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Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of Computational Intelligence (CI) to a wide spectrum of research cases. The book covers the main approaches used in Computational Intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing.
Computational intelligence (CI) is a paradigm of natural environment-inspirited computational methodologies, which provides solutions to strenuous real-life problems where mathematical and traditional modeling are rendered useless. It may be impossible to translate several real-life problems into the binary (0 or 1) format due to their stochastic nature or the uncertainties associated with them. It is in these scenarios where CI comes as a rescue to researchers. CI is the extensive umbrella under which three core technologies are present, namely Machine Learning (ML), genetic algorithms (GAs), and neural networks (NNs), among several others. These technologies either individually or in combination can be effectively utilized to fix crucial real-life issues. One essential dilemma in our daily lives is protecting and ensuring the confidentiality, integrity, and authenticity of the information both in transit and storage from intrusions by unauthorized adversaries. CI has the potential to prove to be one of the obvious choices to contribute extensively to the field of cryptography. These contributions of CI in the field of cryptography have been studied elaborately in the Chapter 2