Mainstream strategies for addressing imbalanced monitoring data distributions, such as data sampling and cost-sensitive learning methods, face multiple challenges, including data heterogeneity, uncertainty, biased attention toward normal or fault classes, and reduced discriminability of fault features. These issues collectively lead to a degradation in fault diagnosis (FD) performance. To overcome the aforementioned bottlenecks, this article proposes a unified FD framework, termed a granular computing.based deep neural network (GDNN), which is designed from the novel perspective of granular ball (GB) embedding. Within this framework, the input data are first transformed into suboptimal latent features through a generic deep neural network (DNN).based feature extraction component. Subsequently, an information granulation component reorganizes these features into class-specific local neighborhood structures in the form of GBs. Finally, a Softmax-based fully connected classifier performs FD. The GDNN framework supports end-to-end learning by minimizing a composite loss function consisting of the cross-entropy loss and the justifiable granularity (JG) loss. The main contribution of this work lies in a novel GB embedding mechanism that guides DNNs to focus on GB-based structures rather than isolated individual samples in imbalanced scenarios. Each class is represented by a GB that encapsulates its structural knowledge in the latent feature space, thereby ensuring consistent attention across classes and effectively mitigating severe overfitting. Experimental results on two industrial processes demonstrate that the proposed framework performs robustly across diverse imbalanced scenarios and adapts well to different DNN architectures, providing a novel perspective and a generalizable paradigm for imbalanced FD.

Federated Fuzzy C-Means (FCM) clustering methods have demonstrated success in standard federated learning (FL) environments, but model heterogeneity presents a significant challenge. This heterogeneity hampers communication content selection and makes traditional aggregation methods, such as federated averaging (FedAvg), insufficient for diverse client models. Additionally, handling non-IID data distributions and learning robust feature representations remain unresolved in some existing approaches. To tackle these issues, we propose FFCMD, a federated FCM clustering framework designed to address model heterogeneity while enhancing feature learning and accommodating non-IID data. FFCMD integrates an autoencoder (AE) with a fuzzy C-Means network (FCMN), augmented by mutual knowledge distillation (mKD), to improve data feature learning, communication, and aggregation under heterogeneous conditions. The AE incorporates a clustering-oriented penalty term to learn discriminative features, boosting performance on complex datasets. Rather than direct parameter aggregation, mKD updates the global model using local devices' soft assignments, naturally handling structural variations across client models. Moreover, FFCMD employs discrepancy-aware weighting mechanisms to dynamically align local and global category distributions during soft assignment embedding, effectively addressing the challenges posed by non-IID data. Extensive experiments on multiple public datasets show that FFCMD outperforms state-of-the-art methods, particularly in non-IID scenarios, and excels even in the presence of model heterogeneity.

Classification tasks involving tabular data often require a balance between exceptional performance and heightened interpretability. To address this challenge, we propose a linguistically interpretable deep fuzzy classification system called FFT-FFR-RBFC. The system employs a Fuzzy Feature Transformation (FFT) unit, formed by employing a stacked architecture of multiple Takagi-Sugeno-Kang (TSK) fuzzy models with non-linear conclusions, to distill high-level fuzzy features from the input data, a Rule-Based Fuzzy Classifier (RBFC) unit to perform classification using these features, while a Fuzzy Feature Reconstruction (FFR) unit in tandem with the FFT to enhance the system's linguistic interpretability by remapping the high-level features back to their original space. The proposed approach is optimized by minimizing a composite loss function that balances classification and reconstruction losses, ensuring a harmonious interplay between performance and interpretability. Comprehensive evaluation across 20 diverse datasets demonstrates that the system's is exceptionally promising, particularly for high-dimensional or large-scale tabular data classification tasks, achieving superior classification performance while maintaining a high degree of interpretability.

Convolutional neural networks (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of mixed-variable encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the Gaussian process and particle swarm optimization (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter mixed-variable problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on image-classification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

As interactions between individuals and services increase, requests are more frequent and policy sets are larger. The evaluation performance of PDP (Policy Decision Point) plays a key role in the operation of a system. In order to solve bottlenecks of improving the PDP evaluation performance for large-scale policy sets, we propose an evaluation engine based on artificial neural networks, namely ANNPDP. We transform rules in a large-scale policy set described in the XACML (eXtensible Access Control Markup Language) into numerical rules. Evaluation networks are established and trained by the numerical rules. In order to ensure the accuracy, a misjudgment set is constructed for error corrections and stored by hash indexes. By simulating the arrival of requests, ANNPDP is compared with the Sun PDP, HPEngine, XEngine, and SBA-XACML. The experiment results show that ANNPDP has: 1) high performance: if the number of requests reaches 10,000, the evaluation time of ANNPDP on the large-scale policy set with 100,000 rules is approximately 0.46, 0.93, 0.71, and 1.43 percent of that of the Sun PDP, HPEngine, XEngine, and SBA-XACML, respectively, and 2) stability: as the size of the large-scale policy set and the number of requests increase, the evaluation time of ANNPDP grows linearly. ANNPDP can satisfy the requirements of an authorization system with large-scale policy sets.

In time-series forecasting, it is an important task to make an accurate and interpretable long-term prediction. In this article, we present a novel approach developed from the perspective of granular computing (GrC) to realize the long-term prediction of time series. The proposed method first employs a sliding window strategy to smooth on the raw time series. Subsequently, the smoothed time series is transformed into the corresponding granular time series that is depicted by evolving shape with the aid of the clustering algorithm based on the dynamic time warping (DTW) distance. Finally, a Takagi–Sugeno (TS) architecture-like granular model (GrM) is formed by deriving the relations implying in the granular time series and offers the granular output in the numeric vector format. The GrM adopts the pattern-to-pattern inference mechanism to realize the long-term prediction of time series at the vector level. Experiments on several datasets demonstrate that the proposed method not only has the ability to circumvent the cumulative error but also makes the resulting GrM equip better interpretability.

