Deep hashing cross-modal image-text retrieval has the advantage of low storage cost and high retrieval efficiency by mapping different modal data into a Hamming space. However, the existing unsupervised deep hashing methods generally relied on the …

Knowledge graph completion (KGC) infers missing knowledge triples based on the facts in the knowledge base. In recent years, many representation learning models for knowledge reasoning have achieved promising link prediction results, especially …

Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal …

As an extension of Fuzzy C-Means (FCM), Evidence C-Means (ECM) is proposed in the framework of Dempster–Shafer theory (DST) and has been applied to many fields. However, the objective function of ECM involves only the distortion between the object …

Semi-supervised learning (SSL) employs unlabeled data with limited labeled samples to enhance deep networks, but imbalance degrades performance due to biased pseudo-labels skewing decision boundaries. To address this challenge, we propose two …

In the world of big data, extracting meaningful insights from large and continually growing distributed datasets is a major challenge. Classical clustering algorithms are effective at identifying clusters with convex structures. However, they fall …

Multi-label image classification (MLIC) is a fundamental and highly challenging task in the field of computer vision. Most methods usually only focus on the inter-label association or the way to extract image semantics, ignoring the relevance of …

Graph Neural Network (GNN) has attracted considerable research interest in various graph data modeling tasks. Most GNNs require efficient and sufficient label information during training phase. However, in open environments, the performance of …

With the advent of Industry 4.0, intelligent manufacturing has emerged as a prominent trend for future development. The integration of intelligent manufacturing scenarios with reinforcement learning offers significant advantages and potential.

Compared to conventional artificial neural networks, Graph Neural Networks (GNNs) better handle graph-structured data. Graph topology plays an important role in learning graph representations and impacts the performance of GNNs. However, existing …

Link prediction typically studies the probability of future interconnection among nodes with the observation in a single social network. More often than not, real scenario is presented as a multiplex network with common (anchor) users active in …

Infrared and visible image fusion (IVIF) is to achieve the fused images with multimodal complementary information of source images. To effectively fuse the complementary information, a dual-encoder network based on multi-layer feature fusion for …

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other …

In recommender systems, sequence information is crucial. Sequence data contains user preferences and reflects the evolution of user interests over time. Therefore, how to utilize sequence information to capture dynamic user interests is a critical …

As a widely used data preprocessing method, feature selection with rough sets aims to delete redundant conditional features. However, most of the traditional feature selection methods target to the static data set environment, and the importance …

Information fusion enables the integration and transformation of complimentary data from different sources, providing a unified representation for centralized knowledge discovery, which can contribute to effective decision-making, classification …

In this research, we tackled the critical challenge of detecting fake news in a bilingual context, focusing on English and Urdu. This issue is particularly important in the digital age, where misinformation can impact society and politics. To …

In the real world, numerous heterophilic networks effectively model the tendency of similar entities to repel each other and dissimilar entities to be attracted to each other within complex systems. Concerning the node classification problem in …

Regression and classification are the two main learning tasks in supervised learning, and both of them can be solved by learning a hyperplane from training samples. However, the hyperplane in regression task aims at approximating the labels of …

Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained …