An Approach toward a Q-Neutrosophic Soft Set and Its Application in Decision Making

A neutrosophic set was proposed as an approach to study neutral uncertain information. It is characterized through three memberships, T , I and F, such that these independent functions stand for the truth, indeterminate, and false-membership degrees of an object. The neutrosophic set presents a symmetric form since truth enrolment T is symmetric to its opposite false enrolment F with respect to indeterminacy enrolment I that acts as an axis of symmetry. The neutrosophic set was further extended to a Q-neutrosophic soft set, which is a hybrid model that keeps the features of the neutrosophic soft set in dealing with uncertainty, and the features of a Q-fuzzy soft set that handles two-dimensional information. In this study, we discuss some operations of Q-neutrosophic soft sets, such as subset, equality, complement, intersection, union, AND operation, and OR operation. We also define the necessity and possibility operations of a Q-neutrosophic soft set. Several properties and illustrative examples are discussed. Then, we define the Q-neutrosophic-set aggregation operator and use it to develop an algorithm for using a Q-neutrosophic soft set in decision-making issues that have indeterminate and uncertain data, followed by an illustrative real-life example.

Fuzzy logics and medical diagnosis of nursing assessment

This paper argues that fuzzy representations are appropriate in applications where there are major sources of imprecision and / or uncertainty. Case studies of fuzzy approaches to specific problems of medical diagnosis and classification are described in support of this argument. The solutions use a variety of fuzzy methods including clustering, fuzzy set aggregation and type- 2 fuzzy set and Type-2 fuzzy relation modeling of linguistic approximations. It is concluded that the fuzzy approach to the development of artificial intelligence in application systems is beneficial in these contexts because of the need to focus on uncertainty as a main issue. DOI: http://dx.doi.org/10.3329/bjsir.v49i4.22631 Bangladesh J. Sci. Ind. Res. 49(4), 271-274, 2014

Data Aggregation for Failure Tolerance in Wireless Sensor Network

One of the critical tasks in designing a wireless sensor network is to monitor, detect, and report various useful occurrences of events in the network domain which are determined by the result of data aggregation. Fault tolerance is critical to the efficiency of data aggregation scheme. One important reason is that sensor nodes are neither reliable nor stabile. In this paper, we present an improved k-means data aggregation algorithm considering the proposal of isolated point. Each cluster includes three types of sets: aggregation data, fault data set and abnormal data set. Aggregation data comes from normal sensors in this cluster through the improved K-means aggregation algorithm and abnormal nodes can be detected according to the aggregation result.