Using a Fuzzy Modeling in Decision Making for Planning Under Uncertainty with Risk Analysis Paradigm

Association analysis of amino acids in molecular sequences can reveal crucial information and knowledge for understanding structure, function and interaction of proteins. The traditional methods of association rule mining like apriori, F-P Growth etc. fail to generate appropriate patterns due to inherent uncertainty present in data. The uncertainty in sequence data caused by variation in the length of sequences and lack of parameterization lead to under prediction and over prediction of the results. In this paper an attempt has been made to develop a soft set based approach for mining fuzzy association patterns in peptide sequences of dengue virus. The fuzzy set approach is employed to incorporate the degree of relationships among amino acids due to variation in length of the sequences. The soft set approach is employed to incorporate the relationship of parameters with amino acid association patterns. The 12,581 sequences of dengue virus are downloaded from NCBI and screened for redundancy to obtain non redundant 6995 sequences. The amino acid associations are explored and analyzed using soft fuzzy approach. Also the results obtained by soft fuzzy approach are compared with the results obtained individually by ordinary, fuzzy and soft set approaches. The soft fuzzy approach is able to overcome the issue of under prediction and over prediction of the results obtained by other approaches. Also the interesting association rules have been generated to predict the structure and physico chemical properties of the peptide sequences.

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.

In this paper, the production process is decomposed for typical manufacturing small and medium sized enterprises (SMEs) and the metrics of the defined sub processes, based on the requirements of ISO 9001:2008, are developed. The weight values of production process performance indicators are defined, using the experience of decision makers from the analysed manufacturing SMEs, and calculated using the fuzzy set approach. Finally, the developed solution, based on the genetic algorithm approach, is presented and tested on data from 112 Serbian manufacturing SMEs. The presented solution enables quality assessment of a production process, the ranking of indicators, optimisation and provides the basis for successful improvement of the production process quality.

Lot output time prediction is a critical task to a wafer fab (fabrication plant). Traditional studies are focused on prediction accuracy and efficiency. Another performance measure that is as important but has been ignored in traditional studies is the achievability of an output time forecast, which is defined as the possibility that the fabrication on a wafer lot can be finished in time before the output time forecast. Theoretically, if a probability distribution can be obtained for the output time forecast, then the achievability can be evaluated with the cumulative probability of the probability distribution before the given date. However, there are many managerial actions that are more influential to the achievability. For this reason, a fuzzy set approach is proposed for evaluating the achievability of the output time forecast. The fuzzy set approach is composed of two parts: a fuzzy back propagation network (FBPN) and a set of fuzzy inference rules (FIRs). An example is used to demonstrate the applicability of the proposed methodology.

Limbic System

Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction

• Propose novel behavioral membership functions within the fuzzy goal programming approach. • Model decision-makers’ behavioral aspects and risk perceptions. • Develop a behavior-based fuzzy goal programming approach. • Enhance utility functions within the fuzzy goal programming methodology. • Demonstrate applicability and efficacy through a venture capital investment problem. A relevant extension of traditional goal programming (GP), fuzzy goal programming (FGP) can handle uncertainty and imprecision in multi-objective optimization problems. Based on fuzzy set theory, the notion of membership functions has been introduced to consider the fuzziness related to objectives and constraints. These membership functions are mainly intended for fuzziness in the GP rather than modeling the decision-maker's (DM's) preferences and his/her attitude toward risk in the decision-making process. In the satisfying philosophy of FGP, little attention has been given to how preferences evolve in terms of the behavior of the decision-maker (DM) and how these preferences may affect decisions in risky scenarios. To address this issue, we suggest novel behavioral-type utility functions for the FGP approach by introducing the concept of behavioral membership functions. This concept offers an innovative procedure for simulating the DM's behavioral preferences in the FGP approach. First, two main categories of objectives in relation to the DM's behavioral preferences are distinguished in this work. A risk aversion parameter is integrated into membership functions according to the nature of each objective type, obtaining the so-called behavioral membership functions. A behavioral FGP approach is subsequently formulated. Finally, an illustrative example of venture capital investments, a sensitivity analysis, and comparisons with other FGP approaches are provided to demonstrate the validity and practicality of our proposed approach.

