Pattern Analysis and Intelligent Systems
shahrzad Oveisi
Articles in Press, Accepted Manuscript, Available Online from 02 June 2022
Abstract
Nowadays, we are witnessing the growth of financial theft cases and thereby a high degree of financial losses concomitant with the growth of IoT technology, as well as development and utilization of this technology in banking and financial fields along with the increase in the volume of transactions. ...
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Nowadays, we are witnessing the growth of financial theft cases and thereby a high degree of financial losses concomitant with the growth of IoT technology, as well as development and utilization of this technology in banking and financial fields along with the increase in the volume of transactions. Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples and well-defined patterns or signatures with those that do not conform to the expected profiles. Various methods have been introduced to identify, detect and prevent such thefts. This paper presents a LSTM based approach for detecting fraudulent transactions. I express the fraud detection problem as a sequence classification task and employ LSTM based method to incorporate transaction sequences. Then, the results are assessed using two classifier methods, namely random forest and decision tree and Mean Square Error. In the second case, because the data are imbalanced, undersampling and oversampling techniques have been used, after which I employ LSTM neural network and results assessed with MSE. Finally, the results of the two groups were compared with confusion matrix. According to the evaluations, my proposed method with Random forest classifier gives the best results.
Software Engineering and Information Systems
shahrzad Oveisi; Mohammad Nadjafi; Mohammad Ali Farsi; Ali moeini; Mahmood Shabankhah
Volume 6, Issue 3 , August 2020, , Pages 187-200
Abstract
One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software ...
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One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software risks based on the FMEA indices with respect to reduce the risk level by means of experts’ opinions. To this end, TOPSIS as one of the most applicable methods of prioritizing and ordering the significance of events has been used. Since uncertainty in the data is inevitable, the entropy principle has been applied with the help of fuzzy theory to overcome this problem to weigh the specified indices.The applicability and effectiveness of the proposed approach is validated through a real case study risk analysis of an Air/Space software system. The results show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision making of our software system that is in the early stages of software life cycle. After obtaining the events and assessing their risk using the existing method, finally, suggestions are given to reduce the risk of the event with a higher risk rating.