Fuzzy Rough Set Research Articles

An Extension of VIKOR Approach for MCDM Using Bipolar Fuzzy Preference δ-Covering Based Bipolar Fuzzy Rough Set Model

Bipolarity highlights the positive and negative facets of a certain dilemma. This script aims to propose a novel multi-criteria decision-making (MCDM) approach based on bipolar fuzzy preference δ-covering based bipolar fuzzy rough set (BFPδC-BFRS) model by combining the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Rasenje) scheme. The VIKOR scheme is viewed as a beneficial MCDM strategy, particularly in situations where an expert is incapable of making the right decision at the beginning of system design. The VIKOR method works well for problems with competing attributes because it operates under the presumptions that compromise is acceptable in conflict resolution, the expert seeks a solution that is extremely close to the best, and all developed attributes are taken into consideration when processing the various alternatives. In this study, firstly, we proposed an integrated MCDM based on BFPδC-BFRSs using the VIKOR methodology. Moreover, we solve a real-world illustration to show the effectiveness of the expanded VIKOR approach. Finally, we demonstrate a detailed comparative analysis of the proposed methodology with some prevalent decision-making approaches to substantiate the accountability of the recommended scheme.

Information entropy-assisted intuitionistic fuzzy rough feature subset selection

Fuzzy-rough set (FRS) has been effectively implemented as a powerful pre-processing tool to cope with the issues such as vagueness, imprecision, noise and uncertainty in high-dimensional data analysis. However, FRS fails to handle the uncertainty due to identification, which is very much common in heterogeneous data. Moreover, it is always challenging to handle different issues such as vagueness, uncertainty, and noise during elimination of irrelevant and redundant features. This paper investigates an intuitionistic fuzzy rough set (IFRS)-aided feature selection method by using information entropy notion to tackle these issues simultaneously. We establish an intuitionistic fuzzy (IF) similarity relation. Then IFRS model is discussed based on this relation. Next, we present a novel IF granular structure. Based on the granular structure, we present lambda-conditional entropy for IF framework. Further, relevant mathematical theorems are proved to justify the theoretical aspect of the models. Moreover, a positive region preserved attribute selection method is proposed. A comprehensive experimental study is discussed to demonstrate the effectiveness and practical validation of the proposed method. Finally, a methodology is developed based on proposed models, which increases accuracy for the minority RNA silencing suppressors against majority class non-suppressors as evident from better sensitivity and G-means metrics.

Covering rough set models, fuzzy rough set models and soft rough set models induced by covering similarity

In order to study the rule of quantity characteristics of rough set theory, a novel notion of covering similarity in a covering approximation space is introduced. Based on this new notion, novel covering rough set models, novel fuzzy rough set models and novel soft rough set models are defined. Further, the concept of similarity matrix is presented, and then the matrix calculation formulas of all the new concepts and new models are deduced. Moreover, aim to better solve the practical problems by comparing the similarity, a concept of similarity interval is introduced, then the covering classification is carried out and a partial order of all the classes is derived. Finally, a few practical examples are given to illustrate the simple applications of all the new concepts and models in this paper.

Measurement and distribution characteristics of urban green productivity: based on fuzzy rough set and spatial autocorrelation analysis

Measurement and distribution characteristics of urban green productivity: based on fuzzy rough set and spatial autocorrelation analysis

Selection of an Appropriate Global Partner for Companies Using the Innovative Extension of the TOPSIS Method with Intuitionistic Hesitant Fuzzy Rough Information

