Adaptation, Machine Learning, and the Immune System: A Review Paper (International Journal of Innovative Research in Computer Science & Technology, 2021)
The immunologic system is a critical  dynamic system whose goal it is to detect and eliminate  foreign matter. In order to do any of this, this must be able  to tell the difference across much particles (or antigens) and  the particles self. The cells are able to perceive, learn, and  retain patterns. By employing techniques of genetic  engineering on a temporal scale fast enough seeing  practically, the immune system may recognize novel forms  need preprogramming. We give a good dynamical body's  classification based on Jerne's phone system hypothesis that  is simple to execute on a web page. This terminology is  similar to Yorkshire's classification algorithm, a teaching  students and computational tool. We explain how discrete - time systems may be used to describe simple releases of the  algorithm is proposed, and we go through the immune and  classifier systems in depth. We aim to learn more about  how they do particular tasks by comparing them, as well as  propose new methods that may be useful in learning  systems.
Keywords: Adaptation; Controller; Data Set; Immune System; Machine learning