检索结果-内蒙古大学图书馆

Training Neural Networks by Enhance grasshopper optimization algorithm for Spam Detection System

IEEE ACCESS 2021年 9卷 116768-116813页

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Fadzli, Syed Abdullah Ghanem, Waheed Ali H. M. Univ Sultan Zainal Abidin Fac Informat & Comp Kuala Terengganu 22200 Malaysia Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Kuala Terengganu 21030 Malaysia Univ Aden Fac Engn Aden Yemen Univ Aden Fac Educ Aden & Saber Aden Yemen

A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced grasshopper optimization algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.

关键词： Unsolicited e-mail Filtering Classification algorithms optimization Training Sociology Convergence Metaheuristic grasshopper optimization algorithm classification artificial neural network multilayer perceptron spam email spam detection system

来源：评论

学校读者我要写书评

暂无评论

A novel grasshopper optimization algorithm based on swarm state difference and its application

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5973-5986页

作者： Lv, Zhaoming Peng, Rong Wuhan Univ Sch Comp Sci Wuhan Peoples R China

The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population;the second stage merges the learned population into the gregarious population and updates each grasshopper;and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems.

关键词： Meta-heuristic algorithms grasshopper optimization algorithm solitarious and gregarious states chemotaxis operator ontology matching

来源：评论

学校读者我要写书评

暂无评论

An improved grasshopper optimization algorithm with application to financial stress prediction

引用

APPLIED MATHEMATICAL MODELLING 2018年 64卷 654-668页

作者： Luo, Jie Chen, Huiling Zhang, Qian Xu, Yueting Huang, Hui Zhao, Xuehua Wenzhou Univ Dept Comp Sci Wenzhou 325035 Zhejiang Peoples R China Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China

This study proposed an improved grasshopper optimization algorithm (GOA) for continuous optimization and applied it successfully to the financial stress prediction problem. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behavior of grasshoppers. This algorithm is proved to be efficient in solving global unconstrained and constrained optimization problems. However, the original GOA has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these shortcomings, an improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established. Firstly, Gaussian mutation is employed to increase population diversity, which can make GOA has stronger local search ability. Then, Levy-flight strategy was adopted to enhance the randomness of the search agent's movement, which can make GOA have a stronger global exploration capability. Furthermore, opposition-based learning was introduced into GOA for more efficient search solution space. Based on the improved GOA, an effective kernel extreme learning machine model was developed for financial stress prediction. As the experimental results show, the three strategies can significantly boost the performance of GOA and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Kernel extreme learning machine grasshopper optimization algorithm Parameter optimization Opposition-based learning Levy-flight Gaussian mutation

来源：评论

学校读者我要写书评

暂无评论

Parameter Extraction of Solar Photovoltaic Models Using Enhanced Levy Flight Based grasshopper optimization algorithm

引用

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第1期16卷 171-179页

作者： Mokeddem, Diab Univ Ferhat Abbas Setif 1 Fac Technol Dept Elect Engn Setif Algeria

Recently, solar photovoltaic (PV) systems are becoming the tendency theme motivating researchers focus. The appropriate design of PV cells is an important task, challenged by the development of a useful model able to simulate the current vs voltage characteristics of the real solar cell and the accurate estimation of the PV cell's parameter values. This paper proposes an improved Levy flight based grasshopper optimization algorithm (LGOA) to estimate the parameters of three PV models, i.e., single diode, double diode, and PV module. The incorporation of Levy flight trajectory to the basic grasshopper optimization algorithm (GOA), ensure solutions diversity and enhances the exploration and exploitation capabilities as well. To further validate its effectiveness LGOA is applied to the Sharp ND-R250A5 module under different operating conditions of irradiance and temperature. Experimental results demonstrate that LGOA has the ability to extract the parameters of PV models with high performance and good accuracy compared to the standard GOA.

关键词： Photovoltaic Estimation Levy flight grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

引用

TEXTILE RESEARCH JOURNAL 2022年第9-10期92卷 1415-1428页

作者： Zhou, Zhiyu Deng, Wenxiong Wang, Yaming Zhu, Zefei Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China Lashay Univ Hangzhou Peoples R China Hangzhou Jianzi Univ Sch Mech Engn Hangzhou Peoples R China

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.

