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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 48320

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Dr. Giuseppe De Pietro

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National Research Council of Italy (CNR) - Institute for High Performance Computing & Networking (ICAR), Via P Castellino 111, 80131 Naples, Italy
Interests: decision support systems; pervasive computing; e-health
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Pota

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Guest Editor

Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Naples, Italy
Interests: fuzzy modeling; explainable AI; natural language processing; deep neural networks
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). 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

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Published Papers (13 papers)

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Editorial

Jump to: Research

3 pages, 193 KiB

Open AccessEditorial

Special Issue “Fuzzy Decision Making and Soft Computing Applications”

by Giuseppe De Pietro and Marco Pota

Appl. Syst. Innov. 2022, 5(3), 54; https://doi.org/10.3390/asi5030054 - 10 Jun 2022

Viewed by 1514

Abstract

Research on fuzzy logic [...] Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

Research

Jump to: Editorial

52 pages, 7055 KiB

Open AccessArticle

Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization

by Yury K. Mashunin

Appl. Syst. Innov. 2019, 2(4), 32; https://doi.org/10.3390/asi2040032 - 11 Oct 2019

Cited by 5 | Viewed by 3171

Abstract

We present a problem of “acceptance of an optimal solution” as a mathematical model in the form of a vector problem of mathematical programming. For the solution of such a class of problems, we show the theory of vector optimization as a mathematical apparatus of acceptance of optimal solutions. Methods of solution of vector problems are directed to problem solving with equivalent criteria and with the given priority of a criterion. Following our research, the analysis and problem definition of decision making under the conditions of certainty and uncertainty are presented. We show the transformation of a mathematical model under the conditions of uncertainty into a model under the conditions of certainty. We present problems of acceptance of an optimal solution under the conditions of uncertainty with data that are represented by up to four parameters, and also show geometrical interpretation of results of the decision. Each numerical example includes input data (requirement specification) for modeling, transformation of a mathematical model under the conditions of uncertainty into a model under the conditions of certainty, making optimal decisions with equivalent criteria (solving a numerical model), and, making an optimal decision with a given priority criterion. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

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25 pages, 392 KiB

Open AccessArticle

A Performance Study of the Impact of Different Perturbation Methods on the Efficiency of GVNS for Solving TSP

by Christos Papalitsas, Panayiotis Karakostas and Theodore Andronikos

Appl. Syst. Innov. 2019, 2(4), 31; https://doi.org/10.3390/asi2040031 - 20 Sep 2019

Cited by 9 | Viewed by 3478

Abstract

The purpose of this paper is to assess how three shaking procedures affect the performance of a metaheuristic GVNS algorithm. The first shaking procedure is generally known in the literature as intensified shaking method. The second is a quantum-inspired perturbation method, and the third is a shuffle method. The GVNS schemes are evaluated using a search strategy for both First and Best improvement and a time limit of one and two minutes. The formed GVNS schemes were applied on Traveling Salesman Problem (sTSP, nTSP) benchmark instances from the well-known TSPLib. To examine the potential advantage of any of the three metaheuristic schemes, extensive statistical analysis was performed on the reported results. The experimental data shows that for aTSP instances the first two methods perform roughly equivalently and, in any case, much better than the shuffle approach. In addition, the first method performs better than the other two when using the First Improvement strategy, while the second method gives results quite similar to the third. However, no significant deviations were observed when different methods of perturbation were used for Symmetric TSP instances (sTSP, nTSP). Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

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20 pages, 1253 KiB

Open AccessArticle

Relation “Greater than or Equal to” between Ordered Fuzzy Numbers

by Krzysztof Piasecki

Appl. Syst. Innov. 2019, 2(3), 26; https://doi.org/10.3390/asi2030026 - 3 Aug 2019

Cited by 9 | Viewed by 3115

Abstract

The ordered fuzzy number (OFN) is determined as an ordered pair of fuzzy number (FN) and its orientation. FN is widely interpreted as imprecise number approximating real number. We interpret any OFN as an imprecise number equipped with additional information about the location of the approximated number. This additional information is given as orientation of OFN. The main goal of this paper is to determine the relation “greater than or equal to” on the space of all OFNs. This relation is unambiguously defined as an extension of analogous relations on the space of all FN. All properties of the introduced relation are investigated on the basis of the revised OFNs’ theory. It is shown here that this relation is a fuzzy one. The relations “greater than” and “equal to” also are considered. It is proven that the introduced relations are independent on the orientation of the compared OFNs. This result makes it easier to solve optimization tasks using OFNs. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

