Automatic Computing Communication: A New Perspective Algorithm to Cognition for Automatic Computer Communication Systems

Abstract: The aim of this paper was to optimize the system and the method of identifying communication systems and evaluating the scope of system communication. Algorithmic technique was used to simulate the article. The name of the data set was a Mehr Bank data set in Iran with the number of connection routes of 80 cases and the prediction of 2 models (optimal and distorted). The algorithms used included a combined neural network and genetic algorithm, support vector machine (SVM). In the results of the research, we showed that in relation to the reduction of the cases of distorted route data and the increase of optimal routes, the accuracy of detecting the routes of connection to bank users in optimal routes is increasing. Using a combined neural network and genetic algorithm, the backup vector machine improves the accuracy of detecting connection paths to bank users. By recognizing the information, the system proposed in this paper can transfer less data when transferring data with the same amount. Using two types of algorithms to explain the level of accuracy and power of algorithms in identifying and monitoring the connection paths of inter-system communication. The algorithms used included a combined neural network and genetic algorithm, support vector machine (SVM). Examination of the ability of each of the hybrid algorithms the combined neural network and genetic algorithm and support vector machine (SVM) showed that in the major items of classification and identification of interconnection pathways and their identification, the neural network and genetic hybrid algorithm is more successful. And the percentage of identification and classification of this algorithm in order to identify computer communication systems was higher than the support vector machine (SVM). Keywords: Combined Neural Network and Genetics Algorithm, Support Vector Machine, Communication Systems, Connection Paths