The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit ca...The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.展开更多
This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two pe...This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.展开更多
Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,usin...Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.展开更多
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore,...While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.展开更多
RVON-8（RTS^TM Voice Over Network）卡可以直接安装入ADAM^TM矩阵内部通讯系统，它扩展了ADAM^TM矩阵内部通讯系统的连通性，有8个音频输入和输出通道，每个通道都有可配置的网络和带宽两种参数：能够追踪每个网络的功能特点，而且这...RVON-8（RTS^TM Voice Over Network）卡可以直接安装入ADAM^TM矩阵内部通讯系统，它扩展了ADAM^TM矩阵内部通讯系统的连通性，有8个音频输入和输出通道，每个通道都有可配置的网络和带宽两种参数：能够追踪每个网络的功能特点，而且这些辅助资料能够用于通讯面板和中断设备控制：能支持所有规格的产品，展开更多
Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in c...Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in credit card fraud detection research,i.e.,data category imbalance and data drift.However,the oversampling algorithm used in current research suffers from excessive noise,and the Long Short-Term Memory Network(LSTM)based temporal model suffers from gradient dispersion,which can lead to loss of model performance.To address the above problems,a credit card fraud detection method based on Random Forest-Wasserstein Generative Adversarial NetworkTemporal Convolutional Network(RF-WGAN-TCN)is proposed.First,the credit card data is preprocessed,the feature importance scores are calculated by Random Forest(RF),the features with lower importance are eliminated,and then the remaining features are standardized.Second,the Wasserstein Distance Improvement Generative Adversarial Network(GAN)is introduced to construct the Wasserstein Generative Adversarial Network(WGAN),the preprocessed data is input into the WGAN,and under the mutual game training of generator and discriminator,the fraud samples that meet the target distribution are obtained.Finally,the temporal convolutional network(TCN)is utilized to extract the long-time dependencies,and the classification results are output through the Softmax layer.Experimental results on the European cardholder dataset show that the method proposed in the paper achieves 91.96%,98.22%,and 81.95%in F1-Score,Area Under Curve(AUC),and Area Under the Precision-Recall Curve(AUPRC)metrics,respectively,and has higher prediction accuracy and classification performance compared with existing mainstream methods.展开更多
Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequen...Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.展开更多
In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump...In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.展开更多
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
This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on ...This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.展开更多
Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the abilit...Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the ability to defraud people through online transactions.Fraudsters make assumptions about consumers’routine behaviour,and fraud develops swiftly.Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques.Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current algorithms.When compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN.In this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.展开更多
Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling an...Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.展开更多
目前数据中心规模迅速扩大和网络带宽大幅度提升,传统软件网络协议栈的处理器开销较大,并且难以满足众多数据中心应用程序在吞吐、延迟等方面的需求.远程直接内存访问(remote direct memory access,RDMA)技术采用零拷贝、内核旁路和处...目前数据中心规模迅速扩大和网络带宽大幅度提升,传统软件网络协议栈的处理器开销较大,并且难以满足众多数据中心应用程序在吞吐、延迟等方面的需求.远程直接内存访问(remote direct memory access,RDMA)技术采用零拷贝、内核旁路和处理器功能卸载等思想,能够高带宽、低延迟地读写远端主机内存数据.兼容以太网的RDMA技术正在数据中心领域展开应用,以太网RDMA网卡作为主要功能承载设备,对其部署发挥重要作用.综述从架构、优化和实现评估3个方面进行分析:1)对以太网RDMA网卡的通用架构进行了总结,并对其关键功能部件进行了介绍;2)重点阐述了存储资源、可靠传输和应用相关3方面的优化技术,包括面向网卡缓存资源的连接可扩展性和面向主机内存资源的注册访问优化,面向有损以太网实现可靠传输的拥塞控制、流量控制和重传机制优化,面向分布式存储中不同存储类型、数据库系统、云存储系统以及面向数据中心应用的多租户性能隔离、安全性、可编程性等方面的优化工作;3)调研了不同实现方式、评估方式.最后,给出总结和展望.展开更多
基金supported in part by the National Natural Science Foundation of China(61972241)the Natural Science Foundation of Shanghai(24ZR1427500,22ZR1427100)+1 种基金the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04).
