Dear all,
you are cordially invited to join the master's defense of Namrata Jain on 05.3.21 at 14:30. You can join via Zoom as follows:
Meeting link: https://uni-bamberg.zoom.us/j/97759886756
Kenncode: .fc0$^
Namrata Jain will defend her thesis about Deep Transfer Learning for Timeseries
Regression in Chemical Production.
Abstract:
This thesis introduces the systematic approach of using transfer learning for
time-series regression to build softsensors for the chemical industry. Motivated
by the recent success of transfer learning in image related tasks, we show the applicability
of transfer learning on time-series data. In this thesis, we refer to low resolution
data for manually drawn laboratory samples and to high-resolution
data for automatically generated process data by sensors. The approach to predict
a target component in low-resolution starts with the modeling of a Long short
term memory (LSTM) machine learning model on high-resolution data
and use the trained model on low-resolution data for prediction. Comparing
model predictions using root mean square error (RMSE) shows that LSTM
outperforms machine learning algorithms like Linear Regression, Decision Tree
Regression, Random Forest Regression, and K-Nearest Neighbor Regression.