Fuzzy Concept Research Articles

Concept-drift detection index based on fuzzy formal concept analysis for fake news classifiers

Unpredictable changes in the underlying distribution of the streaming data over time are known as concept drift. The development of procedures and techniques for drift detection, interpretation, and adaptation is central to concept-drift research. Data research has demonstrated that machine learning in a concept-drift environment produces poor learning results if drift is not handled. This study focuses on defining the concept-drift detection index to predict when the performance of a machine learning model for text-stream classifiers is low. It proposes an index that relies on the Fuzzy Formal Concept Analysis theory. The index exploits the formal lattice to understand whether new incoming facts (e.g., news) are well represented in the training data used to build the machine-learning model. Fake news was deemed ideal for testing this new measure because its typical application scenario required handling a stream of unstructured content and concept-drift awareness. Experiments on three news datasets revealed a relevant correlation (i.e., 73.9 %, 80.8 %, and 81 %) between the Accuracy of Random Forest (RF), Naïve Bayes (NB), and Passive Aggressive (PA) models, respectively, and the proposed index. This strong correlation suggests that the new index can avoid incorrect classifications and help in retraining decisions.

Three-way decision for three-stage ranking pattern with criterion fuzzy concept

The application of three-way decision models in solving multi-criteria ranking problems has been widely recognized and noticed by researchers in recent years. However, most types of three-way decision models will not be directly usable when there are individual criterion minimum requirements set by decision-maker in a multi-criteria problem. For this problem, a two-stage three-way ranking pattern oriented to criterion fuzzy concept has been proposed. But, when many objects are directly identified as accepted or rejected, using this ranking pattern, there will be some objects for which the ranking results cannot be achieved. In view of this, considering criterion fuzzy concept and three-way decision, a three-stage ranking pattern is proposed to provide a more realistic and reliable ranking in a multi-criteria environment. Firstly, a new risk-loss function is defined according to the level of risk involved in decision behavior. Then, the decision thresholds are calculated. Secondly, a fuzzy description of an object is obtained using a maximum-k-means clustering algorithm. A feasible way to calculate the conditional probability value of an object is provided by this means. Finally, a new three-way ranking rule is constructed and a feasible three-stage ranking pattern is derived. Meanwhile, the feasibility of the new ranking pattern is analyzed by a computer hardware selection problem and the definition of two ranking indices. In addition, according to the experimental results on several data sets, the ranking indices between the three-stage ranking pattern and some existing ranking patterns are mostly higher than 0.8. The effectiveness of three-stage ranking pattern in solving different types of decision problems is provided by this experiment results. Also, the advantages of the three-stage ranking pattern are demonstrated based on the qualitative analysis between several ranking patterns.

Design, implementation, and automation of a risk management approach for man-at-the-End software protection

The last years have seen an increase in Man-at-the-End (MATE) attacks against software applications, both in number and severity. However, software protection, which aims at mitigating MATE attacks, is dominated by fuzzy concepts and security-through-obscurity. This paper presents a rationale for adopting and standardizing the protection of software as a risk management process according to the NIST SP800-39 approach. We examine the relevant constructs, models, and methods needed for formalizing and automating the activities in this process in the context of MATE software protection. We highlight the open issues that the research community still has to address. We discuss the benefits that such an approach can bring to all stakeholders. In addition, we present a Proof of Concept (PoC) decision support system that instantiates many of the discussed construct, models, and methods and automates many activities in the risk analysis methodology for the protection of software. Despite being a prototype, the PoC’s validation with industry experts indicated that several aspects of the proposed risk management process can already be formalized and automated with our existing toolbox and that it can actually assist decision making in industrially relevant settings.

A Novel Image Encryption Scheme Using Chaotic Maps and Fuzzy Numbers for Secure Transmission of Information

The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.

