Interpretable Machine-Learning for Predicting Molecular Weight of PLA Based on Artificial Bee Colony Optimization Algorithm and Adaptive Neurofuzzy Inference System-Reference-Cited by-同舟云学术

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Interpretable Machine-Learning for Predicting Molecular Weight of PLA Based on Artificial Bee Colony Optimization Algorithm and Adaptive Neurofuzzy Inference System

Published:2024-06-13 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 35th Irish Signals and Systems Conference (ISSC)
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Author:

Masoumi Amir Pouya¹,Creedon Leo¹,Ghosh Ramen¹,Munir Nimra¹,McMorrow Ross¹,McAfee Marion¹

Affiliation:

1. Atlantic Technological University,Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE),Sligo,Ireland

Funder

Science Foundation Ireland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10602774/10602795/10603031.pdf?arnumber=10603031

Reference14 articles.

1. Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid

2. A novel multivariate filter method for feature selection in text classification problems

3. Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

4. A tutorial-based survey on feature selection: Recent advancements on feature selection

5. Improved ReliefF-based feature selection algorithm for cancer histology

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