Utilization of artificial immune system in prediction of paddy production

A. B M Khidzir, M. A. Malek, Amelia Ritahani Ismail, Ju Neng Liew, Ting Sie Chun

Research output: Contribution to journalArticle

Abstract

This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production.

Original languageEnglish
Pages (from-to)1462-1467
Number of pages6
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number3
Publication statusPublished - 2015

Fingerprint

Immune system
Pattern recognition
Climate change
Mean square error
Testing

Keywords

  • Artificial Immune System (AIS)
  • Climate
  • Clonal Selection Based Algorithm (CSA)
  • Paddy production
  • Prediction

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Khidzir, A. B. M., Malek, M. A., Ismail, A. R., Liew, J. N., & Chun, T. S. (2015). Utilization of artificial immune system in prediction of paddy production. ARPN Journal of Engineering and Applied Sciences, 10(3), 1462-1467.

Utilization of artificial immune system in prediction of paddy production. / Khidzir, A. B M; Malek, M. A.; Ismail, Amelia Ritahani; Liew, Ju Neng; Chun, Ting Sie.

In: ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 3, 2015, p. 1462-1467.

Research output: Contribution to journalArticle

Khidzir, ABM, Malek, MA, Ismail, AR, Liew, JN & Chun, TS 2015, 'Utilization of artificial immune system in prediction of paddy production', ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 3, pp. 1462-1467.
Khidzir, A. B M ; Malek, M. A. ; Ismail, Amelia Ritahani ; Liew, Ju Neng ; Chun, Ting Sie. / Utilization of artificial immune system in prediction of paddy production. In: ARPN Journal of Engineering and Applied Sciences. 2015 ; Vol. 10, No. 3. pp. 1462-1467.
@article{ed76b4fbb3fe482baad57560fa5cea07,
title = "Utilization of artificial immune system in prediction of paddy production",
abstract = "This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90{\%}-92{\%} is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production.",
keywords = "Artificial Immune System (AIS), Climate, Clonal Selection Based Algorithm (CSA), Paddy production, Prediction",
author = "Khidzir, {A. B M} and Malek, {M. A.} and Ismail, {Amelia Ritahani} and Liew, {Ju Neng} and Chun, {Ting Sie}",
year = "2015",
language = "English",
volume = "10",
pages = "1462--1467",
journal = "ARPN Journal of Engineering and Applied Sciences",
issn = "1819-6608",
publisher = "Asian Research Publishing Network (ARPN)",
number = "3",

}

TY - JOUR

T1 - Utilization of artificial immune system in prediction of paddy production

AU - Khidzir, A. B M

AU - Malek, M. A.

AU - Ismail, Amelia Ritahani

AU - Liew, Ju Neng

AU - Chun, Ting Sie

PY - 2015

Y1 - 2015

N2 - This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production.

AB - This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production.

KW - Artificial Immune System (AIS)

KW - Climate

KW - Clonal Selection Based Algorithm (CSA)

KW - Paddy production

KW - Prediction

UR - http://www.scopus.com/inward/record.url?scp=84923838698&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923838698&partnerID=8YFLogxK

M3 - Article

VL - 10

SP - 1462

EP - 1467

JO - ARPN Journal of Engineering and Applied Sciences

JF - ARPN Journal of Engineering and Applied Sciences

SN - 1819-6608

IS - 3

ER -