Identification and Education of Students with Gifts and Talents Based on the Fuzzy Conception of Giftedness

The purpose of this article is to review the Fuzzy Conception of Giftedness (FCG) and discuss its implications for the identification and education of gifted students. According to the Fuzzy Conception of Giftedness, the manifestation of giftedness results from the interplay between personal dispositions and stimulus conditions; thus, giftedness exists in the interaction between a person and the environment (e.g., stimulus conditions). While a person is disposed to carry out actions, the environment is potent to allow these actions. In line with this proposition, the identification and education of gifted students should be built on interactions. Interactive models are useful to identify and educate students who have overachievement potential. The FCG proposes three components to define giftedness that could be used in identifying and educating gifted students: intellective and non-intellective dispositions, stimulus conditions, and interaction.

Attribute granulation in fuzzy formal contexts based on L-fuzzy concepts

Formal concept analysis is an effective tool for granular computing. In applications, the selection of attributes greatly determines the structure and size of the obtained concept lattice. The existing studies have dealt with this problem on the basis of ordinary formal concepts. And thus, these studies are not capable of dealing with uncertainties caused by fuzziness. As a solution, we investigate attribute granulation in the settings of fuzzy contexts based on L-fuzzy concepts. Firstly, we present the mathematical descriptions of this open problem as well as the detailed meanings of this study with an illustrative example. And then, we present zoom-in algorithm to get more specific fuzzy concepts, and zoom-out algorithm to derive more abstract fuzzy concepts. Finally, systematic experiments are performed to show the influence of granulating of attributes on the sizes of fuzzy concept lattices.

Optimizing system reliability through selective maintenance allocation: A novel multi‐objective programming approach using neutrosophic fuzzy concept

AbstractSelective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied.

New Applications of Fuzzy Set Concept in the Geometric Theory of Analytic Functions

Zadeh’s fuzzy set theory offers a logical, adaptable solution to the challenge of defining, assessing and contrasting various sustainability scenarios. The results presented in this paper use the fuzzy set concept embedded into the theories of differential subordination and superordination established and developed in geometric function theory. As an extension of the classical concept of differential subordination, fuzzy differential subordination was first introduced in geometric function theory in 2011. In order to generalize the idea of fuzzy differential superordination, the dual notion of fuzzy differential superordination was developed later, in 2017. The two dual concepts are applied in this article making use of the previously introduced operator defined as the convolution product of the generalized Sălgean operator and the Ruscheweyh derivative. Using this operator, a new subclass of functions, normalized analytic in U, is defined and investigated. It is proved that this class is convex, and new fuzzy differential subordinations are established by applying known lemmas and using the functions from the new class and the aforementioned operator. When possible, the fuzzy best dominants are also indicated for the fuzzy differential subordinations. Furthermore, dual results involving the theory of fuzzy differential superordinations and the convolution operator are established for which the best subordinants are also given. Certain corollaries obtained by using particular convex functions as fuzzy best dominants or fuzzy best subordinants in the proved theorems and the numerous examples constructed both for the fuzzy differential subordinations and for the fuzzy differential superordinations prove the applicability of the new theoretical results presented in this study.

Optimization and analysis of a hydrogen supply chain in terms of cost, CO2 emissions, and risk: the case of Turkey

The transportation sector, which is largely dependent on oil, is faced with many problems such as the danger of depletion of fossil fuels that are harmful to the environment. Moreover, the situations such as epidemics and war cause excessive fluctuations in oil prices. Therefore, there is a need for new solutions based on alternative energy sources for a sustainable transportation sector. Hydrogen fuel cell electric vehicles (HFCEV) are one of the significant alternatives for an efficient, zero emissions and sustainable transportation system. Considering the potential investment in HFCEV technology, the need for a cost effective, green, and low risk Hydrogen Supply Chain (HSC) network infrastructure is inevitable. In this study, the HSC design of the Turkish transportation sector over a 25-year period (2026–2050) is investigated. The problem is modeled using a multi-period mixed integer linear programming (MIP) model. Three objectives are addresses: cost, carbon dioxide (CO2) emissions and safety risk. In order to consider the uncertainty in the hydrogen demand, five different scenarios are analyzed using fuzzy concept. There are four main results. First, unit hydrogen cost is found to be very high due to low demand and high capital cost in the initial period (2026–2031). Second, HSC network is established in a decentralized setting in all scenario solutions. The level of decentralization gets stronger over time and with increasing demand. Third, short-distance road transport is generally preferred for hydrogen transport. Fourth, since the aim is to minimize cost, CO2 emissions, and risk level, a mixed production strategy based on cost-oriented SMR and zero-emissions-oriented Electrolysis (ELE) is observed in all scenarios.

