Fuzzy Data Sets Research Articles

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Hamacher Aggregation Operators for Pythagorean Fuzzy Set and its Application in Multi-Attribute Decision-Making Problem

Pythagorean fuzzy set is a useful expansion of intuitionistic fuzzy set for dealing with ambiguities, which mostly occur in real-life problems. Hamacher t-norm also has important and compatible norms that incorporate a parameter that offers various options to decision-makers during the information fusion process, thereby enhancing their ability to model decision-making problems effectively compared to alternative methods. In this study, Hamacher operators are being used to introduce several Pythagorean fuzzy Hamacher interactive weighted averaging (PFHIWA), Pythagorean fuzzy Hamacher interactive ordered weighted averaging (PFHIOWA), Pythagorean fuzzy Hamacher interactive weighted geometric (PFHIWG), and Pythagorean fuzzy Hamacher interactive ordered weighted geometric (PFHIOWG) operators. The properties of these operators are examined in detail. The benefit of using progressive operators is that they deliver more understanding of the scenario to the decision-makers. Proposed operators are utilized to elaborate multi-attribute decision-making (MADM). By showing the sensitivity analysis, our proposed operator has high stability related to multi-attribute decision-making (MADM) under the Pythagorean fuzzy data set.

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores.

Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.

Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges

This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD.

An approach to extract topological information from Intuitionistic Fuzzy Sets and their application in obtaining a Natural Hierarchical Clustering Algorithm

Topological data analysis (TDA) is a powerful mathematical framework that extracts valuable insights about the shape and structure of complex datasets by identifying and analyzing underlying topological features, including connected components, holes, voids, and higher-dimensional counterparts. TDA achieves this by constructing a simplicial complex from the data, comprising simple shapes like points, lines, triangles, and higher-dimensional counterparts. The Vietoris–Rips complex (VRC), a widely used method, constructs simplicial complexes by measuring the distances between data points in a metric space. While the link between Intuitionistic Fuzzy Sets (IFSs) and TDA is clear through intuitionistic fuzzy distance measures, the literature has not explored the application of TDA for extracting topological features from uncertain and vague datasets using IFSs. In this paper, we propose a novel approach that leverages Intuitionistic Fuzzy Distance Measures (IFDMs) to generate the Intuitionistic Fuzzy Vietoris–Rips Complex (IFVRC) of IFSs. Specifically, we investigate the persistence of invariant relations of topological features between IFVRCs constructed using different distances, including Atanassov’s Euclidean distance (l2IFS), Boran and Akay’s distance (D), and Liu’s distance measure (dL), over artificially generated IFSs. Furthermore, we demonstrate that the generation of IFVRC can be viewed as a Natural Hierarchical Clustering Algorithm (NHCA) for IFSs. Finally, we apply the proposed IFVRC as a Natural Hierarchical Clustering Algorithm to cluster intuitionistic fuzzy datasets related to Car and Building materials. Moreover, we incorporate a relatively large dataset, the GTZAN dataset from the Kaggle datasets repository, to classify music genres based on their topological information.

An improved loci method for outlier detection in fuzzy datasets based on fractional distance metric and outlierness degree

Density based methods are significant approaches in outlier detection for high dimensional datasets and Local correlation integral (LOCI) is one of the best of them. To extend LOCI for fuzzy datasets, we should employ suitable metrics to measure the distance between two fuzzy numbers. Euclidean distance measure is a classic one in metric learning, but to overcome curse of dimensionality, we apply fractional distance metric too. Then, after introducing the FLOCI outlier detection algorithm for identifying the fuzzy outliers, we study the efficiency of the proposed method by doing some numerical experiments, in which the obtained results were completely successfull. We also compared the results with Fuzzy versions of Distance based ABOD and SOD methods to prove robustness of this approache. More than the above, one of the main advantages of the new approach is the determination of outlierness factor for each data which is not presented in classical LOCI method.

Linear diophantine fuzzy Aczel-Alsina aggregation operators by using fuzzy-C means clustering algorithm

This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes.

