Enguang Zuo

Mixture-of-experts-based hierarchical dynamic multimodal fusion network for dermatological diagnosis

Towards superior graph representation: A multi-level cross-view approach integrating information bottleneck and contrastive learning

Smartphone-Based Fluorescence/Colorimetric Dual-Mode Aptasensor for the Detection of <i>Salmonella</i> Using Multivalent Aptamer and CHA Amplification

An intelligent and active polyvinyl alcohol hydrogel packaging incorporating alizarin and Zn-MOF for shrimp freshness monitoring and preservation

The Raman spectroscopy combined with selective state-space algorithm for constructing a rapid disease diagnosis model

High-order graph convolutional networks for circular Ribonucleic Acid and disease association prediction incorporating multiple biological relationships

TDMFS: Tucker decomposition multimodal fusion model for pan-cancer survival prediction

DTCMMA: Efficient Wind-Power Forecasting Based on Dimensional Transformation Combined with Multidimensional and Multiscale Convolutional Attention Mechanism

A novel multi-feature fusion technology of FTIR spectroscopy based on attention and adaptive gate for disease diagnosis

The MLSE-SCAM architecture combines with the improved DRSN-TIC model for Raman spectroscopy small-sample data learning

KCGAFormer: When Large-Kernel ConvFormer Meets KAN in Semantic Segmentation

MBGNet: Mamba-Based Boundary-Guided Multimodal Medical Image Segmentation Network

FSGAD: Application of Graph Anomaly Detection Based on Multi-View Contrastive Learning in Food Sample Detection

TreeXformer: Extracting tabular feature-context information using tree-structured semantics

GEME In Government Data Governance: Graph Entropy And Attention Coordination Mechanism

VCformer: Variable-Centric Multi-Scale Transformer for Multivariate Time Series Forecasting

Address Anomalies at Critical Crossroads for Graph Anomaly Detection

ScaleMixer: Selective excitation of feature-enhanced food time series prediction model

TimeADF: A Predictive Method for Cross-Level Governmental Data Fusion

Anomaly Detection in government Data Graph Based on Channel and Causal Gating

IGedgeMAE: Topology-Aware Graph Masked Autoencoder with Dynamic Edge Importance Guidance

Anomaly Detection of government data based on multi-scale structure reconstruction

Rethinking unsupervised time series anomaly detection: Dynamic attention based on route inverse-masking

SCGRL: Graph representation learning based on edge structure contrastive self-supervised framework

Beyond Local Features: A Metadata-Driven Image Frequency Modulation Network for Skin Disease Classification

Clinical Experience-inspired Multimodal Fusion Networks for Dermatological Classification

Frequency-Spatial Domain Fusion for Graph Anomaly Detection

Rethinking link prediction: A multi-scale graph masked autoencoder

FSC-MAE: Feature Structure Coordinated Mask Autoencoder

DSFusion: Infrared and visible image fusion method combining detail and scene information