In this research, we introduce the intuitionistic hesitant fuzzy rough set by integrating the notions of an intuitionistic hesitant fuzzy set and rough set and present some intuitionistic hesitant fuzzy rough set theoretical operations. We compile a list of aggregation operators based on the intuitionistic hesitant fuzzy rough set, including the intuitionistic hesitant fuzzy rough Dombi weighted arithmetic averaging aggregation operator, the intuitionistic hesitant fuzzy rough Dombi ordered weighted arithmetic averaging aggregation operator, and the intuitionistic hesitant fuzzy rough Dombi hybrid weighted arithmetic averaging aggregation operator, and demonstrate several essential characteristics of the aforementioned aggregation operators. Furthermore, we provide a multi attribute decision-making approach and the technique of the suggested approach in the context of the intuitionistic hesitant fuzzy rough set. A real-world problem for selecting a suitable worldwide partner for companies is employed to demonstrate the effectiveness of the suggested approach. The sensitivity analysis of the decision-making results of the suggested aggregation operators are evaluated. The demonstrative analysis reveals that the outlined strategy has applicability and flexibility in aggregating intuitionistic hesitant fuzzy rough information and is feasible and insightful for dealing with multi attribute decision making issues based on the intuitionistic hesitant fuzzy rough set. In addition, we present a comparison study with the TOPSIS approach to illustrate the advantages and authenticity of the novel procedure. Furthermore, the characteristics and analytic comparison of the current technique to those outlined in the literature are addressed.

New covering techniques and applications utilizing multigranulation fuzzy rough sets

In order to conduct an in-depth study of Zhan’s methodology pertaining to the covering of multigranulation fuzzy rough sets (CMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {C}_{{MG}}$$\\end{document}FRSs), we build two families: the family of fuzzy β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-minimum descriptions and the family of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-maximum descriptions. Subsequently, utilizing these notions, we proceed to develop two variations of covering via optimistic (pessimistic) multigranuation rough set samples (CO(P)MG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO(P)}_{{MG}}$$\\end{document}FRS). The axiomatic properties are examined. In this study, we examine four models of covering using variable precision multigranulation fuzzy rough sets (CVPMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CVP}_{{MG}}$$\\end{document}FRSs). We proceed with analyzing the features of these models. Interconnections between these planned plans are also elucidated. This study explores algorithms that aim to identify innovative strategies for addressing multiattribute group decision-making problems (MAGDM) and multicriteria group decision-making problems (MCGDM). The test examples have been elucidated to provide an inclusive grasp of the efficacy of the offered samples. Ultimately, the distinctions between our methodologies and the preexisting research have been demonstrated.

Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities

There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.

Human–computer interaction using artificial intelligence-based expert prioritization and neuro quantum fuzzy picture rough sets for identity management choices of non-fungible tokens in the Metaverse

Necessary improvements should be made to increase the effectiveness of non-fungible tokens on the Metaverse platform without having extra costs. For the purpose of handing this process more efficiently, there is a need to determine the most important factors for a more successful integration of non-fungible tokens into this platform. Accordingly, this study aims to determine the appropriate the identity management choices of non-fungible tokens in the Metaverse. There are three different stages in the proposed novel fuzzy decision-making model. The first stage includes prioritizing the expert choices with artificial intelligence-based decision-making methodology. Secondly, the criteria sets for managing non-fungible tokens are weighted by using Quantum picture fuzzy rough sets-based M-SWARA methodology. Finally, the identity management choices regarding non-fungible tokens in the Metaverse are ranked with Quantum picture fuzzy rough sets oriented VIKOR. The main contribution of this study is that artificial intelligence methodology is integrated to the fuzzy decision-making modelling to differentiate the experts. With the help of this situation, it can be possible to create clusters for the experts. Hence, the opinions of experts outside this group may be excluded from the scope. It has been determined that security must be ensured first to increase the use of non-fungible tokens on the Metaverse platform. Similarly, technological infrastructure must also be sufficient to achieve this objective. Moreover, biometrics for unique identification has the best ranking performance among the alternatives. Privacy with authentication plays also critical role for the effectiveness of this process.

Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making

The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute decision-making (MADM) dilemma, as it requires making a complicated decision while considering a number of attributes, and these attributes can have two-dimensional uncertainty as well as indiscernibility. Thus, in this framework, we developed a novel mathematical framework “complex intuitionistic fuzzy rough set” for tackling both two-dimensional uncertainties and indiscernibility. We also developed the elementary operations of the deduced complex intuitionistic fuzzy rough set. Moreover, we developed complex intuitionistic fuzzy rough (weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric) aggregation operators. Afterward, we developed a method of MADM by employing the devised operators and investigated the case study “Selection of optimal AI architecture for autonomous vehicles” to reveal the practicability of the devised method of MADM. Finally, to reveal the dominance and supremacy of our proposed work, a benchmark dilemma was used for comparison with various prevailing techniques.

