Multigranularity Information Fused Contrastive Learning With Multiview Clustering.

Contrastive multiview clustering (MVC) has emerged as a mainstream approach in MVC due to its superior representation learning capabilities. Traditional contrastive multiview learning methods extract both low- and high-level information from raw data. However, only high-level information is utilized for clustering.

Multimodal multi-instance evidence fusion neural networks for cancer survival prediction.

Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopathological images, genomic data, and clinical information, provide complementary and comprehensive information, significantly enhancing the accuracy of this task. However, existing methods, despite achieving some promising results, still exhibit two significant limitations: they fail to effectively utilize global context and overlook the uncertainty of different modalities, which may lead to unreliable predictions.

A shared value network model based on synergy theory in intelligent manufacturing.

With the in-depth development and integration of science and technology, intelligent manufacturing has gradually become a trend in the manufacturing industry. However, it is difficult for the traditional business model to maximize the use of resources. To address these problems, a shared value network model based on synergy theory is first proposed, which consists of three layers: resource network layer, value connection layer and resource coordination layer.

MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning.

Deep-learning models have been widely used in image recognition tasks due to their strong feature-learning ability. However, most of the current deep-learning models are "black box" systems that lack a semantic explanation of how they reached their conclusions. This makes it difficult to apply these methods to complex medical image recognition tasks.

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns.

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence.

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification.

δ-Cut decision-theoretic rough set approach: model and attribute reductions.

Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao's decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation.