Ransomware represents a critical and evolving cybersecurity threat that often evades traditional defenses during its early stages. We present a novel intelligent sensing framework (ISF) designed for proactive, early-stage ransomware detection, center...
To address the challenges of feature extraction and low classification accuracy in fault diagnosis of variable-speed rolling bearings, this paper proposes an intelligent fault diagnosis method based on bandwidth division variational mode decompositio...
The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of to...
Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high d...
Rotor motor fault diagnosis in Unmanned Aerial Vehicles (UAVs) presents significant challenges under variable speeds. Recent advances in deep learning offer promising solutions. To address challenges in extracting spatial, temporal, and hierarchical...
This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memor...
To mitigate inter-symbol interference (ISI) caused by Faster-than-Nyquist (FTN) technology in a multiple input multiple output (MIMO) optical wireless communication (OWC) system, we propose an ISI cancellation algorithm that combines multi-head self-...
This study presents a new image inpainting model based on U-Net and incorporating the Wasserstein Generative Adversarial Network (WGAN). The model uses skip connections to connect every encoder block to the corresponding decoder block, resulting in a...
To address the challenges of spectral unmixing and feature extraction in the hot jet mixtures of two types of aero-engine hot jets, Fourier transform infrared spectrometry was employed to precisely measure these mixtures. The collected data encompass...
Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students’ classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and mul...
Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such condition...
Background: Decoding covert spatial attention (CSA) from dry, low-channel electroencephalography (EEG) is key for gaze-independent brain–computer interfaces (BCIs). Methods: We evaluate, on sixteen participants and three tasks (CSA, motor image...
Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their lim...
With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Conseq...
Accurately identifying the risk of academic failure at the student-course level is fundamental to data-driven early warning and precision student management, which requires a subsequent decision-layer aggregation to inform student-level interventions...
Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However...
Retrieving non-optically active water quality variables, such as total hardness (TH) and permanganate index (CODMn), from hyperspectral data remains challenging because these parameters are not directly linked to spectral reflectance. To improve thei...
Accurately recognizing student classroom behaviors is essential for analyzing teacher–student interactions and enabling intelligent educational assessment. Although deep learning offers promising solutions, existing methods often perform poorly...
Video inpainting aims to complete the missing regions with content that is consistent both spatially and temporally. How to effectively utilize the spatio-temporal information in videos is critical for video inpainting. Recent advances in video inpai...
With the rapid expansion of railway networks and increasing operational complexity, intelligent rail damage detection has become crucial for ensuring safety and improving maintenance efficiency. Traditional physical inspection methods (e.g., ultrason...
The Conformer enhanced Transformer by using convolution serial connected to the multi-head self-attention (MHSA). The method strengthened the local attention calculation and obtained a better effect in auto speech recognition. This paper proposes a h...
In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emoti...
With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of...
With the popularity of the mobile internet, people all over the world can easily create and publish diverse media content such as multilingual and multi-dialectal audio and video. Therefore, language or dialect identification (LID) is increasingly im...
Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensem...
In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper...
This paper introduces a novel retaining wall structure that integrates a traditional mechanically stabilized earth (MSE) retaining wall with foamed lightweight soil (FLS) as the fill material. To evaluate the performance of the structure, a numerical...
In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG...
Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes...
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification d...
Obstructive sleep apnea (OSA) is characterized by frequent episodes of sleep-disordered breathing (SDB), which are often accompanied by leg movement (LM) events, especially periodic limb movements during sleep (PLMS). Traditional single-event detecti...
The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-sca...
Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode-skin interface shi...
In recent years, pseudo point clouds generated from depth completion of RGB images and LiDAR data have provided a robust foundation for multimodal 3D object detection. However, the generation process often introduces noise, reducing data quality and...
The presence of subtle mucosal abnormalities makes small bowel Crohn’s disease (SBCD) and other gastrointestinal lesions difficult to detect, as these features are often very subtle and can closely resemble other disorders. Although the Kvasir...
Bolt connections are the primary component of beam–column joints, which frequently become loose during their service life due to environmental factors. Assessing the tightness of bolts is essential for maintaining structural integrity and safet...
Background: Type 1 pili, as an important virulence factor of E. coli, has certain homology between APEC and UPEC, but the homology degree is not clear enough. Objectives: This study aims to compare the homology between them. Methods: The recombinant...
Accurate forecasting of wind and photovoltaic power remains challenging due to the strong nonlinearity, nonstationarity, and seasonal heterogeneity of renewable generation series. To address this issue, this study proposes a hybrid forecasting framew...
Fish disease and species identification is critical for intelligent aquaculture, directly influencing productivity, sustainability, and economic viability. However, existing approaches largely treat species identification and pathological classificat...
With the rapid development of social media and online interactive platforms, online public opinion has become a vital information source for public emotional expression, social risk perception, and decision support. However, public opinion texts are...
The “cocktail party problem”, the challenge of isolating individual speech signals from a noisy mixture, has traditionally been addressed using statistical methods. However, deep neural networks (DNNs), with their ability to learn complex...
Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine...
Traditional Convolutional Neural Network (ConvNet, CNN)-based image super-resolution (SR) methods have lower computation costs, making them more friendly for real-world scenarios. However, they suffer from lower performance. On the contrary, Vision T...
We study real-time next-window badminton action prediction from wearable IMU streams where the system must predict the action label of the upcoming 100 ms window using past-only (causal) information. To handle severe class imbalance in continuous str...
With the advancement of satellite and sensor technologies, remote sensing images are playing crucial roles in both civilian and military domains. This paper addresses challenges such as complex backgrounds and scale variations in remote sensing image...
This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analys...
The state of health (SOH) estimation of lithium-ion batteries faces significant challenges under complex operating conditions due to transient disturbances and distribution shifts. This paper proposes a deep learning framework named Conformer-KAN, wh...
The study explored if willingness to seek psychotherapy or refer patients to therapy is predicted by either perception of its usefulness or stigma (public and self-stigma), and if there are any differences based on specific psychological disorders fo...
During the drying and processing of raisins, the presence of foreign matter such as fruit stems, branches, stones, and plastics is a common issue. To address this, we propose an enhanced real-time detection approach leveraging an improved YOLOv8 mode...
Accurate and non-contact measurement of cattle body dimensions is essential for precision livestock management. This study presents YOLOv8-DMC, a lightweight deep learning model optimized for anatomical keypoint detection in side-view images of cattl...
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