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 32 for “"LSTM Networks"”.
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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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USE OF LANGUAGE TECHNOLOGY TO IMPROVE MATCHING AND RETRIEVAL IN TRANSLATION MEMORY
… first one is based on Long Short Term Memory (LSTM) networks, while the other one is based on Tree Structured Long Short Term Memory (Tree-LSTM) networks. Eight different models using different datasets and settings are trained. The results are comparable to a baseline which uses simple …
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Detekce anomálií v množství generovaných záznamů o incidentech
… decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T …
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Time series analysis and machine learning studies of biophotovoltaic systems
… models. Here, Long Short-Term Memory (LSTM) networks were explored for their ability to replicate complex time-evolving phenomena without a priori knowledge. Results. Seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) was first used to decompose BPV …
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A comparative analysis of machine learning models for forecasting JSE Stock Returns
… using monthly data from 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 …
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Time series forecasting with recurrent neural networks
… 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 networks and GRU …
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A Study of Machine Learning based on Physical Load Classification
… light-load and heavy-load. The traditional LSTM was able to achieve 71.1% accuracy for the multi-classification problem, and the Bi-LSTM was able to achieve a 74.5% correct classification rate. The excellent performance of multiple Bi-LSTMs for binary classification problems was exploited by …
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Evaluating transformers as memory systems in reinforcement learning
… In recent years, Long Short-Term Memory (LSTM) networks have been the dominant mechanism for providing memory in reinforcement learning, however, the success of transformers in natural language processing tasks has highlighted a promising and viable alternative. Memory in reinforcement …
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Analog On-chip Training and Inference with Non-volatile Memory Devices
As the demand for computation in neural networks continues to rise, conventional computing resources are increasingly constrained by their limited energy efficiency. One promising solution to this challenge is analog in-memory computing (AIMC), which enables efficient matrix-vector multiplications …
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Analysis of bankruptcy prediction of shipping industry - Machine Learning Approach
… Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks. A comprehensive literature review and interviews with industry practitioners were conducted to refine the variables used in the models. These models predict bankruptcy across 1, 3, and 5-year horizons, with Explainable Artificial …
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Predicting Passenger Demand and Optimizing Fleet Allocation: A Machine Learning Approach for Icelandic Tour Operators
… Vector Regression, and Long Short-Term Memory (LSTM) networks. An intelligent bus assignment system using a Best Fit greedy algorithm was developed to utilize these forecasts. The best performance was demonstrated by the LSTM model, with a Mean Absolute Error of 9.08 passengers being achieved. …
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Advanced space-time integration for knowledge discovery in human mobility studies
… human activity-travel patterns and Bayesian networks models with specially designed topology to predict the forthcoming activity at the individual level. Last, a deep convolutional LSTM networks model is proposed to capture the spatial and temporal dependencies in an integrated way to predict …
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Improving Text Classification Using Graph-based Methods
… Several studies employ Long Short- Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), but Graph Convolutional Networks (GCNs) have not yet been investigated for the task. Sequence- based models can successfully capture semantics in local consecutive text sequences. On the other …
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Machine learning based speech quality prediction
… of deep learning architectures, such as CNNs, LSTM networks, and Transformer/self-attention networks were combined and compared. It was found that a network with CNN, Self-Attention, and a proposed attention-pooling delivers the best single-ended speech quality predictions on the considered …
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eMARLIN: Addressing Coordination and Partial Observability in Distributed Reinforcement Learning for Traffic Signal Control
… data sequences and Long Short-Term Memory (LSTM) networks are employed to mitigate these issues. Additionally, the integration of Transformer-based models into eMARLIN demonstrates further advancements in handling temporal information and enhancing coordination among agents. Overall, this …
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Cloud and digital-twin enhanced thermal safety framework for e-mobility battery management systems
… circuit models (ECM), long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM), and Kolmogorov-Arnold network–LSTM fusion networks. These methods achieve real-time core temperature estimation with errors as low as 0.16°C across a wide range of ambient temperatures and …
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Formation control of multiple robots under packet loss
… proposes the use of Long Short-Term Memory (LSTM) networks, which excel at retaining long-term dependencies, for predicting missing data during packet loss. A comparative analysis involving LSTM, Gated Recurrent Units (GRUs), Linear Interpolation Predictor (LIP), and the Memory Consensus …
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BERT-Based Intrusion Detection System for RF Jamming Attacks in Vehicular Network
As vehicular networks continue to evolve toward increased connectivity and autonomy, they become more vulnerable to cybersecurity threats, particularly Radio Frequency (RF) jamming attacks that can severely disrupt communication systems. This thesis presents a comprehensive study on the application …
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A Comparative Analysis of Machine Learning Models and Traditional Statistical Models for Continuous-Time Survival Analysis
… Forest (RSF), and Long Short-Term Memory (LSTM) algorithms. Model performance was assessed using the concordance index (C-index), integrated Brier score (IBS), and Time-dependent Area Under the Curve (AUC) across three secondary datasets with different characteristics: a breast cancer …
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