About this Research Topic
Data-driven problems are relevant to a variety of disciplines and can be constituted, examined, and solved in numerous possible ways. It is common for the information involved in such problems to not be perfect and to include uncertain, imprecise, and vague data. For this reason – because they are likely to involve indeterminate data - data-related problems cannot be satisfactorily solved by using fuzzy set, intuitionistic fuzzy, or their extensions. Neutrosophic theory, on the other hand, is an efficient tool for solving data-driven problems connected with indeterminacy. This based on the capacity of neutrosophic theory to deal with every element of a problem in addition to their conflicts and their hybrid behavior with other types of sets like rough sets, soft sets, and bipolar sets. Such a potential has resulted in the increasing use of neutrosophic theory in the development of applications relevant to a variety of data-driven problems, including data mining, e-learning, image processing, classification of pattern, clustering, medical diagnosis and so forth.
Employing neutrosophic theory in the field of artificial intelligence is becoming more and more popular as it is considered to offer optimum results. In this respect, neutosophic logic, sets, probability, and statistics, have all been used in the development of artificial intelligence applications and tools, including among others robot mapping, automatic decision making, satellite image segmentation, medical diagnosis, neutrosophic cognitive maps, linear and non-linear programming. This vast variety of uses of neutrosophic theory in artificial intelligence has given rise to unique questions and has introduced novel ideas to solve critical problems.
Following these observations, this Research Topic aims at addressing the latest developments in the employment of neurosophic theory in artificial intelligence. Researchers examining the theories and applications of neutrosophic theory in artificial intelligence are invited to submit their manuscripts. We particularly welcome original research articles, research and data report, conceptual analysis, hypothesis and theory, methods, as well as technology and code. Themes can include, but are not limited to the use of neutrosophic theory in the development of artificial intelligence applications in a variety of scientific areas, including modeling and simulation, physics, computer science, engineering, biology and chemistry, industrial and computational engineering and which address issues relevant to the indeterminacy, uncertainty, impreciseness, vagueness, inconsistency, and incompleteness of information.
Keywords: neutrosophic logic, neutrosophic theory, neutrosophic scheduling, neutrosophic reasoning, neutrosophic optimization, neutrosophic networks
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