Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification  

作　　者：Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait 

机构地区：[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

出　　处：《Computer Systems Science & Engineering》2023年第2期1419-1432,共14页计算机系统科学与工程（英文）

基　　金：supported by the Researchers Supporting Program(TUMA-Project-2021-27);Almaarefa University,Riyadh,Saudi Arabia.Taif University Researchers Supporting Project Number(TURSP-2020/161);Taif University,Taif,Saudi Arabia.

摘　　要：Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated 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 classifica-tion(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 exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关 键 词：Soil classification intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]