Although the deep semantic segmentation network (DSSN) has been widely used in remote sensing (RS) image semantic segmentation, it still does not fully mind the spatial relationship cues between objects when extracting deep visual features through co...
Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and intern...
One of the most significant graph data analysis tasks is graph classification, as graphs are complex data structures used for illustrating relationships between entity pairs. Graphs are essential in many domains, such as the description of chemical m...
Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is w...
The accurate detection of railway tracks is essential for ensuring the safe operation of railways. This study introduces an innovative algorithm that utilizes a graph convolutional network (GCN) and deep neural residual network to enhance feature ext...
When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In th...
Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural netwo...
Human activity recognition (HAR) is an important research problem in computer vision. This problem is widely applied to building applications in human–machine interactions, monitoring, etc. Especially, HAR based on the human skeleton creates in...
Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately foreca...
Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and te...
Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state,...
In the current deep learning model, the computation between each feature and parameter is defined in the real number field. This, together with the nonlinearity of the deep learning model, makes it difficult to analyze the relationship between the va...
Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have been widely applied to hyperspectral image classification tasks, but both exhibit certain limitations. To address these issues, this paper proposes a multi-scale featur...
The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First...
In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use bas...
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...
Detecting fake news is a challenging task and an important area of research for social media researchers. This task also involves clarifying accountability mechanisms that demonstrate the credibility of quotable sources, such as networks that documen...
Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing pe...
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results i...
Drilling and completion condition identification is of great significance in improving operational efficiency, reducing safety risks and optimizing resource utilization. However, traditional methods rely on experts’ experience and rules and hav...
The development of emotionally intelligent computers depends on emotion recognition based on richer multimodal inputs, such as text, speech, and visual cues, as multiple modalities complement one another. The effectiveness of complex relationships be...
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...
Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unla...
With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) hav...
Groundwater contaminant transport modeling is a vitally important topic. Since modeled processes include uncertainties, Monte Carlo methods are adopted to obtain some statistics. However, accurate models have a substantial computational cost. This dr...
Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EE...
Advancements in computing platform deployment have acted as both push and pull elements for the advancement of engineering design and scientific knowledge. Historically, improvements in computing platforms were mostly dependent on simultaneous develo...
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in...
Error detection has a vital function in the production stages. Computer-aided error detection applications bring significant technological innovation to the production process to control the quality of products. As a result, the control of product qu...
Malware remains a major threat to computer systems, posing serious risks to security and privacy by stealing sensitive data, disrupting services, and compromising system integrity. Traditional detection methods are often ineffective against rapidly e...
Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation o...
Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper propos...
Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis met...
Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an un...
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep...
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artifici...
The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling...
Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large extent; however, these are computationally expensive. Moreover, existing ap...
This study proposes improvements to a behavioral malware detection method based on graph convolutional networks (GCNs). Three main modifications were investigated: improved normalization of the adjacency matrix, a multi-layer GCN architecture, and a...
The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of...
A method for training and white boxing of deep learning (DL) binary decision trees (BDT), random forest (RF) as well as mind maps (MM) based on graph neural networks (GNN) is proposed. By representing DL, BDT, RF, and MM as graphs, these can be train...
Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body...
Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fau...
In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict...
Hyperspectral image change detection (HSI-CD) is becoming increasingly important in understanding how the Earth’s surface evolves over time, from monitoring ecosystems to tracking urban expansion. Unlike traditional pixel-based or hand-crafted...
Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardw...
Mesh subdivision is a common mesh-processing algorithm used to improve model accuracy and surface smoothness. Its classical scheme adopts a fixed linear vertex update strategy and is implemented iteratively, which often results in excessive mesh smoo...
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...
Deep learning has become an important tool for wind power forecasting because it can help improve wind energy utilization and support reliable grid-connected operation. For wind farms, accurate turbine-level forecasting depends on spatial interaction...
Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algori...
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