Traditional single-center learning algorithms often face significant limitations in handling heterogeneous data integration, including insufficient generalization ability, weak privacy protection, and difficulties adapting to multi-center scenarios. To address these challenges, multi-center learning has emerged as a critical technological framework. Although our previously proposed MKTC-R0T algorithm partially addressed the integration and modeling of multi-center data through the knowledge transfer calibration strategy, it still exhibits notable shortcomings in terms of knowledge fusion stability, model interpretability and generalization, as well as the utilization of complementary information across centers. To overcome these limitations, we propose a Knowledge Calibration Fusion and Label Space Graph Regularization-based Multi-center TSK Fuzzy System (KCF-LSG-MTSK). Specifically, we introduce an enhanced knowledge calibration and fusion strategy to effectively integrate heterogeneous information between the base center (BC) and auxiliary center (AC). We also propose a novel label space graph regularization scheme that constructs both intracenter and intercenter graph structures, leveraging data consistency and complementarity to enhance the quality of knowledge sharing. Furthermore, building upon firstorder TSK fuzzy system optimization, our approach incorporates a projected maximum mean discrepancy (PMMD) transfer term to effectively reduce data distribution discrepancies between the BC and AC. Experimental results on thirteen benchmark datasets demonstrate that KCFLSGMTSK achieves an average accuracy of 88.7%, significantly outperforming stateoftheart singlecenter and multicenter methods, thereby validating the superiority of our approach in heterogeneous data integration, knowledge transfer, and interpretable classification.

Effective pulmonary disease monitoring and diagnosis are crucial for improving patient outcomes and guiding clinical interventions. Pulmonary Electrical Impedance Tomography (EIT) faces challenges due to its nonlinearity and complex data, which has led researchers to directly use raw EIT boundary voltage data for disease classification. However, the data's high non-stationarity, multi-scale time-frequency features, and long-term temporal dependencies limit traditional classification methods. To address these challenges, we propose the Time-Frequency Multi-Scale Convolutional Network (TFMSCNet), a novel architecture that simultaneously captures temporal dynamics and frequency patterns through parallel processing branches. The integrated Multi-Scale Feature Enhancement (MSFE) module adaptively fuses features across different temporal resolutions, enabling robust representation learning from complex EIT signals. Experiments show that TFMSCNet achieves 99.50% accuracy on a private EIT dataset, outperforming eleven baseline methods. Validation on ten public datasets confirms its strong generalization, highlighting its potential for clinical pulmonary disease diagnosis.

Hyperspectral imaging can capture the spectrum of each pixel in an image across various wavelengths, providing unparalleled opportunities for precise detection, classification, and analysis of transportation infrastructure. However, traditional methods often struggle with the curse of dimensionality, inter-class variability, and the spectral-spatial trade-off inherent in hyperspectral data. To address these challenges, we introduce a novel Multi-Source Patch Feature fusion based Neighborhood Flash Attention Transformer (MSPF-NFAT) for pixel-level vehicle and road recognition in hyperspectral images (HSIs). Our methodology hinges on the insight that the integration of complementary features from multiple sources and scales can significantly enhance classification performance. Specifically, the MSPF is designed to aggregate and harmonize features extracted from both spectral and spatial dimensions, as well as from different contextual scales within the image. This fusion process ensures a richer representation of the data, capturing both the fine-grained details and the broader contextual information essential for accurate classification. Building upon this enriched feature set, we employ the NFAT, a state-of-the-art attention mechanism that focuses on capturing local spatial relationships while efficiently scaling to accommodate the high-resolution characteristics of hyperspectral data. In addition, extensive experimental results on four widely used HSIs datasets show that our newly proposed method provides superior performance compared to other state-of-the-art methods.

Skin diseases rank among the most prevalent ailments in humans, which underscores the critical importance of early detection and diagnosis. Considering clinical practice, the integration of information from various modalities holds significant potential to enhance diagnostic accuracy and precision. In this study, we propose a multi-modal and multi-attention fusion network for multi-task skin disease classification. We initially devise the Shifted Window Cross-attention Fusion (SWCF) module based on the shifted window self-attention mechanism, leveraging the advantages of the attention mechanism to learn the correlations between different modalities and fully integrate multi-modal features. Subsequently, taking into account the heterogeneity of the data, we employ the Heterogeneous Data Cross-attention Fusion (HDCF) module using the idea of information decoupling and separation processing to integrate image and text data. Additionally, we suggest the Dynamic Loss Weight Allocation (DLWA) method for multi-task learning to refine the training procedure. We confirm the superiority of the proposed method on the publicly available multi-modal skin lesion dataset, Derm7pt. The average accuracy of multi-modal skin disease classification is 79.14%, surpassing the current state-of-the-art methods.

Knowledge graph completion (KGC) aims to infer missing or incomplete parts in knowledge graph.The existing models are generally divided into structure-based and descriptionbased models, among description-based models often require longer training and inference times as well as increased memory usage.In this paper, we propose Pre-Encoded Masked Language Model (PEMLM) 1 to efficiently solve KGC problem.By encoding textual descriptions into semantic representations before training, the necessary resources are significantly reduced.Furthermore, we introduce a straightforward but effective fusion framework to integrate structural embedding with pre-encoded semantic description, which enhances the model's prediction performance on 1-N relations.The experimental results demonstrate that our proposed strategy attains state-of-the-art performance on the WN18RR (MRR+5.4% and Hits@1+6.4%)and UMLS datasets.Compared to existing models, we have increased inference speed by 30x and reduced training memory by approximately 60%.

The one‐class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph–regularized term and a sparse projection matrix–regularized term. The former aims to preserve the within‐modal structural and between‐modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS‐SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available at https://github.com/wongchuang/CCS_SVDD .

The diagnosis and treatment of epilepsy necessitate the precise identification and classification of electroencephalogram (EEG) signals. However, EEG samples from different medical institutions often exhibit variability due to factors such as institutional characteristics, geographic locations, and the professional levels of physicians. This variability limits the widespread application of existing methods in small or single medical institutions, as they typically rely on large-scale and high-quality datasets. To rapidly assist small or single medical institutions in constructing models for the diagnosis and classification of epileptic EEG signals that are both highly generalizable and interpretable while ensuring patient privacy, this article proposes an innovative learning framework named Multicenter Knowledge Transfer Calibration with rapid zeroth-order TSK fuzzy system (MKTC-R0T). This method employs the zeroth-order TSK fuzzy system as the baseline model for each center and integrates a knowledge transfer calibration strategy within a multicenter learning framework, aiming to enhance the model's generalizability and classification accuracy in the face of inconsistent sample quality and sample heterogeneity. Specifically, MKTC-R0T first establishes a base center model in a large medical institution, and then, by imitating the forgetting mechanism of the human brain, a portion of the knowledge at the base center is randomly forgotten, while the remaining knowledge is utilized to assist the auxiliary centers in rapidly deploying models. Ultimately, through a knowledge integration strategy, all centers collectively guide the target center in building an efficient linear system for the diagnosis and classification of epileptic EEG signals. Extensive experiments conducted on 12 epilepsy EEG signal datasets have validated that MKTC-R0T outperforms other typical algorithms in terms of running time, deployment speed, rule complexity, and the model's generalization and robustness, which demonstrates the substantial potential of MKTC-R0T in the field of epilepsy EEG signal diagnosis and classification.