A convolutional neural network based classification for fuzzy datasets using 2-D transformation

Researches on deep learning methods have been actively conducted for the past 10 years, and various deep learning techniques have been proposed by many researchers. In addition, prediction methods using deep learning are widely used in various fields. In particular, convolution neural network (CNN) is most commonly applied to analyze visual images, but it can be also applied to many other data. On the other hand, fuzzy theory has been applied to deep learning techniques in traffic problem, agriculture, and airline customer service. In the case of data containing ambiguous information, data analysis can be performed using soft methods. In particular, the fuzzy theory is widely used to deal with such data. So, when the data includes vague information a fuzzy number can be applied to input/output data. In this paper, seven models using CNN have been proposed to analyze fuzzy input containing ambiguous or linguistic information. Our proposed models use five activation functions. For the data analysis, three datasets including Iris data, US Health Insurance data, Wine quality data are used to compare the seven proposed Fuzzy CNN models.

Machine Learning Algorithms in Identifying Balanced Diet Plan for Healthy Life style

The present generation of all ages is terribly facing the challenges of obesity in recent times. The people suffering from this disorder practice different diet plans for weight reduction without considering the balanced proportion of nutrients in their diet. This paper aims in highlighting the ill effects of unbalanced diet plans and proposes a machine learning (ML) model based on support vector machine to make decisions on the balanced nature of the diet. The efficiency of the proposed ML model is compared with other ML algorithms. The accuracy results of the proposed model are more convincing in comparison with other ML algorithms. The proposed ML model is applied to deterministic type of secondary data sets and this shall be extended by applying to fuzzy data sets. This research work applies the algorithms of machine learning to health-based decision-making systems

New similarity measure of Pythagorean fuzzy sets based on the Jaccard index with its application to clustering

A similarity measure is being widely used as a tool to create the degree of similarity between two different objects or sets. Several similarity measures between two Pythagorean fuzzy sets (PFSs) are suggested in literature. As far as our knowledge is concerned no one has considered similarity measure between two Pythagorean fuzzy sets (PFSs) based on Jaccard index (JI) so far. Therefore, in this manuscript a novel similarity measure based on JI between two PFSs is suggested. The JI is statics used to compare similarity as well as variety of simple sets. The advantages of proposed similarity based on Jaccard index includes: (i) It can be utilized to compare any two sets and it can also be applied to text data, numerical data and even categorical data. (ii) It is robust to outliers, which means that it is not significantly affected by presence of rare or unusual items exist in the data. This makes it useful in variety of applications including clustering and anomaly detection (iii) It is simple and efficient in computation which make it right choice in many applications where fast processing time is important. Then we gave several numerical examples to show the reliability of suggested similarity measures. Our proposed similarity measure between PFSs based on JI give better properties with respect to numerical results. Based on the numerical analysis, our proposed method is reliable, reasonable and better than the existing ones. Furthermore, we utilized proposed similarity measure with Pythagorean Fuzzy Technique of Order Preference by Similarity to Ideal Solution (PF-TOPSIS) in handling complex daily life issues involving multicriteria decision-making (MCDM) processes. Furthermore, similarity measure between two PFSs is also very useful in clustering. Therefore, we also apply our proposed similarity measure in an application to clustering based on Pythagorean fuzzy data set. Final results revealed that our suggested methods are well suited and reasonable in multicriteria decision-making and clustering environment.

Granular Computing Approach to Evaluate Spatio-Temporal Events in Intuitionistic Fuzzy Sets Data through Formal Concept Analysis

Knowledge discovery through spatial and temporal aspects of data related to occurrences of events has many applications in digital forensics. Specifically, in electronic surveillance, it is helpful to construct a timeline to analyze information. The existing techniques only analyze the occurrence and co-occurrence of events; however, in general, there are three aspects of events: occurrences (and co-occurrences), nonoccurrences, and uncertainty of occurrences/non-occurrences with respect to spatial and temporal aspects of data. These three aspects of events have to be considered to better analyze periodicity and predict future events. This study focuses on the spatial and temporal aspects given in intuitionistic fuzzy (IF) datasets using the granular computing (GrC) paradigm; formal concept analysis (FCA) was used to understand the granularity of data. The originality of the proposed approach is to discover the periodicity of events data given in IF sets through FCA and the GrC paradigm that helps to predict future events. An experimental evaluation was also performed to understand the applicability of the proposed methodology.

Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty

To solve the problem regarding the large-scale grid-connected consumption of a high proportion of new energy sources, a concentrating solar power (CSP)-photovoltaic (PV)-wind power day-ahead and intraday-coordinated optimal dispatching method considering source load uncertainty is proposed. First, the uncertainty of day-ahead wind power output prediction is described by the multi-scenario stochastic planning method, and the uncertainty of intraday source-load is characterized by the trapezoidal fuzzy number equivalence model. Second, based on the combined scenario set of day-ahead wind power output prediction, the day-ahead optimal dispatch is performed by combining thermal and CSP plants, and the day-ahead thermal and CSP plant dispatch output and intraday source load fuzzy dataset are used as the input quantities for the day-ahead dispatch. Thus, the scheduling output and rotating backup plan for thermal power and CSP plants were determined; finally, the validity and feasibility of the model were verified using arithmetic examples.

How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets

This work introduces a data-driven approach based on k-means clustering with datasets elicited under a Picture fuzzy set (PFS) environment. With the vision, mission, and goals statement as a proxy for organizational identity, an actual case study is reported to demonstrate the application of the proposed approach. Four attributes were introduced to describe organizational identification: knowledge, perception, linking, and future attributes. Results revealed that out of 1,911 members, 56.67% of them are considered “proactive citizens”, 32.50% are “ambivalent citizens”, and 10.83% are “disengaged citizens”. Sensitivity analysis shows that the proposed approach is robust to changes in the model parameters. Characteristics of the types of citizens were discussed, and some managerial insights were outlined. Succinctly, this work contributes to the literature by exploring the integration of PFS to k-means clustering to characterize the extent of organizational identification of organizational stakeholders when presented with a new organizational identity – a relatively novel application in organization science.

Do you think that the home delivery is good for retailing?

Order placing and delivery always play a significant role in any business industry. Nowadays, most customers want useful products at their doorstep without visiting any retail store. In this current study, an advanced dual-channel supply chain model is constructed for a single product with two players. Customers can order online or offline from the retail store. In contrast, retailers use only offline mode to take the product from the manufacturer, and retailers charge a different amount for delivery. To optimize profit and satisfy the consumers, retailers only provide a free transportation service when customers order a certain quantity. If customers order less than the desired amount, a transportation charge will be applied to the customers. Contrary to the existing research, demand for the products varies with the selling price online and offline and is also affected by the free home delivery service. Manufacturers use a single-setup multi-delivery (SSMD) transportation strategy to enrich the system's profit. To make the study more applicable, all unit costs are considered fuzzy. Signed-distance defuzzification technique is adopted to defuzzify the fuzzy values. Finally, the total profit of the supply chain is optimized along with the optimized value of the decision-making variable through an uncertain optimization technique. Numerical experiments are carried out along with sensitivity analysis to show the applicability and optimality of this study. Though the crisp data set provided a 0.72% better result compared to the fuzzy data set, but fuzzy data set provided a more realistic result. Numerical results prove that the home delivery service provided 14.59% better profit. Finally, it can be stated that the home delivery policy with some charged amount based on ordered quantity is the best retailing strategy for the dual-channel supply chain.

Fuzzy Rule-Based Classification Method for Incremental Rule Learning

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due to the diversity of data types, how to improve the readability of the extracted granular rules while ensuring efficiency is always a challenge. Since granular reduct in granular computing (GrC) can simplify real complex problem and dataset, this article carries out granular rule learning from the perspective of granular reduct by taking formal concept analysis (FCA)-based GrC method as a framework. Specifically, for achieving classification task, we first propose a method to update the granular reduct, and then explore the updating mechanism of fuzzy granular rule in a reduced dataset. Second, a novel fuzzy rule-based classification model named FRCM is presented for fuzzy granular rule learning. In order to verify the effectiveness of the proposed model, some numerical experiments for incremental learning and fuzzy rule mining are conducted to demonstrate that FRCM can achieve the state-of-the-art classification performance.

Fuzzy Confidence Intervals by the Likelihood Ratio: Testing Equality of Means—Application on Swiss SILC Data

We propose a practical procedure of construction of fuzzy confidence intervals by the likelihood method where the observations and the hypotheses are considered to be fuzzy. We use the bootstrap technique to estimate the distribution of the likelihood ratio. The chosen bootstrap algorithm consists on randomly drawing observations by preserving the location and dispersion measures of the original fuzzy data set. A metric d_{SGD}^{theta ^{star }} based on the well-known signed distance measure is considered in this case. We expose a simulation study to investigate the influence of the fuzziness of the computed maximum likelihood estimator on the constructed confidence intervals. Based on these intervals, we introduce a hypothesis test for the equality of means of two groups with its corresponding decision rule. The highlight of this paper is the application of the defended approach on the Swiss SILC Surveys. We empirically investigate the influence of the fuzziness vs. the randomness of the data as well as of the maximum likelihood estimator on the confidence intervals. In addition, we perform an empirical analysis where we compare the mean of the group “Swiss nationality” to the group “Other nationalities” for the variables Satisfaction of health situation and Satisfaction of financial situation.

Multigranulation Rough Set Methods and Applications Based on Neighborhood Dominance Relation in Intuitionistic Fuzzy Datasets

Multigranulation Rough Set Methods and Applications Based on Neighborhood Dominance Relation in Intuitionistic Fuzzy Datasets

Approximation of 3D trapezoidal fuzzy data using radial basis functions

We present a new methodology to approximate a trapezoidal fuzzy numbers set by using smoothing radial basis functions (RBFs). The methodology uses different error and similarity indices to determine and compare the accuracy of the approximation of the given trapezoidal fuzzy data. For the proposed approximation method a fuzzy radial basis functions type are defined, called fuzzy smoothing radial basis functions under tension. The computation of one of these approximation functions from a given trapezoidal fuzzy data set is described and some convergence results are proved. Finally, some examples in two-dimensions are given to compare the behavior of the presented method by using the proposed error and similarity indices for different configurations of the fuzzy smoothing radial basis functions under tension.

Improved subspace-based and angle-based outlier detections for fuzzy datasets with a real case study

To overcome curse of dimensionality for outlier detecting in high dimensional dataset, axis-parallel subspace (SOD) and angle-based outlier detection (ABOD) methods were presented. These methods are also friendly used distance-based to detect outliers. In this regard, based on the reality of fuzzy data for explaining the world phenomena, this paper introduces an extended version of both methods for fuzzy dataset. First, the basic concepts of both methods are explained. Next we provide two metrics based on Euclidean and analytic distance to measure distance between fuzzy objects; also Cosine similarity measure formula for calculating the cosine of angle between two difference vectors in high-dimensional fuzzy dataset is illustrated. Then the algorithms to determine outliers of fuzzy datasets by using these metrics and Cosine similarity measure, based on ABOD and SOD algorithms, are presented. Some numerical experimental examples are also presented, in which both real and synthesis datasets are used, For a real numerical examination, we have applied proposed algorithms to data from 15 Iranian petrochemical companies in a fully fuzzy environment. The obtained results show the significant properties of the new methods in detecting outliers.

Research on robot scene recognition based on improved feature point matching algorithm

A new feature description method based on the fusion of fast retina keypoint (FREAK) and the rotation-aware binary robust independent elementary features (rRBRIEF) is proposed to realize the effective combination of efficiency and accuracy of the two feature descriptions. In addition, in the elimination stage of mismatched point pairs, by setting the base point and its neighborhood, an improved neighborhood parallel random sample consensus (RANSAC) algorithm is proposed to achieve efficient parallel operation of the algorithm in multiple local neighborhoods. The improved feature point matching algorithm and the existing algorithm were tested in different scales, different rotations, different illuminations, and different fuzzy data sets. The experimental results show that the improved algorithm improves the average scene recognition accuracy by 18.21%, improves the efficiency by 15.58%, and shows good robustness.