文摘The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.
文摘This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.
基金This work is part of the Consumer Data Research Centre project(ES/L011840/1)funded by the UK Economic and Social Research Council(grant number 1477365).
文摘Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
文摘While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.
文摘RVON-8（RTS^TM Voice Over Network）卡可以直接安装入ADAM^TM矩阵内部通讯系统，它扩展了ADAM^TM矩阵内部通讯系统的连通性，有8个音频输入和输出通道，每个通道都有可配置的网络和带宽两种参数：能够追踪每个网络的功能特点，而且这些辅助资料能够用于通讯面板和中断设备控制：能支持所有规格的产品，
基金supported by the National Natural Science Foundation of China under Grant No.62466001the Talent Plan Project of Fuzhou City of Jiangxi Province of China under the Grant No.2021ED008+1 种基金the Opening Project of Jiangxi Key Laboratory of Cybersecurity Intelligent Perception under the Grant No.JKLCIP202202the Priority Unveiled Marshalling Project of Fuzhou City of Jiangxi Province of China under the Grant No.2023JBB026.
文摘Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in credit card fraud detection research,i.e.,data category imbalance and data drift.However,the oversampling algorithm used in current research suffers from excessive noise,and the Long Short-Term Memory Network(LSTM)based temporal model suffers from gradient dispersion,which can lead to loss of model performance.To address the above problems,a credit card fraud detection method based on Random Forest-Wasserstein Generative Adversarial NetworkTemporal Convolutional Network(RF-WGAN-TCN)is proposed.First,the credit card data is preprocessed,the feature importance scores are calculated by Random Forest(RF),the features with lower importance are eliminated,and then the remaining features are standardized.Second,the Wasserstein Distance Improvement Generative Adversarial Network(GAN)is introduced to construct the Wasserstein Generative Adversarial Network(WGAN),the preprocessed data is input into the WGAN,and under the mutual game training of generator and discriminator,the fraud samples that meet the target distribution are obtained.Finally,the temporal convolutional network(TCN)is utilized to extract the long-time dependencies,and the classification results are output through the Softmax layer.Experimental results on the European cardholder dataset show that the method proposed in the paper achieves 91.96%,98.22%,and 81.95%in F1-Score,Area Under Curve(AUC),and Area Under the Precision-Recall Curve(AUPRC)metrics,respectively,and has higher prediction accuracy and classification performance compared with existing mainstream methods.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(RS-2023-00249743)Additionally,this research was supported by the Global-Learning&Academic Research Institution for Master’s,PhD Students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2024-00443714)This research was also supported by the“Research Base Construction Fund Support Program”funded by Jeonbuk National University in 2025.
文摘Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.
文摘In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
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.
文摘This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.
文摘Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the ability to defraud people through online transactions.Fraudsters make assumptions about consumers’routine behaviour,and fraud develops swiftly.Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques.Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current algorithms.When compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN.In this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.
文摘Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.
文摘目前数据中心规模迅速扩大和网络带宽大幅度提升,传统软件网络协议栈的处理器开销较大,并且难以满足众多数据中心应用程序在吞吐、延迟等方面的需求.远程直接内存访问(remote direct memory access,RDMA)技术采用零拷贝、内核旁路和处理器功能卸载等思想,能够高带宽、低延迟地读写远端主机内存数据.兼容以太网的RDMA技术正在数据中心领域展开应用,以太网RDMA网卡作为主要功能承载设备,对其部署发挥重要作用.综述从架构、优化和实现评估3个方面进行分析:1)对以太网RDMA网卡的通用架构进行了总结,并对其关键功能部件进行了介绍;2)重点阐述了存储资源、可靠传输和应用相关3方面的优化技术,包括面向网卡缓存资源的连接可扩展性和面向主机内存资源的注册访问优化,面向有损以太网实现可靠传输的拥塞控制、流量控制和重传机制优化,面向分布式存储中不同存储类型、数据库系统、云存储系统以及面向数据中心应用的多租户性能隔离、安全性、可编程性等方面的优化工作;3)调研了不同实现方式、评估方式.最后,给出总结和展望.
