METHODS OF DATA PROCESSING AND INTELLECTUAL ANALYSIS USING ARTIFICIAL IMMUNE SYSTEMS

Abstract

Relevance. Artificial immune systems, especially algorithms of negative selection and dendritic cells, demonstrate high efficiency in detecting rare and unknown anomalies. Distributed computing systems and networks require effective monitoring and protection methods. Their ability to recognize specific patterns and classify medical images makes them indispensable in these fields. They are also actively used to detect and prevent cyber threats. They can analyze network traffic, detect anomalous activity, and provide real-time protection. Thus, the relevance of using artificial immune systems for data processing and classification lies in their unique properties of adaptability, ability to recognize complex patterns and anomalies, as well as effective work in distributed systems. The purpose is to research existing methods of processing and artificial analysis of data using artificial immune systems. The object of the study is the intellectual analysis of data by artificial immune systems. The subject are the methods of intellectual analysis of data by artificial immune systems. Results an analysis of existing methods of data processing and intellectual analysis using artificial immune systems was carried out. Immune clustering algorithms are a powerful tool for data analysis and processing. They allow you to efficiently group data, detect anomalies, and adapt to changes in the data environment, making them valuable for a wide range of applications, from marketing and medicine to finance and industry. Artillery algorithms are powerful tools for intelligent data analysis, offering efficient, accurate and productive methods of optimization and analysis. They find applications in many industries, from finance and logistics to medicine and cyber security, providing solutions to complex data processing and decision-making tasks. Dendritic cell algorithms are a powerful tool for intelligent data analysis, particularly for anomaly detection and data classification. Their ability to aggregate different types of signals and make decisions based on the overall level of danger makes them particularly effective in complex and dynamic environments such as cyber security and bioinformatics. they provide high sensitivity and adaptability, which allows them to be successfully used in various fields to solve data processing and analysis problems.