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Symmetry
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Fuzzy Techniques for Emerging Conditions & Digital Transformation
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Fuzzy Techniques for Emerging Conditions & Digital Transformation

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 16477

Special Issue Editor

Prof. Dr. Cengız Kahraman

E-Mail Website
Guest Editor
Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
Interests: engineering economics; quality control and management; statistical decision making; multicriteria decision making; fuzzy decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers symmetry and asymmetry phenomena occurring in intelligent and fuzzy research problems. We invite authors to submit their theoretical or experimental research presenting engineering models under fuzziness and intelligence, dealing with the the symmetry or asymmetry of different types of information. Emerging conditions such as pandemics, wars, natural disasters and various high technologies force us to make significant changes to business and our social lives. The pandemic has caused all of us to live under quarantine for a certain period of time and serious restrictions in our business and social lives. We have clearly seen how important digital technologies are and how great the need for them is during this period. Digital transformation is the adoption of digital technologies to transform services or businesses through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technologies. This may enable—in addition to improving efficiency via automation—new types of innovation and creativity, rather than simply enhancing and supporting traditional methods. This Special Issue focuses on revealing the digital transformation in our business and social lives under emerging conditions through intelligent and fuzzy systems. This Special Issue is on the theory and practice of fuzzy techniques for smart and innovative solutions. The topics of interest include, but are not limited to, the following:

Theoretical and/or practical developments of the following for emerging conditions and digital transformation:

  • Type-2 fuzzy sets;
  • Hesitant fuzzy sets;
  • Intuitionistic fuzzy sets;
  • Spherical fuzzy sets;
  • Picture fuzzy sets;
  • Pythagorean fuzzy sets;
  • Q-rung orthopair fuzzy sets;
  • Neutrosophic sets;
  • Fermatean fuzzy sets.

The integration of fuzzy sets theory with the following for smart and innovative solutions:

  • Bayesian networks;
  • Chaotic systems;
  • Combinatorial search;
  • Complex systems;
  • Distributed artificial intelligence;
  • Embedded systems;
  • Evolutionary systems;
  • Genetic algorithms;
  • Genetic programming;
  • Machine learning;
  • Multi-agent systems;
  • Neural fuzzy systems;
  • Neural genetic systems;
  • Neural network;
  • Pattern recognition;
  • Qualitative reasoning;
  • Quantum computing;
  • Reinforcement learning;
  • Support vector machines;
  • Swarm intelligence.

Prof. Dr. Cengız Kahraman
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. Symmetry is an international peer-reviewed open access monthly 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 2400 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

26 pages, 2614 KiB  
Article
Enhancing BDI Agents Using Fuzzy Logic for CPS and IoT Interoperability Using the JaCa Platform
by Burak Karaduman, Baris Tekin Tezel and Moharram Challenger
Symmetry 2022, 14(7), 1447; https://doi.org/10.3390/sym14071447 - 14 Jul 2022
Cited by 4 | Viewed by 1864
Abstract
Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, [...] Read more.
Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent & artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Emerging Conditions & Digital Transformation)
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36 pages, 9906 KiB  
Article
Generalized Type-2 Fuzzy Parameter Adaptation in the Marine Predator Algorithm for Fuzzy Controller Parameterization in Mobile Robots
by Felizardo Cuevas, Oscar Castillo and Prometeo Cortés-Antonio
Symmetry 2022, 14(5), 859; https://doi.org/10.3390/sym14050859 - 21 Apr 2022
Cited by 33 | Viewed by 2162
Abstract
This article is oriented to the application of generalized type-2 fuzzy systems in the dynamic adjustment of the parameters of a recent metaheuristic based on nature that follows the rules of the best feeding strategies of predators and prey in ecosystems. This metaheuristic [...] Read more.
This article is oriented to the application of generalized type-2 fuzzy systems in the dynamic adjustment of the parameters of a recent metaheuristic based on nature that follows the rules of the best feeding strategies of predators and prey in ecosystems. This metaheuristic is called fuzzy marine predator algorithm (FMPA) and is presented as an improved variant of the original marine predator algorithm (MPA). The FMPA balances the degree of exploration and exploitation through its iterations according to the advancement of the predator. In the state of the art, it has been shown that type-2 fuzzy increases metaheuristic performance when adapting parameters, although there is also an increase in the execution time. The FMPA with generalized type-2 and interval type-2 parameter adaptations was applied to a group of benchmark functions introduced in the competition on evolutionary computation (CEC2017); the results show that generalized FMPA provides better solutions. A second case for FMPA is also presented, which is the optimal fuzzy control design, in the search for the optimal membership function parameters. A symmetrical distribution of these functions is assumed for reducing complexity in the search process for optimal parameters. Simulations were carried out considering different degrees of noise when analyzing the performance when simulating each of the used fuzzy methods. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Emerging Conditions & Digital Transformation)
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14 pages, 2454 KiB  
Article
Improving Classification Performance of Fully Connected Layers by Fuzzy Clustering in Transformed Feature Space
by Tolga Ahmet Kalaycı and Umut Asan
Symmetry 2022, 14(4), 658; https://doi.org/10.3390/sym14040658 - 24 Mar 2022
Cited by 10 | Viewed by 4537
Abstract
Fully connected (FC) layers are used in almost all neural network architectures ranging from multilayer perceptrons to deep neural networks. FC layers allow any kind of symmetric/asymmetric interaction between features without making any assumption about the structure of the data. However, success of [...] Read more.
Fully connected (FC) layers are used in almost all neural network architectures ranging from multilayer perceptrons to deep neural networks. FC layers allow any kind of symmetric/asymmetric interaction between features without making any assumption about the structure of the data. However, success of convolutional and recursive layers and findings of many studies have proven that the intrinsic structure of a dataset holds a great potential to improve the success of a classification problem. Leveraging clustering to explore and exploit this intrinsic structure in classification problems has been the subject of various studies. In this paper, we propose a new training pipeline for fully connected layers which enables them to make more accurate classification predictions. The proposed method aims to reflect the clustering patterns in the original feature space of the training dataset to the transformed feature space created by the FC layer. In this way, we intend to enhance the representation ability of the extracted features and accordingly increase the classification accuracy. The Fuzzy C-Means algorithm is employed in this study as the clustering tool. To evaluate the performance of the proposed method, 11 experiments were conducted on 9 benchmark UCI datasets. Empirical results show that the proposed method works well in practice and gives higher classification accuracies compared to a regular FC layer in most datasets. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Emerging Conditions & Digital Transformation)
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22 pages, 3572 KiB  
Article
Evaluation of Classification for Project Features with Machine Learning Algorithms
by Ching-Lung Fan
Symmetry 2022, 14(2), 372; https://doi.org/10.3390/sym14020372 - 13 Feb 2022
Cited by 7 | Viewed by 3271
Abstract
Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for [...] Read more.
Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Emerging Conditions & Digital Transformation)
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17 pages, 588 KiB  
Article
Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics
by Alejandra Mancilla, Mario García-Valdez, Oscar Castillo and Juan Julian Merelo-Guervós
Symmetry 2022, 14(2), 202; https://doi.org/10.3390/sym14020202 - 21 Jan 2022
Cited by 27 | Viewed by 3442
Abstract
In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human [...] Read more.
In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained. Full article
(This article belongs to the Special Issue Fuzzy Techniques for Emerging Conditions & Digital Transformation)
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