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...

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Vydáno v:	Process integration and optimization for sustainability Ročník 9; číslo 1; s. 197 - 207
Hlavní autoři:	Jin, Hai Tao, Chen, Zhiyuan, Law, Chung Lim
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.03.2025 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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