Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study

Design and implementation of a broker for cloud additive manufacturing services

The growing number of cloud Additive Manufacturing (AM) services, offered by different providers over the Internet, makes it challenging for consumers to compare these cloud AM services to select a service of their choice. In addition, it is even more challenging for consumers to compare these cloud AM services against their personal preferences. This is because, consumers personal preferences on multiple service attributes such as price, material, accuracy, and schedule, should be considered for cloud AM service selection. The decentralized nature of these cloud AM services coupled by the need to consider consumers personal preferences during cloud AM service selection, requires a system that will serve as a broker between cloud AM services and consumers. But, existing frameworks of cloud manufacturing either do not have brokers between cloud manufacturing service providers and consumers or do not support personalized preference and tradeoff based brokerage. To address these issues, we propose a cloud additive manufacturing framework which consists of a service broker system for cloud AM services that provides consumers with a single point of access to a large number of cloud AM services from many additive manufacturing service providers. This broker system also incorporates the first real application of service selection with fuzzy logic based personalized preferences and tradeoff. We also develop a method to generate fuzzy membership functions for each service attribute. This makes it easy for consumers to specify their fuzzy membership functions. We present an application case study to demonstrate the feasibility of brokerage in cloud AM services and finally evaluate our method in terms of performance --Abstract, page iii

Methods and algorithms for service selection and recommendation (preference and aggregation based)

In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users\u27 service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request. Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences. The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users\u27 personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users\u27 personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance --Abstract, page iii

Developing an integrated AHP and Intuitionistic FuzzyTOPSIS methodology

U ovom se istraživanju daje pregled analitičkog hijerarhijskog postupka (AHP) i intuicijskih FuzzyTOPSIS (IFT) metoda. Rad se bavi procjenom metodologije zasnovane na AHP-IFT gdje se nesigurnosti opisuju lingvističkim vrijednostima. Najprije se problem izbora dobavljača formulira primjenom AHP, a zatim se koristi za određivanje težina kriterija. Kasnije se IFT koristi za postizanje rangiranja među alternativama temeljenim na mišljenju donositelja odluka (DMs). Ovaj model omogućuje točnu i laku klasifikaciju svojstava dobavljača prema tome kako su rangirani u hibridnom modelu. Daje se numerički primjer kako bi se objasnio glavni dobiveni rezultat u radu.This research gives an overview of the Analytic Hierarchy Process (AHP) and Intuitionistic FuzzyTOPSIS (IFT) methods. This study deals with an evaluation methodology based on the AHP-IFT where the uncertainties are handled with linguistic values. First, the supplier selection problem is formulated using AHP and, then, it is used to determine the weights of the criteria. Later, IFT is used to obtain full- ranking among alternatives based on opinion of the Decision Makers (DMs). The present model provides an accurate and easy classification in supplier attributes by those that have been prioritized in the hybrid model. A numerical example is given to clarify the main developed result in this paper

Developing an integrated AHP and Intuitionistic FuzzyTOPSIS methodology

U ovom se istraživanju daje pregled analitičkog hijerarhijskog postupka (AHP) i intuicijskih FuzzyTOPSIS (IFT) metoda. Rad se bavi procjenom metodologije zasnovane na AHP-IFT gdje se nesigurnosti opisuju lingvističkim vrijednostima. Najprije se problem izbora dobavljača formulira primjenom AHP, a zatim se koristi za određivanje težina kriterija. Kasnije se IFT koristi za postizanje rangiranja među alternativama temeljenim na mišljenju donositelja odluka (DMs). Ovaj model omogućuje točnu i laku klasifikaciju svojstava dobavljača prema tome kako su rangirani u hibridnom modelu. Daje se numerički primjer kako bi se objasnio glavni dobiveni rezultat u radu.This research gives an overview of the Analytic Hierarchy Process (AHP) and Intuitionistic FuzzyTOPSIS (IFT) methods. This study deals with an evaluation methodology based on the AHP-IFT where the uncertainties are handled with linguistic values. First, the supplier selection problem is formulated using AHP and, then, it is used to determine the weights of the criteria. Later, IFT is used to obtain full- ranking among alternatives based on opinion of the Decision Makers (DMs). The present model provides an accurate and easy classification in supplier attributes by those that have been prioritized in the hybrid model. A numerical example is given to clarify the main developed result in this paper

The interval-valued intuitionistic fuzzy geometric choquet aggregation operator based on the generalized banzhaf index and 2-additive measure

Based on the operational laws on interval-valued intuitionistic fuzzy sets, the generalized Banzhaf interval-valued intuitionistic fuzzy geometric Choquet (GBIVIFGC) operator is proposed, which is also an interval-valued intuitionistic fuzzy value. It is worth pointing out that the GBIVIFGC operator can be seen as an extension of some geometric mean operators. Since the fuzzy measure is defined on the power set, it makes the problem exponentially complex. In order to overall reflect the interaction among elements and reduce the complexity of solving a fuzzy measure, we further introduce the GBIVIFGC operator w.r.t. 2-additive measures. Furthermore, if the information about weights of experts and attributes is incompletely known, the models of obtaining the optimal 2-additive measures on criteria set and expert set are given by using the introduced cross entropy measure and the Banzhaf index. Finally, an approach to pattern recognition and multi-criteria group decision making under interval-valued intuitionistic fuzzy environment is developed, respectively

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier