2 research outputs found
ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction
The pulp and paper manufacturing industry requires precise quality control to
ensure pure, contaminant-free end products suitable for various applications.
Fungal spore concentration is a crucial metric that affects paper usability,
and current testing methods are labor-intensive with delayed results, hindering
real-time control strategies. To address this, a machine learning algorithm
utilizing time-series data and domain knowledge was proposed. The optimal model
employed Ridge Regression achieving an MSE of 2.90 on training and validation
data. This approach could lead to significant improvements in efficiency and
sustainability by providing real-time predictions for fungal spore
concentrations. This paper showcases a promising method for real-time fungal
spore concentration prediction, enabling stringent quality control measures in
the pulp-and-paper industry
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
In this paper, we introduce a framework ARBEx, a novel attentive feature
extraction framework driven by Vision Transformer with reliability balancing to
cope against poor class distributions, bias, and uncertainty in the facial
expression learning (FEL) task. We reinforce several data pre-processing and
refinement methods along with a window-based cross-attention ViT to squeeze the
best of the data. We also employ learnable anchor points in the embedding space
with label distributions and multi-head self-attention mechanism to optimize
performance against weak predictions with reliability balancing, which is a
strategy that leverages anchor points, attention scores, and confidence values
to enhance the resilience of label predictions. To ensure correct label
classification and improve the models' discriminative power, we introduce
anchor loss, which encourages large margins between anchor points.
Additionally, the multi-head self-attention mechanism, which is also trainable,
plays an integral role in identifying accurate labels. This approach provides
critical elements for improving the reliability of predictions and has a
substantial positive effect on final prediction capabilities. Our adaptive
model can be integrated with any deep neural network to forestall challenges in
various recognition tasks. Our strategy outperforms current state-of-the-art
methodologies, according to extensive experiments conducted in a variety of
contexts.Comment: 10 pages, 7 figures. Code: https://github.com/takihasan/ARBE