Palash Dutta

Uncertainty Modeling in Risk Assessment Based on Dempster–Shafer Theory of Evidence with Generalized Fuzzy Focal Elements

Abstract Dempster–Shafer theory of evidence is one of the important tools for decision making under uncertainty. It is more useful in situations when cost of technical difficulties is involved or uniqueness of the situation under study makes it difficult/impossible to cover enough observations to quantify the models with real data. Consequently, experts provide opinions in terms of basic probability assignment for focal elements. Usually, it is seen that experts provide basic probability assignment for interval (or crisp) focal elements. However, due to presence of uncertainty focal elements can sometimes be treated as normal/generalized triangular fuzzy number (TFN in short) instead of intervals or crisp sets. TFN encodes only most likely value (mode) and the spread. This paper presents an attempt to combine Dempster–Shafer structures (DSS in short) with generalized/normal fuzzy focal elements using possibilistic sampling technique. To this end, human health risk assessment is carried out under such setting.

Methods to Obtain Basic Probability Assignment in Evidence Theory

Dempster-Shafer Theory (DST) of Evidence is a powerful and flexible mathematical tool for handling uncertainty, impreciseness, and incomplete information. It can be used when both epistemic and aleatory uncertainties are present in the problem under consideration. The fundamental and important object of this theory of evidence is the primitive function called basic probability assignment (bpa). In the absence of empirical data, experts in related fields provide necessary information (bpa). However how to obtain BPA is still an open issue. In this paper, we propose methods to determine BPA when only the minimum, maximum and most likely values of the parameter are known. An example is illustrated to demonstrate and check the efficiency of the proposed methods. We have also developed an extended version of uncertainty measurement in evidence theory in order to calculated total uncertainty in the body of evidence obtained by the proposed methods.

A Hybrid Method to Deal with Aleatory and Epistemic Uncertainty in Risk Assessment

Risk assessment is an important and significant aid in the decision making process. Risk assessment is performed using ‘model’ and a model is a function of parameters which are usually affected by uncertainty. Some model parameters are affected by aleatory uncertainty and some others are affected by epistemic uncertainty. In this paper we propose a hybrid method to deal with propagation of both kinds of uncertainty within the same computation of risk.

Uncertainty Modeling in Risk Analysis: A Fuzzy Set Approach

isk assessment is a popular and important tool in decision making process. Risk assessment is generally performed using models and model is a function of some parameters which are usually affected by uncertainty. Here, we consider that model parameters are affected by epistemic uncertainty. To represent epistemic uncertainty in general triangular fuzzy number or trapezoidal fuzzy numbers are used. In this paper, we study Gaussian fuzzy number to represent epistemic type uncertainty and try to fuse with triangular fuzzy numbers and also risk assessment is carried out under fuzzy environment.

Bell-shaped Fuzzy Soft Sets and Their Application in Medical Diagnosis

Abstract A soft set theory is a new general mathematical tool to dealing with imprecision. The theory differs from traditional mathematical tools to dealing with uncertainties in which many mathematical theories failed. In this paper, we proposed the concept of Bell shaped fuzzy soft set and its application in medical diagnosis using arithmetic operations of discrete- Gaussian fuzzy number, triangular-Cauchy fuzzy number and Gaussian-Cauchy fuzzy number. As an integral part of this paper, we discuss the parametric form of Gaussian membership function is discussed with some important properties.

Comparison of Arithmetic Operations of Generalized Fuzzy Numbers: Case Study in Risk Assessment

ABSTRACT This study attempts to perform arithmetic operations of generalized triangular fuzzy numbers apart from the existing approaches. Also, average width of generalized fuzzy numbers is defined. Numerical examples are illustrated and uncertainty is measured by using defined average width, and finally, the results are compared with the existing approaches. A case study for health risk assessment is carried out in this comparison.

Modeling of variability and uncertainty in human health risk assessment

Graphical abstract

Dempster Shafer Structure-Fuzzy Number Based Uncertainty Modeling in Human Health Risk Assessment

In risk assessment, generally model parameters are affected by uncertainty arises due to vagueness, imprecision, lack of data, small sample sizes etc. Fuzzy set theory and Dempster-Shafer theory In short DST of evidence should be explored to handle this type of uncertainty. Representation of parameters of risk assessment models may be Dempster-Shafer structure in short DSS and fuzzy numbers. To deal with such situations, it is important to device new techniques. This paper presents two algorithms to combine Dempster-Shafer structure with generalized/normal fuzzy focal elements, generalized/normal fuzzy numbers within the same framework. Sampling technique for evidence theory and alpha-cut for fuzzy numbers are considered to execute the algorithms. Finally, results are obtained in the form of fuzzy numbers normal/generalized at different fractiles.

An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment

Risk assessment is an important and popular aid in the decision making process. The aim of risk assessment is to estimate the severity and likelihood of harm to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. In risk assessment, it is most important to know the nature of all available information, data or model parameters. More often, it is seen that available information model parameters, data are usually tainted with aleatory and epistemic uncertainty or both type of uncertainty. When some model parameters are affected by aleatory uncertainty and other some parameters are affected by epistemic uncertainty, how far computation of the risk is concern, one can either transform all the uncertainties to one type of format or need for joint propagation of uncertainties. In this paper, an effort has been made to combine probability distributions, normal fuzzy numbers and generalized interval valued fuzzy numbers (IVFNs) within the same framework.

Human Health Risk Assessment Under Uncertain Environment and Its SWOT Analysis

Received: October 06, 2017 Revised: January 03, 2018 Accepted: February 02, 2018 Abstract: Introduction: Due to release of pollutants, chemical or hazardous materials into the environment from manmade or artificial sources, human being as well as animal populations are affected directly or indirectly through inhalation of air, intake of water, intake of food, dermal contact, etc. Then, human being may suffer from types of health effect such as acute radiation sickness, cancer, teratogenic (fetal) damage, hereditary changes, etc.