Regional economic resilience: A scoping review

Since the late 2000s, the concept of regional economic resilience has become the new buzzword in economic geography. Despite considerable attention, a common sentiment in the literature is that regional economic resilience is an underdeveloped and fuzzy concept. Therefore, this paper conducted a scoping review of 168 articles on the concept of regional economic resilience from 2000 to 2022 to assess its present conceptual state. The paper finds that the notion of regional economic resilience has become a well-developed concept and does not bear the markers of a fuzzy concept anymore. A conceptual framework is advanced.

Hybrid Optimization Algorithms of Firefly with GA and PSO for the Optimal Design of Water Distribution Networks

A novel two-hybrid optimization model of particle swarm optimization (FAPSO) and firefly algorithm with genetic algorithm (FAGA) are introduced to improve the performance of the conventional firefly algorithm for the least cost design of water distribution networks. The performance of the models is tested through application to three of the well-known benchmark networks available in the literature and also to the real case study of the El-Mostakbal City network, Ismailia, Egypt. The performance of the different algorithms was determined by evaluating the minimum, maximum, mean and standard deviation of costs, the function evaluation number, the consumed computational time for 1000 evaluations and the success rate calculated using the fuzzy logic concept for different optimal solutions slightly greater than the known optimal solution (by about 1.0% and 2.0%) were utilized for testing the convergence and search capabilities of the models. It was found that the FAGA model is superior to the standard firefly and FAPSO models in exploring the search space, exploiting the promising areas and convergence to the optimal solution and can be considered as a reasonable optimization technique for the management of water distribution networks.

Dealing with Evaluative Expressions and Hate Speech Metaphors with Fuzzy Property Grammar Systems

We introduce a Fuzzy Property Grammar System (FPGS), a formalism that integrates a Fuzzy Property Grammar into a linguistic grammar system to formally characterize metaphorical evaluative expressions. The main scope of this paper is to present the formalism of FPGS and to show how it might provide a formal characterization of hate speech linguistic evaluative expressions with metaphors (as fuzzy concepts), together with evaluating their degree of linguistic violence. Linguistic metaphors are full of semantic coercions. It is necessary to formally characterize the context of the communication to acknowledge the extralinguistic constraints of the pragmatic domain, which establishes whether an utterance is violent. To show the applicability of our formalism, we present a proof of concept. By compiling and tagging a 3000-tweet corpus, we have extracted a lexicon of hate speech metaphors. Furthermore, we show how FPGS architecture can deal with different types of hate speech and can identify implicit violent figurative evaluative expressions by context and type. Although we are still in the experimental phase of our project and cannot present conclusive results at the computational level, the proof-of-concept results show that our formalism can achieve the desired outcome.

