International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 8, Issue 12 (December 2021), Pages: 110-116

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 Original Research Paper

 Title: Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

 Author(s): Vinu Sherimon 1, *, P. C. Sherimon 2, Alaa Ismaeel 2

 Affiliation(s):

 1Department of Information Technology, University of Technology and Applied Sciences, Muscat, Oman
 2Faculty of Computer Studies, Arab Open University, Muscat, Oman

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4923-2841

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.12.013

 Abstract:

For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas. 

 © 2021 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Prediction of seafood quality, Linear regression-based prediction, Least-square method, LSM, Fish quality

 Article History: Received 17 July 2021, Received in revised form 23 October 2021, Accepted 23 October 2021

 Acknowledgment 

No Acknowledgment.

 Funding 

The research leading to these results has received funding from the Research Council (TRC) of the Sultanate of Oman under the Block Funding Program BFP/ RGP/ ICT/ 18/ 113.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Sherimon V, Sherimon PC, and Ismaeel A (2021). Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers. International Journal of Advanced and Applied Sciences, 8(12): 110-116

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 Figures

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 Tables

 No Table  

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