Essays about: "recurrent neural network"

Showing result 6 - 10 of 255 essays containing the words recurrent neural network.

1. 6. Object Recognition in Satellite imagesusing improved ConvolutionalRecurrent Neural Network

   University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Author : TARUN NATTALA; [2023]
   Keywords : CRNN; CNN; RNN; Machine Learning and Satellite Image Recognition.;

   Abstract : Background:The background of this research lies in detecting the images from satellites. The recognition of images from satellites has become increasingly importantdue to the vast amount of data that can be obtained from satellites. This thesisaims to develop a method for the recognition of images from satellites using machinelearning techniques. READ MORE

3. 7. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks

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

   Author : Eddie Nevander Hellström; Johan Slettengren; [2023]
   Keywords : ;

   Abstract : Brain-inspired computing is a promising research field, with potential to encouragebreakthroughs within machine learning and enable us to solve complex problems in a moreefficient way. This study aims to compare the performance of brain-like machine learningalgorithms for time series forecasting. READ MORE

4. 8. Unsupervised Online Anomaly Detection in Multivariate Time-Series

   University essay from Uppsala universitet/Datorteknik

   Author : Ludvig Segerholm; [2023]
   Keywords : unsupervised; online; anomaly detection; explainable ai; machine learning; mahalanobis distance;

   Abstract : This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. READ MORE

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

   University essay from KTH/Mekatronik och inbyggda styrsystem

   Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Keywords : 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;

   Abstract : 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

6. 10. 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)

   Author : Luca Colasanti; [2023]
   Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   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