Moisture Prediction in Bird’s Nest Drying with Machine Learning Models

The moisture content plays a pivotal role in determining the quality of dried food products. With the aim of refining moisture estimation accuracy during the drying process and streamlining workflow efficiency and automation, this study investigates machine learning models for predicting moisture le...

Full description

Saved in:

Bibliographic Details
Published in:	Process integration and optimization for sustainability Vol. 9; no. 1; pp. 197 - 207
Main Authors:	Jin, Hai Tao, Chen, Zhiyuan, Law, Chung Lim
Format:	Journal Article
Language:	English
Published:	Singapore Springer Nature Singapore 01.03.2025 Springer Nature B.V
Subjects:	Accuracy Artificial intelligence Automation Birds Datasets Drying Economics and Management Energy Policy Engineering Experiments Food Food quality Genetic algorithms Heat Humidity Industrial and Production Engineering Industrial Chemistry/Chemical Engineering Learning algorithms Machine learning Moisture content Multilayer perceptrons Multilayers Neural networks Original Research Paper Sustainable Development Temperature Waste Management/Waste Technology Water content Workflow Selangor Malaysia Malaysia Hyperparameter optimization Food drying Machine learning Prediction
ISSN:	2509-4238, 2509-4246
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!