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Special Issue "Advances and Applications of Soft Computing"

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 6 June 2024 | Viewed by 1547

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Special Issue Editor

Prof. Dr. Michael Voskoglou

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Guest Editor

Graduate Technological Educational Institute (T.E.I.) of Western Greece, School of Technological Applications, 263 34 Patras, Greece
Interests: fuzzy sets and logic; markov chains; abstract and linear algebra; artificial intelligence; mathematics education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In contrast to the conventional methods of hard computing, which are based on symbolic logical reasoning and numerical modeling, soft computing deals with approximate reasoning and processes that provide solutions to complex real-life problems, which cannot be modeled or are too difficult to be modelled mathematically. Soft computing is a synthesis of several computing paradigms that mainly include probabilistic reasoning, fuzzy logic, artificial neural networks, and genetic algorithms. These paradigms are complementary to each other and can be used simultaneously for solving a given problem. Although soft computing only appeared during the 1980s, its techniques are used nowadays successfully in many domestic, commercial, and industrial applications, becoming a major research object in automatic control engineering and having the potential to expand further in the forthcoming era of the Fourth Industrial Revolution and the advanced Internet of things. The target of the present Special Issue of the MDPI journal Mathematics is to provide experts in the field (academics, researchers, practitioners, etc.) with the opportunity to present recent theoretical advances in this field as well as the best practices for a wide range of applications. Papers dealing with case studies and experimental as well as theoretical works, along with their applications to real-life situations, are of particular interest.

Submissions for this Special Issue should address, but are not limited to, the following related topics: probability, Bayesian reasoning, fuzzy sets as well as systems and their extensions/generalizations, fuzzy logic, fuzzy control, fuzzy graphs, intuitionistic fuzzy sets, neutrosophic sets, soft sets, rough sets, grey systems, intelligent systems, artificial neural networks, genetic algorithms, evolutionary computing, Industry 4.0, the Internet of things (IoT), cyber–physical systems, applications of soft computing to education.

Prof. Dr. Michael Voskoglou
Guest Editor

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. Mathematics 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 2600 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

probability
Bayesian reasoning
fuzzy sets and systems and their extensions/generalizations
fuzzy logic
fuzzy control
fuzzy graphs
intuitionistic fuzzy sets
neutrosophic sets
soft sets
rough sets
grey systems
artificial intelligence
intelligent systems
artificial neural networks
genetic algorithms
evolutionary computing
Industry 4.0
Internet of Things (IoT)
cyber-physical systems
applications of soft computing to education

Published Papers (4 papers)

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Research

Open AccessArticle

Utilizing m-Polar Fuzzy Saturation Graphs for Optimized Allocation Problem Solutions

and

Mathematics 2023, 11(19), 4136; https://doi.org/10.3390/math11194136 (registering DOI) - 30 Sep 2023

Viewed by 57

Abstract

It is well known that crisp graph theory is saturated. However, saturation in a fuzzy environment has only lately been created and extensively researched. It is necessary to consider m components for each node and edge in an m-polar fuzzy graph. Since there is only one component for this idea, we are unable to manage this kind of circumstance using the fuzzy model since we take into account m components for each node as well as edges. Again, since each edge or node only has two components, we are unable to apply a bipolar or intuitionistic fuzzy graph model. In contrast to other fuzzy models, mPFG models produce outcomes of fuzziness that are more effective. Additionally, we develop and analyze these kinds of mPFGs using examples and related theorems. Considering all those things together, we define saturation for a m-polar fuzzy graph (mPFG) with multiple membership values for both vertices and edges; thus, a novel approach is required. In this context, we present a novel method for defining saturation in mPFG involving m saturations for each element in the membership value array of a vertex. This explains

α

-saturation and

β

-saturation. We investigate intriguing properties such as

α

-vertex count and

β

-vertex count and establish upper bounds for particular instances of mPFGs. Using the concept of

α

-saturation and

α

-saturation, block and bridge of mPFG are characterized. To identify the

α

-saturation and

β

-saturation mPFGs, two algorithms are designed and, using these algorithms, the saturated mPFG is determined. The time complexity of these algorithms is

O (| V |^{3})

, where

| V |

is the number of vertices of the given graph. In addition, we demonstrate a practical application where the concept of saturation in mPFG is applicable. In this application, an appropriate location is determined for the allocation of a facility point. Full article

