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 8 of 8 for “"LSTM-RNN"”.
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Long short-term memory recurrent neural networks for classification of acute hypotensive episodes
… Long short-term memory recurrent neural network (LSTM RNN) models which predict whether a patient will experience an AHE or not based on a time series of mean arterial blood pressure (ABP). A 2-layer, 128-hidden unit LSTM RNN trained with rmsprop and dropout regularization achieves sensitivity of …
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Machine Learning with FEARS index: does the inclusion of investor sentiment improve a machine learning model's ability to predict volatility?
… Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) methods. A consolidated dataset, where all G7 countries were combined into a single series, as well as an individualised dataset, where each individual country is analysed independently, were used to test the different ML methods' …
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Aircraft Bird Strike Risk Prediction Using Machine Learning and Analytic Hierarchy Process
… Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air …
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Feasibility Study of Transfer Learning on LSTM Recurrent Neural Networks for Fiber Manufacturing Commercialization
… long short-term memory recurrent neural network (LSTM RNN) model for a desktop fiber extrusion system that mimics the fiber extrusion process on the manufacturing floor. Transfer learning on the LSTM RNN was then implemented to explore the feasibility of reusing a well-developed machine learning …
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Exploring deep learning based methods for information retrieval in Indian classical music
… Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music …
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A Predictive Analysis of Electronic Healthcare Records for Stroke Symptoms
… Backpropagation; Recurrent Neural Network (RNN); and Long Short-Term Memory - Recurrent Neural Network (LSTM-RNN). These are powerful and widely used techniques in machine learning and bioinformatics. First, we decoded ICD-10th codes into the health records, as well as other potential risk …
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Building a similarity engine
… which predicts the possible words nearby, the LSTM-RNN method, which forms semantic representations of sentences by learning more about the sentence as it iterates through a sentence, using single convolution neural networks, and several other methods. Using these theories, we are trying to …
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ADVANCED MACHINE LEARNING MODELS IN PREDICTION OF MEDICAL CONDITIONS
… were used as inputs to a Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD. The LSTM RNN method performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. Accurate prediction of …