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 136 for “"Long-short-term-memory (LSTM)"”.
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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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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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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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Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… forecasting and compared the success of the Long Short-Term Memory (LSTM) model to the performance of AutoRegressive Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe …
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Predicting continuous hand pose from wearable EMG sensor data using transformer-based deep-learning models
… in a hand-centered frame. Compared with a Long Short-Term Memory (LSTM)-based model, the Transformer-based model reduced whole-hand median prediction error from 3.6° to 3.3° for a joint angle model and from 17.4° to 15.1° for a bone orientation model. Experimental results demonstrated that …
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AstroBug: automatic game bug detection using deep learning
… new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in games as anomalies. The detected buggy frames are then clustered to determine the category of the manifested bug. The framework was evaluated on two First Person Shooter (FPS) games. We …
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Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations
… (CSMS) and end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, …
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SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries
… and statement-level suggestions that rely on Long Short-term Memory (LSTM) Recurrent Neural Networks language models trained on historical queries. The word-level model is integrated in a responsive command-line interface database client which is evaluated quantitatively and qualitatively. …
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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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A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution
… Rapid Refresh (HRRR) meteorology model, and a long-short-term-memory (LSTM) neural network to forecast and interpolate ozone values at high spatiotemporal resolution of 1 hour and 3 km. The accuracies of the LSTM models are analyzed using lagged ozone at various forecast horizons and across the …
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Machine Learning Application in Energy Storage System’s State Estimation: State of Health (SOH)
… The accuracy of all the developed DL models with long short-term memory (LSTM), convolutional LSTM (ConvLSTM), and deep convolutional neural network (DCNN) architecture are acceptable by industry standards, with mean absolute percentage error (MAPE) less than 3%. The promising results obtained in …
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Gaze-Aware Driver Maneuver Prediction Using Object Detection and Sequential Deep Learning Models for Advanced Driver Assistance Systems
… architectures: Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and the Temporal Convolutional Network (TCN). Results indicate that incorporating gaze-based object detection features slightly increased the time available to anticipate maneuvers but did not improve prediction …
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Gesture Recognition in Tennis Biomechanics
… motion capture data of a tennis player to determine which biomechanical aspects of a tennis swing best correlate to a swing efficacy. For our learning set this work aimed to record 50 tennis athletes of similar competency with the Microsoft Kinect performing standard tennis swings in the …
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IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING
… (MIL-STD-1399). This research then uses a long short term memory (LSTM) network machine learning algorithm, modeled in Python, to create a system for detecting anomalies in a shipboard microgrid. The model generates predictive data, and by comparing the predictive data to current trends, it …
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Machine Learning Regularized Solution of the Lippmann-Schwinger Equation
… network: a recurrent neural network with long short-term memory (LSTM). We train the LSTM using typical scattering potentials and their corresponding scattered fields. For the evaluation of the LSTM, two scattering cases are considered: electromagnetic scattering by dielectric objects, and …
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A Data-Driven Approach to System Dynamics Modeling and Control Design
… data during the fiber drawing process, a long short-term memory (LSTM) neural network is architected, implemented, and trained to model the process dynamics of the fiber drawing plant. Training experiments were conducted to investigate the effect of several parameters on the model’s …
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Application of Machine Learning in Process Control in Optical Fiber Manufacturing
… production data from the fiber drawing tower, a long short-term memory (LSTM) neural network structure is used to find the correlation between the inputs and outputs of the process. Different experiments were conducted on the physical draw tower and the simulation to gauge the accuracy of the …
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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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