• Online Journals
specialty journal of electronic and computer sciences
Volume 4, 2018, Issue 1
Determination of the Required Data Type and Position in the EDG Algorithm in Wireless Sensor Networks
Alireza Froozani Fard, Hossein Royat
Pages: 1-8
The wireless sensor networks can be used for a number of military purposes such as monitoring the activities of the forces and protecting them. Initially, through equipping this network with proper sensors, the friendly forces can be distinguished from the enemy forces and the status of enemy’s movements can be analyzed. The current study has aimed to, by the use of Emergency Data GTS (EDG) algorithm features and improving it, identify the data required for a region and determine the position and the scope of the data, so that the best decisions in time period can be made. Therefore, the current study will be most usable for military purposes. At the end of the article, the results of the simulation along with the latitude and longitude, will be provided.
Detection of the Suspicious Transactions by Integrating the Neural Network and Bat Algorithm
Nikfar Safari, Touraj Banirostam
Pages: 9-19
Banking system fraud is one of the challenges of banking and e-commerce development. One of the main challenges of machine learning and data mining techniques in bank fraud detection is their low accuracy in identifying these transactions. This research presented a hybrid method based on a multi-layer artificial neural network and bat algorithm to reduce the fraud detection fault. In the proposed method, the parameters of the neural network such as weights and bias are selected optimally by the bat algorithm to reduce the fault rate in the fraud detection. The proposed method is a type of learning intensification in which the bat algorithm improves the learning of the neural network. In the proposed method, the Kmeans clustering is used to remove data from the dataset to increase the accuracy of the proposed method. MATLAB software was used to run the data. The data related to bank fraud indicate that the accuracy, sensitivity, and specificity of the proposed method for detecting bank fraud were as much as 91.46%, 88.97%, and 90.32%, respectively. The comparison of our proposed method with other methods shows that the proposed method is more accurate than methods such as regression and backup machine

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specialty journal of electronic and computer sciences Issue 3, Volume 4, 2018