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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
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Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 221-232 |
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Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
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Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 6 pp. 200-206 |
|
Abstract |
Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models. |
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Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
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