Sökning: "RNN-modeller"

Hittade 5 uppsatser innehållade ordet RNN-modeller.

1. 1. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

3. 2. Remaining Useful Life Prediction of Power Electronic Devices Using Recurrent Neural Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Congrui Cai; [2023]
   Nyckelord :Power electronics; Prognostics and health management; Remaining useful life; Recurrent neural network; Kraftelektronik; Prognostik och hälsoledning; Återstående livslängd; Återkommande neurala nätverk;

   Sammanfattning : The growing demand for sustainable technology has led to an increased application of power electronics. As these devices are often exposed to harsh conditions, their reliability is a primary concern for both manufacturers and users. LÄS MER

4. 3. Sequential Deep Learning Models for Neonatal Sepsis Detection : A suitability assessment of deep learning models for event detection in physiological data

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Henrik Alex Siren; [2022]
   Nyckelord :Neonatal sepsis; Deep learning; Recurrent models; Convolutional models; Physiological data; Neonatal sepsis; Djupinlärning; RNN-modeller; CNN-modeller; Fysiologisk data;

   Sammanfattning : Sepsis is a life-threatening condition that neonatal patients are especially susceptible to. Fortunately, improved bedside monitoring has enabled the collection and use of continuous vital signs data for the purpose of detecting conditions such as sepsis. LÄS MER

5. 4. Predicting average response sentiments to mass sent emails using RNN

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Adel Bavey; [2021]
   Nyckelord :Sentiment Analysis; Sentiment Forecasting; Natural Language Processing; Machine Learning; Recurrent Neural Nets; E-mails; Sentiment Analys; Sentiment Förutsägning; Naturlig Språkbehandling; Maskininlärning; Recurrenta Neurala nät; E-mails;

   Sammanfattning : This study is concerned with using the popular Recurrent Neural Network (RNN) model, and its variants Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), on the novel problem of Sentiment Forecasting (SF). The goal of SF is to predict what the sentiment of a response will be in a conversation, using only the previous utterance. LÄS MER

7. 5. Spelling Correction in a Music Entity Search Engine by Learning from Historical Search Queries

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Maria Movin; [2018]
   Nyckelord :Machine Learning; LSTM; Music search; Query Suggestion;

   Sammanfattning : Query spelling correction is an important component of modern search engines that can help users to express their intent, and thus improve search quality. In this study, we investigated with what accuracy a sequence-to-sequence recurrent neural network (RNN) can recognise and correct misspellings in a music search engine, when the model is trained with old search queries. LÄS MER