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Showing result 1 - 5 of 62 essays matching the above criteria.

1. 1. Time Series Forecasting on Database Storage

   University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Author : Pranav Patel; [2024]
   Keywords : Machine Learning; Time Series Forecasting; Prediction; Neural Networks; CNN; RNN; Database Storage;

   Abstract : Time Series Forecasting has become vital in various industries ranging from weather forecasting to business forecasting. There is a need to research database storage solutions for companies in order to optimize resource allocation, enhance decision making process and enable predictive data storage maintenance. READ MORE

3. 2. Heart rate estimation from wrist-PPG signals in activity by deep learning methods

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Marie-Ange Stefanos; [2023]
   Keywords : Deep Learning; Medical Data; Signal Processing; Heart Rate Estimation; Wrist Photoplethysmography; Djup lärning; Medicinska Data; Signalbehandling; Pulsuppskattning; Handledsfotopletysmograf;

   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

4. 3. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data

   University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

   Author : Deepthy Prasad; Swathi Hampapura Sripada; [2023]
   Keywords : multivariate - time series; anomaly detection; neural networks; autoencoders; interpretability; counterfactuals;

   Abstract : 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

5. 4. Anomaly Detection in the EtherCAT Network of a Power Station : Improving a Graph Convolutional Neural Network Framework

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Niklas Barth; [2023]
   Keywords : Unsupervised Learning; Multivariate Time Series; Graph Convolutional Neural Networks; Anomaly Detection; Industrial Control System; EtherCAT; Power Station; Electricity Grid;

   Abstract : In this thesis, an anomaly detection framework is assessed and fine-tuned to detect and explain anomalies in a power station, where EtherCAT, an Industrial Control System, is employed for monitoring. The chosen framework is based on a previously published Graph Neural Network (GNN) model, utilizing attention mechanisms to capture complex relationships between diverse measurements within the EtherCAT system. READ MORE

6. 5. Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

   University essay from KTH/Fysik

   Author : Jeanette Marie Victoria Skeppland Hole; [2023]
   Keywords : 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;

   Abstract : 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). READ MORE