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 17 of 17 for “"Gated Recurrent Unit (GRU)"”.
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Using deep learning to classify community network traffic
… DL Models (Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL …
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On the computational power of RNNs
… neural network architectures such as the basic recurrent neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks. But what is the computational power of such systems? We prove that finite precision RNNs …
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Predicting football match outcomes with fantasy league data and deep learning
… used to predict the outcome of matches. We use a Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU) layers to predict the outcome of matches. Our combined method shows good results, improving significantly on state of the art.
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Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries
… temperature estimation method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… Trees Classifier—and two deep learning models, Gated Recurrent Unit (GRU) and Neural Network (NN). Performance was evaluated using Recall, Precision, F1 Score, ROC-AUC Score, and Accuracy. The Bagging Classifier and Random Forest Classifier models exhibit exceptional performance, achieving …
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INCORPORATING EMR AND GENOMIC DATA USING NLP AND MACHINE LEARNING TO REFINE CANCER TREATMENT
… extract NGS-related information. Three types of recurrent neural network (RNN), including gated recurrent unit (GRU), long-short term memory (LSTM), and bidirectional LSTM (LSTM_Bi), were applied to classify documents to treatment-change group and no-treatment-change group. The performances of …
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Data-Driven Decoding of Quantum Surface Codes using Recurrent Neural Networks
… an approach to decode QEC data. In particular, recurrent neural network (RNN) architectures, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are investigated as their are able to capture temporal dependencies and model sequential data. GRU and LSTM models are trained …
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Forecasting Energy Consumption using Sequence to Sequence Attention models
… costs, and identify energy savings opportunities, it is essential to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely-used smart meters, have created possibilities for sensor based energy forecasting. Machine learning algorithms commonly used for …
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Path Following using Frequency Modulated Continuous Wave millimetre-wave Automotive Radar
… LiDAR, GNSS, cameras, inertial measurement units, ultrasonic, and radar. In previous research, the use of radar in these systems is to aid with the detection of obstacles, which could then be used to determine paths, not as a single modality system. This thesis presents research into using …
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H2OGAN: A Deep Learning Approach for Detecting and Generating Cyber-Physical Anomalies
… sensor anomalies, and malicious attacks. A recurrent neural network (RNN) model, i.e., gated recurrent unit (GRU), is used to classify and capture the temporal dynamics those events. Subsequently, experiments with real-world data from Alexandria Renew Enterprises (AlexRenew), a wastewater …
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Machine learning approaches for malware classification based on hybrid artefacts
… For consecutive sequences, concatenating the Gated Recurrent Unit (GRU) and Transformers model can yield the highest accuracy at 97% for Noriben operations, while GRU can achieve the maximum accuracy for Opcode sequences at 89%.
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Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security.
… with a large number of electronic control units (ECUs), which are interconnected through the controller area network (CAN) bus for real-time data exchange. However, the CAN bus lacks security measures, rendering it susceptible to cyberattacks, endangering passenger safety. Although …
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Application of data-driven neural networks to bio-inspired lattice design and prediction of multiphysics solution fields
… under dynamic transverse compression. A gated recurrent unit (GRU) predicts stress-strain curves and energy absorption from geometric inputs. Systematic variations of design parameters, such as tubule shapes and orientations, enable the creation of a dataset comprising 128,000 designs. …
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Biological applications, visualizations, and extensions of the long short-term memory network
… implementation. Given the success of the gated recurrent unit (GRU), which has two gates, a natural question is whether any of the LSTM gates are redundant. Research has shown that the forget gate is one of the most important gates in the LSTM. Hence, we propose a forget-gate-only version …
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Data-Driven Research On Engineering Design Thinking And Behaviors In Computer-Aided Systems Design: Analysis, Modeling, And Prediction
… the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model.</p> <p>In the work on analysis, this dissertation focuses primarily on different clustering analysis techniques. Based on the behaviors modeled, designers showing similar behavioral patterns can be clustered, from …
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Gallium Nitride Power Electronics using Machine Learning
… of such devices which are new to the power community, there is a steep learning curve involved, with dispersed information on how best to employ these devices. This work aims to solve this problem through the development of a universal GaN power device and circuit model and the formulation of …
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Gaze-Aware Driver Maneuver Prediction Using Object Detection and Sequential Deep Learning Models for Advanced Driver Assistance Systems
… and evaluate three deep learning 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 …