Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

A brief review of modeling approaches based on fuzzy time series

Abstract: Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this survey, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The article ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

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

Abstract: 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.

Multiparametric similarity measures on Pythagorean fuzzy sets with applications to pattern recognition

Abstract: Pythagorean fuzzy sets (PFSs), characterized by membership degrees and non-membership degrees, are a more effective and flexible way than intuitionistic fuzzy sets (IFSs) to capture indeterminacy. In this paper, some new diverse types of similarity measures, overcoming the blemishes of the existing similarity measures, for PFSs with multiple parameters are studied, along with their detailed proofs. The various desirable properties among the developed similarity measures and distance measures have also been derived. A comparison between the proposed and the existing similarity measures has been performed in terms of the division by zero problem, unsatisfied similarity axiom conditions, and counter-intuitive cases for showing their effectiveness and feasibility. The initiated similarity measures have been illustrated with case studies of pattern recognition, along with the effect of the different parameters on the ordering and classification of the patterns.