Fuzzy Concept Research Articles

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.

Protein Binding: A Fuzzy Concept.

Our understanding of protein binding interactions has matured significantly over the last few years, largely as a result of trying to make sense of the binding interactions of intrinsically disordered proteins. Here, we bring together some disparate ideas that have largely developed independently, and show that they can be linked into a coherent picture that provides insight into quantitative aspects of protein interactions, in particular that transient protein interactions are often optimised for speed, rather than tight binding.

Set-based integer-coded fuzzy granular evolutionary algorithms for high-dimensional feature selection

Feature selection plays a pivotal role in handling today’s high-dimensional databases by keeping only the most valuable features, leading to less computation, improved performance, and higher transparency in decision-making processes. Despite the considerable advances in combinatorial optimization, this data-preprocessing step is computationally NP-hard and continues to pose critical challenges, particularly for very high-dimensional (VHD) databases. Here, we propose integer coding and fuzzy granulation (FG) as an integral part of evolutionary wrapper-based feature selection. Based on this integer coding, we further propose crossover and mutation operators that employ set operations such as ‘union,’ ‘intersection,’ and ‘complement’ for higher transparency in their evolutionary explorative and exploitative search processes. In addition to its common use as a surrogate technique to avoid unnecessary computations by recognizing similarities, the fuzzy granulation concept also operates as a repulsive strategy that searches for dissimilarities in the elitist and population initialization routines to reach higher population diversity. An ablation study is implemented to discover the role of individual components of this multi-prong approach. The results are then compared on 22 benchmark problems, ranging from 64 to 138672 attributes, with nine competing methods. Superior performance is shown for the proposed approach in terms of accuracy (in 15 of 22 cases) and achieving a substantially smaller (as much as six times less) feature set with considerably less computational cost (by an average of 30 percent), particularly for VHD feature selection

Attribute reduction and information granulation in Pythagorean fuzzy formal contexts

Pythagorean fuzzy set theory is one of the significant tools to deal with real-life problems which are prone to imprecision, partial truth or uncertainty. A Pythagorean fuzzy set reports the degree of truthness as well as falsity of a statement in order to illustrate the imprecision of that statement. Motivated by the resourcefulness of this theory, this paper is aimed to explore formal concept analysis in Pythagorean fuzzy environment. It defines Pythagorean fuzzy formal concept and Pythagorean fuzzy concept lattice associated with a Pythagorean fuzzy formal context. The proposed Pythagorean fuzzy formal concept is a duple whose first argument is (crisp) extension and second argument is Pythagorean fuzzy intension. Moreover, the join of different combinations of object concepts of a Pythagorean fuzzy formal context returns different Pythagorean fuzzy formal concepts. We also studied the knowledge reduction in Pythagorean fuzzy formal contexts in association with Pythagorean fuzzy concept lattice. The key ideas of attribute reduct, core and discernibility matrix for a Pythagorean fuzzy formal context are illustrated. Further, we merged the notions of information granulation and attribute reduction with Pythagorean fuzzy formal context. Various results of formal concepts and concept lattices are extended in Pythagorean fuzzy set theory. At last, an algorithm is developed which discovers non-redundant Pythagorean fuzzy formal concepts for a given Pythagorean fuzzy formal context. Eventually, an example is presented which elaborates the procedure of this algorithm.

Human intelligence-based metaverse for co-learning of students and smart machines.

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.

Level operators over primary interval-valued intuitionistic fuzzy M group

The concept of interval-valued intuitionistic fuzzy M group is extended by introducing primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group using this concept primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group is defined and using level operators and their properties are established.

Orbital fuzzy iterated function systems

In this paper we introduce the concept of orbital fuzzy iterated function system and prove that the fuzzy operator associated with such a system is weakly Picard. An example is provided.

A fuzzy risk assessment model used for assessing the introduction of African swine fever into Australia from overseas

African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.

Some Properties Of Cartesian Product Of Two Fuzzy Normed Spaces

In this paper , the concept of the Cartesian Product of two fuzzy&#x0D; normed spaces is presented. Some basic properties and theorems on this&#x0D; concept are proved. The main goal of this paper is to prove that the&#x0D; Cartesian product of two complete fuzzy normed spaces is a complete&#x0D; fuzzy normed space.&#x0D; Key words:Fuzzy normed space , Cartesian product , Cauchy sequence ,&#x0D; complete fuzzy normed space.&#x0D; 1- Introduction&#x0D; &#x0D; The fuzzy set concepts was introduced in mathematics by K.Menger&#x0D; in 1942 and reintroduced in the system theory by L.A.Zadeh in 1965.&#x0D; In 1984, Katsaras [ 1 ] , first introduced the notation of fuzzy norm on&#x0D; linear space, in the same year Wu and Fang [ 4 ] also introduced a notion of&#x0D; fuzzy normed space . Later on many other mathematicians like Felbin [ 2 ]&#x0D; , Cheng and Mordeson [ 10] , Bag and Samanta [12], J.Xiao and X.Zhu&#x0D; [8,9] , Krishna and Sarma [11] , Balopoulos and Papadopoulos [ 13] etc,&#x0D; have given different definitions of fuzzy normed spaces .&#x0D; J.Kider introduced the definition of fuzzy normed space[ 7 ] , we use this&#x0D; definition to prove that the Cartesian product of two fuzzy normed spaces&#x0D; is also fuzzy normed space.&#x0D; &#x0D; The structure of the paper is as follow : In section 2 we&#x0D; present some fundamental concepts . In section 3, the definition of fuzzy&#x0D; normed space appeared [7] is used to prove that the cartesain product of&#x0D; two fuzzy normed spaces is also fuzzy normed space, then we prove that&#x0D; the cartesain product of two complete fuzzy normed spaces is complete&#x0D; fuzzy normed space.&#x0D; &#x0D; 2. Preliminaries&#x0D; In this section, we briefly recall some definitions and preliminary&#x0D; results which are used in this paper.

Analysis of Stock Selection Model Strategy in Indonesia's Capital Market

The impact of investor behavior and decision-making on investment interest in the Indonesian capital market is ultimately tied to the stock selection model used to form an optimal portfolio. Therefore, this study aims to develop an optimal stock selection model in the Indonesian capital market. And thus, the novelty of this study relates to other studies that demonstrate how to improve investment decisions in the capital market in Indonesia. Whereas previous research focused solely on the associations between variables, this study examines the variables themselves. This investigation was carried out in the Greater Jakarta of Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi). This research utilizes the Fuzzy-Analytical Hierarchy Process (FAHP) methodology. FAHP is the combination of AHP and a fuzzy concept approach. The FAHP method uses qualitative and quantitative data to make decisions. The data collection method employs primary data from the Survey Questionnaire and discussions, secondary data consisting of market data, stock price movement data from the Indonesia Stock Exchange, Indonesian Central Securities Depository, Bank Indonesia, and empirical literature about stock selection models. The results of the analysis indicate that forming the optimal portfolio in the Indonesian capital market involves predicting the price or stock return using a single factor that is thought to impact security. The primary objective assumed to be realized by the optimal portfolio formation approach on the Indonesian capital market is the optimization of stock combinations. Keywords: investor behavior, investor decision-making, capital market, investment, stocks

A kernel-based intuitionistic weight fuzzy k-modes algorithm using coupled chained P system combines DNA genetic rules for categorical data

A kernel-based intuitionistic weight fuzzy k-modes algorithm (KIWFKM) is proposed in this paper which can improve the clustering performance of categorical data. The current FKM algorithm which were proposed by researchers generally have three drawbacks. Firstly, the algorithms were easily limited to the local optimal solution. Secondly, most of algorithms were considering all attributes equally. Thirdly, most algorithms are sensitive to noise data points. So the intuitionistic fuzzy sets (IFS), kernel trick and weight concept are introduced into the objective function which can not only solve the problem of all attributes equally but also improve the robustness to noise. In addition, a coupled DCP (chained tissue-like P system combines DNA genetic rules) system is established which is used for realizing the KIWFKM algorithm (KIWFKM-DCP). The uncertainty and implicit parallelism of the DCP system can help the KIWFKM algorithm jump out of the local optimal solution and find better solution. Finally, we conduct experiments and compare experiment results with six state-of-the-art clustering methods. Experimental results conduct that the KIWFKM-DCP algorithm perform better than the other comparison clustering algorithms.

Decision Analysis on the Financial Performance of Companies Using Integrated Entropy-Fuzzy TOPSIS Model

Sustainable economic development plans have been shattered by the devastating COVID-19 crisis, which brought about an economic recession. The companies are suffering from financial losses, leading to financial distress and disengagement from sustainable economic goals. Many companies fail to achieve considerable financial performances, which may lead to unachieved organizational goal and a loss of direction in decision-making and investment. According to the past studies, there has been no comprehensive study done on the financial performance of the companies based on liquidity, solvency, efficiency, and profitability ratios by integrating the entropy method and fuzzy technique for order reference based on similarity to the ideal solution (TOPSIS) model in portfolio investment. Therefore, this paper aims to propose a multi-criteria decision-making (MCDM) model, namely the entropy-fuzzy TOPSIS model, to evaluate the financial performances of companies based on these important financial ratios for portfolio investment. The fuzzy concept helps reduce vagueness and strengthen the meaningful information extracted from the financial ratios. The proposed model is illustrated using the financial ratios of companies in the Dow Jones Industrial Average (DJIA). The results show that return on equity and debt-to-equity ratios are the most influential financial ratios for the performance evaluation of the companies. The companies with good financial performance, such as the best HD company, have been determined based on the proposed model for portfolio selection. A mean-variance (MV) model is used to validate the proposed model in the portfolio investment. At a minimum level of risk, the proposed model is able to generate a higher mean return than the benchmark DJIA index. This paper is significant as it helps to evaluate the financial performance of the companies and select the well-performing companies with the proposed model for portfolio investment.

