Thapar University

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

A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making

Abstract: The objective of this work is to present novel correlation coefficient measures for measuring the relationship between the two complex intuitionistic fuzzy sets (CIFSs) In the existing studies of fuzzy and its extension, the uncertainties present in the data are handled with the help of degrees of membership which are the subset of real numbers, and may lose some useful information and hence consequently affect on the decision results An alternative to these, complex intuitionistic fuzzy set handles the uncertainties with the degrees whose ranges are extended from real subset to the complex subset with unit disc and hence handle the two-dimensional information in a single set Thus, motivated by this, we develop correlation and weighted correlation coefficients under the CIFS environment in which pairs of the membership degrees represent the two-dimensional information Also, some of the desirable properties of it are investigated Further, based on these measures, a multicriteria decision-making approach is presented under the CIFS environment Two illustrative examples are taken to demonstrate the efficiency of the proposed approach and validate it by comparing their results with the several existing approaches’ results

Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making

Abstract: Intuitionistic fuzzy soft set (IFSS) theory acts as a fundamental tool for handling the uncertainty in the data by adding a parameterizing factor during the process as compared to fuzzy and intuitionistic fuzzy set (IFS) theories. In this paper, an attempt has been made to this effect to describe the concept of generalized IFSS (GIFSS), as well as the group-based generalized intuitionistic fuzzy soft set (GGIFSS) in which the evaluation of the object is done by the group of experts rather than a single expert. Based on this information, a new weighted averaging and geometric aggregation operator has been proposed by taking the intuitionistic fuzzy parameter. Finally, a decision-making approach based on the proposed operator is being built to solve the problems under the intuitionistic fuzzy environment. An illustrative example of the selection of the optimal alternative has been given to show the developed method. Comparison analysis between the proposed and the existing operators have been performed in term of counter-intuitive cases for showing the superiority of the approach.

Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective

Abstract: With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multiedge-cloud environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedge-cloud environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide cost-effective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.