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 13 of 13 for “"Recurrent Neural Networks (RNN)"”.
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Time series forecasting with recurrent neural networks
… to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM …
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Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data
… such 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 …
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Gesture Recognition in Tennis Biomechanics
… as variable-length sequence classifiers, recurrent neural networks (RNN), to predict tennis ball trajectory. In attempt to learn temporal dependencies within a tennis swing, we implemented gate-augmented RNNs. This study compared the RNN to two gated models; gated recurrent units (GRU), …
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Recurrent neural network language models in the context of under-resourced South African languages
Over the past five years neural network models have been successful across a range of computational linguistic tasks. However, these triumphs have been concentrated in languages with significant resources such as large datasets. Thus, many languages, which are commonly referred to as …
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Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals
… vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from …
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Application of probabilistic deep learning models to simulate thermal power plant processes
… mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both …
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An improved data classification framework based on fractional particle swarm optimization
… is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then …
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One Size Does Not Fit All: Optimizing Sequence Length with Recurrent Neural Networks for Spectrum Sensing
… Machine Learning (RFML), techniques like deep neural networks and reinforcement learning have been used to develop more complex spectrum sensing systems that are not reliant on expert features. Architectures like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have shown …
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Machine learning methods for individual acoustic recognition in a species of field cricket
… It is for this very reason that convolutional neural networks (CNN) and recurrent neural networks (RNN) were utilised in this research. The results of these models were compared to results of a baseline random forest (RF) model as RFs can also be used to make acoustic classifications. …
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Improving federated learning performance with similarity guided feature extraction and pruning.
… to improve FL performance, with a focus on neural architectures for classification tasks. The models considered include Multinomial Logistic Regression (MLR), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Multi-layer Perceptrons (MLP) trained using Stochastic …
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Sequence modelling using deep learning approaches for spatiotemporal public transport data.
… passengers. This will make the use of urban bus networks more convenient for passengers and, thus, will play a crucial role in shifting traffic to public transport. Ultimately, this will alleviate pollution and congestion and save a substantial amount of cost to society associated with the use of …
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Scalable and Reconfigurable True-Time Delay Line for Integrated Radio-Frequency Recurrent Neural Processors
… and tunable delay-line architecture for analog recurrent neural networks (RNNs) operating directly in the RF-domain. Previous research has shown the RF-domain RNNs are capable of performing real-time anomaly detection in wireless systems while reducing the inference latency of the wireless …
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Accelerated deep learning for the edge-to-cloud continuum: A specialized full stack derived from algorithms
… a major driver for the rapid evolution of Deep Neural Networks (DNN). Due to their insatiable demand for compute power, naturally, both the research community as well the industry have turned to accelerators to accommodate modern DNN computation. Furthermore, DNNs are gaining prevalence and have …