An η-Intuitionistic Fuzzy Rings Structure

In this article, we present the structure of η-intuitionistic fuzzy ring. An η-intuitionistic fuzzy ring is a structure which is built with combinating the definition of fuzzy ring, intuitionistic fuzzy set, and η-intuitionistic fuzzy set. The η-intuitionistic fuzzy set is characterized by any value η∈[0,1], where the degree of membership μ_(A^η ) (k) is obtained based on the averaging operator of the degree of membership μ_A (k) and the value of η∈[0,1]. While the degree of non membership ν_(A^η ) (k) is obtained based on the averaging operator of the degree of non membership ν_A (k) and the value of 1-η∈[0,1]. In its development, new concepts were obtained, namely the η-intuitionistic fuzzy ideal and its properties related to the sum and product operation of η-intuitionistic fuzzy ideals. Furthermore, the η-intuitionistic fuzzy ideals concept can be developed into an η-intuitionistic fuzzy quotient ring, η-intuitionistic fuzzy homomorphism, and its properties on the next research.

Fuzzy logic-based health risk assessment of fluoride in groundwater used as drinking source in Sira region, Tumkur, India.

Fluoride contamination in drinking water is a serious public health concern across the world, and more than 90 million people in India are affected by health risks associated with fluoride. Though the fatality due to fluoride chronic toxicity is uncommon, the exposure to fluoride at different concentration levels shows various adverse health effects such as dental and skeletal fluorosis, multiorgan failure, cognitive and behavioural effects. Hence, the objectives of the present study are to understand the hydrogeochemistry and drinking water suitability of groundwater of the Sira region, Karnataka, India, and to understand the occurrence of fluoride and its health risks using the United State Environmental Protection Agency (US EPA) method and fuzzy logic concepts. Forty-six samples were collected from each pre and post-monsoon season. The hydrogeochemistry studied through Chadha's diagram and Gibb's diagram indicated that the groundwater in this region is of Na-Cl type and the hydrogeochemistry is majorly controlled by rock-water interaction and followed by evaporative dominance. Water quality parameters were compared with drinking water standards guidelines, and the results showed that around 50% of the samples were contaminated with fluoride. The occurrence of high levels of fluoride in the study region is associated to the presence of granitic rocks and it is influenced by high pH and low calcium dissolution in the groundwater. Based on US EPA method, the order ofpopulation group under the risk of dental and skeletal fluorosis, is children > adolescents > adults. A fuzzy inference system model is developed to assess the health risk due to fluoride and its output gives severity levels of each type of health risk, i.e. dental caries, dental fluorosis and skeletal fluorosis. The results of the application of the fuzzy inference system model in the Sira region showed that the children (< 8Yr.) are more susceptible to the moderate risk of dental caries, dental fluorosis and skeletal fluorosis. Whereas adolescents (8-18Yr.) and adults (> 18Yr.) are less susceptible to low-very low risk. Hence, health risks associated with fluoride can be better addressed with the help of a fuzzy inference system model which can be used for more reliable and grounded results to improve the quality of decision-making.

Powerset operators induced by fuzzy relations as a basis for fuzzification of various mathematical structures

We propose a unified approach to the theory of forward and backward powerset operators induced by fuzzy relations with special attention to the algebraic and topological properties of these operators. On the basis of this theory, we reconsider previous studies done by various authors in the fields of fuzzy rough sets, fuzzy concept analysis and fuzzy mathematical morphology as well as obtain some new results in these areas. In this way we contribute to the development of the theory of fuzzy rough sets, fuzzy concept analysis and fuzzy mathematical morphology on the basis of fuzzy powerset operators induced by fuzzy relations.

Optimised ensemble learning-based IoT-enabled heart disease monitoring system: an optimal fuzzy ranking concept

ABSTRACT Generally, patients with heart diseases enquire for medical services while sick. They will not experience sickness often until they reach the last stage of heart disease, and so the damages are not reversible. Thus, the solution for reducing the death rate of heart disease healthcare is becoming the passive medical care type. Physicians usually monitor the physical status of patients, which helps in deciding when to provide medical services by considering the real-time analysis of patients. Moreover, a significant division of pervasive clinical care is taken as the development of practical monitoring systems. An IoT approaches are overwhelming the progression in addressing the issue of heart disease patient care since they can modify the service mode in a pervasive way. A remote monitoring model is more significant for realising pervasive healthcare services. Here, an ‘optimised ensemble learning’-based IoT-enabled heart disease monitoring system through an optimised ensemble fuzzy ranking (OEFR) strategy, deep feature extraction and heuristic improvement. Moreover, the hyperparameter of each classifier is optimised through a new improved dingo optimiser (I-DOX) algorithm. Throughout the result analysis, the accuracy of the designed I-DOX-based OEFR model is attained 96.72%. Thus, the analysis exhibits an overall performance analysis in terms of high performance.