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Developing an algorithm for the application of Bayesian method to software using artificial immune systems

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This paper develops a new algorithm by applying the Bayesian method to software using artificial immune systems. An artificial immune system is an adaptive computing system that uses models, principles, mechanisms, and functions used to solve problems in theoretical immunology. Its application to various fields of science is studied. The role that artificial immune systems play in software is invaluable. Methods for detecting malware are explored. Some works in the field of artificial immune system are analyzed and issues to be addressed are identified. The Bayesian method accurately calculates the probability of occurrence of any event under certain conditions. Therefore, the Bayesian method is applied to software using artificial immune systems. By applying this method, fast software performance can be achieved. For this, a new algorithm is developed and experiments are conducted. The developed algorithm is one of the new ones. The results of the experiments provide good performance.

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The author thanks the editors and anonymous reviewers for their helpful comments and suggestions that have led to this improved version of the paper.

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Correspondence to Shafagat Mahmudova.

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Communicated by Harini Balasubramanian.

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Mahmudova, S. Developing an algorithm for the application of Bayesian method to software using artificial immune systems. Soft Comput 25, 11837–11843 (2021).

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