Enhanced Elman Spike neural network optimized with Red Fox optimization algorithm for sugarcane yield grade prediction

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES Smart Science Pub Date : 2023-07-03 DOI:10.1080/23080477.2023.2229173
M. Deepanayaki, Vidyaathulasiraman
{"title":"Enhanced Elman Spike neural network optimized with Red Fox optimization algorithm for sugarcane yield grade prediction","authors":"M. Deepanayaki, Vidyaathulasiraman","doi":"10.1080/23080477.2023.2229173","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods. Graphical abstract In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"11 1","pages":"568 - 582"},"PeriodicalIF":2.4000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2229173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods. Graphical abstract In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
红狐优化算法优化的增强型Elman Spike神经网络用于甘蔗产量等级预测
摘要本文提出了用Red Fox优化算法优化的增强Elman Spike神经网络(EESNN)用于甘蔗产量等级预测(SYGD-EESNN-RFOA)。最初,采用糖产量预测数据集。然后通过形态学滤波和扩展经验小波变换(MF-EEWT)的混合分解方法对输入数据进行预处理,以检索缺失值。这些预处理的输出被提供给特征选择方法。在特征选择过程中,采用了基于熵-峰度的特征选择方法。这些提取的特征被输入EESNN,然后将甘蔗产量分为低等级、中等等级和高等级。通常,EESNN方法没有指示使用任何优化策略来计算最佳参数,以确保准确的甘蔗产量预测。为了准确预测甘蔗产量,提出了红狐优化算法(RFOA)。所提出的方法是在Python中执行的;其性能根据性能指标进行评估,如精度、均方根误差、均方误差、平均绝对百分比误差、收敛曲线和2021-2027年甘蔗产量变化的预测百分比。与现有方法相比,所提出的SYGP-EESNN-RFOA框架具有更高的准确性,分别为27.5%、16.65%和9.13%,特异性分别高15.21%。本文提出了用Red Fox优化算法优化的增强Elman Spike神经网络(EESNN)用于甘蔗产量等级预测(SYGD-EESNN-RFOA)。EESNN方法没有表明使用任何优化策略来计算最佳参数,以确保准确的甘蔗产量预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Smart Science
Smart Science Engineering-Engineering (all)
CiteScore
4.70
自引率
4.30%
发文量
21
期刊介绍: Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials
期刊最新文献
MFCDFT and impedance characteristic-based adaptive technique for fault and power swing discrimination Frequency and voltage stability of multi microgrid system using 2-DOF TIDF FUZZY controller AI-based fault recognition and classification in the IEEE 9-bus system interconnected to PV systems A cost-emission based scheme for residential energy hub management considering comfortable lifestyle and responsible demand Intelligent faults diagnostics of turbine vibration’s via Fourier transform and neuro-fuzzy systems with wavelets exploitation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1