Branch Mathematics and Statistics Faculty and Staff Publications

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It is common knowledge that common models with their limited boundaries of truth and falsehood are not su¢ cient to detect the reality so there is a need to discover other systems which are able to address the daily life problems. In every branch of science problems arise which abound with uncertainties and impaction. Some of these problems are related to human life, some others are subjective while others are objective and classical methods are not su¢ cient to solve such problems because they can not handle various ambiguities involved. To overcome this problem, Zadeh [67] introduced the concept of a fuzzy set which provides a useful mathematical toolfordescribingthebehaviorofsystemsthatareeithertoocomplexorare ill-dened to admit precise mathematical analysis by classical methods. The literature in fuzzy set and neutrosophic set theories is rapidly expanding and application of this concept can be seen in a variety of disciplines such as articialintelligence,computerscience,controlengineering,expertsystems, operating research, management science, and robotics. Zadeh introduced the degree of membership of an element with respect to a set in 1965, Atanassov introduced the degree of non-membership in 1986, and Smarandache introduced the degree of indeterminacy (i.e. neither membership, nor non-membership) as independent component in 1995 and defined the neutrosophic set. In 2003 W. B. Vasantha Kan- dasamy and Florentin Smarandache introduced for the rst time the I- neutrosophic algebraic structures (such as neutrosophic semigroup, neutro- sophic ring, neutrosophic vector space, etc.) based on neutrosophic num- bers of the form a + bI, where


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groupoid, Abel Grassmann Groupoid, neutrosophic logic

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.