Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

Reconstruction and stability of Fe_(3)O_(4)(001)surface:An investigation based on particle swarm optimization and machine learning

Magnetite nanoparticles show promising applications in drug delivery,catalysis,and spintronics.The surface of magnetite plays an important role in these applications.Therefore,it is critical to understand the surface structure of Fe_(3)O_(4)at atomic scale.Here,using a combination of first-principles calculations,particle swarm optimization(PSO)method and machine learning,we investigate the possible reconstruction and stability of Fe_(3)O_(4)(001)surface.The results show that besides the subsurface cation vacancy(SCV)reconstruction,an A layer with Fe vacancy(A-layer-V_(Fe))reconstruction of the(001)surface also shows very low surface energy especially at oxygen poor condition.Molecular dynamics simulation based on the iron–oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-V_(Fe)reconstruction.Our results are also instructive for the study of surface reconstruction of other metal oxides.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Image classification is a core field in the research area of image proces-sing and computer vision in which vehicle classification is a critical domain.The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security,traffic analysis,and self-driving and autonomous vehicles.The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional,and handcrafted means of solving image analysis problems.In this paper,a combina-tion of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme,particle swarm optimization(PSO),was employed for autonomous vehi-cle classification.The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented.The trained model was classified using several classifiers;however,the Cubic SVM(CSVM)classifier was found to out-perform the others in both time consumption and accuracy(94.8%).The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accu-racy(94.8%)but also in terms of training time(82.7 s)and speed prediction(380 obs/sec).

Aero-engine Thrust Estimation Based on Ensemble of Improved Wavelet Extreme Learning Machine

Aero-engine direct thrust control can not only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance.However,it is a big challenge to estimate engine thrust accurately.To tackle this problem,this paper proposes an ensemble of improved wavelet extreme learning machine(EW-ELM)for aircraft engine thrust estimation.Extreme learning machine(ELM)has been proved as an emerging learning technique with high efficiency.Since the combination of ELM and wavelet theory has the both excellent properties,wavelet activation functions are used in the hidden nodes to enhance non-linearity dealing ability.Besides,as original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes,particle swarm optimization(PSO)algorithm is adopted to select the input weights and hidden biases.Furthermore,the ensemble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust.The simulation results verify the effectiveness and efficiency of the developed method and show that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique

An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.

Optimizing the Multi-Objective Discrete Particle Swarm Optimization Algorithm by Deep Deterministic Policy Gradient Algorithm

Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.

Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel.In this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation.The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized.Twelve input variables were selected according to results from analysis of influencing factors.The prediction performance of two models,PSO-RF and RF(default)were compared.The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings.The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R^(2)=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

Objective: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer(PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine(TCM) syndromes.Methods: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining10,060 electronic medical records, which were randomly divided into a training set and a test set.Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.Results: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%,respectively. The classification accuracy rates of the models for all syndromes in this paper were between82.15% and 93.82%.Conclusion: Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment.The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production,to rising sea levels that deteriorates the risk of catastrophic flooding.Among all aspects related to global warming,there is a growing concern on water resource management.This field is targeted at preventing future water crisis threatening human beings.The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions.Numerous prediction models,methods and tools,in this case,have been developed and applied so far.In line with trend,the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron(MLP)network to explore any meaningful connection between large-scale climate indices(LSCIs)and precipitation in the capital of Iran,a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming.This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran.Therefore,the current and future environmental conditions of this city especially its water supply conditions are of great importance.To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed.To this end,the present study investigates three training methods namely backpropagation(BP),genetic algorithms(GAs),and particle swarm optimization(PSO)algorithms on a MLP platform.Two frameworks distinguished by their input compositions are denoted in this study:Concurrent Model Framework(CMF)and Integrated Model Framework(IMF).Through these two frameworks,13 cases are generated:12 cases within CMF,each of which contains all selected LSCIs in the same lead-times,and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time.Following the evaluation of all model performances through related statistical tests,Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms,the best of which is found to be MLP-PSO in IMF.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

