Sökning: "Long Short-Term Memory Networks"

Visar resultat 11 - 15 av 175 uppsatser innehållade orden Long Short-Term Memory Networks.

1. 11. Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

   Master-uppsats, KTH/Fysik

   Författare :Jeanette Marie Victoria Skeppland Hole; [2023]
   Nyckelord :ECG; ECG-analysis; QRS detector; Artificial Intelligence; Machine Learning; Deep neural networks; Long short-term memory; Convolutional neural network; Multilayer perceptron; EKG; EKG-analys; QRS detektor; Artificiell intelligens; Maskininlärning; Djupa neurala nätverk; Long short-term memory; Convolutional neural network; Multilayer perceptron;

   Sammanfattning : This study aimed to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in implementing a QRS detector forambulatory electrocardiography (ECG) monitoring devices. Three ML models, namely long short-term memory (LSTM), convolutional neural network (CNN), and multilayer perceptron (MLP), were compared and evaluated using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH noise stress test database (NSTDB). LÄS MER

3. 12. Demand Forecasting of Outbound Logistics Using Neural Networks

   Master-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Författare :Enobong Paul Otuodung; Gulten Gorhan; [2023]
   Nyckelord :Time Series Prediction; Demand Forecasting; Outbound Logistics; Machine Learning; Deep Learning; Univariate Forecasting; Multivariate Forecasting; Multi-Step Forecasting; LSTM; CNN-LSTM; ConvLSTM; Encoder-Decoder; Design science; Design science;

   Sammanfattning : Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. LÄS MER

4. 13. Latent Data-Structures for Complex State Representation : A Steppingstone to Generating Synthetic 5G RAN data using Deep Learning

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Högenergifysik

   Författare :Jakob Häggström; [2023]
   Nyckelord :Data Science; Machine Learning; Generative models; Artificial Intelligence; 5GRAN;

   Sammanfattning : The aim of this thesis is to investigate the feasibility of applying generative deep learning models on data related to 5G Radio Access Networks (5GRAN). Simulated data is used in order to develop the generative models, and this project serves as a proof of concept for further applications on real data. LÄS MER

5. 14. Temporal Localization of Representations in Recurrent Neural Networks

   Master-uppsats, Högskolan Dalarna/Institutionen för information och teknik

   Författare :Asadullah Najam; [2023]
   Nyckelord :Recurrent Neural Networks RNNs ; Deep Learning; Time Series Prediction; Exploding Values; Gradient Decay; Long Short-Term Memory LSTMs ; Gated Recurrent Units GRUs ; Attention Mechanism; Moving Representations; Localizing Representations;

   Sammanfattning : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. LÄS MER

7. 15. Local Planning for Unmanned Ground Vehicles using Imitation Learning

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :Johan Henningsson; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Mobile robotics is an expanding field worldwide leading to the need for advanced path-planning algorithms that can traverse various environments. Current state-ofthe- art path-planning algorithms used at the Swedish Defence Research Agency, FOI, tend to be inflexible and parameter dependent. LÄS MER