Holistic evaluation of energy transition technology investments using an integrated recommender system and artificial intelligence-based fuzzy decision-making approach

The most essential criteria should be determined in the selection of the suitable energy transition technologies due to budget deficit problem. Therefore, it is necessary to identify the most important criteria in energy transition technology selection. Therefore, a new study is needed to determine the most prominent issues in the correct selection of energy transition technologies. The purpose of this study is to identify the most appropriate energy transition technology alternative. Within this framework, a novel artificial intelligence (AI)-based fuzzy decision-making model has been presented. In the first part, the experts are prioritized by the help of AI methodology. In the next section, missing evaluations of energy transition technology investments are estimated via expert recommender system. Thirdly, the weights of the criteria for energy transition technology selection are computed by quantum picture fuzzy rough sets (QPFR) M-Stepwise Weight Assessment Ratio Analysis (SWARA). At the final stage, selected energy transition technology alternatives are ranked via QPFR-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The main contribution of this study is the integration of AI technique to the proposed model. Similar to this issue, using M-SWARA methodology in the process of criteria weighting increases the quality of the findings. This methodology helps to consider the impact relation map of the criteria. The findings demonstrate that the most important factor is cost-effectiveness of energy transition. Similarly, it is also found that the local ecosystem is the second most significant issue. On the other side, the ranking results denote that compact renewable systems for small scale production is the most optimal solution of energy transition technology alternatives.

Explainable Prediction Technique for Analyzing Information Disorder Using Fuzzy Rough Sets

A persistent challenge in the domain of social media pertains to the comprehension and representation of information disorder incidents, which include but are not limited to erroneous information, theories of conspiracy, and racial discrimination. These issues frequently become apparent in the written content of posts that are shared on networking platforms and multiple sites. The complexity of information disorder research stems from the wide range of internet epidemic genres and the diverse effects that such information can have on people. Furthermore, the difficulty is exacerbated by the emergence of generative AI models that are capable of generating information that is difficult to identify as synthetic. The findings can assist analysts and decision-makers in acquiring a more in-depth comprehension of the phenomenon occurring in the field of information disorder. Hence, Information Disorder Prediction using Fuzzy Rough Set (IDP-FRS) has been proposed to predict the disinformation in the collected domain accurately and identify fake and real information. Through the incorporation of fuzzy sets of data into modelling and prediction techniques, the research improves the accuracy and interpretability of models, hence facilitating the discovery of disinformation in a more trustworthy manner. Moreover, the introduction of explainable prediction approaches gives users the ability to make educated choices concerning the appropriateness and dissemination of information, which in turn promotes openness and confidence in predictive models.

Correction: Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs

Correction: Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs

An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach

Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (T−SHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel T−SHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.

FRRI: A novel algorithm for fuzzy-rough rule induction

Interpretability is the next frontier in machine learning research. In the search for white box models — as opposed to black box models, like random forests or neural networks — rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this archetype, almost always separately. As both approaches offer different ways to deal with imprecise and uncertain information, often with the use of an indiscernibility relation, it is natural to combine them. The QuickRules [20] algorithm was a first attempt at using fuzzy rough set theory for rule induction. It is based on QuickReduct, a greedy algorithm for building decision superreducts. QuickRules already showed an improvement over other rule induction methods. However, to evaluate the full potential of a fuzzy rough rule induction algorithm, one needs to start from the foundations. Accordingly, the novel rule induction algorithm, Fuzzy Rough Rule Induction (FRRI), we introduce in this paper, uses an approach that has not yet been utilised in this setting. We provide background and explain the workings of our algorithm. Furthermore, we perform a computational experiment to evaluate the performance of our algorithm and compare it to other state-of-the-art rule induction approaches. We find that our algorithm is more accurate while creating small rulesets consisting of relatively short rules.

