Essays about: "application of Neural Networks"

Showing result 6 - 10 of 215 essays containing the words application of Neural Networks.

1. 6. 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

3. 7. 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

4. 8. Comparing dropout regularization algorithms for convolutional neural networks identifying malignant cells for diagnosis of leukemia

   University essay from Uppsala universitet/Statistiska institutionen

   Author : Hampus Engström; Alexander Koutakis; [2023]
   Keywords : classification; Bernoulli; Gaussian; spatial; machine learning; cancer; myeloid; myeloproliferative neoplasms;

   Abstract : Fast and high quality classifications of cells inflicted with malignant mutations is essential for diagnosing patients with different forms of leukemia, to quickly be able give patients the crucial care they need. Convolutional neural networks (CNNs) can be trained and used for this purpose. READ MORE

5. 9. The Impact of the Retrieval Text Set for Text Sentiment Classification With the Retrieval-Augmented Language Model REALM

   University essay from KTH/Matematik (Inst.)

   Author : Oscar Blommegård; [2023]
   Keywords : The Impact of the Retrieval Text Set for Text Sentiment Classification With the Retrieval-Augmented Language Model REALM; Hämtningsförstärkta språkmodeller; Natural Language Processing; Transformers; Djupinlärning; Textklassificering;

   Abstract : Large Language Models (LLMs) have demonstrated impressive results across various language technology tasks. By training on large corpora of diverse text collections from the internet, these models learn to process text effectively, allowing them to acquire comprehensive world knowledge. READ MORE

6. 10. Low-power Implementation of Neural Network Extension for RISC-V CPU

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

   Author : Dario Lo Presti Costantino; [2023]
   Keywords : Artificial intelligence; Deep learning; Neural networks; Edge computing; Convolutional neural networks; Low-power electronics; RISC-V; AI accelerators; Parallel processing; Artificiell intelligens; Deep learning; Neurala nätverk; Edge computing; konvolutionella neurala nätverk; Lågeffektelektronik; RISC-V; AI-acceleratorer; Parallell bearbetning;

   Abstract : Deep Learning and Neural Networks have been studied and developed for many years as of today, but there is still a great need of research on this field, because the industry needs are rapidly changing. The new challenge in this field is called edge inference and it is the deployment of Deep Learning on small, simple and cheap devices, such as low-power microcontrollers. READ MORE