Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown pr...Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.展开更多
A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all...A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.展开更多
Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment re...Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.展开更多
With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this...With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.展开更多
[Objective] To evaluate the control effect of yellow sticky cards and sex pheromone on Plutella xylostella and Liriomyza spp.,which can provide reference for large area extension of the technique.[Method] The integrat...[Objective] To evaluate the control effect of yellow sticky cards and sex pheromone on Plutella xylostella and Liriomyza spp.,which can provide reference for large area extension of the technique.[Method] The integrated assessment of trapping efficiency of colored sticky cards and sex pheromone on Liriomyza spp.and Plutella xylostella was conducted by field surveys and structured interview in Tonghai County,Yunnan Province,China.[Result] The results showed that yellow sticky cards and sex pheromone have strong power of trapping Liriomyza spp.and Plutella xylostella（3 414±720 and（219±157） head/piece,respectively）;the control cost by the usage of pesticide is the highest,（10 099.5±2 752.5） yuan/hm^2,yellow sticky cards and sex pheromone takes the second place,（1 125.0±465.0） yuan/hm^2,the control cost by the usage of yellow sticky cards is the lowest,（450.0 ±186.0）yuan/hm^2.Without the usage of yellow sticky cards and sex pheromone,pesticide application times and costs are（15.0±2.7） times and（12 070.5±2 136.0） yuan/hm^2;combined with usage of yellow sticky cards and sex pheromone,pesticide application times and costs reduce by 5.7 times and 4 618.5 yuan/hm^2.The ratio of trapped beneficial insects and target pests was 1 ∶1 131,which showed that the negative effect of yellow sticky cards and sex pheromone on the non-target insects was very limited.[Conclusion] The trapping approach has become popular among all the local farmers.Looking at the above factors,the trapping technology has strong application prospect and promotion value in pest control field.展开更多
文摘Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.
基金supported by the Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
基金supported by grants from the Guangdong Provincial Department of Finance Project in 2022(KS0120220267,KS0120220268,KS0120220272,KS0120220271)Guangdong Basic and Applied Basic Research Natural Science Funding(2023A1515012485)+1 种基金Science and Technology Projects in Guangzhou(202102020058)Launch funding of the National Natural Science Foundation of China(8210101099).
文摘Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.
文摘With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.
基金Supported by Development Program of Misereor Foundation of Germany(335-0311028Z)~~
文摘[Objective] To evaluate the control effect of yellow sticky cards and sex pheromone on Plutella xylostella and Liriomyza spp.,which can provide reference for large area extension of the technique.[Method] The integrated assessment of trapping efficiency of colored sticky cards and sex pheromone on Liriomyza spp.and Plutella xylostella was conducted by field surveys and structured interview in Tonghai County,Yunnan Province,China.[Result] The results showed that yellow sticky cards and sex pheromone have strong power of trapping Liriomyza spp.and Plutella xylostella（3 414±720 and（219±157） head/piece,respectively）;the control cost by the usage of pesticide is the highest,（10 099.5±2 752.5） yuan/hm^2,yellow sticky cards and sex pheromone takes the second place,（1 125.0±465.0） yuan/hm^2,the control cost by the usage of yellow sticky cards is the lowest,（450.0 ±186.0）yuan/hm^2.Without the usage of yellow sticky cards and sex pheromone,pesticide application times and costs are（15.0±2.7） times and（12 070.5±2 136.0） yuan/hm^2;combined with usage of yellow sticky cards and sex pheromone,pesticide application times and costs reduce by 5.7 times and 4 618.5 yuan/hm^2.The ratio of trapped beneficial insects and target pests was 1 ∶1 131,which showed that the negative effect of yellow sticky cards and sex pheromone on the non-target insects was very limited.[Conclusion] The trapping approach has become popular among all the local farmers.Looking at the above factors,the trapping technology has strong application prospect and promotion value in pest control field.
