An m-polar fuzzy model plays a vital role in modeling of real-world problems that involve multi-attribute, multi-polar information and uncertainty. The m-polar fuzzy models give increasing precision and flexibility to the system as compared to the fu...
Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling ev...
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...
Feature selection is a core step in data analysis and is referred to as attribute reduction in rough set theory. Granular ball computing has emerged as a novel data analysis paradigm characterized by high computational efficiency, robustness, and sca...
Three-way decisions, as a general model for uncertain information processing and decisions, mainly utilize the threshold generated by the decision cost matrix to determine the decision category of the object. However, the determination of the thresho...
The K-means clustering algorithm is widely applied in various clustering tasks due to its high computational efficiency and simple implementation. However, its performance significantly deteriorates when dealing with non-convex structures, fuzzy boun...
Data incompleteness is a common problem in real-life datasets. This is caused by acquisition problems, sensor failures, human errors, and so on. Missing values and their subsequent imputation can significantly affect the performance of data-driven mo...
In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular...
Trajectory data are embedded within driving paths, GPS positioning systems, and mobile signaling information. A vast amount of trajectory data play a crucial role in the development of smart cities. However, these trajectory data contain a significan...
Unsupervised anomaly detection has been extensively studied. However, most existing methods are designed for either numerical or nominal data, which struggle to detect anomalies effectively in real-world mixed-type datasets. Fuzzy information granula...
As a pivotal research method in the field of granular computing (GrC), fuzzy rough sets (FRSs) have garnered significant attention due to their successful overcoming of the limitations of traditional rough sets in handling continuous data. This paper...
Corporate defaults represent a critical risk factor for financial institutions and stakeholders. In today’s complex economic environment, precise and timely risk assessment has become an essential component of financial strategies. One promisin...
The article presents a multidimensional type 2 epistemic fuzzy arithmetic (MT2EF-arithmetic) based on the new body definition of fuzzy set type 2 (T2FS), which in the authors’ opinion, is more suitable for fuzzy computing than the current versions of...
This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic system...
Clustering is a fundamental cornerstone in unsupervised learning, playing a pivotal role in various data mining techniques. The precise and efficient classification of data stands as a central focus for numerous researchers and practitioners alike. I...
Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box cla...
In this paper, we define q-rung picture fuzzy hypergraphs and illustrate the formation of granular structures using q-rung picture fuzzy hypergraphs and level hypergraphs. Further, we define the q-rung picture fuzzy equivalence relation and q-rung pi...
Maintaining optimal microclimatic conditions within greenhouses represents a significant challenge in modern agricultural contexts, where prediction systems play a crucial role in regulating temperature and humidity, thereby enabling timely intervent...
As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which...
Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation...
Fuzzy rough set theory has been successfully applied to many attribute reduction methods, in which the lower approximation set plays a pivotal role. However, the definition of lower approximation used has ignored the information conveyed by the upper...
In human–computer interaction, translating information between binary and numerical representations and human concepts remains an active research area aimed at bridging the gap while preserving the core meaning of the information. The field con...
Granular structure plays a very important role in the model construction, theoretical analysis and algorithm design of a granular computing method. The granular structures of classical rough sets and fuzzy rough sets have been proven to be clear. In...
The development of accurate models to forecast load demand across different time horizons is challenging due to demand patterns and endogenous variables that affect short-term and long-term demand. This paper presents two contributions. First, it add...
The construction of information granules is a significant and interesting topic of Granular Computing (GrC) in which information granules play a vital role in representing and describing data, and it has become one of the most effective frameworks fo...
In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online...
This study proposes a novel multi-granularity broad fuzzy Apriori classifier (MGB-FAC) that addresses the high computational complexity and low generalization capability of FAC while sharing FAC’s high linguistic interpretability and strong unc...
As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information...
Granular computing techniques are a huge discipline in which the basic component is to operate on groups of similar objects according to a fixed similarity measure. The first references to the granular computing can be seen in the works of Zadeh in f...
In this paper, we use the fundamental idea of the incremental model (IM) and develop the design framework. The design method of IM is composed of two steps. In the first step, we perform a linear regression (LR) as the global model. In the second ste...
Efficient aero-acoustic regression is critical for unmanned aerial vehicle (UAV) design and urban air mobility operations, where noise mitigation is essential for regulatory compliance and public acceptance. While data-driven fuzzy Takagi–Sugen...
In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional la...
This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluat...
Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component obj...
Considering that data quality and model confidence bring threats to the confidence of decision-making, a three-way decision with uncertain data description is more meaningful in system analyses. In this paper, an advanced method for forming classific...
Texture image retrieval based on subjective visual descriptions remains a significant challenge due to the “semantic gap”, where conventional Content-Based Image Retrieval (CBIR) methods rely on low-level features or reference images that...