Securing and Optimizing Access in Large Data Systems Based on Computational Intelligence

Dear Colleagues,

Computational Intelligence is the set of biologically inspired artificial intelligence techniques capable of modeling and optimizing complex phenomena. Its main techniques are neural networks, including deep learning, evolutionary computing, and fuzzy logic.

These methods, especially deep learning, are reaching a level of maturity that allows their application in real commercial products and services. We are starting to see software incorporating these techniques in photographic packages, natural language tools, surveillance systems, and autonomous driving, to name just a few.

The transition from purely theoretical research techniques to real-world applications is not a trivial process. It involves significant application and adaptation efforts due to the real needs of the field of use. Sometimes, applicability may require modification of some theoretical foundations. This arises from an iterative process, in which theory is revised for the sake of applicability to real problems in society. This is the gap between science and engineering.

In this sense, we are looking for works that show real applications of Computational Intelligence in the field of large data systems, especially in securing and optimizing access to them, both from the designer’s point of view (what theory is applied, with what restrictions, how that theory is adapted to make it applicable), and from the user’s point of view (what can be done that was not possible before, what restrictions are imposed, what benefits it provides) and, if applicable, from society’s point of view (what aspects of people’s lives change, what ethical problems arise).

This Special Issue aims to bring a set of high-quality articles on new practical contributions to industry and business in modeling, optimizing, planning, securing, and optimizing particularly large data systems, which have led to a significant improvement in the productive capacity of companies and organizations.

Topics include but are not limited to:

• Securing data systems;
• Optimizing access in large data systems;
• Cryptographic models;
• Practical applications of deep learning;
• Meta-heuristics techniques in real systems;
• Applications on intelligent transportation systems;
• Artificial Intelligence applications towards Industry 4.0 and IoT;
• Modeling economics and business;
• Improving society and education through intelligent systems;
• Deep learning in arts and science;
• Computational intelligence applied to cybersecurity;
• Explainable computational intelligence.

Dr. Nuria Gómez
Guest Editor

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