(This article belongs to the Special Issue Advances and Applications of Soft Computing)

Open AccessArticle

Modeling and Verification of Uncertain Cyber-Physical System Based on Decision Processes

Na Chen

Shengling Geng

and

Yongming Li

Mathematics 2023, 11(19), 4122; https://doi.org/10.3390/math11194122 - 29 Sep 2023

Viewed by 120

Abstract

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty. Full article

(This article belongs to the Special Issue Advances and Applications of Soft Computing)

Open AccessArticle

Faculty Performance Evaluation through Multi-Criteria Decision Analysis Using Interval-Valued Fermatean Neutrosophic Sets

Said Broumi

Raman Sundareswaran

Marayanagaraj Shanmugapriya

Prem Kumar Singh

Michael Voskoglou

and

Mohamed Talea

Mathematics 2023, 11(18), 3817; https://doi.org/10.3390/math11183817 - 05 Sep 2023

Viewed by 372

Abstract

The Neutrosophic Set

(N_{s e t})

represents the uncertainty in data with fuzzy attributes beyond true and false values independently. The problem arises when the summation of true

(T r)

, false

(F a)

, and [...] Read more.

The Neutrosophic Set

(N_{s e t})

represents the uncertainty in data with fuzzy attributes beyond true and false values independently. The problem arises when the summation of true

(T r)

, false

(F a)

, and indeterminacy

(I n)

values crosses the membership value of one, that is,

T r + I n + F a < 1

. It becomes more crucial during decision-making processes like medical diagnoses or any data sets where

T r + I n + F a < 1

. To achieve this goal, the

F N_{s e t}

is recently introduced. This study employs the Interval-Valued Fermatean Neutrosophic Set (

I V F N_{s e t}

) as its chosen framework to address instances of partial ignorance within the domains of truth, falsehood, or uncertainty. This selection stands out due to its unique approach to managing such complexities within multi-decision processes when compared to alternative methodologies. Furthermore, the proposed method reduces the propensity for information loss often encountered in other techniques. IVFNS excels at preserving intricate relationships between variables even when dealing with incomplete or vague information. In the present work, we introduce the

I V F N_{s e t}

, which deals with partial ignorance in true, false, or uncertain regions independently for multi-decision processes. The

I V F N_{s e t}

contains the interval-valued

{T r}_{m e m b e r s h i p}

value,

{I n}_{m e m b e r s h i p}

value, and

{F a}_{m e m b e r s h i p}

for knowledge representation. The algebraic properties and set theory between the interval-valued

F N_{s e t}

have also been presented with an illustrative example. Full article

(This article belongs to the Special Issue Advances and Applications of Soft Computing)

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Open AccessArticle

A Fuzzy Graph Theory Approach to the Facility Location Problem: A Case Study in the Indian Banking System

Anushree Bhattacharya

and

Madhumangal Pal

Mathematics 2023, 11(13), 2992; https://doi.org/10.3390/math11132992 - 04 Jul 2023

Viewed by 582

Abstract

A fuzzy graph G is stated to have a set of trees as its tree cover if all the vertices of G are in their union. The maximum weight tree in the tree cover is assumed to be the cost of a tree [...] Read more.

A fuzzy graph G is stated to have a set of trees as its tree cover if all the vertices of G are in their union. The maximum weight tree in the tree cover is assumed to be the cost of a tree cover for a fuzzy graph. For an integer

β > 0

, finding a set of trees to cover all the vertices of a graph with minimum cost and at most

β

number of spanning trees is known as the

β

-tree cover problem. Combining the tree-covering concept and facility location problem in a fuzzy environment for solving critical real-life problems in the recent era is a more fruitful approach. This issue strongly inspires us to develop a model with a practical algorithm. This paper provides an algorithm and complexity analysis to determine the number of rooted trees s covering the given fuzzy graph. In addition, a model is constructed with three optimization programming problems in the facility location problem and a tree covering fuzzy graphs. The model includes two types of the facility location problem, simultaneously addressing a variable covering radius and a fixed covering radius. A numerical example is provided to further describe the model, then, in the application part of the paper, the proposed model is applied to solve the real-life problem of maximizing demand saturation by minimizing the number of small denominations in the Indian banking system. This problem involves the data input of different indicators in the banking system along with details of the denominations of banknotes. Full article

(This article belongs to the Special Issue Advances and Applications of Soft Computing)

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