Fuzzy Concept Research Articles

A Kaleidoscope of play: a new approach to play analysis in childhood

ABSTRACT Play is a frequent and relevant activity during childhood, and developmental psychologists agree that it offers a unique window on development. Play, however, remains a fuzzy concept, and difficulties persist in its definition, often leading to obstacles in building and comparing experimental studies. This may be due to widespread tendencies to define play by referring to non-observable inner states, to consider playing something that occurs in the head rather than in-the-world and to overreliance on developmental stages. Enactive approaches to child play have instead recently stressed the importance of play contexts, considering child play an activity in-the-world rather than a mental state, thereby de-intellectualizing play and pretense. Along these lines, in this paper the authors propose a novel approach to the definition of play types by considering the roles of organism, environment, and task constraints, within the framework of Material Engagement Theory. Focusing on the material world surrounding the child and the interactions which characterize play, we critically review the strategy of resorting to non-observable categories in the study of play, and we propose a new model (the Kaleidoscope Model) for play analysis.

Dimensionality reduction technique under picture fuzzy environment and its application in decision making

The notion of soft matrix plays a vital role in many engineering applications and socio-economic and financial problems. A picture fuzzy set has been used to handle uncertainty data in modeling human opinion. In this work, we recall the picture fuzzy soft matrix concept and its different subsequent classes. Also, different kinds of binary operations over the proposed matrices have been provided. The main contribution of this paper is that using the concept of choice matrix and its weighted form and the score matrix, a new algorithm for decision-making has been outlined by considering the picture of fuzzy soft matrices. The current challenge In the decision-making problems is that many qualitative and quantitative criteria are involved. Hence, the dimensionality reduction technique plays an essential role in simplicity and broader applicability in the decision-making processes. We present an algorithm for the reduction process using the proposed definitions of the object and parameter-oriented picture fuzzy soft matrix and the technique to find the threshold value for the provided information. Then, illustrative numerical examples have also been provided for each proposed algorithm. A detailed comparative study of the proposed techniques has also been carried out in contrast with other existing techniques.

Robust Variable Threshold Fuzzy Concept Lattice with Application to Medical Diagnosis

Robust Variable Threshold Fuzzy Concept Lattice with Application to Medical Diagnosis

Inverse Graphs in m-Polar Fuzzy Environments and Their Application in Robotics Manufacturing Allocation Problems with New Techniques of Resolvability

The inverse in crisp graph theory is a well-known topic. However, the inverse concept for fuzzy graphs has recently been created, and its numerous characteristics are being examined. Each node and edge in m-polar fuzzy graphs (mPFG) include m components, which are interlinked through a minimum relationship. However, if one wants to maximize the relationship between nodes and edges, then the m-polar fuzzy graph concept is inappropriate. Considering everything we wish to obtain here, we present an inverse graph under an m-polar fuzzy environment. An inverse mPFG is one in which each component’s membership value (MV) is greater than or equal to that of each component of the incidence nodes. This is in contrast to an mPFG, where each component’s MV is less than or equal to the MV of each component’s incidence nodes. An inverse mPFG’s characteristics and some of its isomorphic features are introduced. The α-cut concept is also studied here. Here, we also define the composition and decomposition of an inverse mPFG uniquely with a proper explanation. The connectivity concept, that is, the strength of connectedness, cut nodes, bridges, etc., is also developed on an inverse mPF environment, and some of the properties of this concept are also discussed in detail. Lastly, a real-life application based on the robotics manufacturing allocation problem is solved with the help of an inverse mPFG.

Light weight concrete mix design through fuzzy logic with Cosine similarity aided optimized rules

In this paper, a novel model of mix design built on fuzzy concepts is elaborated. For the development of the model, around 300 lightweight mix design data which is a blend of manually generated data and the data garnered through standard research publications reporting laboratory research and field mix designs have been used. The manual designs are based on ACI guidelines and were generated over spread sheet. have been considered. The model has five inputs viz., density of course aggregate, nominal size of course aggregate, water-cement ratio, target strength and the slump. The outputs are the quantities of cement, water, lightweight aggregate and fine aggregate. This data driven model consisted of four phases, i. Fuzzification of inputs and outputs with appropriate granulation using linguistic terms, ii. Framing of conjunctive rules based on approximate reasoning mapping the antecedent space to consequent space, iii. Selection of similar rules using cosine similarity measure and selection of the rules that generated outputs with minimum error, and iv. testing and validation of the system using error metrics such as root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The so developed model with compact and optimized rule set was tested with test and validation data (15% of the data for each). The results of the system are encouraging with low values of error metrics such as RMSE (10%- 13%), MAE(7–14), and MAPE(7%-14%) over the predicted outputs. The system has also shown high values of coefficient of determination R2 (0.87–0.96) across the predicted outputs. The correlation coefficient (R) authenticating the closeness of predicted values of the outputs with actual values stood in the range of 0.93 – 0.96.

Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris.

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of Pichia pastoris. First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the Pichia pastoris fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of Pichia pastoris. Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of Pichia pastoris under different operating conditions.

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.