The imputation of missing values is an important research content in incomplete data analysis. Based on the auto associative neural network (AANN), this paper conducts regression modeling for incomplete data and imputes missing values. Since the AANN can estimate missing values in multiple missingness patterns efficiently, we introduce incomplete records into the modeling process and propose an attribute cross fitting model (ACFM) based on AANN. ACFM reconstructs the path of data transmission between output and input neurons and optimizes the model parameters by training errors of existing data, thereby improving its own ability to fit relations between attributes of incomplete data. Besides, for the problem of incomplete model input, this paper proposes a model training scheme, which sets missing values as variables and makes missing value variables update with model parameters iteratively. The method of local learning and global approximation increases the precision of model fitting and the imputation accuracy of missing values. Finally, experiments based on several datasets verify the effectiveness of the proposed method.

The learning of fuzzy cognitive maps (FCMs) is a timely issue pursued by numerous researchers. Many learning methods, such as population-based algorithms and some hybrid algorithms, have been developed and applied to many fields resulting in better performance. However, those learning algorithms also exhibit obvious limitations: some of them either are difficult to handle the learning problem of large-scale FCM or extremely time-consuming with high computational overload. Furthermore, the learning problem of FCM with noisy data is rarely considered in existing algorithms. In this article, a fast and efficient method for learning FCM is proposed. It first transforms the learning problem of the FCM into a convex optimization problem with constraints, and then, the classic interior-point methods are invoked to solve the optimization problem to obtain the optimized weight matrix of the FCM. A series of experiments involving synthetic data, noisy synthetic data, real data, and publicly available data with noise are considered to demonstrate that the proposed method can rapidly and efficiently learn small-scale and large-scale FCMs and deal with the problem of learning FCM from noisy historical data.

Missing values are a common phenomenon in real-world datasets, which decreases the quality and reliability of data mining. Traditional regression-based imputation method estimates missing values through the relationship between attributes inferred by complete records. In order to describe the relationship more appropriately and make better use of present values, a rule-based incomplete data modeling method is proposed to impute missing values in this paper. The method utilizes incomplete records together with complete records for establishing Takagi-Sugeno (TS) models. In this process, the incomplete dataset is divided into several subsets and the linear functions containing only significant variables are built to describe the relationships between attributes in each subset. Experimental results demonstrate that the proposed method can effectively improve the performance of missing value imputation.

At present, the development of most of the granular fuzzy models depends upon some well-established numeric ones. In this study, a layered approach used to directly construct granular fuzzy models based on multidimensional numeric data is presented by engaging design methodology of granular computing. The crux of the approach involves a construction of interval information granules in the output space and the corresponding hyperbox information granules in the input space. A method of constructing these information granules and the hyperbox-based granular fuzzy model formed around them is studied in detail. Two different schemes to decode the formed hyperbox-based granular fuzzy model are also presented. Furthermore, a measure of a composite quality of the formed hyperbox-based granular fuzzy model is proposed along with the concept of coverage and specificity of resulting information granules. A number of experimental studies are reported, which offer a useful insight into the effectiveness of the presented approach, as well as reveal the impact of critical parameters on the performance of the established models.

Incomplete data clustering is often encountered in practice. Here the treatment of missing attribute value and the optimization procedure of clustering are the important factors impacting the clustering performance. In this study, a missing attribute value becomes an information granule and is represented as a certain interval. To avoid intervals determined by different cluster information, we propose a congeneric nearest-neighbor rule-based architecture of the preclassification result, which can improve the effectiveness of estimation of missing attribute interval. Furthermore, a global fuzzy clustering approach using particle swarm optimization assisted by the Fuzzy C-Means is proposed. A novel encoding scheme where particles are composed of the cluster prototypes and the missing attribute values is considered in the optimization procedure. The proposed approach improves the accuracy of clustering results, moreover, the missing attribute imputation can be implemented at the same time. The experimental results of several UCI data sets show the efficiency of the proposed approach.

In this paper, a fuzzy c-means clustering algorithm based on interval-valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval-valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient-based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human–computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well-known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c-means clustering algorithm.

Most researchers of time series forecasting devote to design and develop quantitative models for pursuing high accuracy of forecasting on the numerical level. However, in real world, the numerical accuracy is sometimes not necessary for human cognition and decision-making and the numerical results of forecasting based on quantitative model are deficient in interpretability, thus the development of qualitative forecasting model of time series becomes an evident challenge. In this paper, the improved fuzzy cognitive map (IFCM) are proposed first, and then it is applied to develop qualitative model for linguistic forecasting of time series together with fuzzy c-means clustering technology and real-coded genetic algorithm (RCGA). Two real life time series are used to test the developed forecasting model and compare with another method based on FCM, whose results show the developed FCM forecasting model is more simpler and high quality on the linguistic level.

Access control is an important measure to ensure the security of the management information system in enterprise environment. Taking the characteristics of task-role-based access control (TRBAC) and the requirement of practical application into consideration, the paper introduces TRBAC which is suitable for enterprise environment into the design of MIS. Task is classified to support both passive and active access control and permission assignment is also analyzed according to task and task instance, and then the complexity of permission management is reduced. At the same time, responsibility separation is introduced to increase the security and performance of the system. Finally, an application instance of TRBAC in MIS is given to show the superiority of TRBAC in MIS