What is not mentioned in the famous article by Edmund Gettier

The paper aims to unfold the «internal» content of Gettier’s argument as a skeptical argument against knowledge in terms of answering the question: «why he could be right when he says what he says». Our initial hypothesis is that E. Gettier does not say anything about the «accidentality of the fact that Smith has 10 coins in his pocket», but he uses the words «entailment» and «deduction», which substantiates the «truth of the conclusion», and on the basis of which he attributes «knowledge» to Smith. The article comes out at a certain time, E. Gettier uses the rhetoric of necessary and sufficient conditions, with regard to justification an analogue of the closure principle is given, there are exactly two examples – all this lead to the assumption that E. Gettier’s «justification» in the form he speaks about it in the staging part of the article – is a logical relationship between beliefs that are interconnected by the relationship of entailment and are fixed in the same way in the person’s belief system. Much of the evidence on pages two and three is an illustration of why this «logical interpretation» of reasoning doesn’t work. Contrary to most popular points of view, E. Gettier managed to show only that the implementation of formal relations between beliefs does not allow one to get to «knowledge» in the form in which he defines it. In particular, such a reading makes it possible to block various attempts to propose a «fourth condition». It must meet the task set – to restore confidence that the fulfillment of the formal relations between beliefs can help to get to «knowledge». However, within the framework of the linguistic turn paradigm, in which E. Gettier works, this is impossible, because in general, it simply lacks adequate means of discussing the persuasiveness of the inference. In this sense, «knowledge» is, by definition, a fuzzy concept that brings us back to «classical notions» in which the persuasiveness of an argument implies not only validity, but also soundness, relevance, and usefulness.

Similarity measure-based three-way decisions in Pythagorean fuzzy information systems and its application in FANETs

The existing researchers generalize the decision-theoretic rough sets (DTRSs) model from the viewpoint of the cost function, whether the information system is complete, and so on. Few of them consider multiple different strategies to rank the expected losses. Furthermore, under the circumstance of Pythagorean fuzzy, we can’t directly define the partition of the objects set by employing equivalence relation, there is a need for constructing the general binary relation. Aiming at these problems, in present paper, we propose the similarity measure-based three-way decisions (3WD) in Pythagorean fuzzy information systems, both the binary relation and the similarity neighborhood are induced by similarity measure between objects. Each object has its own losses, different strategies are designed to rank the expected losses. Further, the similarity measure-based DTRSs dealing with crisp concept and the similarity measure-based Pythagorean fuzzy DTRSs dealing with Pythagorean fuzzy concept are developed to establish the three regions of similarity measure-based 3WD. Finally, the proposed models are used to make decisions for classifying the network nodes of flying ad-hoc networks (FANETs) into normal nodes also called safe nodes, suspicious nodes, and malicious nodes also called unsafe nodes under the evaluation of Pythagorean fuzzy information.

An attribute fuzzy concept-oriented three-way utility decision model in multi-attribute environments

In recent years, an increasing number of researchers have focused on how to leverage limited information to make more reasonable decisions, thereby reducing the decision-making risk for decision-makers. Three-way decision, as a method for risky decision-making, offers a new approach to address this challenge and has gained significant attention. In view of this, based on the utility theory, this paper proposes a novel three-way utility decision model oriented to attribute fuzzy concept in multi-attribute environments. Firstly, an attribute fuzzy concept is constructed based on the decision-maker’s minimum requirements on each attribute. Subsequently, a new method for calculating the relative utility function is proposed, which is used to calculate the corresponding decision thresholds. Additionally, a generalized conditional probability calculation method is presented based on the attribute fuzzy concept and the basic semantics of conditional probability. Finally, a new three-way decision model is established to obtain the classification and ranking results of all objects. Through application analysis, comparative study and experimental analysis, the proposed method’s rationality and effectiveness are verified.

Fuzzy-based concept-cognitive learning: An investigation of novel approach to tumor diagnosis analysis

Medical decision-making with high-dimensional complex data has recently become a focus and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data mining technology, from the perspective of gene analysis, can effectively improve the prediction accuracy of patients. For gene databases of tumors with high-dimensional attributes and small sample sizes, tumor classification based on gene analysis is a significant step in the intervention and treatment of tumors. The existing research on the tumor classification of gene data has one prevalent disadvantage: gene obtained via the classification performance evaluation has weak interpretability and universality. This paper presents a concept-cognitive learning system with the three-way analysis (CCL3S) in the fuzzy context for the problem of tumor diagnosis with high-dimensional data, a new fuzzy classifier good at tumor diagnosis. The main steps of the CCL3S include: designing fuzzy recognition to extract the core gene, constructing a fuzzy three-way concept space via the core gene, and finally completing the tumor diagnosis based on the minimum recognition degree. Experimental results on nine tumor gene expression datasets demonstrate that CCL3S achieves better classification performance than some related methods.

