Fuzzy logic programming based knowledge analysis for qualitative comparative analysis

2016 ◽  
Vol 51 (5) ◽  
pp. 2101-2113 ◽  
Author(s):  
Pushkin Kachroo ◽  
Anjala Krishen ◽  
Shaurya Agarwal
Keyword(s):  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Logic Programming ◽  
Qualitative Comparative Analysis ◽  
Knowledge Analysis

scholarly journals Finding the Links between Risk Management and Project Success: Evidence from International Development Projects in Colombia

2020 ◽  
Vol 12 (21) ◽  
pp. 9294
Author(s):  
Rocío Rodríguez-Rivero ◽  
Isabel Ortiz-Marcos ◽  
Javier Romero ◽  
Luis Ballesteros-Sánchez
Keyword(s):  
Risk Management ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
International Development ◽  
Cultural Differences ◽  
Direct Relationship ◽  
Project Success ◽  
Qualitative Comparative Analysis ◽  
Development Projects ◽  
Political Factors

The aim of this research is to help improve the effectiveness of international development projects (IDPs) with a focus on enhancing their success. For this purpose, this work seeks to identify links between the management of risks among five projects executed in Cauca (Colombia) and the success of these projects in terms of project management and impacts on the beneficiary communities. An analysis of these projects reveals the most critical risks encountered and the relationships between the management of those risks and the success of the projects. The use of fuzzy logic through the fuzzy-set qualitative comparative analysis (fsQCA) program is key to performing this difficult task. The results of a qualitative study reveal that the most important risks correspond to economic, cultural, and political factors. A quantitative analysis by fsQCA shows a direct relationship between the management of cultural differences and the positive impacts of IDPs on the beneficiary communities.

Fuzzy Multiple Attribute Conditions in fsQCA: Problems and Solutions

2017 ◽  
Vol 49 (2) ◽  
pp. 312-355 ◽  
Author(s):  
Francesco Veri
Keyword(s):  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Qualitative Comparative Analysis ◽  
Arithmetic Mean ◽  
Aggregation Techniques ◽  
Multiple Attribute ◽  
Different Types ◽  
Problems And Solutions ◽  
Problematic Issue

This article aims to clarify the fundamental aspects of aggregating fuzzy scores of conditions with multiple attributes in fuzzy set qualitative comparative analysis (fsQCA). Fuzzy multiple attribute conditions (FMACs) are conditions that are built using different types of concepts. FMACs are flexible conditions that express the ontological nature of the concept and deals with the causal heterogeneity. In fact, researchers can add a new attribute to a concept in order to consider the concept’s meaning vis-à-vis to the outcome of interest instead of only considering the theory. In relation to FMAC fuzzy scores, we have individuated one problematic issue which is the aggregation strategy of attributes that are already calibrated that should be able to capture conceptual properties of membership and similarity. In this article, we will employ an empirical example in order to deal with causal heterogeneity and aggregation strategies. After discussing the disadvantages of the aggregation techniques used by QCA scholars, we individuate an axiomatic framework for defining logical conjunction operators that allows one to aggregate parts of concepts in accordance with membership and similarity. Then, we propose a technique to assign fuzzy scores to FMAC using the arithmetic mean–based compensatory fuzzy logic. This technique indirectly affects the solution formula(s) following the QCA and allows one to better locate cases in the XY plot during the post-QCA analysis.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Impact of Globalisation on Pandemics: A comparative study on SARS and Covid-19 (Preprint)

2020 ◽  
Author(s):  
Johann Johann And Devika
Keyword(s):  
Comparative Analysis ◽  
Comparative Study ◽  
Descriptive Analysis ◽  
Qualitative Comparative Analysis ◽  
Global Scale ◽  
Global Level ◽  
Economic Stability ◽  
The Past ◽  
The World ◽  
Close Relationship

BACKGROUND Since November 2019, Covid - 19 has spread across the globe costing people their lives and countries their economic stability. The world has become more interconnected over the past few decades owing to globalisation and such pandemics as the Covid -19 are cons of that. This paper attempts to gain deeper understanding into the correlation between globalisation and pandemics. It is a descriptive analysis on how one of the factors that was responsible for the spread of this virus on a global scale is globalisation. OBJECTIVE - To understand the close relationship that globalisation and pandemics share. - To understand the scale of the spread of viruses on a global scale though a comparison between SARS and Covid -19. - To understand the sale of globalisation present during SARS and Covid - 19. METHODS A descriptive qualitative comparative analysis was used throughout this research. RESULTS Globalisation does play a significant role in the spread of pandemics on a global level. CONCLUSIONS - SARS and Covid - 19 were varied in terms of severity and spread. - The scale of globalisation was different during the time of SARS and Covid - 19. - Globalisation can be the reason for the faster spread in Pandemics.

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