Essays about: "long short-term memory recurrent neural network"
Showing result 1 - 5 of 98 essays containing the words long short-term memory recurrent neural network.
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1. Drivers of sea level variability using neural networks
University essay from Göteborgs universitet/Institutionen för geovetenskaperAbstract : Understanding the forcing of regional sea level variability is crucial as many people all over the world live along the coasts and are endangered by extreme sea levels and the global sea level rise. The adding of fresh water into the oceans due to melting of the Earth’s land ice together with thermosteric changes has led to a rise of the global mean sea level with an accelerating rate during the twentieth century. READ MORE
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2. Primary Drivers of Sea Level Variability in the North – Baltic Sea Transition Using Machine Learning
University essay from Göteborgs universitet/Institutionen för geovetenskaperAbstract : Global mean sea level is rising, however not uniformly. Regional deviations of sea surface height (SSH) are common due to local drivers, including surface winds, ocean density stratifications, vertical land- & crustal movements and more. READ MORE
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3. Heart rate estimation from wrist-PPG signals in activity by deep learning methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. READ MORE
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4. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition
University essay from KTH/Mekatronik och inbyggda styrsystemAbstract : 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. READ MORE
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5. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE
