Fuzzy Choquet Integral Research Articles

A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation

A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation

An integrated interval-valued spherical fuzzy Choquet integral based decision making model for prioritizing risk in Fine-Kinney

The Fine-Kinney model has been expanded to serve as a tool for analyzing systemic risk in different industries, including occupational hazards. However, existing models for prioritizing risk in Fine-Kinney fail to account for the interconnectedness of experts’ cognitive information in interval-valued spherical fuzzy situations. This paper creates a new fuzzy compromise ranking of alternatives from distance to ideal solution (CRADIS) method to address the limitations of the Fine-Kinney model in occupational risk analysis. The interval-valued spherical fuzzy numbers are extended into the conventional risk scales in the Fine-Kinney model to generate a processing method for uncertain risk rating data. Then, the weighted averaging operator is developed based on the Choquet integral and interval-valued spherical fuzzy sets to construct the collective risk assessment matrix. This developed operator can capture inter-dependencies among risk data. Next, an enhanced CRADIS method with interval-valued spherical fuzzy sets and entropy measures is presented to address the risk ranking issue in the application of Fine-Kinney. To demonstrate the implementation of the synthesized occupational risk framework, a case study analyzing the occupational hazards in the metro construction process uses numerical methods. The proposed framework undergoes parameter sensitivity analysis to test its rationality. An evaluation compares the enhanced framework with the risk-prioritizing methods used in F–K to assess its advantages. The outcome suggests that the framework offers an appropriate and dependable approach to prioritizing occupational risks in a subjective and ambiguous scenario.

A Security Resilience Metric Framework Based on the Evolution of Attack and Defense Scenarios

The frequent attacks show that no information system is absolutely safe and the security capabilities are relative. Security resilience becomes a complementary priority for improving information systems’ continuous service and security capabilities in the face of attacks such as unknown vulnerabilities and backdoors. Endogenous security defense technology has become an important research aspect to improve the security resilience of information systems. However, there are some limitations in the research of the information system security resilience evaluation model, such as lacking indexes to characterize the system security resilience under an attack environment. In this paper, a security resilience enhancement strategy based on dynamic defense is constructed to improve the security performance of the system through IP port hopping and attack surface conversion. For the adversarial behaviors of attackers and defenders, we propose a security resilience metric framework based on the evolution of attack and defense scenarios, which is evaluated using a resilient security evaluation model based on the fuzzy Choquet integral. In the model, the weights of evaluation indicators are calculated based on the DEMATEL method. The 2-addable fuzzy measures of each indicator are calculated secondarily. Then the security performance of the system is calculated using the fuzzy Choquet integral. Absorptive capacity, adaptive capacity, and resilience factor are proposed to better supervise the metric framework’s validity. Finally, four groups of control cases were created by building the Web service system after the endogenous security transformation as the experimental simulation scenario. Experimental simulation results show the superiority of the proposed metric model.

Generalized linear diophantine fuzzy Choquet integral with application to the project management and risk analysis

Generalized linear diophantine fuzzy Choquet integral with application to the project management and risk analysis

Picture fuzzy Choquet integral based Einstein operations and its application in selection of the best mobile apps for online education

Picture fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to attributes into one unique output. The Choquet integral operator has been proven more ideal than traditional aggregation operators in the modelling of interaction phenomena among the attributes in decision-making problems. Firstly, we propose the Choquet integral picture fuzzy Einstein geometric aggregation operator and Choquet integral picture fuzzy Einstein ordered geometric aggregation operator with certain properties of these operators being established. We validate the functioning of the operators with illustrative examples. The proposed operators clearly capture the comprehensive correlative relationships of attributes in a simpler manner. Furthermore, the algorithm for a multi attribute decision-making problem based on proposed operators is given. The application of the proposed operators was explored to deal with the selection of the best mobile apps for online education. Finally, comparisons are conducted to illustrate the discussion and advantages of the proposed operators.

Effective detection algorithm of electronic information and signal processing based on multi-sensor data fusion

Appropriate vibration diagnostics when cutting a variety of products is critical for cutting quality and scrap and consumables reduction. Collecting and combining data from various sensors allows for real-time data collection during processing for system monitoring. The purpose of this study is to develop a method for effectively diagnosing milling machine vibrations using multi-sensor data fusion. It is proposed that sound and frequency signals be preprocessed to isolate specific vibration features, and then classified and aggregated for a complete vibration diagnostics system. After preliminary wavelet signal conversion, a deep belief network has been developed for each sensor to classify vibration features. When combining classification results, the fuzzy Choquet integral algorithm is used. Thus, a comprehensive judgment is obtained. It has been demonstrated that for neural networks, 100 epochs are sufficient for learning to take place. It has been discovered that the accuracy of classification and inference of the correct solution increases from 75% to 98% as the number of vibration feature combinations increases. When compared to the Dempster-Schafer evidence theory, the fuzzy integral fusion algorithm provides a high level of vibration detection accuracy of up to 98.7%. The resulting diagnostic procedure is simple and effective. It can be used to control the cutting process on machine tools in industrial settings. Improvements to the scheme are suggested, including the use of intelligent technologies to automate the process.

Occupational Hazards Evaluation in Residential Construction Projects: Novel Sorting Methods Based on q-Rung Orthopair Fuzzy Choquet Integral

Despite multiple efforts to improve safety in construction, insufficient hazard identification remains a significant concern. Failure to address these hazards can lead to severe safety incidents that harm workers and a firm’s reputation. This problem is especially prevalent in construction small and medium enterprises (SMEs) due to their limited resources, reliance on manual labor, and lack of technical expertise regarding safety concerns. Thus, this study addresses the gap by offering a computational framework that provides a comprehensive evaluation of occupational hazards, considering multiple factors, such as severity, frequency of occurrence, and the likelihood of detection, which are risk dimensions of failure mode effect analysis (FMEA). Notwithstanding the FMEA-based evaluation methods for safety evaluation in the construction sector, drawbacks attributed to the interdependencies of the risk dimensions and the handling of judgment uncertainties are evident. In this work, an extension of the FMEA is developed that assigns an occupational hazard to a risk category under a holistic framework that better addresses the current limitations of the FMEA. In particular, the study offers a two-fold contribution: (1) putting forward the proposed Choquet–FMEA–Sort methods under a q-rung orthopair fuzzy set (q-ROFS) environment and (2) demonstrating an actual case study in the Philippines that comprehensively evaluates occupational hazards in construction SMEs. Results of a demonstrative case of residential construction projects show that out of the 26 identified occupational hazards, 18 pose a high risk to workers, while the remaining eight pose a moderate risk. High-risk occupational hazards require more attention for mitigation efforts, especially in residential construction SMEs facing resource constraints. The computational framework offered in this work aids decision-makers in identifying high-risk occupational hazards in a more systematic approach. The robustness and stability of the proposed methods were tested using layers of sensitivity and comparative analyses.

Interval Valued q- Rung Orthopair Hesitant Fuzzy Choquet Aggregating Operators in Multi-Criteria Decision Making Problems

Abstract: In this paper, we introduce Interval valued q- Rung Orthopair Hesitant fuzzy sets (IVq-ROHFS) with motivation of Interval valued pythagorean Hesitant fuzzy sets [44] as a new concept. Then, we give some basic operations as complement, union, intersection, addition, scalar multiplication, scalar power. Also, we combine to (IVq-ROHFS) and choquet integral , together with aggregating operators, and develop to Interval valued q- rung orthopair hesitant fuzzy Choquet averaging operator (IVq-ROHCA) and Interval valued q- rung orthopair hesitant fuzzy Choquet geometric operator (IVq-ROHCG). Then, we offer to indicate soft approach of proposed IVq-ROHCA and IVq-ROHCG an example adopted from Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS (IVIHCI) [43]. The obtained results are agreement with IVIHCI but presented IVq-ROHCA and IVq-ROHCG have more advantages than existing structures as Interval-valued intuitionistic hesitant fuzzy sets (IVIHFS), interval-valued Pythagorean Hesitant fuzzy sets (IVPHFS) with reasons changing according to need, requirement, prefer of decision makers and moreover, being IVIHFS and IVPHFS are special cases of IVq-ROHFS. It is open from comparative analysis that the while some of offered approaches are giving no solution for some values, our operators present to needed results.

Spherical Fuzzy Choquet Integral-Based VIKOR Method for Multi-Criteria Group Decision-Making Problems

Inter-dependency among the decision criteria and difficulty of handling the information in form of “yes,” “abstain,” “no,” and “refusal” are two important issues to be addressed in multicriteria group decision-making (MCGDM) problems in the environment of uncertainty. Recently, spherical fuzzy set (SFS) has gained the attention of the researchers for MCGDM problems due to their capability of handling decision makers’ preferences in the form “yes,” “abstain,” “no,” and “refusal” more efficiently than picture fuzzy set. Choquet integral operator has an edge over traditional aggregation operator in the modeling of interaction among the preferences of decision makers and decision criteria and therefore, we define Choquet integral operator for SFS. The main objective of this study is to define the spherical fuzzy Choquet integral (SFCI) operator and to extend the VIsekriterijumska optimizacija I Kmpromisno Resenje (VIKOR) for spherical fuzzy environment. In this study we define spherical fuzzy Choquet average, spherical fuzzy Choquet geometric and spherical fuzzy Choquet integral distance (SFCID) operators and propose SFCI-based VIKOR method for MCGDM problems. Proposed SFCI-based VIKOR method is applied on warehouse selection problems and results are compared with existing MCGDM methods.

Secure Intelligent Fuzzy Blockchain Framework: Effective Threat Detection in IoT Networks

Integrating blockchain into the Internet of Things (IoT) for security is a new development in computational communication systems. While security threats are changing their strategies and constructing new threats on blockchain-based IoT systems. Also, in combining blockchain with IoT networks, malicious transactions and active attacks deliver more vulnerabilities, privacy issues, and security threats. The concept of blockchain-based IoT attacks is a hot topic in both IoT and blockchain disciplines. Network attacks are a type of security and privacy threat and cover the exact scope of threats related to the combination of IoT and blockchain. Even though blockchain has potential security benefits, new cyberattacks have emerged that make blockchain alone insufficient to deal with threats and attacks in IoT networks since vagueness and ambiguity issues are unavoidable in IoT data. The heterogeneous nature of IoT sources has made uncertainty a critical issue in IoT networks. Deep Learning (DL) models have difficulty dealing with uncertainty issues and cannot manage them efficiently as an essential tool in security techniques. Thus, we need better security, privacy, and practical approaches, such as efficient threat detection against network attacks in blockchain-based IoT environments. Also helpful to consider fuzzy logic to tackle deterministic issues when DL models face uncertainty. This paper designs and implements a secure, intelligent fuzzy blockchain framework. This framework utilizes a novel fuzzy DL model, optimized adaptive neuro-fuzzy inference system (ANFIS)-based attack detection, fuzzy matching (FM), and fuzzy control system (FCS) for detection of network attacks. The proposed fuzzy DL applies the fuzzy Choquet integral to have a powerful nonlinear aggregation function in the detection. We use metaheuristic algorithms to optimize the attack detection error function in ANFIS. We also validate transactions via FM to tackle fraud detection and efficiency in the blockchain layer. This framework is the first secure, intelligent fuzzy blockchain framework that identifies and detects security threats while considering uncertainty issues in IoT networks and having more flexibility in decision-making and accepting transactions in the blockchain layer. Evaluation results verify the efficiency of the blockchain layer in throughput and latency metrics and the intelligent fuzzy layer in performance metrics (Accuracy, Precision, Recall, and F1-Score) in threat detection on both blockchain and IoT network sides. Additionally, FCS demonstrates that we obtain an effective system (stable model) for threat detection in blockchain-based IoT networks.

Picture Fuzzy Choquet Integral Based Geometric Aggregation Operators and Its Application to Multi Attribute Decision-Making

Picture fuzzy set is an extension of intuitionistic fuzzy set, which allows interpreting uncertain data in decision-making problems. This study provides aggregation operators based on Choquet integral, namely Choquet integral picture fuzzy geometric aggregation (CIPFGA) operator and Choquet integral picture fuzzy hybrid geometric aggregation (CIPFHGA) operator with certain properties of these operators are established. We validate the functioning of the operators with illustrative examples. The CIPFHGA operator has an added benefit of combining the weights of positions along with capturing the comprehensive correlative relationships of the criteria. Further, the proposed operators enable us to solve a numerical problem in picture fuzzy Multi attribute decision-making problem and also allows to make a comparison study with the existing literature.

Risk Assessment Method on Haze Networks Public Opinion Based on Intuitionistic Fuzzy Choquet Integral

Risk Assessment Method on Haze Networks Public Opinion Based on Intuitionistic Fuzzy Choquet Integral

Location Selection for a Covid-19 Field Hospital Using Fuzzy Choquet Integral Method

Location Selection for a Covid-19 Field Hospital Using Fuzzy Choquet Integral Method

Powerpoint Presentation Evaluation Based on Aggregation of Quality Criteria

Today, there is no consensus about proper timing and conditions for integration of PowerPoint presentations into the educational process. But the model-based evaluation can make a decision-making process easier when it comes to using presentations. The purpose of this study is to build a formal model to evaluate presentations. In order to build a formal model, the authors suggest employing hierarchical structure consisting of aggregation operators, such as the weighted averaging operator, minimum operator, and fuzzy Choquet integral. The proposed formal model shows experts' knowledge of the interdependencies between the criteria. The experiment described in the paper demonstrates the effectiveness of such a model as it allows to formalize expert preferences gradually, which may include interdependencies between the quality criteria of a presentation. Thus, this model will allow to store, analyze, and compare presentations properties that are necessary for their successful application.

Novel Regularization for Learning the Fuzzy Choquet Integral With Limited Training Data

Fuzzy integrals (FIs) are powerful aggregation operators that fuse information from multiple sources. The aggregation is parameterized using a fuzzy measure (FM), which encodes the worths of all subsets of sources. Since the FI is defined with respect to an FM, much consideration must be given to defining the FM. However, in practice this is a difficult task—the number of values in an FM scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^n$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of input sources, thus manually specifying an FM quickly becomes tedious. In this article, we review an automatic, data-supported method of learning the FM by minimizing a sum-of-squared error objective function in the context of decision-level fusion of classifiers using the Choquet FI. While this solves the specification problem, we illuminate an issue encountered with many real-world data sets; i.e., if the training data do not contain a significant number of all possible sort orders, many of the FM values are not supported by the data. We propose various regularization strategies to alleviate this issue by pushing the learned FM toward a predefined structure; these regularizers allow the user to encode knowledge of the underlying FM to the learning problem. Furthermore, we propose another regularization strategy that constrains the learned FM's structure to be a linear order statistic. Finally, we perform several experiments using synthetic and real-world data sets and show that our proposed extensions can improve the learned FM behavior and classification accuracy. A previously proposed visualization technique is employed to simultaneously quantitatively illustrate the FM as well as the FI.

Weighted Moving Averages for a Series of Fuzzy Numbers Based on Nonadditive Measures with σ − λ Rules and Choquet Integral of Fuzzy-Number-Valued Function

The aim of this study is to generalize moving average by means of Choquet integral. First, by employing nonadditive measures with δ − λ rules, the calculation of the moving average for a series of fuzzy numbers can be transformed into Choquet integration of fuzzy-number-valued function under discrete case. Meanwhile, the Choquet integral of fuzzy number and Choquet integral of fuzzy number vector are defined. Finally, some properties are investigated by means of convolution formula of Choquet integral. It shows that the results obtained in this paper extend the previous conclusions.

Incorporating the Qualitative Variable Comfort into the Design of a Wearable Body Sensor System

The design of mechatronic devices commonly includes qualitative design objectives that can play a significant role in the consumer appeal and the success of the product. This article presents an approach to incorporate the qualitative design objective comfort into the design process of a wearable device by determining the most suitable locations to mount such hardware. The objective is modeled through multiple criteria to represent comfort. The model is formulated through the theory of fuzzy measures and the Choquet integral. The fuzzy measures are determined through two different and new methods. One method uses a least squares algorithm and the other determines fuzzy measures using a preference ranking of alternatives. In this approach, the preference rankings are locations, ordered from the most comfortable to the least comfortable. This order of locations can be replicated with the determined fuzzy measures and Choquet integral, to build the comfort model. An error that quantifies the inaccuracy in the order of preferred to nonpreferred alternatives is introduced. The comfort model that is established in this article is validated using a training set and a test set. A comparison between the two methods that determine the fuzzy measures is presented.

Q ‐Rung orthopair fuzzy Choquet integral aggregation and its application in heterogeneous multicriteria two‐sided matching decision making

<i>q</i> ‐Rung orthopair fuzzy Choquet integral aggregation and its application in heterogeneous multicriteria two‐sided matching decision making

Safety Assessment of Complex Electromechanical Systems Based on Hesitant Interval-Valued Intuitionistic Fuzzy Theory

This paper proposes a novel framework for assessing the system safety of complex electromechanical systems (CEMSs). From the perspective of system topology, the fault pervasion probability (FPP) is first proposed to analyze fault propagation mechanisms in combination with historical failure data. This approach can easily identify all failure propagation paths and rapidly locate fault nodes, thereby providing a valuable reference for maintenance engineers. To overcome the influence of subjective factors, the hesitant interval-valued intuitionistic fuzzy element (HIVIFE) is used to describe the failure consequences of components and fault paths. Then, a system safety indicator is proposed to measure the system state and provide support to managers and operators through integration of the failure consequences based on the hesitant interval-valued intuitionistic fuzzy Choquet integral (HIVIFCI). The bogie system of a high-speed train is selected as a case study to verify the effectiveness and applicability of the proposed approach. The results indicate that the proposed approach can (i) achieve a more accurate result for system safety assessment and (ii) identify all possible fault propagation paths. This study provides a basis for formulating maintenance strategies and reducing accident losses, which have important theoretical value and practical significance.

Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.