Inteligência Computacional são resultantes de pesquisas e investigações empíricas em dados do mundo real. Neste contexto; estes modelos são extraídos para comparação de duas grandes técnicas de aprendizagem de máquina Redes Neuro-Fuzzy e de Regressão aplicadas no intuito de estimar um parâmetro de qualidade do produto em um ambiente industrial sob processo contínuo. Heuristicamente; esses Modelos de Predição são aplicados e comparados em um mesmo ambiente de simulação com intuito de mensurar os níveis de adequação dos mesmos, o poder de desempenho e generalização dos dados empíricos que compõem este cenário (ambiente industrial de mineração).

abstract: Gas turbine engine for aircraft propulsion represents one of the most physics-complex and safety-critical systems in the world. Its failure diagnostic is challenging due to the complexity of the model system, difficulty involved in practical testing and the infeasibility of creating homogeneous diagnostic performance evaluation criteria for the diverse engine makes. NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem. This study focuses on a different machine learning approach to the diagnostic problem. Some most common machine learning techniques, such as support vector machine, multi-layer perceptron, and self-organizing map are used to help gain insight into the different engine failure modes from the perspective of big data. They are organically integrated to achieve good performance based on a good understanding of the complex dataset. The study presents a new hierarchical machine learning structure to enhance classification accuracy in NASA's engine diagnostic benchmark problem. The designed hierarchical structure produces an average diagnostic accuracy of 73.6%, which outperforms comparable studies that were most recently published. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Made available in DSpace on 2014-06-12T17:43:09Z (GMT). No. of bitstreams: 2 arquivo981_1.pdf: 1519617 bytes, checksum: 1476b8d5238437f951709f3c7a7be61b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Confiabilidade é uma métrica crítica para as organizações, uma vez que ela influencia diretamente seus desempenhos face à concorrência e é essencial para a manutenção da disponibilidade de seus sistemas produtivos. A previsão dessa métrica quantitativa é então de grande interresse, pois ela pode antecipar o conhecimento de falhas do sistema e permitir que as organizações possam evitar ou superar essas situações indesejadas. A confiabilidade de sistemas depende tanto dos efeitos inerentes da idade assim como das condições operacionais a que o sistema é submetido. Isso pode tornar a modelagem da confiabilidade muito complexa de forma que processos estocásticos tradicionais falhem em prever de forma acurada o seu comportamento ao longo do tempo. Nesse contexto, métodos de aprendizado como Support Vector Machines surgem como alternativa para superar essa questão. Uma das principais vantagens de se utilizar SVMs é o fato de não ser necessário supor ou conhecer previamente a função ou o processo que mapeia as variáveis de entrada (input) em saída (output). No entanto, seu desempenho está associado a um conjunto de parâmetros que aparecem no problema de aprendizado. Isso dá origem ao problema de seleção de modelo para SVM, que consiste basicamente em escolher os valores apropriados para esses parâmetros. Nesse trabalho, tal problema é resolvido por meio de Otimização via Nuvens de Partículas (Particle Swarm Optimization - PSO), uma abordagem probabilística que é inspirada no comportamento de organismos biológicos que se movem em grupos. Além disso, é proposta uma metodologia PSO+SVM para resolver problemas de previsão de confiabilidade, que é validada por meio da resolução de exemplos da literatura baseados em dados de séries temporais. As soluções encontradas, comparadas às provenientes de outras ferramentas de previsão como Redes Neurais (Neural Networks - NNs), indicam que a metodologia proposta é capaz de fornecer previsões de confiabilidade competitivas ou até mesmo mais acuradas. Além disso, a metodologia proposta é utilizada para resolver um exemplo de aplicação envolvendo dados de poços de produção de petróleo