基于PSO-SVR模型预测粮食孔隙率

利用自制粮食孔隙率测定仪,采用直接测量法对不同受压状态下的粮食单元体孔隙率进行测量,得到不同粮种、不同含水率和不同压力下的粮食单元体孔隙率。通过粒子群算法(PSO)优化支持向量回归(SVR),建立基于PSO-SVR粮食单元体孔隙率的预测模型,并与随机森林(RF)模型、SVR模型对比分析其性能。结果表明:PSO-SVR模型的各项性能指标均优于RF模型和SVR模型。PSO-SVR模型测试样本的均方误差(MSE)为0.0660、决定系数(R2)为0.9340、平均绝对误差(MAE)为0.2000,相较其他2种模型,该模型的预测结果误差小,具有较高的预测精度,可以有效预测粮食在不同压力下的孔隙率。

基于IAOA-KELM的储气库注采管柱内腐蚀速率预测

针对储气库注采管柱的内腐蚀速率预测问题,建立了基于阿基米德优化算法(Archimedes Optimization Algorithm,AOA)与核极限学习机(Kernel Extreme Learning Machine,KELM)相结合的模型提高腐蚀速率预测精度。通过引入佳点集、改进密度降低因子、采用黄金正弦算法缩小搜索空间,提高局部开发能力,利用改进阿基米德优化算法(Improved Archimedes Optimization Algorithm,IAOA)优化KELM正则化系数(C)和核函数参数(γ),进而建立IAOA-KELM储气库注采管柱内腐蚀速率预测模型;使用MATLAB软件运用该模型对某注采管柱内腐蚀数据集进行学习与预测,将IAOA-KELM模型与KELM、粒子群优化算法(Particle Swarm Optimization,PSO)-KELM、AOA-KELM结果进行预测误差对比。结果表明,IAOA-KELM模型的预测值与实际值较为拟合,其E RMSE为0.65%,E MAE为0.39%,R 2为99.83%,均优于其他模型。研究表明,IAOA-KELM模型能够更为准确地预测储气库注采管柱内腐蚀速率,为储气库注采管柱的运维及储气库的健康管理提供参考。

基于多影响因素联合的某抽水蓄能电站主厂房洞室围岩变形预测

为保证地下洞室围岩环境的安全状态,提出了一种以变分模态分解(VMD)方法分解原始数据和粒子群优化算法(PSO)提高预测精度为基础,基于多影响因素联合核极限学习机(KELM)方法的洞室围岩变形预测方法。该方法首先采用VMD方法将监测位移分解为受趋势性因素影响的趋势项位移和受周期性因素影响的周期项位移,去除影响因素的干扰项,其次将演化状态及影响因素作为PSO-KELM的输入数据,预测各影响因素所对应的趋势项或周期项位移,最后叠加两种分项位移,并将多影响因素结合的KELM方法与其他预测方法进行精度比较。对某抽水蓄能电站工程实测数据的验证结果表明,预测结果与原始位移的RRMSE相差仅0.76%,且二者的R为0.986,所提预测方法具有较高的预测精度,可为同类工程的围岩变形预测提供参考。

基于改进粒子群算法优化的染色木材颜色检测算法研究

为提高染色木材颜色的检测精度和速度,对樟子松木材单板进行染色,选取染色单板的光谱反射率作为输入,以极限学习机模型为基础构建预测模型,对染色单板的色度参数L^(*)、a^(*)、b^(*)进行预测,运用粒子群算法对ELM权值和阈值进行寻优,并引入非线性惯性权重和新的位置与速度更新策略改进粒子群算法,以消除其易陷入局部最优的缺点。此外,以L^(*)、a^(*)、b^(*)平均绝对误差为评价指标,与基础ELM模型及其他模型作对比,发现优化后的模型平均绝对误差为0.16,测色效果相较于基础ELM的0.68、麻雀算法优化的ELM的0.37等具有明显优势,这对于提高木材染色生产效率具有重要意义。