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 312 for “"LSTM"”.
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Learning from videos with deep convolutional LSTM networks
… This research explores the use of convolution LSTMs to simultaneously learn spatial- and temporal-information in videos. A deep network of convolutional LSTMs allows the model to access the entire range of temporal information at all spatial scales of the data. This work first constructs an …
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A Bi-Encoder LSTM Model for Learning Unstructured Dialogs
… 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 Corpus Version 2 (UDCv2) …
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LSTM and extended dead reckoning automobile route prediction using smartphone sensors
… The two approaches, Extended Dead Reckoning and LSTM, are compared for their advantages and disadvantages. These concepts are explored by recording nearly one thousand miles of driving data from Virginia to Indiana to Illinois. The GPS data is used to train the LSTM neural network along with the …
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Predicting social unrest events in South Africa using LSTM neural networks
… implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the patterns of the social unrest time series were analysed using time series exploratory techniques. The social unrest time …
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Pronóstico de volatilidad de la TRM mediante un modelo híbrido LSTM-GARCH
En este trabajo se propone un modelo híbrido LSTM-GARCH para el pronóstico de la volatilidad de la tasa representativa del mercado (TRM). Este modelo es una red neuronal recurrente LSTM, en la cual se incluyen como variables explicativas los coeficientes de modelos de series de tiempo GARCH, EGARCH …
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An Attention LSTM U-Net Model for Drosophila Melanogaster Heart Tube Segmentation
… model, FlyNet 2.0+, is a fully convolutional LSTM U-Net model. However, the performance of the model diminishes in the presence of artifacts, such as image reflection and heart movement, resulting in time-consuming manual intervention for mask correction. Therefore, we developed the FlyNet 3.0 …
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IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
… technique to enhance efficiency. An optimized LSTM neural network enables accurate bot detection and identification, with hyperparameters selected using the Bayesian Optimization algorithm. SHAP analysis provides insightful individual and collective bot characteristic profiles. The model’s …
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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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LIDS: An Extended LSTM Based Web Intrusion Detection System With Active and Distributed Learning
Intrusion detection systems are an integral part of web application security. As Internet use continues to increase, the demand for fast, accurate intrusion detection systems has grown. Various IDSs like Snort, Zeek, Solarwinds SEM, and Sleuth9, detect malicious intent based on existing patterns of …
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Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM
As stocks have been developing over decades, the trend and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid …
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LSTM prediction capability on the South African JSE Top 40 of historical and live data
… the efficacy of Long Short-Term Memory (LSTM) models in stock price forecasting using data from the South African FTSE/JSE Top 40 index, a domain yet to be extensively explored, particularly in real-time data analysis. Addressing the gap in existing research, this study assesses LSTM …
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A CNN–LSTM–Attention Hybrid Architecture for Real-Time Intrusion Detection at the Data Link Layer
… Neural Networks (CNNs), Long Short-Term Memory (LSTM) units, and an Attention mechanism for real-time detection of Layer 2 intrusions. A novel dataset, BCCC-DLLayer-IDS-2025, was developed as part of this research, comprising over 4.6 million labeled flow records collected in a controlled …
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Deciphering Emotional Responses to Music: A Fusion of Psychophysiological Data Analysis and Bi-LSTM Predictive Modeling
This research explores the temporal patterns of psychophysiological responses to musical excerpts by analyzing the expansive Emotion in Motion dataset, the most comprehensive of its kind. Utilizing the Dynamic Time Warping and T-test analysis techniques, we examined data from participants across …
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Comparative research on code vulnerability detection: Open-source vs. proprietary large language models and LSTM neural network
… security risks and compares them to Word2Vec+LSTM neural networks. The findings reveal that CodeGen2 emerges as the most reliable model for vulnerability detection, achieving near-perfect precision and balanced recall, leading to superior F1 and AUC scores. LLaMA2-7b delivers reasonable …
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