Algebraic coding theory is a new and rapidly developing subject, popular for its many practical applications and for its fascinatingly rich mathematical structure. This book provides an elementary yet rigorous introduction to the theory of error-correcting codes. Based on courses given by the author over several years to advanced undergraduates and first-year graduated students, this guide includes a large number of exercises, all with solutions, making the book highly suitable for individual study.

A first course in coding theory, by Raymond Hill. Pp 251. £12·50. 1986. ISBN 0-19-853803-0 (Oxford University Press)

This paper presents three novel single-valued neutrosophic soft set (SVNSS) methods. First, we initiate a new axiomatic definition of single-valued neutrosophic similarity measure, which is expressed by single-valued neutrosophic number (SVNN) that will reduce the information loss and remain more original information. Then, the objective weights of various parameters are determined via grey system theory.

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Algorithms for Neutrosophic Soft Decision Making Based on Edas, New Similarity Measure and Level Soft Set

In this paper, we expand the Hamy mean (HM) operator, weighted Hamy mean (WHM), dual Hamy mean (DHM) operator, and weighted dual Hamy mean (WDHM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose a 2-tuple linguistic neutrosophic Hamy mean (2TLNHM) operator, 2-tuple linguistic neutrosophic weighted Hamy mean (2TLNWHM) operator, 2-tuple linguistic neutrosophic dual Hamy mean (2TLNDHM) operator, and 2-tuple linguistic neutrosophic weighted dual Hamy mean (2TLNWDHM) operator. Then, the multiple attribute decision-making (MADM) methods are proposed with these operators. Finally, we utilize an applicable example in risk assessment for construction engineering projects to prove the proposed methods.

Research on Construction Engineering Project Risk Assessment with Some 2-Tuple Linguistic Neutrosophic Hamy Mean Operators

The theory of intuitionistic fuzzy soft set (IFSS) is an extension of the soft set theory which is utilized to precise the deficiency, indeterminacy, and uncertainty of the evaluation while making decisions. The conspicuous characteristic of this mathematical concept is that it considers two distinctive sorts of information, namely the membership and non-membership degrees. The present paper partitioned into two folds: (ⅰ) to define the correlation measures for IFSSs; (ⅱ) to introduce the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS) for IFSS information. Further, few properties identified with these measures are examined thoroughly. In view of these techniques, an approach is presented to solve decision-making problems by utilizing the proposed TOPSIS method based on correlation measures. At last, an illustrative example is enlightened to demonstrate the appropriateness of the proposed approach. Also, its suitability and attainability are checked by contrasting its outcomes and the prevailing methodologies results.

TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information

Since its introduction by Molodstov (Computers & Mathematics with Applications 37(4):19–31 1999), soft set theory has been widely applied in various fields of study. Soft set theory has also been combined with other theories like fuzzy sets theory, rough sets theory, and probability theory. The combination of soft sets and probability theory generates probabilistic soft set theory. However, decision-making based on the probabilistic soft set theory has not been discussed in the literature. In this paper, we propose new algorithms for decision-making based on the probabilistic soft set theory. An example to show the application of these algorithms is given, and its possible extensions and reinterpretations are discussed. Inspired by realistic situations, the notion of dual probabilistic soft sets is proposed, and also, its application in decision-making is investigated.

Probabilistic soft sets and dual probabilistic soft sets in decision-making

In this paper, the notion of bipolar N-soft set, which is the bipolar extension of N-soft set, and its fundamental properties are introduced. This new idea is illustrated with real-life examples. Moreover, some useful operations and products on the bipolar N-soft sets are derived. We thoroughly discuss the idempotent, commutative, associative, and distributive laws for these emerging operations and products. Also, we set forth two outstanding algorithms to handle the decision-making problems under bipolar N-soft set environments. We give potential applications and comparison analysis to demonstrate the efficiency and advantages of algorithms.

Bipolar N-soft set theory with applications

In this paper, we first generalize the products of two fuzzy soft matrices. Through these generalizations, three or more fuzzy soft matrices in the different types can be multiplied. Furthermore, we introduce the mean operators and normalized fuzzy weighted mean operators of the fuzzy soft matrices. We discuss the theoretical aspects of these operators. We describe the multicriteria group decision making (MCGDM) problem with different evaluation criterion sets, and then we create two algorithms using the mean operators and generalized products of fuzzy soft matrices to deal with such problems. To show the advantages of the proposed ones, we present the comparison results with some of the preexisting decision making algorithms of fuzzy soft sets. Finally, we create Scilab codes of our algorithms to expedite and facilitate the decision making process.

The mean operators and generalized products of fuzzy soft matrices and their applications in MCGDM

The main objective of this paper is to introduce some algebraic properties of finite linear Diophantine fuzzy subsets of group, ring and field. Relatedly, we define the concepts of linear Diophantine fuzzy subgroup and normal subgroup of a group, linear Diophantine fuzzy subring and ideal of a ring, and linear Diophantine fuzzy subfield of a field. We investigate their basic properties, relations and characterizations in detail. Furthermore, we establish the homomorphic images and preimages of the emerged linear Diophantine fuzzy algebraic structures. Finally, we describe linear Diophantine fuzzy code and investigate the relationships between this code and some linear Diophantine fuzzy algebraic structures.

Linear Diophantine fuzzy algebraic structures

A soft matrix multiplication of matrices in different types was not possible so far. In this study, we generalize the soft matrix products And, And–Not, Or, Or–Not defined in Cagman and Enginoglu (Comput Math Appl 59:3308–3314, 2010) so as to multiply soft matrices in different types. Furthermore, these generalizations allow us to multiply soft matrices more than two soft matrices. Therefore, we can solve decision making problems with multiple decision makers using a single product. These new operations make the process of solving decision making problems faster, easier and more convenient. Then we construct some effective decision making methods called soft distributive max–min (max–max, min–min, min–max) decision making methods. We also provided Scilab codes to demonstrate our methods.

Hüseyin Kamacı

Papers

Bipolar N-soft set theory with applications

The mean operators and generalized products of fuzzy soft matrices and their applications in MCGDM

Linear Diophantine fuzzy algebraic structures

Reduced soft matrices and generalized products with applications in decision making

Row-products of soft matrices with applications in multiple-disjoint decision making