Sökning: "Long short-term memory network"

Visar resultat 6 - 10 av 207 uppsatser innehållade orden Long short-term memory network.

1. 6. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Luca Colasanti; [2023]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

3. 7. AI/ML Development for RAN Applications : Deep Learning in Log Event Prediction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yuxin Sun; [2023]
   Nyckelord :LSTM; Anomaly Detection; Failure Prediction; Log Mining; Deep Learning; LSTM; Anomali Detection; Failure Prediction; Log Mining; Deep Learning;

   Sammanfattning : Since many log tracing application and diagnostic commands are now available on nodes at base station, event log can easily be collected, parsed and structured for network performance analysis. In order to improve In Service Performance of customer network, a sequential machine learning model can be trained, test, and deployed on each node to learn from the past events to predict future crashes or a failure. LÄS MER

4. 8. Plant yield prediction in indoor farming using machine learning

   Magister-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

   Författare :Anjali Ashok; Mary Adesoba; [2023]
   Nyckelord :Yield prediction; Machine Learning; Hyperparameter tweaking; Support Vector Regression; Long Short-Term Memory; Artificial Neural Network;

   Sammanfattning : Agricultural industry has started to rely more on data driven approaches to improve productivity and utilize their resources effectively. This thesis project was carried out in collaboration with Ljusgårda AB, it explores plant yield prediction using machine learning models and hyperparameter tweaking. LÄS MER

5. 9. Unauthorised Session Detection with RNN-LSTM Models and Topological Data Analysis

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Nazar Maksymchuk Netterström; [2023]
   Nyckelord :Recurrent Neural Network; Long-Short-Term-Memory; Topological Data Analysis; Session based data; Anomaly detection; Time-series analysis; Imbalanced data; Master thesis; Neurala nätverk; Topologisk data analys; Detektion av avvikelse; Sessionsbaserad data; Tidserieanalys; Inbalancerad data; Masteruppsats;

   Sammanfattning : This thesis explores the possibility of using session-based customers data from Svenska Handelsbanken AB to detect fraudulent sessions. Tools within Topological Data Analysis are employed to analyse customers behavior and examine topological properties such as homology and stable rank at the individual level. LÄS MER

7. 10. Artificial Neural Networks for Financial Time Series Prediction

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Dana Malas; [2023]
   Nyckelord :artificial neural networks; time series analysis; deep learning; finance; long short-term memory; simple moving average;

   Sammanfattning : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. LÄS MER