Machine learning algorithms have become a very essential tool in the fields of math and engineering, as well as for industrial purposes (fabric, medicine, sport, etc.). This research leverages classical machine learning algorithms for innovative accurate and efficient fabric protrusion detection. We present an approach for improving model training with a small dataset. We use a few classic statistics machine learning algorithms (decision trees, logistic regression, etc.) and a fully connected neural network (NN) model. We also present an approach to optimize a model accuracy rate and execution time for finding the best accuracy using parallel processing with Dask (Python).
|State||Published - 1 Nov 2022|
- fabric protrusion
- machine learning
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Hardware and Architecture
- Computer Networks and Communications
- Electrical and Electronic Engineering
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Research from Ben-Gurion University of the Negev Has Provided New Data on Machine Learning (Meta Classification Model of Surface Appearance for Small Dataset Using Parallel Processing)
Ofer Hadar & Raz Birman
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