Predicting carboxymethyl cellulase assay (CMCase) production using artificial neural network and explicit feature selection approach

• Main Authors: Ullah Miah, Md Saef, Junaida, Sulaiman, Kamal Zuhairi, Zamli, Samiur Rashid, Shah, Khan Chowdhury, Ahmed Jalal
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/38772/1/Predicting%20carboxymethyl%20cellulase%20assay%20%28CMCase%29%20production%20using%20artificial.pdf; http://umpir.ump.edu.my/id/eprint/38772/2/Predicting%20carboxymethyl%20cellulase%20assay%20%28CMCase%29%20production%20using%20artificial%20neural%20network%20and%20explicit%20feature%20selection%20approach_ABS.pdf; http://umpir.ump.edu.my/id/eprint/38772/; https://doi.org/10.1109/I2CT57861.2023.10126364

This paper presents a method for predicting carboxymethyl cellulase (CMCase) production using artificial neural networks (ANNs) and an explicit feature selection approach. A dataset of CMCase production experiments was collected, and an explicit feature selection approach was applied to select the m...