Back-loaded connection model with wrap loss.

Back-loaded connection model with wrap loss.

<div><p>In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the temperatures in a city can vary...

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Bibliographic Details
Main Author: Md Khalid Hossen (20706979) (author)
Other Authors: Yan-Tsung Peng (20706982) (author), Meng Chang Chen (12551601) (author)
Published: 2025
Subjects:
Medicine
Sociology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
neural network models
various unclear reasons
proposed two models
highest drifting stations
identify data drifting
data drifting scenario
data drifting problem
data drifting issue
loaded connection model
baselines bilstm model
previous year ’
div >< p
accurate results compared
data drifting
various stations
sub ><
proposed front
weather data
data used
data collected
comparing data
experimental results
accurate results
model training
cnn model
year due
training process
th </
term memory
specific locations
significant drift
p values
p value
mape metrics
long short
highlight areas
five years
different periods
different contexts
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