Sökning: "LSTM"
Visar resultat 1 - 5 av 476 uppsatser innehållade ordet LSTM.
1. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). LÄS MER
2. Predicting Navigational Patterns in Web Applications using Machine Learning Techniques
Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknikSammanfattning : In large corporations, customer support is a costly service, and an area of constant optimization. Solutions to increase efficiency and decrease bottlenecks are constantly needed. LÄS MER
3. Time Series Forecasting on Database Storage
Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Sammanfattning : 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. LÄS MER
4. CNN-LSTM architecture for predicting hazardous driving situations
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : This study aims to investigate how a CNN-LSTM model can be used together with recorded vehicle data from trucks and external weather data in order to predict a hazardous driving situation. The dataset consists of three-second-long driving snippets from customer and development trucks registered within Europe. LÄS MER
5. Audio Anomaly Detection in Cars
Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaperSammanfattning : Audio anomaly detection in the context of car driving is a crucial task for ensuring vehicle safety and identifying potential faults. This paper aims to investigate and compare different methods for unsupervised audio anomaly detection using a data set consisting of recorded audio data from fault injections and normal "no fault" driving. LÄS MER