New categories of coverings in terms of rough fuzzy sets

New categories of coverings in terms of rough fuzzy sets

Multi-label-Specific Features Learning Algorithm Based on Label Importance and Fuzzy Rough Set

Multi-label-Specific Features Learning Algorithm Based on Label Importance and Fuzzy Rough Set

Fuzzy–Rough Analysis of ESG Ratings and Financial and Growth Ratios on the Stock Returns of Blue-Chip Stocks in Taiwan

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan’s blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions.

Financial multidimensional assessment of a green hydrogen generation process via an integrated artificial intelligence-based four-stage fuzzy decision-making model

It is widely accepted that there is an urgent need to make green hydrogen (GH2) projects financially viable to reduce global warming. However, any form of improvements to these GH2 projects lead to substantial cost increase. Due to this cost increase, making many improvements negatively affects the financial profitability of hydrogen projects. This is why there is a need for new advanced financial priority analysis tools so that it is easier to develop GH2 projects globally. Accordingly, the aim of this study is to identify and then define the most important factors affecting GH2 generation projects. To achieve this aim, this work proposes a new fuzzy multi-criteria decision-making model based on artificial intelligence (AI). First, experts are weighted with AI technique. Second, the missing evaluations are filled via a recommender system. Third, criteria weights are calculated by the M-SWARA technique integrated with quantum picture fuzzy rough (QPFR) sets. Finally, GH2 energy generation processes are listed by the QPFR-VIKOR approach. Overall, the main contribution of this study is the generation of a comprehensive AI oriented fuzzy decision-making model to make a detailed evaluation with respect to the financial potential improvements of the GH2 generation projects. The main originality of this model is the consideration of AI to calculate the weights of the criteria. Similarly, another benefit of the proposed model, that increases its superiority against other models, is the completion of missing evaluations by experts thanks to the recommender system. It is concluded that the most important criterion affecting green hydrogen investments is organizational effectiveness.

The novel WASPAS method for roughness of bipolar fuzzy sets based bipolar fuzzy covering

The research employs Covering-based Rough Set and Fuzzy Set theories to handle uncertainty in data analysis. However, when dealing with data uncertainty and bipolarity in various scenarios, the Bipolar Fuzzy Set (BFS) theory proves advantageous by simultaneously managing positive and negative information. Other sets, such as traditional fuzzy sets, often fail to capture this duality, leading to less comprehensive data analysis. This study pioneers a new methodology called the Theory of Roughness of Bipolar Fuzzy Sets, integrating Fuzzy Covering, Monotone Fuzzy Covering, and Bipolar Fuzzy Covering to propose an innovative decision-making approach. This novel concept undergoes comprehensive structural analysis. By incorporating the bipolar fuzzy covering based bipolar fuzzy rough set ( BFCBFRS ) model into conventional decision-making methods like the WASPAS technique, the research introduces a fresh perspective to address Multi-Criteria Decision-Making ( MCDM ) challenges. The efficacy of this extended method is evaluated by applying it to agricultural diagnosis, demonstrating its superiority over existing approaches through comparative analysis.

Understanding the market potential of crypto mining with quantum mechanics and golden cut-based picture fuzzy rough sets

Significant improvements should be made to increase the market potential of crypto mining. However, it is not financially feasible to make too many improvements because all actions lead to cost increases. In this context, it is necessary to determine the factors that most affect this process. Accordingly, the purpose of this study is to understand the main indicators to improve the market potential of crypto mining activities. Therefore, the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities. In this context, a new model has been constructed to answer this question. First, significant indicators are identified based on the literature evaluation. After that, these factors are weighted via quantum picture fuzzy rough set-based M-SWARA. The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of the crypto mining activities. The M-SWARA model is taken into consideration for criteria weighting. Owing to this issue, the causal relationships between the items can be identified. The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry. Furthermore, technological developments also play an important role in this regard.