Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"artificial data"”.
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New algorithm for neural network data discrimination applied to Markarian 421 high energy gamma rays
… is observed in the EXOR problem and in an artificial data discrimination (Toy Data) problem. The learning time is found to be about 1/15 of backpropagation learning time for the parity problem. The algorithm is applied to cosmic high energy gamma ray detection and is further used for …
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Neural network based learnings in support of two application domains
Artificial intelligence (AI) empowers machines to mimic the behaviors and thoughts of humans. With this technology now being used in all walks of life, it powers many real-world applications, ranging from language understanding to facial recognition. This thesis explores AI applications in two …
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Privacy-Preserving Synthetic Medical Data Generation with Deep Learning
… Language Processing. However, the utilization of data-driven methods in healthcare raises privacy concerns, which creates limitations for collaborative research. A remedy to this problem is to generate and employ synthetic data to address privacy concerns. Existing methods for artificial data …
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Bayesian Approaches to File Linking with Faulty Data
… or more sources of information, creating linked data bases. From linking school records to track student progress across years, to official statistics and linking patient health files, linked data bases allow analysts to use existing sources of information to perform rich statistical analysis. …
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Improved adaptive semi-unsupervised weighted oversampling (IA-SUWO) using sparsity factor for imbalanced datasets
The imbalanced data problem is common in data mining nowadays due to the skewed nature of data, which impact the classification process negatively in machine learning. For preprocessing, oversampling techniques significantly benefitted the imbalanced domain, in which artificial data is generated in …
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Discrimination of Computer Generated versus Natural Human Faces
… realism to computer generated multimedia data, e.g., scenes, human characters and other objects, making them achieve a very high quality level. However, these synthetic objects may be used to create situations which may not be present in real world, hence raising the demand of having …
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Evaluating visually grounded language capabilities using microworlds
… comprising a broad set of reasoning abilities. Datasets in this context typically act not just as application-focused benchmark, but also as basis to examine higher-level model capabilities. This thesis argues that emerging issues related to dataset quality, experimental practice and learned …
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Towards Personalized Medicine Using Systems Biology And Machine Learning
<p>The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well …
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Diabetes Mellitus Glucose Prediction by Linear and Bayesian Ensemble Modeling
… simplified models were evaluated on 47 patient data records from the first DIAdvisor trial. Qualitatively physiological correct responses were imposed, and model-based prediction, up to two hours ahead, and specifically for low blood glucose detection, was evaluated. The glucose raising, and …
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Summary statistics and sequential methods for approximate Bayesian computation
… likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data to summary statistics of the observed data. This thesis looks at two related methodological issues for ABC. Firstly a method is proposed to …
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Synthetic Electronic Medical Record Generation using Generative Adversarial Networks
… and other areas. The private nature of EHR data has prevented public access to EHR datasets. There are many obstacles to create a deep learning model with EHR data. Because EHR data are primarily consisting of huge sparse matrices, these challenges are mostly unique to this field. Due to …
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Development of Wastewater Pipe Performance Index and Performance Prediction Model
… After reviewing all relevant reports and utility databases, a set of standard pipe parameter list (data structure) and a pipe data collection methodology were developed. These parameters includes physical/structural, operational/functional, environmental and other parameters, for not only the …
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REAL-TIME CLASSIFIER BASED ON ADAPTIVE COMPETITIVE SELF-ORGANIZING ALGORITHM
… with implementing it on both real and artificial data sets as well as comparing with other well-known clustering methods. ACS method showed a better clustering performance in some categories and an overall comparable rendition. System dynamics is simulated with two optimizers Gradient …
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Restricting Supervised Learning: Feature Selection and Feature Space Partition
… as bioinformatics or image processing, whose data are sampled in a high dimensional space, suffer the curse of dimensionality, and there are not enough observations to obtain good estimates. Therefore, it is necessary to reduce such features under consideration. Another issue of supervised …
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EXPLORING MULTIPLEX NETWORKS
… to address them. For each problem, we utilize artificial data to study their effectiveness, understand their intrinsic properties and evaluate their behavior under a controlled network structure. Then, we report applications on real-world data sets, from variety of domains, and compare our …
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The Single Imputation Technique in the Gaussian Mixture Model Framework
Missing data is a common issue in data analysis. Numerous techniques have been proposed to deal with the missing data problem. Imputation is the most popular strategy for handling the missing data. Imputation for data analysis is the process to replace the missing values with any plausible values. …
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Sources of Fluctuations in Emerging Markets: DSGE Estimation with Mixed Frequency Data
… present short quarterly national accounts data, usually since the late eighties, but longer annual series, since 1950. To use this information efficiently, I estimate the model implementing a Bayesian mixed frequency strategy that combines quarterly and annual data for 1950-2010. The mixed …
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Well-log based determination of rock thermal conductivity in the North German Basin
… determine thermal conductivity based on well-log data are desirable. In this study rock thermal conductivity was investigated on different scales by (1) providing thermal-conductivity measurements on Mesozoic rocks, (2) evaluating and improving commonly applied mixing models which were used to …
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La utilización de Inteligencia de Fuentes Abiertas en la Investigación Penal y los problemas que implica su admisibilidad a la luz de la IA
… analizado a la luz del auge de la Inteligencia Artificial en nuestra sociedad, y de la problemática que puede presentarse a partir de ella con las pruebas de fuentes abiertas, y a partir de ello se ofrecerán criterios concretos y recomendaciones para el mejor tratamiento de este tipo de pruebas.
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Uma metodologia para tratamento de dados de curvas de carga baseada em técnicas de inteligência artificial
Data quality is critical in the short-term load forecasting. Frequently, load data show aberrant values (outliers), discontinuities, and gaps (missing data) caused by the abnormal operation of the electrical system or failures and problems in the measurement system. The presence of corrupted data …