As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonl...As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.展开更多
Atopic dermatitis(AD)is the most common chronic inflammatory skin disease seriously affecting the quality of life of patients.Reliable and accurate evaluation methods are necessary for early diagnosis and effective AD...Atopic dermatitis(AD)is the most common chronic inflammatory skin disease seriously affecting the quality of life of patients.Reliable and accurate evaluation methods are necessary for early diagnosis and effective AD treatment.Thus,this study used machine learning(ML)to explore a novel diagnostic and therapeutic effect evaluation model for AD.Firstly,candidate model genes were screened from an integrated data set of four AD-related microarray datasets:GSE133477,GSE32924,GSE58558,and GSE107361,using Robust Rank Aggregation(RRA),and protein-protein interaction network(PPI).Next,three recognized models(REC)and three AD-associated gene models(AAG)established with LASSO,Logistic linear regression(LR),and random forest(RF)were developed and tested separately using GSE130588 and GSE99802 datasets.The results revealed that REC model of LASSO(model genes including IL7R,KRT16,CCL2,CD53,CCL18 and CCL22),REC model of LR(including IL7R,KRT16,CCL18)and AAG model of LR(including MX1,CCNB1,SERPINB13,ADAM19,CEP55,VMP1,TTC39A,and FCHSD1)accurately classified AD lesions and non-lesions based on the good AUCs(LASSO(REC):0.8761,and LR(REC and AAG):0.8302 in GSE130588;LASSO(REC):0.7761,and LR(REC and AAG):0.8783 in GSE99802).In Dupilumab,Crisaborole,and fezakinumab-treated samples,the LASSO(REC)and LR(AAG)models were positively correlated with SCORD(Pearson correlation coefficients of 0.55 and 0.69,respectively)and negatively correlated with the treatment length.In addition,the two models also accurately predicted the infiltration of immune cells in the skin lesions and non-lesions.Therefore,the ML-based predictive model provides a new approach to predicting AD diagnosis and the therapeutic effect of AD treatment options.展开更多
In university English teaching,the integration of deep learning and wisdom classroom is very important to improve students’English thinking ability.The article first explains the connotation and correlation of deep l...In university English teaching,the integration of deep learning and wisdom classroom is very important to improve students’English thinking ability.The article first explains the connotation and correlation of deep learning and smart classroom.Then,it puts forward how to promote students’deep participation and thinking in college English teaching through the establishment of wisdom teaching objectives,the creation of teaching situations,the design of teaching activities and the development of teaching evaluation.It aims to build an efficient college English wisdom classroom and promote the development of students’comprehensive English ability.In order to test whether intelligent teaching environment and deep learning model can effectively improve the traditional college English teaching model:Students have low sense of learning,low ability of knowledge transfer and application,and low ability to deal with practical problems,improve teaching quality and efficiency,build college English in-depth classroom teaching mode based on intelligent teaching environment,and course teaching implementation process,and take a college English course as a sample to conduct a unit teaching quasi-experiment and effect evaluation.展开更多
Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c...Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.展开更多
基金the National Defense Science and Technology Key Laboratory Fund of China(XM2020XT1023).
文摘As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.
基金supported by the National Natural Science Foundation of China(82003326,82103704)the Natural Science Foundation of Hunan Province(2021JJ40924)+1 种基金the Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University(YX202007)the Science and Technology Innovation Program of Hunan Province(2021RC3035).
文摘Atopic dermatitis(AD)is the most common chronic inflammatory skin disease seriously affecting the quality of life of patients.Reliable and accurate evaluation methods are necessary for early diagnosis and effective AD treatment.Thus,this study used machine learning(ML)to explore a novel diagnostic and therapeutic effect evaluation model for AD.Firstly,candidate model genes were screened from an integrated data set of four AD-related microarray datasets:GSE133477,GSE32924,GSE58558,and GSE107361,using Robust Rank Aggregation(RRA),and protein-protein interaction network(PPI).Next,three recognized models(REC)and three AD-associated gene models(AAG)established with LASSO,Logistic linear regression(LR),and random forest(RF)were developed and tested separately using GSE130588 and GSE99802 datasets.The results revealed that REC model of LASSO(model genes including IL7R,KRT16,CCL2,CD53,CCL18 and CCL22),REC model of LR(including IL7R,KRT16,CCL18)and AAG model of LR(including MX1,CCNB1,SERPINB13,ADAM19,CEP55,VMP1,TTC39A,and FCHSD1)accurately classified AD lesions and non-lesions based on the good AUCs(LASSO(REC):0.8761,and LR(REC and AAG):0.8302 in GSE130588;LASSO(REC):0.7761,and LR(REC and AAG):0.8783 in GSE99802).In Dupilumab,Crisaborole,and fezakinumab-treated samples,the LASSO(REC)and LR(AAG)models were positively correlated with SCORD(Pearson correlation coefficients of 0.55 and 0.69,respectively)and negatively correlated with the treatment length.In addition,the two models also accurately predicted the infiltration of immune cells in the skin lesions and non-lesions.Therefore,the ML-based predictive model provides a new approach to predicting AD diagnosis and the therapeutic effect of AD treatment options.
文摘In university English teaching,the integration of deep learning and wisdom classroom is very important to improve students’English thinking ability.The article first explains the connotation and correlation of deep learning and smart classroom.Then,it puts forward how to promote students’deep participation and thinking in college English teaching through the establishment of wisdom teaching objectives,the creation of teaching situations,the design of teaching activities and the development of teaching evaluation.It aims to build an efficient college English wisdom classroom and promote the development of students’comprehensive English ability.In order to test whether intelligent teaching environment and deep learning model can effectively improve the traditional college English teaching model:Students have low sense of learning,low ability of knowledge transfer and application,and low ability to deal with practical problems,improve teaching quality and efficiency,build college English in-depth classroom teaching mode based on intelligent teaching environment,and course teaching implementation process,and take a college English course as a sample to conduct a unit teaching quasi-experiment and effect evaluation.
基金sponsored by the National Defense Science and Technology Key Laboratory Fund(Grant No.61422062205)the Equipment Pre-Research Fund(Grant No.JCKYS2022LD9)。
文摘Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.
