Special Issue "Fuzzy Decision Making and Soft Computing Applications"

A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Giuseppe De Pietro

National Research Council of Italy (CNR) - Institute for High Performance Computing & Networking (ICAR), Via P Castellino 111, 80131 Naples, Italy.
Website | E-Mail
Phone: +390816139511
Interests: Decision Support Systems; Pervasive Computing; E-Health
Guest Editor
Dr. Marco Pota

National Research Council of Italy (CNR) - Institute for High Performance Computing & Networking (ICAR), Via P Castellino 111, 80131 Naples, Italy.
Website | E-Mail
Interests: Fuzzy Logic; Data Mining; Deep Learning; Knowledge Representation; Natural Language Processing; Predictive Models

Special Issue Information

Dear Colleagues,

Research on Fuzzy Logic and Soft Computing in the field of Decision Making has a long history, but it is still attractive for the possibility of solving many practical problems with the peculiarities of systems built by these approaches. In particular, often, decision-making systems should deal with uncertain data. Moreover, in some fields of application, such as differential diagnosis in medicine, a meaningful confidence measure is required to be associated with the classification result, in order to show all possible outcomes with the relative likelihood. Finally, in the last few years, increasing regard has been paid to semantically meaningful systems, for encapsulating them in interactive frameworks of cognitive systems, or for enabling validation by domain experts, by providing clear and logical interpretation of the inference process. These issues can be accomplished, on the one hand, by modelling uncertain numerical data by terms of interpretable linguistic variables; on the other hand, fuzzy rules show a clear and logic justification for each conclusion. Finally, if desired, fuzzy systems allow presenting classification results associated with a confidence measure, such as the probability of different classes. The remarkable progresses made by these approaches in various fields underline their benefits and stimulate further research and applications.

The aim of this Special Issue is to collect original research articles, as well as review articles, on the most recent developments and research efforts in this field, with the purpose of providing guidelines for future research directions. Potential topics include, but are not limited to:

  • Theory of fuzzy systems and soft computing;
  • Procedures for learning fuzzy systems;
  • Interpretability of fuzzy systems;
  • Decision making applications employing fuzzy logic and soft computing.

Prof. Giuseppe De Pietro
Dr. Marco Pota
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied System Innovation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fuzzy logic and soft computing
  • Decision making
  • Classification robustness
  • Interpretability
  • Confidence-weighted classification

Published Papers (5 papers)