16 pages, 9157 KiB

Open AccessArticle

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

by Mahmut Dirik, Oscar Castillo and Adnan Fatih Kocamaz

Appl. Syst. Innov. 2019, 2(2), 14; https://doi.org/10.3390/asi2020014 - 24 Apr 2019

Cited by 10 | Viewed by 4652

Abstract

Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot’s motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye movements supply significant features of nature task to realization. This paper presents an intelligent vision-based gaze guided robot control (GGC) platform that uses a user-computer interface based on gaze tracking enables a user to control the motion of a mobile robot using eyes gaze coordinate as inputs to the system. In this paper, an overhead camera, eyes tracking device, a differential drive mobile robot, vision and interval type-2 fuzzy inference (IT2FIS) tools are utilized. The methodology incorporates two basic behaviors; map generation and go-to-goal behavior. Go-to-goal behavior based on an IT2FIS is more soft and steady progress in data processing with uncertainties to generate better performance. The algorithms are implemented in the indoor environment with the presence of obstacles. Experiments and simulation results indicated that intelligent vision-based gaze guided robot control (GGC) system can be successfully applied and the IT2FIS can successfully make operator intention, modulate speed and direction accordingly. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

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34 pages, 422 KiB

Open AccessArticle

Using Dual Double Fuzzy Semi-Metric to Study the Convergence

by Hsien-Chung Wu

Appl. Syst. Innov. 2019, 2(2), 13; https://doi.org/10.3390/asi2020013 - 11 Apr 2019

Cited by 1 | Viewed by 2508

Abstract

Convergence using dual double fuzzy semi-metric is studied in this paper. Two types of dual double fuzzy semi-metric are proposed in this paper, which are called the infimum type of dual double fuzzy semi-metric and the supremum type of dual double fuzzy semi-metric. Under these settings, we also propose different types of triangle inequalities that are used to investigate the convergence using dual double fuzzy semi-metric. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

11 pages, 1324 KiB

Open AccessArticle

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

by Salaheddin Hosseinzadeh, Hadi Larijani, Krystyna Curtis and Andrew Wixted

Appl. Syst. Innov. 2019, 2(1), 10; https://doi.org/10.3390/asi2010010 - 18 Mar 2019

Cited by 12 | Viewed by 3572

Abstract

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

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9 pages, 1473 KiB

Open AccessArticle

A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

by Salaheddin Hosseinzadeh

Appl. Syst. Innov. 2018, 1(4), 48; https://doi.org/10.3390/asi1040048 - 4 Dec 2018

Cited by 3 | Viewed by 3454

Abstract

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 μm wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper’s findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded. Full article

(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)

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25 pages, 460 KiB

Open AccessArticle

New Approximation Methods Based on Fuzzy Transform for Solving SODEs: II

by Hussein ALKasasbeh, Irina Perfilieva, Muhammad Zaini Ahmad and Zainor Ridzuan Yahya

Appl. Syst. Innov. 2018, 1(3), 30; https://doi.org/10.3390/asi1030030 - 23 Aug 2018

Cited by 5 | Viewed by 3321

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 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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28 pages, 385 KiB

Open AccessArticle

New Approximation Methods Based on Fuzzy Transform for Solving SODEs: I

by Hussein ALKasasbeh, Irina Perfilieva, Muhammad Zaini Ahmad and Zainor Ridzuan Yahya

Appl. Syst. Innov. 2018, 1(3), 29; https://doi.org/10.3390/asi1030029 - 23 Aug 2018

Cited by 4 | Viewed by 3189

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 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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10 pages, 1013 KiB

Open AccessArticle

Causal Graphs and Concept-Mapping Assumptions

by Eli Levine and J. S. Butler

Appl. Syst. Innov. 2018, 1(3), 25; https://doi.org/10.3390/asi1030025 - 24 Jul 2018

Cited by 1 | Viewed by 5015

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 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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15 pages, 1503 KiB

Open AccessArticle

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

by Dipankar Mandal

Appl. Syst. Innov. 2018, 1(2), 19; https://doi.org/10.3390/asi1020019 - 20 Jun 2018

Cited by 8 | Viewed by 6006

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 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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16 pages, 359 KiB

Open AccessArticle

New Fuzzy Numerical Methods for Solving Cauchy Problems

by Hussein ALKasasbeh, Irina Perfilieva, Muhammad Zaini Ahmad and Zainor Ridzuan Yahya

Appl. Syst. Innov. 2018, 1(2), 15; https://doi.org/10.3390/asi1020015 - 11 May 2018

Cited by 5 | Viewed by 4105

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 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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