Generalized Interval Valued Neutrosophic Graphs of First Type

In this paper, motivated by the notion of generalized single valued neutrosophic graphs of first type, we defined a new neutrosophic graphs named generalized interval valued neutrosophic graphs of first type (GIVNG1) and presented a matrix representation for it and studied few properties of this new concept. The concept of GIVNG1 is an extension of generalized fuzzy graphs (GFG1) and generalized single valued neutrosophic of first type (GSVNG1)

New Trends in Neutrosophic Theory and Applications Volume II

Neutrosophic set has been derived from a new branch of philosophy, namely Neutrosophy. Neutrosophic set is capable of dealing with uncertainty, indeterminacy and inconsistent information. Neutrosophic set approaches are suitable to modeling problems with uncertainty, indeterminacy and inconsistent information in which human knowledge is necessary, and human evaluation is needed. Neutrosophic set theory was proposed in 1998 by Florentin Smarandache, who also developed the concept of single valued neutrosophic set, oriented towards real world scientific and engineering applications. Since then, the single valued neutrosophic set theory has been extensively studied in books and monographs introducing neutrosophic sets and its applications, by many authors around the world. Also, an international journal - Neutrosophic Sets and Systems started its journey in 2013. Single valued neutrosophic sets have found their way into several hybrid systems, such as neutrosophic soft set, rough neutrosophic set, neutrosophic bipolar set, neutrosophic expert set, rough bipolar neutrosophic set, neutrosophic hesitant fuzzy set, etc. Successful applications of single valued neutrosophic sets have been developed in multiple criteria and multiple attribute decision making. This second volume collects original research and application papers from different perspectives covering different areas of neutrosophic studies, such as decision making, graph theory, image processing, probability theory, topology, and some theoretical papers. This volume contains four sections: DECISION MAKING, NEUTROSOPHIC GRAPH THEORY, IMAGE PROCESSING, ALGEBRA AND OTHER PAPERS. First paper (Pu Ji, Peng-fei Cheng, Hongyu Zhang, Jianqiang Wang. Interval valued neutrosophic Bonferroni mean operators and the application in the selection of renewable energy) aims to construct selection approaches for renewable energy considering the interrelationships among criteria. To do that, Bonferroni mean (BM) and geometric BM (GBM) are employed

Formal Concept Analysis Applications in Bioinformatics

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed. Adviser: Jitender S. Deogu

Relational Approach to the L-Fuzzy Concept Analysis

Modern industrial production systems benefit from the classification and processing of objects and their attributes. In general, the object classification procedure can coincide with vagueness. Vagueness is a common problem in object analysis that exists at various stages of classification, including ambiguity in input data, overlapping boundaries between classes or regions, and uncertainty in defining or extracting the properties and relationships of objects. To manage the ambiguity mentioned in the classification of objects, using a framework for L-fuzzy relations, and displaying such uncertainties by it can be a solution. Obtaining the least unreliable and uncertain output associated with the original data is the main concern of this thesis. Therefore, my general approach to this research can be categorized as follows: We developed an L-Fuzzy Concept Analysis as a generalization of a regular Concept Analysis. We start our work by providing the input data. Data is stored in a table (database). The next step is the creation of the contexts and concepts from the given original data using some structures. In the next stage, rules, or patterns (Attribute Implications) from the data will be generated. This includes all rules and a minimal base of rules. All of them are using L-fuzziness due to uncertainty. This requires L-fuzzy relations that will be implemented as L -valued matrices. In the end, everything is nicely packed in a convenient application and implemented in Java programming language. Generally, our approach is done in an algebraic framework that covers both regular and L -Fuzzy FCA, simultaneously. The tables we started with are already L-valued (not crisp) in our implementation. In other words, we work with the L-Fuzzy data directly. This is the idea here. We start with vague data. In simple terms, the data is shown using L -valued tables (vague data) trying to relate objects with their attributes at the start of the implementation. Generating attribute implications from many-valued contexts by a relational theory is the purpose of this thesis, i.e, a range of degrees is used to indicate the relationship between objects and their properties. The smallest degree corresponds to the classical no and the greatest degree corresponds to the classical yes in the table

Novel System and Method For Telephone Network Planing Based on Neutrosophic Graph

Telephony is gaining momentum in the daily lives of individuals and in the activities of all companies. With the great trend towards telephony networks, whether analogue or digital known as Voice over IP (VoIP), the number of calls an individual can receive becomes considerably high. However, effective management of incoming calls to subscribers becomes a necessity. Recently, much attention has been paid towards applications of single-valued neutrosophic graphs in various research fields. One of the suitable reason is it provides a generalized representation of fuzzy graphs (FGs) for dealing with human nature more effectively when compared to existing models i.e. intuitionistic fuzzy graphs (IFGs), inter-valued fuzzy graphs (IVFGs) and bipolar-valued fuzzy graphs (BPVFGs) etc. In this paper we focused on precise analysis of useful information extracted by calls received, not received due to some reasons using the properties of SVNGs. Hence the proposed method introduced one of the first kind of mathematical model for precise analysis of instantaneous traffic beyond the Erlang unit. To achieve this goal an algorithm is proposed for a neutrosophic mobile network model (NMNM) based on a hypothetical data set. In addition, the drawback and further improvement of proposed method with a mathematical proposition is established for it precise applications