A Fuzzy Model for Reasoning and Predicting Student’s Academic Performance

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies.

Introduction: Alternetworks and contrapublics in digital activism

In recent years Media and Communication disciplinary conferences have begun to highlight the issue of #communicationsowhite (Chakravartty et al., 2018). The International Communication Association has also continued to intensify its efforts at internationalization even as members continue to debate what this “internationalization” would look like in terms of inclusion of historically marginalized populations worldwide. Yet in a larger disciplinary context of knowledge building, our theoretical and methodological constructs are still based in academic hierarchies that privilege the very same Global North ontologies and epistemologies that are universalized under implicit and unspoken assumptions that such constructs are universal and innately democratic. Thus, when attempts are made to include, engage or privilege “Global South” scholars in these venues, the token inclusion that occurs potentially creates further erasures by flattening and invisibilizing historically oppressed groups from the geo-locations identified as located in “the Global South.” Some of this has to do with how we use the north/south binary even as academics, which leads to tokenizing. Our terminologies and self-identifications themselves sometimes create erasures where the very people we want to include get disappeared. For instance, the terminology around the word “activism” is problematized in this special issue by Shakuntala Banaji in her commentary and criticism essay. She cautions us to be vigilant to “the dangers of allowing a warm fuzzy conception of activism to divert attention from the fascist politics being enacted online across vast swathes of the Global South.” Warm fuzzy ideas of what it means to write from and about the Global South are equally problematic.

Extracting Concepts From Fuzzy Relational Context Families

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fuzzy relational formal concept analysis (FRCA)</i> mines collections of fuzzy concept lattices from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy relational context families</i> , which are special datasets made of fuzzy formal contexts and fuzzy relations between objects of different types. Mainly, FRCA consists of the following procedures: first, an initial fuzzy relational context family is transformed into a collection of fuzzy formal contexts; second, a fuzzy concept lattice is generated from each fuzzy formal context by using one of the techniques existing in the literature. The principal tools to transform a fuzzy context family into a set of fuzzy formal contexts are the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy scaling quantifiers</i> , which are particular fuzzy quantifiers based on the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evaluative linguistic expression</i> . FRCA can be applied whenever information needs to be extracted from multirelational datasets including vagueness, and it can be viewed as an extension of both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relational concept analysis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy formal concept analysis</i> . This article contributes to the development of fuzzy relational concept analysis by achieving the following goals. First of all, we present and study a new class of fuzzy quantifiers, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-scaling quantifiers</i> , to extract fuzzy concepts from fuzzy relational context families. Subsequently, we provide an algorithm to generate, given a t-scaling quantifier, a collection of fuzzy concept lattices from a special fuzzy relational context family, which is composed of a pair of fuzzy formal contexts and a fuzzy relation between their objects. After that, we introduce an ordered relation on the set of all t-scaling quantifiers, which allows us to discover a correspondence among fuzzy concept lattices deriving from different t-scaling quantifiers. Finally, we discuss how the results obtained for t-scaling quantifiers can be extended to the class of fuzzy scaling quantifies. Therefore, this analysis highlights the main differences between t-scaling and fuzzy quantifiers.

A Simple Fuzzy Decision Support Model for Determining the Indonesian Green Cities

Green city is every bodies’ expectation. Every country highly competes to increase the number of green city. Thus several types of measurement standard for city green level appear and being practically implemented. The study’s objective is to develop a decision support model (DSM) for determining the Indonesian green cities based on the main method fuzzy logic. At this juncture, the Indonesian green city determined by green score of each city in Indonesia. The main method fuzzy logic interconnected to statistics and mathematics approaches. Slope calculation that combined with fuzzy logic conception was operated to treat three of five parameters believed in the model. The model design was delivered by using the object oriented approach and the model itself constructed via programming language Python in Google Colab platform. Finally, the constructed model is able to exhibit a hundred Indonesian cities’ green scores.