IEEE Transactions on Fuzzy Systems

It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech ...

Medical visual question answering (medical VQA) is a critical cross-modal interaction task that garnered considerable attention in the medical domain. Several existing methods commonly leverage the vision-and-language pretraining paradigms to mitigate the ...

Addressing the challenge posed by the unpredictable and recurrent nature of epileptic seizures, which stand among the most significant neurological conditions, remains imperative, especially within settings inundated with high patient flow. The prompt ...

Glaucoma is a progressive eye disorder that can lead to permanent vision loss if not identified and treated promptly. Thus, timely glaucoma detection is paramount to developing a more efficient treatment plan and saving vision loss. Despite the promising ...

Intraclass heterogeneity of medical diagnostic objects poses a challenge for accurate intraclass classification of medical fine-grained images (MFGIs) within deep learning. To accurately classify MFGIs, we propose a novel approach termed fuzzy channel ...

Automatic lung organ segmentation on computerized tomography images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negative/positive and leakage issues in numerous state-of-...

Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in ...

As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the ...

Yellow fever is a vigorous, phlebotomic, vector-borne disease that poses a significant public health threat in regions with high mosquito density and inadequate vaccination coverage. The disease's toxic phase is lethal, making prompt identification ...

Extracting latent disease patterns from electronic health records (EHRs) is a crucial solution for disease analysis, significantly facilitating healthcare decision-making. Multiview learning presents itself as a promising approach that offers a ...

The hybrid parallel fuzzy convolutional neural network (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel ...

Most of the previous machine learning-based models for multimodal medical diagnosis, primarily designed for unimodal images, usually do not fully leverage the potential of multimodal medical images, leading to limited classification accuracy. These ...

Breast cancer is a significant global health concern, with invasive ductal carcinoma (IDC) being a significant subtype. Detecting IDC is a challenging task that can be hindered by the oversight of important contextual cues within whole slide images (WSIs)...

The rapid advancement of medical technology has led to an exponential increase in the volume of medical images. To optimize clinical practice, physicians often require efficient retrieval of images from various medical image databases. However, the ...

Evidential clustering is a promising clustering framework using Dempster–Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the ...

The abundant deposits of unstructured and scarcely labeled data over social networks make text classification (TC) vital for structuring and extracting useful information. In addition, ignoring dialectal variations significantly hinders the performance of ...

In practical applications, sampled-data systems are often affected by unforeseen physical constraints that may cause deviations in the sampling interval from the expected value and result in fluctuations in a probabilistic way, where the probability ...

Multistream classification aims to predict the target stream by transferring knowledge from labeled source streams amid nonstationary processes with concept drifts. While existing methods address label scarcity, covariate shift, and asynchronous concept ...

In this article, the event-triggered saturation-tolerant control problem of nonlinear multiagent systems (MASs) is investigated based on the finite-time fuzzy composite learning approach. Specifically, a novel concept, named as deferred saturation-...

This article focuses on the fixed-time (FxT) synchronization of discontinuous fuzzy competitive neural networks (DFCNNs) with leakage-delays and state-dependent switching. Discontinuous activations, fuzzy terms, leakage delays, and state-dependent ...

In an effective process of cluster analysis, it is indispensable to validate the goodness of different partitions after clustering. Existing internal validation indexes are implemented based on distance and variance, which cannot catpure the real “...

In this article, we present a dimensionality reduction methodology of reinforced fuzzy-rule-based neural networks (FRNNs) realized with the help of determination/correlation coefficient-based streamlined feature selection strategy and fuzzy ...

Fuzzy-rough sets (FRS) provide an outstanding theoretical tool for feature selection (FS). Whilst promising, the FRS model is sensitive to noisy information and ineffectively applicable to the data with large class density difference, with existing FRS-...

The Bayesian network (BN) method has been identified as a research hotspot in dynamic risk assessment (DRA) for systems. The traditional BN inference process relies on crisp probabilities; however, it is inapplicable in an interval type-2 fuzzy set (IT2FS)...

DNA N4-methylcytosine (4mC) site identification is a crucial field in bioinformatics, where machine learning methods have been effectively utilized. Due to the presence of noise, the existing deep learning methods for detecting 4mC have consistently low ...

This article investigates leader-following formation problems for second-order fuzzy multiagent systems (MASs) with input saturation constraints, by using an impulsive control strategy. Traditional communication methods generate large amounts of ...

In this article, a decentralized event-triggered (ET) regulation problem is tackled for networked interconnected systems (NISs) with control constraints and unmatched interference. Foremost, the decentralized regulation issue is converted into the optimal ...

This article develops a method to address the observer-based fuzzy control for a class of discrete-time nonlinear networked control systems under multichannel attacks, in which taking semi-Markov process to characterize the switching among various attack ...

Semantic textual similarity (STS) is a fundamental task in the field of natural language processing (NLP). Recent advances demonstrate that deep-learning-based approaches can achieve excitingly accurate STS measurement. However, existing studies cannot ...

Support vector clustering (SVC) is a powerful algorithm for density-based clustering, offering advantages such as handling arbitrary cluster shapes and determining the number of classes without prior knowledge. However, its practical application is ...