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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. 7 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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URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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. 7 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. 7 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. 7 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. 7 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. |
|
URL |
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Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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. |
|
URL |
|
|
Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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. |
|
URL |
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|
Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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. |
|
Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
|
URL |
|
|
Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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. |
|
Keywords |
Artificial neural network, fraud detection, e-commerce, Backpropagation, Steepest Descent, Gauss-Newton algorithm, QuickProp. |
|
URL |
|
|
Title |
Fraud Detection in E-Commerce |
|
Author |
Sara Alqethami, Badriah Almutanni, and Manal AlGhamdi |
| Citation |
Vol. 21 No. 7 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 |
Artificial Intelligence and Pattern Recognition Using Data Mining Algorithms |
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Author |
Abdulkawi Yahya Radman Al-Shamiri |
| Citation |
Vol. 21 No. 7 pp. 221-232 |
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Abstract |
In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses. |
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Keywords |
Data Mining, Classification Algorithms, Regression Algorithms, Association Rules Algorithms, Clustering Algorithms, Steps of Data Mining Process. |
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URL |
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Title |
Artificial Intelligence and Pattern Recognition Using Data Mining Algorithms |
|
Author |
Abdulkawi Yahya Radman Al-Shamiri |
| Citation |
Vol. 21 No. 7 pp. 221-232 |
|
Abstract |
In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses. |
|
Keywords |
Data Mining, Classification Algorithms, Regression Algorithms, Association Rules Algorithms, Clustering Algorithms, Steps of Data Mining Process. |
|
URL |
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Title |
Artificial Intelligence and Pattern Recognition Using Data Mining Algorithms |
|
Author |
Abdulkawi Yahya Radman Al-Shamiri |
| Citation |
Vol. 21 No. 7 pp. 221-232 |
|
Abstract |
In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses. |
|
Keywords |
Data Mining, Classification Algorithms, Regression Algorithms, Association Rules Algorithms, Clustering Algorithms, Steps of Data Mining Process. |
|
URL |
|
|
Title |
Artificial Intelligence and Pattern Recognition Using Data Mining Algorithms |
|
Author |
Abdulkawi Yahya Radman Al-Shamiri |
| Citation |
Vol. 21 No. 7 pp. 221-232 |
|
Abstract |
In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses. |
|
Keywords |
Data Mining, Classification Algorithms, Regression Algorithms, Association Rules Algorithms, Clustering Algorithms, Steps of Data Mining Process. |
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URL |
