An interesting attempt is taken to form a hybrid model by integrating Fuzzy Cognitive Map (FCM), the Delphi Method, and the DEMATEL method through the Haar ranking of Hexagonal Fuzzy Number. To examine this model, the problem of solid waste management is taken. Solid waste arises from human and animal activities can generate major health problems and horrible living environment when they are not dumped of safely and appropriately. The proper disposal of solid waste helps to reduce the terrible impacts on both human health and environment to sustain economic growth and better quality of life. Therefore, this present study analyzes the solid waste management through the Haar FCM with DEMATEL technique.

Haar FCM with DEMATEL Technique to Analyze the Solid Waste Management

Development of a decision support system to use in the strategic purchasing of dental implants

Multiple Criteria/Attribute Decision Making (MCDM) models are often used to solve various decision making and selection problems. There are well known methods for solving MCDM problems such as AHP, DEMATEL, TOPSIS, VIKOR, etc., have been developed by the soft computing researchers. Though the DEMATEL method is applied successfully in the crisp environment, it is not able deal the situations when the data is full of uncertain and vagueness. Therefore, Haar fuzzy DEMATEL method can be proposed using trapezoidal fuzzy number and Haar ranking for analyzing the climate change. 

Haar fuzzy DEMATEL technique to analyze climate change

The necessity for good and tenable communication systems has motivated researchers to develop mobile communication networks (MCN). On the other hand, the huge functionalities of the global system of mobile (GSM) communication have given an increasing number of users. As the subscribers grow, the necessity for efficient and productive planning of the limited frequency spectrum of the GSM is inevitable, especially in highly dense areas. Researchers have proposed various algorithms for frequency or channel allocation (CA), as the discussions about CA methods to resolve the various practical issues in CA are going on. The literature reveals that the “Manhattan distance” concept can be used in scheduling and optimization problems. Similarly, the same idea makes it possible to discover a more tenable telecommunication system with “ease of connectivity” among subscribers, even when many users are on a common channel. Graph labeling is the most interesting idea in graph theory that has numerous uses in different fields, particularly in communication networks. 2-odd labeling assigns distinct integers to the nodes of a graph $$G\left( {V,E} \right)$$ such that the positive difference of adjacent nodes is either 2 or an odd integer, $$2k \pm 1,\;k \in N$$ . The motivation behind the development of this article is to study the applications of graph theory in communication networks through the concept of 2-odd labeling in graphs. 

Studying the Applications of Graph Labeling in Satellite Communication Through 2-Odd Labeling of Graphs

Type-2 Diabetes mellitus is one of the most alarming diseases in both developed and developing countries. The WHO predicted that 90% of people around the globe will suffer from T2DM (WHO, 2016). Most of the people are living in India without knowing that they are affected with T2DM. So, the undiagnosed T2DM leads to the complication in heart, kidney disease, eye and feet. Even though Type 2 diabetes has many risk factors associated to it, lifestyle changes play a vital role in triggering the Type 2 diabetes. Hence, the objective of the present study is to analyze and identify the most influencing risk factors of T2DM. Determining the most influencing risk factor of T2DM is not an easy task as there are lot of complexity and uncertainty involved in it. To tackle this issue, a novel decision making system is designed by combining the salient features of The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy Cognitive Map (FCM). Eight risk factors are chosen as the input variables for the system. The proposed system elucidates that Blindness, Obesity, Physical Inactivity are the most influencing factors for the type 2 diabetes mellitus.

A. Parthiban

Papers

Soft computing decision making system to analyze the risk factors of T2DM

Certain Results on Prime and Prime Distance Labeling of Graphs