121 Results Found

Grapevine (Vitis vinifera L.) is a globally significant economic crop. However, its widely cultivated varieties are highly susceptible to white rot disease. To elucidate the mechanisms of resistance in grapevine against this disease, we utilized time...

The MADS-box gene family constitutes a vital group of transcription factors that play significant roles in regulating plant growth, development, and signal transduction processes. However, research on the MADS-box genes in cassava (Manihot esculenta)...

The spread of corona virus disease 2019 (COVID-19) has coincided with the rise of Transformer and graph neural networks, leading several studies to propose using them to better predict the evolution of a pandemic. The inconveniences of infectious dis...

This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graph...

Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly reduce the cost of constructing a knowledge graph. The Graph Convoluti...

A longstanding challenge is to understand how ribosomes parse mRNA open reading frames (ORFs). Significantly, GCN codons are over-represented in the initial codons of ORFs of prokaryote and eukaryote mRNAs. We describe a ribosome rRNA-protein surface...

Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperat...

The research on aspect-based sentiment analysis (ABSA) mostly relies on a single attention mechanism or grammatical semantic information, which makes it less effective in dealing with complex language structures. To address the challenges in fine-gra...

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to impr...

Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successful...

Interval prediction is to predict the range of power prediction intervals, which can guide electricity production and usage better. To improve further improve the performance of the prediction interval, this paper aims to investigate he wind power in...

Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-le...

Modern network attacks are becoming stealthier and smarter. Attackers use encryption to cover up malicious traffic, which makes it really hard to detect. To solve this problem, this paper introduces a new model called Graph Convolutional Network with...

Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation predic...

Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows....

Deep learning techniques have brought substantial performance gains to remote sensing image classification. Among them, convolutional neural networks (CNN) can extract rich spatial and spectral features from hyperspectral images in a short-range regi...

Hand action recognition is an important part of intelligent monitoring, human–computer interaction, robotics and other fields. Compared with other methods, the hand action recognition method using skeleton information can ignore the error effec...

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a...

Encryption is widely used to ensure the security and confidentiality of information. Because people trust in encryption technology, a series of attack methods based on certificates have been derived. Malicious certificates protect many malicious beha...

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stat...

Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional...

Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attraction...

Community search is a basic problem in graph analysis. In many applications, network nodes have certain properties that are important for the community to make sense of the application; hence, attributes are associated with nodes to capture their pro...

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics...

In this study, a hand pose estimation method based on GCN feature enhancement is proposed to address the problem of the time-consuming nature and neglection of the internal relationships between hand joint points, which results in the low accuracy of...

Sodium hypophosphite is a promising green source for generating clean elemental hydrogen without pollutants. This study presents the development of an efficient heterogeneous catalyst, Ru/g-C₃N₄ (Ru/GCN), for hydrogen generation from sodium hypophosp...

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing def...

Polyline simplification is a critical process in cartographic generalization, but the existing methods often fall short in considering the overall geographic morphology or local edge and vertex information of polylines. To enhance the graph convoluti...

Accurate and stable power-load forecasting is crucial for optimizing generation scheduling and ensuring the economic and secure operation of power grids. To address the issues of low prediction accuracy and poor robustness during abrupt load changes,...

Mobile augmented reality (AR) applications require high-performance, energy-efficient deep learning solutions to deliver immersive experiences on resource-constrained devices. We propose SAHA-WS, a Sparsity-Aware Hybrid Architecture with Weight-Stati...

The recommendation system based on graphs aims to infer the symmetrical relationship between unconnected users and items nodes. Graph convolutional neural networks (GCNs) are powerful deep learning models widely used in recommender systems, showcasin...

In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to insuffi...

The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The m...

To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GC...

Skeleton-based action recognition, which aims to classify human actions through the coordinates of body joints and their connectivity, is a significant research area in computer vision with broad application potential. Although Graph Convolutional Ne...

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep lea...

The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of...

The de novo peptide-sequencing method can be used to directly infer the peptide sequence from a tandem mass spectrum. It has the advantage of not relying on protein databases and plays a key role in the determination of the protein sequences of unkno...

Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the ass...

In some complex labor production and human–machine interactions, such as subway driving, to ensure both the efficient and rapid completion of work and the personal safety of staff and the integrity of operating equipment, the level of mental wo...

EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human i...

Graphitic carbon nitride (gCN) is a promising n-type semiconductor widely investigated for photo-assisted water splitting, but less studied for the (photo)electrochemical degradation of aqueous organic pollutants. In these fields, attractive perspect...

We present a skeleton-based approach to baseball pitch type classification from broadcast video. We leverage Human Pose Estimation and an ST-GCN architecture, improved with a projection-gated temporal downsampler, to learn kinematic signatures of the...

Forest resources are among the most important ecosystems on the earth. The semantic segmentation and accurate positioning of ground objects in forest remote sensing (RS) imagery are crucial to the emergency treatment of forest natural disasters, espe...

The existing multimodal image classification methods often suffer from several key limitations: difficulty in effectively balancing local detail and global topological relationships in hyperspectral image (HSI) feature extraction; insufficient multi-...

This study proposes an improved link prediction model for predicting the “suitable for people” relationship within the knowledge graph of tea. The relationships between various types of tea and suitable target groups have yet to be fully...

Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being play...

Despite notable advancements in bot detection methods based on Graph Neural Networks (GNNs). The efficacy of Graph Neural Networks relies heavily on the homophily assumption, which posits that nodes with the same label are more likely to form connect...

Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary code simi...

Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different perio...