Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.

Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.

Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines

Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

Hybrid Optimization of Support Vector Machine for Intrusion Detection

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.

Adjustable entropy function method for support vector machine

Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.

Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.

On-line Chatter Detection Using an Improved Support Vector Machine

On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.

基于KPCA-WOA-SVM的住宅工程造价预测

在项目决策阶段,准确预测住宅工程造价对提高工程项目决策的科学性至关重要,引入人工智能及机器技术能进一步提高预测的精准度。通过文献梳理,确定决策阶段住宅工程造价的影响指标,用核主成分分析(KPCA)对影响指标进行降维,利用鲸鱼优化算法(WOA)确定支持向量机(SVM)的惩罚参数与核参数,最终构建基于KPCA-WOA-SVM的住宅工程造价预测模型。采用江苏省近5年的70组住宅工程造价数据对模型进行验证,结果表明:与BP神经网络模型、SVM模型和WOA-SVM模型相比,KPCA-WOA-SVM模型预测精准度更高,适用性更好。

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Data mining optimization of laidback fan-shaped hole to improve film cooling performance

To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate 32 groups of training samples. Least square support vector machine(LS-SVM) was applied for surrogate model, and the optimal design parameters were determined by a kind of chaotic optimization algorithm. As hole length, lateral expansion angle and forward expansion angle are 90 mm, 20° and 5°, the area-averaged film cooling effectiveness can reach its maximum value in the design space. LS-SVM coupled with chaotic optimization algorithm is a promising scheme for the optimization of shaped film cooling holes.

基于EEMD-WOA-SVM的土石坝渗流量预测

为准确预报土石坝渗流量的变化趋势,针对传统的时间序列模型存在非线性处理能力较差、捕捉序列依赖关系能力不足等问题,建立了基于EEMD-WOA-SVM的土石坝渗流量预测模型.该模型采用集合经验模态(EEMD)有效分解土石坝渗流时间序列,引入鲸鱼优化算法(WOA)寻找支持向量机模型(SVM)的最优超参数组合,再将各模态分解分量代入组合模型预测并重构预测结果.案例分析结果表明,所建EEMD-WOA-SVM模型与传统的SVM模型相比,其拟合优度R2提升了19.8%,均方误差EMS、均方根误差ERMS、平均绝对误差EMA和平均绝对百分比误差EMAP分别降低了76%、50.3%、45.2%和43.2%.另外,与GA-SVM和WOA-SVM模型相比,R2值达0.9486,EMS、ERMS、EMA和EMAP分别降低至0.0012、0.0352、0.0289和0.0176,进一步说明了该组合模型具有较高的预测精度,为土石坝渗流量的精确预测提供了新途径.

Efficient Reliability-Based Optimization Scheme for Piezoelectric Composite Beams with Delamination under Axial Compression

An efficient computational framework for reliability analysis and reliability-based design optimization of piezoelectric composite beam with delamination is presented.In the proposed approach,the transverse shear deformation,delamination and piezoelectricity of the beam are taken into account.By introducing the Heaviside step function into the displacement components and using the Rayleigh-Ritz method,and the buckling governing equations for the piezoelectric composite beams is obtained.The reliability of the beams is obtained by integrating the support vector machine method and first order reliability method,further the reliability-based optimization is executed through employing genetic algorithm.The effects of ply style,delamination length and voltage are discussed in details.Numerical results indicate that the comprehensive computational scheme provides a unified numerical framework to analysis the nondeterministic buckling and reliability efficiently.

Hybrid Sooty Tern Optimization and Differential Evolution for Feature Selection

In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets simultaneously.Feature selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of features.In recent years,it has been applied in many practical domains of intelligent systems.The application of SVM in many fields has proved its effectiveness in classification tasks of various types.Its performance is mainly determined by the kernel type and its parameters.One of the most challenging process in machine learning is feature selection,intending to select effective and representative features.The main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow convergence.Therefore,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed method.The superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.

基于GRA-MSWOA-SVM的煤与瓦斯突出危险性预测研究

为提高煤与瓦斯突出危险性预测的精准度,提出一种灰色关联分析(GRA)去噪、混合策略改进鲸鱼算法(MSWOA)优化支持向量机(SVM)的煤与瓦斯突出预测模型。选取山西煤矿的实测数据为样本,经灰色关联分析(GRA)剔除影响程度较小的指标;基于鲸鱼优化算法(WOA),引入非线性时变因子、惯性权重及预选择机制的小生境技术设计MSWOA,利用MSWOA优化SVM的惩罚参数与核参数,构建煤与瓦斯突出危险性预测模型并与其他模型对比。结果表明:基于GRA的数据约简能进一步减少冗余因素,有效提升模型预测精度;MSWOA比鲸鱼优化算法(WOA)提前40代左右收敛,寻优速度更快;与其他预测模型相比,该模型预测精度更高,具有可靠性。