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 74 for “"Numerical Analysis and Scientific Computing"”.
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Construction of efficient indexes from Fuzzy Clusters: preliminary study
… are optimal to queries (i.e. the best case), and the order linearly corresponding to the number of records stored in a database (i.e. the worst case). Currently, the generation of indexes is performed manually by database administrators so that optimality is entirely up to the administrators. …
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Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing
… in case studies involving image classification and natural language processing. In addition, a software library in the form of two-way bridge connecting deep learning models in Keras with ones available in the Fortran programming language is also presented.</p> <p>In Chapter 2, we explore the …
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Modeling repairable system failure data using NHPP reliability growth mode.
… the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the …
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A practical and efficient algorithm for the k-mismatch shortest unique substring finding problem
… shortest unique substring (SUS) finding problem and demonstrates that a technique recently presented in the context of solving the k-mismatch average common substring problem can be adapted and combined with parts of the existing solution, resulting in a new algorithm which has expected time …
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The application of GPU to molecular communication studies
… not fully accounted for structural boundaries and the associated simulation of a massive number of messenger molecule paths for stochastic evaluation. These molecules are influenced by a Brownian motion as well as the flow of the blood, which is modeled using numerical methods based on the …
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Evaluating a Cluster of Low-Power ARM64 Single-Board Computers with MapReduce
… enormous data collection in science, industry, and the cloud, methods for processing massive datasets have become more crucial than ever. MapReduce is a restricted programing model for expressing parallel computations as simple serial functions, and an execution framework for distributing those …
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The Effect of Initial Conditions on the Weather Research and Forecasting Model
<p>Modeling our atmosphere and determining forecasts using numerical methods has been a challenge since the early 20th Century. Most models use a complex dynamical system of equations that prove difficult to solve by hand as they are chaotic by nature. When computer systems became more widely …
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Appearance Based Stage Recognition of Drosophila Embryos
… due to severe variations of illumination and gene expressions. In this research thesis, we propose an appearance based recognition method using orientation histograms and Gabor filter. Furthermore, we apply Principal Component Analysis to reduce the dimension of the low-level features, …
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A Comparative Study of Cognitive Systems for Learning
… of a behavioral tendency by experience. Memory and reasoning are the most important aspects for learning in humans; information is temporarily stored in the short-term memory and processed, compared with existing memories and stored in long-term memory, and can be re-used when needed. One way to …
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A Normal Truncated Skewed-Laplace Model in Stochastic Frontier Analysis
<p>Stochastic frontier analysis is an exciting method of economic production modeling that is relevant to hospitals, stock markets, manufacturing factories, and services. In this paper, we create a new model using the normal distribution and truncated skew-Laplace distribution, namely the …
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Novel Monte Carlo Methods for Large-Scale Linear Algebra Operations
… algebra operations play an important role in scientific computing and data analysis. With increasing data volume and complexity in the "Big Data" era, linear algebra operations are important tools to process massive datasets. On one hand, the advent of modern high-performance computing …
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Stress Levels of CS1 Students During Programming- Measurement and a Cause and Effect Analysis
… that we are overloaded mentally in our thoughts and wonder whether we can cope with those placed upon us. The effects of stress are different for different people when we take their age, profession, gender and other aspects into consideration. Many studies show that stress in a learning …
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Spike-Based Classification of UCI Datasets with Multi-Layer Resume-Like Tempotron
… ``spikes.'' Though predominantly used in neuro-scientific investigations, spiking neural networks (SNN) can be applied to machine learning problems such as classification and regression. SNN are computationally more powerful per neuron than traditional neural networks. Though training time is …
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Data Visualization and Classification of Artificially Created Images
… of multidimensional data is a long-standing challenge in machine learning and knowledge discovery. A problem arises as soon as 4-dimensions are introduced since we live in a 3-dimensional world. There are methods out there which can visualize multidimensional data, but loss of …
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Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation
… to panoramic images in addition to pinhole ones and performing a comparative evaluation. First, we create a simulated depth detection dataset that lends itself to panoramic comparisons and contains pre-made cylindrical and spherical panoramas. We then modify monodepth2 to support cylindrical and …
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Shakespeare in the Eighteenth Century: Algorithm for Quotation Identification
… trend. Initial work focuses on identifying exact and modified sections of texts taken from works of Shakespeare in novels spanning the eighteenth century. We then introduce a novel approach to identifying modified quotes by adapting the Edit Distance metric, which is character based, to a word …
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Prediction of Laser Ablation In Brain: Sensitivity, Calibration, and Validation
… laser induced thermal therapy (MRgLITT) stands to benefit from predictive computational modeling. The dearth of physical model parameter data leads to modeling uncertainty. This work implements a well-accepted framework with three key steps for model-building: model-parameter sensitivity …
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A Comparative Study on Feature Extraction and Classification/Clustering of Fake News and Conspiracy Theories from Twitter Data
<p>Fake news and conspiracy theories have become largely abundant in the expanding world of social media. They predominantly affect the beliefs and thoughts of the public, resulting in chaos. They have always existed throughout the last few decades. They have been linked to prejudice, revolutions …
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