A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α whi...A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.展开更多
This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning ap...This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.展开更多
With a random sample of 10 JCR(Science) subject areas it is shown that the 2-year and the 5-year impact factor of journals lead statistically to the same ranking per category. Yet in a majority of cases, the 5-year im...With a random sample of 10 JCR(Science) subject areas it is shown that the 2-year and the 5-year impact factor of journals lead statistically to the same ranking per category. Yet in a majority of cases, the 5-year impact factor is larger than the 2-year one.展开更多
Nonparametric(distribution-free)control charts have been introduced in recent years when quality characteristics do not follow a specific distribution.When the sample selection is prohibitively expensive,we prefer ran...Nonparametric(distribution-free)control charts have been introduced in recent years when quality characteristics do not follow a specific distribution.When the sample selection is prohibitively expensive,we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts.In this study,we proposed a nonparametric homogeneously weighted moving average based on theWilcoxon signed-rank test with ranked set sampling(NPHWMARSS)control chart for detecting shifts in the process location of a continuous and symmetric distribution.Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the proposed NPHWMARSS control chart.The proposed NPHWMARSS control chart’s performance is compared to that of parametric and nonparametric control charts.These control charts include the exponentially weighted moving average(EWMA)control chart,Wilcoxon signed-rank with simple random sampling based the nonparametric EWMA control chart,the nonparametric EWMA sign control chart,Wilcoxon signed-rank with ranked set sampling-based the nonparametric EWMA control chart,and the homogeneously weighted moving average control charts.The findings show that the proposed NPHWMARSS control chart performs better than its competitors,particularly for the small shifts.Finally,an example is presented to demonstrate how the proposed scheme can be implemented practically.展开更多
Particle swarm optimization(PSO)algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation.However,the slow conve...Particle swarm optimization(PSO)algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation.However,the slow convergence speed and low solution accuracy of the algorithm also restrict its further applications.To solve the above problems,this paper introduces the chromosome crossing characteristics of genetic algorithm(GA),and proposes a comprehensive learning particle swarm optimization based on optimal particle recombination.With the help of the ability of comprehensive learning strategy to efficiently explore the solution space,this method organically combines the excellent information explored by each particle through the optimal particle recombination,so as to obtain a better individual,speed up the convergence of the algorithm,and improve the solution accuracy of the problem.The experimental results of benchmark function show that the proposed algorithm has faster convergence speed and optimization accuracy than the original algorithm,and the results of Friedman test and Wilcoxon signed-rank test prove the feasibility of the optimal particle recombination operation in particle swarm optimization.展开更多
文摘A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU),Grant Number IMSIU-RG23151.
文摘This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.
基金supported by the National Natural Science Foundation of China(Grant No.70673019)
文摘With a random sample of 10 JCR(Science) subject areas it is shown that the 2-year and the 5-year impact factor of journals lead statistically to the same ranking per category. Yet in a majority of cases, the 5-year impact factor is larger than the 2-year one.
基金Funds are available under the Grant No.RGP.2/132/43 at King Khalid University,Kingdom of Saudi Arabia.
文摘Nonparametric(distribution-free)control charts have been introduced in recent years when quality characteristics do not follow a specific distribution.When the sample selection is prohibitively expensive,we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts.In this study,we proposed a nonparametric homogeneously weighted moving average based on theWilcoxon signed-rank test with ranked set sampling(NPHWMARSS)control chart for detecting shifts in the process location of a continuous and symmetric distribution.Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the proposed NPHWMARSS control chart.The proposed NPHWMARSS control chart’s performance is compared to that of parametric and nonparametric control charts.These control charts include the exponentially weighted moving average(EWMA)control chart,Wilcoxon signed-rank with simple random sampling based the nonparametric EWMA control chart,the nonparametric EWMA sign control chart,Wilcoxon signed-rank with ranked set sampling-based the nonparametric EWMA control chart,and the homogeneously weighted moving average control charts.The findings show that the proposed NPHWMARSS control chart performs better than its competitors,particularly for the small shifts.Finally,an example is presented to demonstrate how the proposed scheme can be implemented practically.
基金supported in part by the Scientific Research Program of the Jiangxi Provincial Department of Education(Nos.GJJ210731 and GJJ211331).
文摘Particle swarm optimization(PSO)algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation.However,the slow convergence speed and low solution accuracy of the algorithm also restrict its further applications.To solve the above problems,this paper introduces the chromosome crossing characteristics of genetic algorithm(GA),and proposes a comprehensive learning particle swarm optimization based on optimal particle recombination.With the help of the ability of comprehensive learning strategy to efficiently explore the solution space,this method organically combines the excellent information explored by each particle through the optimal particle recombination,so as to obtain a better individual,speed up the convergence of the algorithm,and improve the solution accuracy of the problem.The experimental results of benchmark function show that the proposed algorithm has faster convergence speed and optimization accuracy than the original algorithm,and the results of Friedman test and Wilcoxon signed-rank test prove the feasibility of the optimal particle recombination operation in particle swarm optimization.