This paper describes a heuristic method and a developed software package for the restoration of distribution networks for the purpose of reconstructing normal operating conditions. The proposed methodology uses a fuzzy set approach, heuristic rules and a mixed integer programming method for solving the network restoration problem. Important implementational aspects of the methodology and the results of its application on real-life distribution networks are presented.

Human coronaviruses (HCoVs) are associated with a range of respiratory complications. In the last two decades, three major outbreaks have been reported due to HCoVs including the current pandemic. In December 2019, a newly emerged virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan city, China. This paper presents a detailed review of the literature and discusses the uncertain spread of coronavirus disease 2019 (COVID-19) using fuzzy set as classical set theory logic to measure uncertainty and vagueness of COVID-19 in China. Our findings show that both infection and death rate touched the peak (normal fuzzy sets) and have shown a decline. The graphs are not convex, which shows that there remains much uncertainty in the spread of COVID-19. Effective vaccines are clearly needed to control and prevent the COVID-19 pandemic.

Weather has an important role in people's lives, such as agriculture, economics, socio-economic, disaster management, and finance. So, weather prediction is very important to be considered. In the prediction process we are often faced with the problem of data incompleteness. Therefore, it needs a proper classification algorithm that able to handle incomplete attribute values in the training data. In this paper, we use two approaches to handle incomplete data, namely are rough set and fuzzy rough set based algorithms. To test the performance of the two algorithms, we use meteorological data to classify rain or dry season. Conclusion of the study showed that the rough set approach is more efficient than the fuzzy rough sets approach. The advantage of fuzzy rough set approach can predict all the conditions that may occur, which can't be done by the rough set approach.

Contractors may be faced with the problem of deciding the bidding price of a construction project, when the likelihood of the occurrence of risk events and the risk-associated consequences (monetary loss and gain) are uncertain. If the monetary loss resulting from risk events is not considered or is underestimated due to associated uncertainties, a construction enterprise may suffer a tremendous loss and eventually fail. In this paper, a risk-pricing method is proposed for analyzing and pricing construction project risk. The method consists of identifying risk elements and quantifying risk-associated consequences. The uncertainty in the values of the quantified consequences are represented by using a fuzzy set approach and incorporated directly into the bidding price decision process. The proposed risk-pricing method will assist contractors in the process of estimation under uncertainty. A real construction project is selected to illustrate how the proposed method can be put into practice.

Confidence sets are generally interpreted in terms of replications of an experiment. However, this interpretation is only valid before observing the sample. After observing the sample, any confidence sets have probability zero or one to contain the parameter value. In this paper, we provide a confidence set analysis for an observed sample based on fuzzy set theory by using the concept of membership functions. We show that the traditional ad hoc thresholds (the confidence and significance levels) can be attained from a general membership function. The applicability of the newly proposed theory is demonstrated by using well-known examples from the statistical literature and an application in the context of contingency tables.

Application of fuzzy logic to land suitability for rubber production in peninsular Thailand

In Calcutta metro, railways, roads and waterways are available to provide mass transport services. An attempt has been made to use non-conventional abstract mathematical theory to solve the multimodal mass transport system. Fuzzy functions have been used as an analytical tool, to express the satisfaction level of attributes as viewed by different interest groups. The central concept of fuzzy set theory is the membership function which represents numerically the degree to which an element belongs to a set. This function takes values from zero to one. For synthesizing information, rating matrices have been constructed with different alternative modes as columns against various attributes/factors as rows. Each rating matrix represents the evaluation made by a respective interest group. Having identified the corridor and selective modal characteristics, the preferred mode may be chosen by this method. Additionally, when the need for introducing some new mode or increasing the services of some old mode are felt, this procedure may be used as a simplified tool.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An important part of predicting responses with computational models is the definition of systems parameters such as material properties. The reliability of model‐based predictions depends on the quality of these data, and the knowledge of their uncertainties. When experiments cannot be performed to determine parameter values, they must be obtained from the literature, namely handbooks, standards, specification sheets, etc. This paper addresses the use of literature‐based parameters and other information for assessing uncertainty in computational predictions. The uncertainties in the data and the solution (prediction) are described in the framework of fuzzy sets. A double‐pipe heat exchanger thermal analysis problem is used to illustrate the difficulties with computational predictions when only informations from the literature are available. It is shown that the resulting uncertainties are significant and cannot be neglected. Copyright © 2010 John Wiley &amp; Sons, Ltd.