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Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Majed Alsanea3, Abdul Rahaman Wahab Sait4

1 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Kingdom of Saudi Arabia
3 Department of Computing, Arabeast Colleges, Riyadh, 11583, Kingdom of Saudi Arabia
4 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email: email

Computer Systems Science and Engineering 2023, 44(2), 1419-1432. https://doi.org/10.32604/csse.2023.027377

Abstract

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classification (FCMCSO-ASC) technique. The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil. To accomplish this, the FCMCSO-ASC technique incorporates local diagonal extrema pattern (LDEP) as a feature extractor for producing a collection of feature vectors. In addition, the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm. For examining the enhanced soil classification outcomes of the FCMCSO-ASC technique, a series of simulations were carried out on benchmark dataset and the experimental outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

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Cite This Article

APA Style
Dutta, A.K., Albagory, Y., Faraj, M.A., Alsanea, M., Sait, A.R.W. (2023). Cat swarm with fuzzy cognitive maps for automated soil classification. Computer Systems Science and Engineering, 44(2), 1419-1432. https://doi.org/10.32604/csse.2023.027377
Vancouver Style
Dutta AK, Albagory Y, Faraj MA, Alsanea M, Sait ARW. Cat swarm with fuzzy cognitive maps for automated soil classification. Comput Syst Sci Eng. 2023;44(2):1419-1432 https://doi.org/10.32604/csse.2023.027377
IEEE Style
A.K. Dutta, Y. Albagory, M.A. Faraj, M. Alsanea, and A.R.W. Sait "Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification," Comput. Syst. Sci. Eng., vol. 44, no. 2, pp. 1419-1432. 2023. https://doi.org/10.32604/csse.2023.027377



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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