Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and di...
In this study, we propose a classification method of expert–novice levels using a graph convolutional network (GCN) with a confidence-aware node-level attention mechanism. In classification using an attention mechanism, highlighted features may...
Currently, attention mechanisms are widely used in aspect-level sentiment analysis tasks. Previous studies have only used attention mechanisms combined with neural networks for aspect-level sentiment classification, and the feature extraction of the...
Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for...
Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes...
Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute informa...
The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central no...
Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature s...
In order to find a suitable designer team for the collaborative design crowdsourcing task of a product, we consider the matching problem between collaborative design crowdsourcing task network graph and the designer network graph. Due to the differen...
The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the ima...
Early and accurate detection of brain tumors is critical for MRI-based diagnosis. Conventional convolutional neural networks often struggle to capture fine-grained details, small or boundary-ambiguous lesions, and hemispheric symmetry patterns. To ad...
The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) sem...
Recent advancements in crime prediction have increasingly focused on street networks, which offer finer granularity and a closer reflection of real-world urban dynamics. However, existing studies on street-level graph representation learning often ov...
Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. Ho...
Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to ext...
Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph),...
Social relationship recognition (SRR) holds significant value in fields such as behavior analysis and intelligent social systems. However, existing methods primarily focus on modeling individual visual traits, interaction patterns, and scene-level co...
We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph at...
Hypergraph neural networks have gained widespread attention due to their effectiveness in handling graph-structured data with complex relationships and multi-dimensional interactions. However, existing hypergraph neural network models mainly rely on...
Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behavio...
Early and accurate detection of dermatological conditions, particularly melanoma, is critical for effective treatment and improved patient outcomes. Misclassifications may lead to delayed diagnosis, disease progression, and severe complications in me...
Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning technique...
Accurate identification of potential drug–target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents s...
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth...
In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and secur...
Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achi...
The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locatio...
The innovation capability of seed enterprises reflects their core competitiveness and serves as a vital foundation for sustainable agricultural development and modernization. Therefore, evaluating this capability is of great importance. However, exis...
Various uncertainties such as communication delay, packet loss and disconnection in the Industrial Internet, as well as the asynchronous sampling of sensors, can cause irregularity, sparsity, and misalignment of sampling sequences, and thereby seriou...
The integration of location-based social networks and POI recommendation systems has the potential to enhance the urban experience by facilitating the exploration of new and relevant locales. The deployment of graph neural networks (GNNs) drives the...
Aspect-based sentiment analysis (ABSA) through joint task learning aims to simultaneously identify aspect terms and predict their sentiment polarities. However, existing methods face two major challenges: (1) Most existing studies focus on the sentim...
Hemerocallis citrina Baroni with different maturity levels has different uses for food and medicine and has different economic benefits and sales value. However, the growth speed of Hemerocallis citrina Baroni is fast, the harvesting cycle is short,...
Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities an...
Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount...
Passion fruit, renowned for its significant nutritional, medicinal, and economic value, is extensively cultivated in subtropical regions such as China, India, and Vietnam. In the production and processing industry, the quality grading of passion frui...
It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for unders...
Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play a...
Link prediction is a key technique for connecting entities and relationships in a graph reasoning field. It leverages known information about the graph structure data to predict missing factual information. Previous studies have either focused on the...
In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic...
The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extend...
Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind ret...
Extractive summarization, a pivotal task in natural language processing, aims to distill essential content from lengthy documents efficiently. Traditional methods often struggle with capturing the nuanced interdependencies between different document...