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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 1 June 2025 | Viewed by 18962

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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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Published Papers (9 papers)

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Research

23 pages, 621 KiB

Open AccessArticle

A Dynamic Trading Approach Based on Walrasian Equilibrium in a Blockchain-Based NFT Framework for Sustainable Waste Management

by Ch Sree Kumar, Aayushman Bhaba Padhy, Akhilendra Pratap Singh and K. Hemant Kumar Reddy

Mathematics 2025, 13(3), 521; https://doi.org/10.3390/math13030521 - 5 Feb 2025

Cited by 1 | Viewed by 851

Abstract

It is becoming harder to manage the growing amounts of waste generated daily at an increasing rate. These problems require an efficient solution that guarantees effectiveness and transparency and maintains trust within the community. To improve the process of traditional waste management, we proposed a unique solution, “GREENLINK”, which uses a combination of blockchain technology with the concept of zero-knowledge proofs (ZKPs), non-fungible tokens (NFTs), and Walrasian equilibrium. Zero-knowledge proofs (cryptographic protocols) are used to verify organizations and prove compliance (e.g., certification, recycling capacity) without disclosing sensitive information. Through an iterative bidding process, the proposed framework employs Walrasian equilibrium, a technique to balance supply and demand, guaranteeing equitable pricing and effective resource distribution among participants. The transactions and waste management activities are securely recorded on an immutable ledger, ensuring accountability, traceability, and transparency. The performance of the proposed model is evaluated. Parameters like average latency, TPS, and memory consumption are calculated using Hyperledger Caliper (a blockchain performance benchmark framework). Full article

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

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18 pages, 583 KiB

Open AccessArticle

A Method to Handle the Missing Values in Multi-Criteria Sorting Problems Based on Dominance Rough Sets

by Ahmet Topal, Nilgun Guler Bayazit and Yasemen Ucan

Mathematics 2024, 12(18), 2944; https://doi.org/10.3390/math12182944 - 22 Sep 2024

Cited by 1 | Viewed by 821

Abstract

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of ‘do not care’ type missing values. Full article

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

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25 pages, 848 KiB

Open AccessArticle

Proposal of a Framework for Evaluating the Importance of Production and Maintenance Integration Supported by the Use of Ordinal Linguistic Fuzzy Modeling

by Ronald Díaz Cazañas, Daynier Rolando Delgado Sobrino, Estrella María De La Paz Martínez, Jana Petru and Carlos Daniel Díaz Tejeda

Mathematics 2024, 12(2), 338; https://doi.org/10.3390/math12020338 - 19 Jan 2024

Cited by 1 | Viewed by 1287

Abstract

Over the years, the integration of Production Management and Maintenance Management has gained significant attention from the scientific community due to its benefits for the company. When searching through the states of the art and practice, it is possible to understand that one of the main challenges for the integration is the lack of systematic, methodological, and scientific approaches and evaluation systems that lead companies into a successful implementation and a clear understanding of the benefits and drawbacks of the process. This paper introduces an original framework that conducts the processes of evaluation, weighting, and aggregation of set of novel indicators proposed by the authors. The main output of the proposal is an integral index that allows us to qualify, in a linguistic domain, the importance of the Production and Maintenance Management integration. At the same time, the proposed framework includes a methodology to evaluate the consensus of the experts, based on the use of linguistic terms with a membership function of the triangular type, which attempts to overcome some deficiencies of previous models identified by the authors in a detailed and complex analysis of the scientific literature. The proposed framework is applied in a plant of the Cuban mechanical industry. The results of this application are clearly presented and discussed, allowing us to verify and validate the proposal while also contributing to its ease of understanding and ultimately to the successful integration of the production and maintenance tasks in the given company. Full article

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

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21 pages, 3352 KiB

Open AccessArticle

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

by Shweta Singhal, Nishtha Jatana, Kavita Sheoran, Geetika Dhand, Shaily Malik, Reena Gupta, Bharti Suri, Mudligiriyappa Niranjanamurthy, Sachi Nandan Mohanty and Nihar Ranjan Pradhan

Mathematics 2023, 11(13), 2983; https://doi.org/10.3390/math11132983 - 4 Jul 2023

Cited by 3 | Viewed by 1471

Abstract

Regression testing of the software during its maintenance phase, requires test case prioritization and selection due to the dearth of the allotted time. The resources and the time in this phase are very limited, thus testers tend to use regression testing methods such as test case prioritization and selection. The current study evaluates the effectiveness of testing with two major goals: (1) Least running time and (2) Maximum fault coverage possible. Ant Colony Optimization (ACO) is a well-known soft computing technique that draws its inspiration from nature and has been widely researched, implemented, analyzed, and validated for regression test prioritization and selection. Many versions of ACO approaches have been prolifically applied to find solutions to many non-polynomial time-solvable problems. Hence, an attempt has been made to enhance the performance of the existing ACO_TCSP algorithm without affecting its time complexity. There have been efforts to enhance the exploration space of various paths in each iteration and with elite exploitation, reducing the total number of iterations required to converge to an optimal path. Counterbalancing enhanced exploration with intelligent exploitation implies that the run time is not adversely affected, the same has also been empirically validated. The enhanced algorithm has been compared with the existing ACO algorithm and with the traditional approaches. The approach has also been validated on four benchmark programs to empirically evaluate the proposed Enhanced ACO_TCSP algorithm. The experiment revealed the increased cost-effectiveness and correctness of the algorithm. The same has also been validated using the statistical test (independent t-test). The results obtained by evaluating the proposed approach against other reference techniques using Average Percentage of Faults Detected (APFD) metrics indicate a near-optimal solution. The multiple objectives of the highest fault coverage and least running time were fruitfully attained using the Enhanced ACO_TCSP approach without compromising the complexity of the algorithm. Full article

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

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38 pages, 16817 KiB

Open AccessArticle

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

by Hector Carreon-Ortiz, Fevrier Valdez and Oscar Castillo

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

Cited by 8 | Viewed by 2631

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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33 pages, 8413 KiB

Open AccessArticle

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

by 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

Cited by 8 | Viewed by 2197

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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21 pages, 5010 KiB

Open AccessArticle

Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application

by Dibyalekha Nayak, Kananbala Ray, Tejaswini Kar and Sachi Nandan Mohanty

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

Cited by 5 | Viewed by 1708

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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15 pages, 439 KiB

Open AccessArticle

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

by Arihant Tanwar, Wajdi Alghamdi, Mohammad D. Alahmadi, Harpreet Singh and Prashant Singh Rana

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

Cited by 2 | Viewed by 2480

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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13 pages, 1924 KiB

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

by Leticia Cervantes, Camilo Caraveo and Oscar Castillo

Mathematics 2023, 11(3), 730; https://doi.org/10.3390/math11030730 - 1 Feb 2023

Cited by 10 | Viewed by 2857

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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