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 228 for “"long-short term memory"”.
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Long short-term memory neural networks for predicting corporate credit ratings
… investments. The existing literature shows that long short-term memory (LSTM) neural networks are the best neural network to predict credit ratings, while random forests have been shown to perform better than regular neural networks. As at the beginning of this study, no study had compared the …
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Biological applications, visualizations, and extensions of the long short-term memory network
… techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus …
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Long short-term memory recurrent neural networks for classification of acute hypotensive episodes
… the development and evaluation of a series of 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 …
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Reliable control of surgical robots and stabilization of long short-term memory neural networks
… the joint torques, without any additional intermediate steps for computing shortest distances or gradients of shortest distances between the links. Furthermore the collision avoidance controller can be augmented to any stable controller with different objectives, such as position tracking, …
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Building occupancy analytics based on deep learning through the use of environmental sensor data
… is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the …
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model
… Random Forest, Recurrent Neural Network, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and LightGBM. Additionally, two hybrid machine learning methods are used for prediction: CNN-LSTM and BiLSTM-BO-LightGBM. After training the models, the algorithm creates an optimal …
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USE OF LANGUAGE TECHNOLOGY TO IMPROVE MATCHING AND RETRIEVAL IN TRANSLATION MEMORY
Current Translation Memory (TM) tools lack semantic knowledge while matching. Most TM tools compute similarity at the string level, which does not take into account semantic aspects in matching. Therefore, semantically similar segments, which differ on the surface form, are often not retrieved. In …
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Random Sequential Encoders for Private Data Release in NLP
… random recurrent neural networks and (2) random long short-term memory networks as encoding schemes for private data release in natural language processing. Experiments were conducted to evaluate the utility and privacy of these encoders against known baseline encoding schemes with less privacy: …
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Anomalijų aptikimas finansų rinkose /
… In the analyzed situation, the algorithm using Long Short Term Memory neural networks showed better results than the algorithm using the Least Absolute Shrinkage and Selection Operator regression, allowing a much earlier detection of an unusual period. The algorithm using Long Short Term Memory …
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A Bi-Encoder LSTM Model for Learning Unstructured Dialogs
… Chatbot systems. This thesis presents a Long Short Term Memory (LSTM) based Recurrent Neural Network architecture that learns unstructured multi-turn dialogs and provides implementation results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog …
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Assessing models for de-identification of Electronic Discharge Summary Using Machine Learning tools
… purpose. The Conditional Random Fields (CRF), Long Short Term Memory (LSTM) and Random Forest models were used, and the performance of each model was assessed. Findings: In order to assess each model’s performance, evaluation metrics were used to compare F-measure, Recall and Precision at token …
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Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques.
… Vector Machine, Convolutional Neural Network + Long-Short Term Memory Network, Feedforward Neural Network, gradient boosting, Gaussian Mixed Model and K-means clustering) to find cost effective localization techniques. Results showed that MFCC feature extraction and Convolutional Neural Network …
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Applying Neural Networks for Tire Pressure Monitoring Systems
… and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of …
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A comparative analysis of machine learning models for forecasting JSE Stock Returns
… 2005 to 2021: neural networks, random forest, long short- term memory (LSTM) networks, and conventional linear regression. The explanatory variables comprise nine firm-specific financial metrics, motivated by prior research. The sample is divided into a training period (2005–2016) and a testing …
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Lipreading with convolutional and recurrent neural network models
… (RNN) model with convolutional layer and one long short-term memory (LSTM) layer to perform the classification on a sequence of input. In both models, the convolutional layers serve as feature extractors. The performance of each model is experimentally evaluated and the detailed network …
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Machine learning for time series anomaly detection
… as anomalous. I used multiple models such as Long Short-Term Memory (LSTM), autoregression, Multi-Layer Perceptron, and Encoder-Decoder LSTM. I used the "Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding" paper as a basis for my analysis, and was able to beat …
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A comparative study of recurrent neural networks and statistical techniques for forecasting the stock prices of JSE-listed securities
… neural networks, Gated Recurrent Units and Long-Short Term Memory Units were thoroughly evaluated with a Convolutional Neural Network and a random forest were used as machine learning benchmarks. Historical data was collected for the period 2 January 2019 to 29 May 2020, with the 2019 …
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Research on the accuracy of Lithuanian speaker’s identification using recurrent neural networks /
… using a Lithuanian speaker dataset in order to determine a classifier most accurately identifying Lithuanian speaking individuals. The performed experimental research allowed concluding that for a Lithuanian speaker higher identification accuracy is achieved by using a recurrent neural network …
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A Spam Transformer Model for SMS Spam Detection
With the prosperity of the Short Message Service (SMS), the increasing number of spam messages has become a serious problem. The need to block spam messages requires us to develop new SMS spam detection technologies. The Transformer, an attention- based sequence to sequence model, has achieved …
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Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks
… 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 short-term …
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