关键词： Parallel convolutional neural network random vector functional link grasshopper optimization algorithm classification of clothing

来源：评论

学校读者我要写书评

暂无评论

E-mail Spam Classification Using grasshopper optimization algorithm and Neural Networks

引用

Computers, Materials & Continua 2022年第6期71卷 4749-4766页

作者： Sanaa A.A.Ghaleb Mumtazimah Mohamad Syed Abdullah Fadzli Waheed A.H.M.Ghanem Faculty of Informatics and Computing Universiti Sultan Zainal AbidinKuala Terengganu22200Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia TerengganuKuala Terengganu21030Malaysia Faculty of Engineering University of AdenAdenYemen Faculty of Education(Aden-Saber) University of AdenAdenYemen

Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of *** only is spam wasting users’time and *** addition,it limits the storage space of the email box as well as the disk ***,spam detection is a challenge for individuals and organizations *** advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and ***,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this ***,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

关键词： grasshopper optimization algorithm multilayer perceptron artificial neural network spam detection approach

来源：评论

学校读者我要写书评

暂无评论

Minimization of blast-induced dust emission using gene-expression programming and grasshopper optimization algorithm: a smart mining solution based on blasting plan optimization

引用

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2022年第8期24卷 2313-2328页

作者： Hosseini, Shahab Monjezi, Masoud Bakhtavar, Ezzeddin Tarbiat Modares Univ Fac Engn Tehran Iran Urmia Univ Technol Fac Environm Orumiyeh Iran

In surface mining, blast-induced dust can be discharged to the atmosphere and impact the surrounding environment and nearby residential areas, especially if a large volume of rock is blasted under inappropriate meteorological conditions such as high wind speed. Many attempts have been done to predict the blast-induced dust emission distance but the literature of the dust reduction is limited to change stemming materials based on water capsules. This study develops a methodology using gene expression programming and grasshopper optimization algorithm to find an optimal blasting plan with minimum blast-induced dust in a mine close to sensitive ecosystem and residential areas. The best gene expression programming model, which indicates relationship between dependent and independent variables, was first determined based on 100 blasting data collected from the mine. The model with the R-2 of 0.9559 and 0.9145, respectively, for training and validating parts was chosen as the best model. The model, as an objective function, was considered in grasshopper optimization algorithm to find the optimal blasting plan with minimum dust emission level. Compared to the old blasting plans of the mine, the optimal plan resulted in a reduction of 76.82% in the emission distance of the blast-induced under constant meteorological conditions. Sensitivity analysis on the system parameters revealed the high sensitivity of the output to wind speed, air temperature, air humidity, powder factor, and stemming. [GRAPHICS] .

关键词： Mine blasting Dust dispersion optimization algorithm Gene expression programming grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization of Interval Type-2 Fuzzy Logic System Using grasshopper optimization algorithm

引用

Computers, Materials & Continua 2022年第5期71卷 3513-3531页

作者： Saima Hassan Mojtaba Ahmadieh Khanesar Nazar Kalaf Hussein Samir Brahim Belhaouari Usman Amjad Wali Khan Mashwani Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Mechanical Materials and Manufacturing EngineeringFaculty of EngineeringUniversity of NottinghamUK College of Computer Sciences and Mathematics Tikrit UniversityIraq Division of Information and Computing Technology College of Science and EngineeringHamad Bin Khalifa UniversityQatar Department of Computer Science Sir Syed University of Engineering and TechnologyPakistan Institute of Numerical Sciences Kohat University of Science and TechnologyKohatPakistan

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

关键词： Parameter optimization grasshopper optimization algorithm interval type-2 fuzzy logic system extreme learning machine electricity market forecasting

来源：评论

学校读者我要写书评

暂无评论

A Hybrid grasshopper optimization algorithm With Invasive Weed for Global optimization

引用

IEEE ACCESS 2020年 8卷 5928-5960页

作者： Yue, Xiaofeng Zhang, Hongbo Yu, Haiyue Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm.

关键词： grasshopper optimization algorithm invasive weed optimization grouping strategy random walk strategy global optimization

来源：评论

学校读者我要写书评

暂无评论

Coordinated Operation of Multi-Integrated Energy System Based on Linear Weighted sum and grasshopper optimization algorithm

引用

IEEE ACCESS 2018年 6卷 42186-42195页

作者： Liu, Jinglu Wang, Anna Qu, Yanhua Wang, Wenhui Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Aiming at tapping the potential of energy complementary, maximizing renewable energy sources utilization and jointly minimizing the operating cost, a coordinated operation model of a multi-integrated energy system based on linearized coupling relationship is established in this paper. In order to establish an accurate model for interconnected energy hubs, an integrated strategy is proposed by the combination of the linear weighted sum and grasshopper optimization algorithm to solve the energy management problem, which improves comprehensive energy efficiency and realize regional coordination optimization. Eventually, the case study indicates that the operating cost of the multi-integrated energy system with coordination is reduced by nearly 3.2%, which can effectively validate the scalability, flexibility, and economic performance of the presented integrated strategy.

关键词： Multi-integrated energy system linearized coupling relationship energy hub grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：