Essays about: "Long Short-Term Memory Neural Network"
Showing result 1 - 5 of 180 essays containing the words Long Short-Term Memory 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. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. READ MORE
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5. 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
