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Special Issue "Fuzzy Decision Making and Soft Computing Applications: Future Perspectives"

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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: 30 September 2023 | Viewed by 3630

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Dr. Sachi Nandan Mohanty

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School of Computer Science & Engineering, VIT-AP University, Amaravati, AP, India
Interests: cognitive science; soft computing; fuzzy decision making
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Special Issue Information

Dear Colleagues,

This Special Issue collects original research articles that discuss cutting-edge technology as well as perspectives on future directions, covering a broad range of theoretical and practical aspects. The following topics are the focus of the Special Issue:

Multi-criteria decision-making applications employing fuzzy rule-based systems and soft computing applications in real-life problem solving.
Consensus in group decision support systems.
Fuzzy model-based forecasting.
Missing preferences in fuzzy consensus and decision making.
Aggregation of fuzzy preferences.
Fuzzy ontologies.
Fuzzy multi-criteria decision making in the banking sector.
Intelligent fuzzy negotiation systems.
Fuzzy consensus and decision making in web frameworks such as social networks, ecommerce activities, e-learning, web security, web quality, digital libraries, etc.
Soft computing applications for air-traffic control, finance, health, fire control, risk management, etc., where fuzzy consensus and decision-making tools are essential to aid experts in making correct decisions for real-life problems.
Fuzzy FinTech applications.

Dr. Sachi Nandan Mohanty
Guest Editor

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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 2100 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.

Published Papers (5 papers)

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Research

Open AccessArticle

Comparative Study of Type-1 and Interval Type-2 Fuzzy Logic Systems in Parameter Adaptation for the Fuzzy Discrete Mycorrhiza Optimization Algorithm

Hector Carreon-Ortiz

Fevrier Valdez

and

Oscar Castillo

Mathematics 2023, 11(11), 2501; https://doi.org/10.3390/math11112501 - 29 May 2023

Viewed by 459

Abstract

The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method using the Discrete Mycorrhiza Optimization Algorithm (DMOA) in combination with type-1 or interval type-2 fuzzy logic system. In this new research, when using T1FLS, membership functions are defined by type-1 fuzzy sets, which allows for a more flexible and natural representation of uncertain and imprecise data. This approach has been successfully applied to several optimization problems, such as in feature selection, image segmentation, and data clustering. On the other hand, when DMOA is using IT2FLS, membership functions are represented by interval type-2 fuzzy sets, which allows for a more robust and accurate representation of uncertainty. This approach has been shown to handle higher levels of uncertainty and noise in the input data and has been successfully applied to various optimization problems, including control systems, pattern recognition, and decision-making. Both DMOA using T1FLS and DMOA using IT2FLS have shown better performance than the original DMOA algorithm in many applications. The combination of DMOA with fuzzy logic systems provides a powerful and flexible optimization framework that can be adapted to various problem domains. In addition, these techniques have the potential to more efficiently and effectively solve real-world problems. Full article

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

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Open AccessArticle

Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems

Manuel Casal-Guisande

Jorge Cerqueiro-Pequeño

José-Benito Bouza-Rodríguez

and

Alberto Comesaña-Campos

Mathematics 2023, 11(11), 2469; https://doi.org/10.3390/math11112469 - 27 May 2023

Viewed by 249

Abstract

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture. Full article

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

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Open AccessArticle

Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application

and

Mathematics 2023, 11(7), 1660; https://doi.org/10.3390/math11071660 - 30 Mar 2023

Viewed by 462

Abstract

Transmission of high volume of data in a restricted wireless sensor network (WSN) has come up as a challenge due to high-energy consumption and larger bandwidth requirement. To address the issues of high-energy consumption and efficient data transmission adaptive block compressive sensing (ABCS) is one of the optimum solution. ABCS framework is well capable to adapt the sampling rate depending on the block’s features information that offers higher sampling rate for less compressible blocks and lower sampling rate for more compressible blocks In this paper, we have proposed a novel fuzzy rule based adaptive compressive sensing approach by leveraging the saliency and the edge features of the image making the sampling rate selection completely automatic. Adaptivity of the block sampling ratio has been decided based on the fuzzy logic system (FLS) by considering two important features i.e., edge and saliency information. The proposed framework is experimented on standard dataset, Kodak data set, CCTV images and the Set5 data set images. It achieved an average PSNR of 34.26 and 33.2 and an average SSIM of 0.87 and 0.865 for standard images and CCTV images respectively. Again for high resolution Kodak data set and Set 5 dataset images, it achieved an average PSNR of 32.95 and 31.72 and SSIM of 0.832 and 0.8 respectively. The experiments and the result analysis show that proposed method is efficacious than the state of the art methods in both subjective and objective evaluation metrics. Full article

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

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Open AccessArticle

A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset

and

Mathematics 2023, 11(4), 920; https://doi.org/10.3390/math11040920 - 11 Feb 2023

Viewed by 853

Abstract

Feature selection is commonly employed for identifying the top n features that significantly contribute to the desired prediction, for example, to find the top 50 or 100 genes responsible for lung or kidney cancer out of 50,000 genes. Thus, it is a huge time- and resource-consuming practice. In this work, we propose a divide-and-conquer technique with fuzzy backward feature elimination (FBFE) that helps to find the important features quickly and accurately. To show the robustness of the proposed method, it is applied to eight different datasets taken from the NCBI database. We compare the proposed method with seven state-of-the-art feature selection methods and find that the proposed method can obtain fast and better classification accuracy. The proposed method will work for qualitative, quantitative, continuous, and discrete datasets. A web service is developed for researchers and academicians to select top n features. Full article

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

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Open AccessArticle

Performance Comparison between Type-1 and Type-2 Fuzzy Logic Control Applied to Insulin Pump Injection in Real Time for Patients with Diabetes

Leticia Cervantes

Camilo Caraveo

and

Oscar Castillo

Mathematics 2023, 11(3), 730; https://doi.org/10.3390/math11030730 - 01 Feb 2023

Viewed by 747

Abstract

Nowadays, type 1 diabetes is unfortunately one of the most common diseases, and people tend to develop it due to external factors or by hereditary factors. If is not treated, this disease can generate serious consequences to people’s health, such as heart disease, neuropathy, pregnancy complications, eye damage, etc. Stress can also affect the condition of patients with diabetes, and our motivation in this work is to help manage the health of people with type 1 diabetes. The contribution of this paper is in presenting the implementation of type-1 and type-2 fuzzy controllers to control the insulin dose to be applied in people with type 1 diabetes in real time and in stressful situations. First, a diagram for the insulin control is presented; second, type-1 and type-2 fuzzy controllers are designed and tested on the insulin pump in real time over a 24 h period covering one day; then, a comparative analysis of the performance of these two controllers using a statistical test is presented with the aim of maintaining a stable health condition of people through an optimal insulin supply. In the model for the insulin control, perturbations (noise/stress levels) were added to find if our proposed fuzzy controller has good insulin control in situations that could generate disturbances in the patient, and the results found were significant; in most of the tests carried out, the type-2 controller proved to be more stable and efficient; more information can be found in the discussion section. Full article

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

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