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

Ullah Miah, Md Saef and Junaida, Sulaiman and Kamal Zuhairi, Zamli and Samiur Rashid, Shah and Khan Chowdhury, Ahmed Jalal (2023) Predicting carboxymethyl cellulase assay (CMCase) production using artificial neural network and explicit feature selection approach. In: 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023 , 7-9 April 2023 , Pune. pp. 1-6.. ISBN 979-835033401-2

Pdf
Predicting carboxymethyl cellulase assay (CMCase) production using artificial.pdf
Restricted to Repository staff only
Download (1MB) | Request a copy

Preview

Pdf
Predicting carboxymethyl cellulase assay (CMCase) production using artificial neural network and explicit feature selection approach_ABS.pdf
Download (351kB) | Preview

DOI/Official URL: https://doi.org/10.1109/I2CT57861.2023.10126364

Abstract

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 most relevant features for CMCase production prediction. The ANN model was trained using both the selected features and all available features of the CMCase production data. The results showed that the explicit feature selection approach improved the performance of the ANN model in terms of prediction accuracy compared to using all the features available in the dataset. The main effect analysis (MEA) was found to be the best method for selecting the explicit features for predicting CMCase production. The ANN model trained using the MEA identified features, achieved 96.3% R2 score and a MAE of 0.057 and a MSE of 0.035. The proposed method is an effective approach for predicting CMCase production and can be used to optimize CMCase production and reduce costs in various industries.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Artificial neural network; CMCase; Enzyme production prediction; Main effect analysis feature selection; Random forest regressor feature selection
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	06 Nov 2023 04:42
Last Modified:	06 Nov 2023 04:42
URI:	http://umpir.ump.edu.my/id/eprint/38772
Download Statistic:	View Download Statistics

Actions (login required)

View Item