Distance measure and similarity measure have been applied to various multi-criteria decision-making environments, like talent selections, fault diagnoses and so on. Some improved distance and similarity measures have been proposed by some researchers. However, hesitancy is reflected in all aspects of life, thus the hesitant information needs to be considered in measures. Then, it can effectively avoid the loss of fuzzy information.

Distance measure and similarity measure have been applied to various multi-criteria decision-making environments, like talent selections, fault diagnoses and so on. Some improved distance and similarity measures have been proposed by some researchers. However, hesitancy is reflected in all aspects of life, thus the hesitant information needs to be considered in measures. Then, it can effectively avoid the loss of fuzzy information. However, regarding fuzzy information, it only reflects the subjective factor. Obviously, this is a shortcoming thatwill result in an inaccurate decision conclusion. Thus, based on the definition of a probabilistic neutrosophic hesitant fuzzy set (PNHFS), as an extended theory of fuzzy set, the basic definition of distance, similarity and entropy measures of PNHFS are established. Next, the interconnection among the distance, similarity and entropy measures are studied. Simultaneously, a novel measure model is established based on the PNHFSs. In addition, the new measure model is compared by some existed measures. Finally, we display their applicability concerning the investment problems, which can be utilized to avoid redundant evaluation processes.

The uncertainty and concurrence of randomness are considered when many practical problems are dealt with. To describe the aleatory uncertainty and imprecision in a neutrosophic environment and prevent the obliteration of more data, the concept of the probabilistic single-valued (interval) neutrosophic hesitant fuzzy set is introduced. By definition, we know that the probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) is a special case of the probabilistic interval neutrosophic hesitant fuzzy set (PINHFS). PSVNHFSs can satisfy all the properties of PINHFSs. An example is given to illustrate that PINHFS compared to PSVNHFS is more general. Then, PINHFS is the main research object. The basic operational relations of PINHFS are studied, and the comparison method of probabilistic interval neutrosophic hesitant fuzzy numbers (PINHFNs) is proposed. Then, the probabilistic interval neutrosophic hesitant fuzzy weighted averaging (PINHFWA) and the probability interval neutrosophic hesitant fuzzy weighted geometric (PINHFWG) operators are presented. Some basic properties are investigated. Next, based on the PINHFWA and PINHFWG operators, a decision-making method under a probabilistic interval neutrosophic hesitant fuzzy circumstance is established. Finally, we apply this method to the issue of investment options. The validity and application of the new approach is demonstrated.

Single-valued neutrosophic hesitant fuzzy set is a merged form of single-valued neutrosophic sets and hesitant fuzzy sets. This set is a useful tool to handle imprecise, incomplete and inconsistent information existing in multi-attribute decision making problems. In multi-attribute decision making, distance measures play an important role to take a decision regarding alternatives.

Covering a wide range of notions concerning hesitant fuzzy set and its extensions, this book provides a comprehensive reference to the topic. In the case where different sources of vagueness appear simultaneously, the concept of fuzzy set is not able to properly model the uncertainty, imprecise and vague information. In order to overcome such a limitation, different types of fuzzy extension have been introduced so far. Among them, hesitant fuzzy set was first introduced in 2010, and the existing extensions of hesitant fuzzy set have been encountering an increasing interest and attracting more and more attentions up to now. It is not an exaggeration to say that the recent decade has seen the blossoming of a larger set of techniques and theoretical outcomes for hesitant fuzzy set together with its extensions as well as applications.As the research has moved beyond its infancy, and now it is entering a maturing phase with increased numbers and types of extensions, this book aims to give a comprehensive review of such researches. Presenting the review of many and important types of hesitant fuzzy extensions, and including references to a large number of related publications, this book will serve as a useful reference book for researchers in this field.

Neutrosophy as science has inclusive attributes that make possible to extract the contributions of neutral values in the analysis of data sets; it builds a unified field of logic for transdisciplinary studies that transcend the boundaries between natural and social sciences. Neutral philosophy seeks to solve the problems of indeterminacy that appear universally, to reform the current natural or social sciences, with an open methodology to promote innovation. The research products related in this special issue start from the premise that the difficulty is not the complexity of the social environment, but the instrumental obsolescence to observe, interpret and manage that complexity, there are bold approaches and proposals for valid solutions that come to enrich the universe of resolution through the use of neutral methods. In the last year, the use of tools related to neutrosophy and its application to the social sciences, modeling of social phenomena based on simulation agents, problems associated with health, psychology, education, environmental management and sustainability solutions and legal sciences has increased in the events organized by the Asociacion Latinoamericana de Ciencias Neutrosoficas (ALCN in Spanish). The methods of higher incidence are cognitive maps, neutral Iadovs, neutral Delphi, analytical hierarchy process methods, neutral statistics, neutral personality models, among the most significant. In this special issue, there is a predominance of research from Ecuadorian universities, demonstrating how neutrosophy and its methods are consolidated as instruments of analysis, inference and research validation.

Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies