Application of artificial neural network to improve pleurotus sp. cultivation modelling

Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Bo...

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Main Authors:	Abdul Sahli, Fakharudin, N., Zainol, Noor Athirah, Dzulkefli
Format:	Conference or Workshop Item
Language:	English
Published:	EDP Sciences 2019
Subjects:	TP Chemical technology
Online Access:	http://umpir.ump.edu.my/id/eprint/25509/ http://umpir.ump.edu.my/id/eprint/25509/ http://umpir.ump.edu.my/id/eprint/25509/1/33.1%20Application%20of%20artificial%20neural%20network%20to%20improve.pdf

id	ump-25509
recordtype	eprints
spelling	ump-255092019-12-12T03:29:07Z http://umpir.ump.edu.my/id/eprint/25509/ Application of artificial neural network to improve pleurotus sp. cultivation modelling Abdul Sahli, Fakharudin N., Zainol Noor Athirah, Dzulkefli TP Chemical technology Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling. EDP Sciences 2019 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25509/1/33.1%20Application%20of%20artificial%20neural%20network%20to%20improve.pdf Abdul Sahli, Fakharudin and N., Zainol and Noor Athirah, Dzulkefli (2019) Application of artificial neural network to improve pleurotus sp. cultivation modelling. In: Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018), 3-5 December 2018 , Sabah, Malaysia. pp. 1-15., 255. ISSN 2261-236X https://doi.org/10.1051/matecconf/201925502010
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Abdul Sahli, Fakharudin N., Zainol Noor Athirah, Dzulkefli Application of artificial neural network to improve pleurotus sp. cultivation modelling
description	Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.
format	Conference or Workshop Item
author	Abdul Sahli, Fakharudin N., Zainol Noor Athirah, Dzulkefli
author_facet	Abdul Sahli, Fakharudin N., Zainol Noor Athirah, Dzulkefli
author_sort	Abdul Sahli, Fakharudin
title	Application of artificial neural network to improve pleurotus sp. cultivation modelling
title_short	Application of artificial neural network to improve pleurotus sp. cultivation modelling
title_full	Application of artificial neural network to improve pleurotus sp. cultivation modelling
title_fullStr	Application of artificial neural network to improve pleurotus sp. cultivation modelling
title_full_unstemmed	Application of artificial neural network to improve pleurotus sp. cultivation modelling
title_sort	application of artificial neural network to improve pleurotus sp. cultivation modelling
publisher	EDP Sciences
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25509/ http://umpir.ump.edu.my/id/eprint/25509/ http://umpir.ump.edu.my/id/eprint/25509/1/33.1%20Application%20of%20artificial%20neural%20network%20to%20improve.pdf
first_indexed	2023-09-18T22:39:12Z
last_indexed	2023-09-18T22:39:12Z
_version_	1777416801093681152

Application of artificial neural network to improve pleurotus sp. cultivation modelling

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