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Research

Open AccessArticle New Approximation Methods Based on Fuzzy Transform for Solving SODEs: II
Appl. Syst. Innov. 2018, 1(3), 30; https://doi.org/10.3390/asi1030030
Received: 17 June 2018 / Revised: 11 August 2018 / Accepted: 14 August 2018 / Published: 23 August 2018
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Abstract
In this research, three approximation methods are used in the new generalized uniform fuzzy partition to solve the system of differential equations (SODEs) based on fuzzy transform (FzT). New representations of basic functions are proposed based on the new types of a uniform
[...] Read more.
In this research, three approximation methods are used in the new generalized uniform fuzzy partition to solve the system of differential equations (SODEs) based on fuzzy transform (FzT). New representations of basic functions are proposed based on the new types of a uniform fuzzy partition and a subnormal generating function. The main properties of a new uniform fuzzy partition are examined. Further, the simpler form of the fuzzy transform is given alongside some of its fundamental results. New theorems and lemmas are proved. In accordance with the three conventional numerical methods: Trapezoidal rule (one step) and Adams Moulton method (two and three step modifications), new iterative methods (NIM) based on the fuzzy transform are proposed. These new fuzzy approximation methods yield more accurate results in comparison with the above-mentioned conventional methods. Full article
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
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Open AccessArticle New Approximation Methods Based on Fuzzy Transform for Solving SODEs: I
Appl. Syst. Innov. 2018, 1(3), 29; https://doi.org/10.3390/asi1030029
Received: 17 June 2018 / Revised: 11 August 2018 / Accepted: 14 August 2018 / Published: 23 August 2018
Cited by 1 | PDF Full-text (385 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, new approximation methods for solving systems of ordinary differential equations (SODEs) by fuzzy transform (FzT) are introduced and discussed. In particular, we propose two modified numerical schemes to solve SODEs where the technique of FzT is combined with one-stage and
[...] Read more.
In this paper, new approximation methods for solving systems of ordinary differential equations (SODEs) by fuzzy transform (FzT) are introduced and discussed. In particular, we propose two modified numerical schemes to solve SODEs where the technique of FzT is combined with one-stage and two-stage numerical methods. Moreover, the error analysis of the new approximation methods is discussed. Finally, numerical examples of the proposed approach are confirmed, and applications are presented. Full article
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
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Open AccessArticle Causal Graphs and Concept-Mapping Assumptions
Appl. Syst. Innov. 2018, 1(3), 25; https://doi.org/10.3390/asi1030025
Received: 11 May 2018 / Revised: 11 July 2018 / Accepted: 20 July 2018 / Published: 24 July 2018
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Abstract
Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are
[...] Read more.
Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are not always easily observed or measured that directly or indirectly influence the dynamic relationships between independent variables and dependent variables. This paper proposes a procedure for helping researchers explicitly understand what their underlying assumptions are, what kind of data and methodology are needed to understand a given relationship, and how to develop explicit assumptions with clear alternatives, such that researchers can then apply a process of probabilistic elimination. The procedure borrows from Pearl’s concept of “causal diagrams” and concept mapping to create a repeatable, step-by-step process for systematically researching complex relationships and, more generally, complex systems. The significance of this methodology is that it can help researchers determine what is more probably accurate and what is less probably accurate in a comprehensive fashion for complex phenomena. This can help resolve many of our current and future political and policy debates by eliminating that which has no evidence in support of it, and that which has evidence against it, from the pool of what can be permitted in research and debates. By defining and streamlining a process for inferring truth in a way that is graspable by human cognition, we can begin to have more productive and effective discussions around political and policy questions. Full article
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
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Open AccessArticle Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique
Appl. Syst. Innov. 2018, 1(2), 19; https://doi.org/10.3390/asi1020019
Received: 10 April 2018 / Revised: 5 June 2018 / Accepted: 15 June 2018 / Published: 20 June 2018
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Abstract
Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati
[...] Read more.
Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with >98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency. Full article
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
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Open AccessArticle New Fuzzy Numerical Methods for Solving Cauchy Problems
Appl. Syst. Innov. 2018, 1(2), 15; https://doi.org/10.3390/asi1020015
Received: 7 April 2018 / Revised: 1 May 2018 / Accepted: 3 May 2018 / Published: 11 May 2018
Cited by 2 | PDF Full-text (359 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, new fuzzy numerical methods based on the fuzzy transform (F-transform or FT) for solving the Cauchy problem are introduced and discussed. In accordance with existing methods such as trapezoidal rule, Adams Moulton methods are improved using FT. We propose three
[...] Read more.
In this paper, new fuzzy numerical methods based on the fuzzy transform (F-transform or FT) for solving the Cauchy problem are introduced and discussed. In accordance with existing methods such as trapezoidal rule, Adams Moulton methods are improved using FT. We propose three new fuzzy methods where the technique of FT is combined with one-step, two-step, and three-step numerical methods. Moreover, the FT with respect to generalized uniform fuzzy partition is able to reduce error. Thus, new representations formulas for generalized uniform fuzzy partition of FT are introduced. As an application, all these schemes are used to solve Cauchy problems. Further, the error analysis of the new fuzzy methods is discussed. Finally, numerical examples are presented to illustrate these methods and compared with the existing methods. It is observed that the new fuzzy numerical methods yield more accurate results than the existing methods. Full article
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
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