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 19 of 19 for “"Long Short Term Memory Networks"”.
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Random Sequential Encoders for Private Data Release in NLP
… vision, lightweight random convolutional networks have shown potential to be an encoder that balances privacy and utility. This thesis takes a novel exploration of random sequential encoders - (1) random recurrent neural networks and (2) random long short-term memory networks as encoding …
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… power have become integral parts of modern power networks. Short-term wind speed prediction is crucial for smart grids, as it can help balance the demand and supply, as well as set the energy price in the market.</p> <p>In this thesis, we simulate and compare various neural network models for …
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Dynamic Hand Gesture Recognition Using Ultrasonic Sonar Sensors and Deep Learning
… are typically based on convolutional neural networks with some applications exploring the use of long short term memory networks. The goal of this study was to build and design a Sonar system that can classify hand gestures using a machine learning approach. Secondly, the study aims to …
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Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data
… as Support Vector Machines (SVM), Basic Neural Networks (BNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN) have been highly used in wildfire prediction. The goal of this research is to discover the best combination of data and …
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Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
… Many existing models and projects focus on long-term trends in coastal water levels particularly in terms of climate change and global warming. This project investigated the application of time series analysis with exogenous meteorological variables to the task of generating accurate …
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Clinical event prediction and understanding with deep neural networks
… In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five intervention tasks: invasive ventilation, non-invasive ventilation, vasopressors, colloid boluses, and crystalloid boluses. Our …
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Assessing the Robustness of Deep Learning Streamflow Models Under Climate Change
Long Short-Term Memory networks provide the most accurate rainfall-runoff predictions to-date, but their reliability under climate change is not well understood. We explore the robustness of these models under climate nonstationarity by creating train and test data splits thatare designed to …
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Deep Hedging of basis risk
… under this approach, the hedge parameters are determined in a model agnostic way. This is achieved using Long Short-Term Memory networks written in TensorFlow. This allows one to make the hedge parameters at each time point a function of current market data and previous hedging decisions. Deep …
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Implementing deep learning techniques for network-scale traffic forecasting
… which can contribute to improving transportation networks and the overall travel experience. In this thesis, we study the use of several machine learning and deep learning techniques to predict travel times on a road network. The two main methods proposed to tackle the problem are Convolutional …
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Reinforcement learning with natural language signals
… models, but using multistage reasoning. We use Long Short-Term Memory Networks to parse the Natural Language input, whose final hidden state is used to compute action scores for the Deep Q-learning algorithm. First part of the thesis introduces the necessary theoretical background, including …
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Neural attentions for natural language understanding and modeling
… a convolutional neural network (CNN) to simulate long short-term memory networks (LSTMs). The model process sequential data in parallel and still achieves competitive performances. We also propose a phrase induction model and headword attention to learn the embedding of following phrases. The …
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Device-type Profiling using Packet Inter-Arrival Time for Network Access Control
… of devices attempting to connect to enterprise networks. However, NAC limitations have led to security threats that can lead to illegal and unauthorised access to networks as well as insider misuse. Current NAC configuration settings rely on point of entry authentication systems including …
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The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
… the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly …
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Predicting mergers and acquisitions using machine learning
… reliant on fundamental and technical metrics along with a few macroeconomic indicators, often struggle to pick up underlying relationships between features and targets. This study investigates the effectiveness of advanced machine learning techniques, which have found large success in stock …
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Predicting diarrhoea outbreak with climate change
… deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was …
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Learning sentiment and semantic relatedness in user generated content using neural models
… of deep learning, this work applies neural networks to solve these tasks. We design neuralbased models including Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) to capture the semantic and sentiment information. Aspect Based Sentiment Analysis is concerned …
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Personalized and Communication Cost Reduction Models in Federated Learning
… we conducted experiments on convolutional neural networks, multi-layer perceptron, and long short-term memory networks using the MNIST, CIFAR-10 and NN5 datasets. These experiments successfully prove the efficacy of the FLICC model as we achieve competitive results compared to conventional …
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SCIENTIFIC MACHINE LEARNING METHODS FOR REACTIVE-TRANSPORT AND THERMAL-TRANSPORT PROBLEMS
… for reactive-transport problems overcomes this shortcoming to improve prediction accuracy using available time-history data. The framework uses convolutional neural networks for capturing spatial patterns and long short-term memory networks for forecasting temporal variations in mixing. The …
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Inferring travel activity pattern from smartphone sensing data using deep learning
… from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other machine learning approaches. For the sequential classification …