Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a mas...
Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce t...
Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study p...
Natural gas wells operate under complex conditions with frequent environmental disturbances. Fault types vary significantly and often present weak signals, affecting both safety and efficiency. This paper proposes an intelligent fault-diagnosis metho...
In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and c...
With the rapid growth and diversification of malware, accurate multi-class detection remains challenging due to severe class imbalance and limited labeled data. This work presents an image-based malware classification framework that converts executab...
Pine trees are more vulnerable to diseases and pests than other trees, so prevention and management are necessary in advance. In this paper, two models of deep learning were mixed to quickly check whether or not to detect pine pests and to perform a...
Terraces constitute a critical form of land surface modification, serving both as essential agricultural resources and effective soil and water conservation measures. However, their morphological diversity, pronounced scale variation, and ambiguous g...
Proactively detecting schizophrenia relapse remains a critical challenge in psychiatric care, where traditional predictive models often fail to capture the complex neurophysiological and behavioral dynamics preceding recurrence. Existing methods typi...
Precipitation nowcasting is a critical part of meteorological services and applications. Recently, mainstream research has been focused on adopting deep learning-based models to generate the predictions, yet existing deep learning models face challen...
Sleepiness at the wheel is an important contributor to road traffic accidents. Slow eye movement (SEM) serves as a reliable physiological indicator for the sleep onset period (SOP). To detect SEM for recognizing drivers’ SOP, a Parallel Multimo...
The electronic nose system is susceptible to cross-sensitivity, noise, and baseline drift, making simultaneous qualitative identification and quantitative prediction of volatile organic compounds (VOCs) challenging. To address these issues, this stud...
The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study pr...
Hybrid CNN–Transformer networks seek to merge the local feature extraction capabilities of CNNs with the long-range dependency modeling abilities of Transformers, aiming to simultaneously address both local details and global contextual informa...
Stock market price prediction remains a persistent challenge owing to the non-stationarity, high noise content, and intricate spatiotemporal dependencies that characterize financial time series. Existing approaches typically excel at either local pat...
Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This p...
Accurate detection of early invisible apple bruises is important for post-harvest quality assessment. Although hyperspectral imaging (HSI) provides rich spectral information, its high dimensionality introduces substantial redundancy and weak-signal i...
This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time...
Printed Arabic Optical Character Recognition (OCR) remains challenging due to complex glyph morphology, typographic variability, and sensitivity to Unicode-preserved evaluation protocols. This work introduces a methodology that explicitly treats deco...
Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions amo...
The rapid growth of the aviation industry and increasing air traffic demand more careful attention to environmental concerns. Among these, aircraft noise is considered one of the main sources of environmental noise, especially after land-based transp...
In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur c...
As an emerging learning paradigm, self-supervised learning (SSL) has attracted extensive attention due to its ability to mine features with effective representation from massive unlabeled data. In particular, SSL, driven by contrastive learning and m...
Soil texture is recognised as one of the key physical properties of soil. Although traditional laboratory testing methods can determine soil texture information with high accuracy, they are often considered time-consuming and costly. To achieve rapid...
Automated analysis of cystoscopic images may support consistent characterization of bladder tissue patterns, but reported performance is highly sensitive to patient composition, data partitioning, and evaluation methodology. Objective: This study pro...
Medical imaging, particularly chest X-rays, plays a vital role in radiological diagnosis. However, interpreting these images and generating detailed diagnostic reports is a time-consuming task for clinicians. To address this challenge, this study pro...
To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Conv...
Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature l...
The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences,...
Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (C...
Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties an...
Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and f...
Postoperative atrial fibrillation (POAF) is a leading cause of stroke and mortality after cardiac surgery, motivating continuous long-term ECG monitoring.This paper presents Cardiac-Net, a wearable ECG system (ESP32-S3/AD8232 BLE device with a Flutte...
High-quality observational data play a crucial role in deepening the investigation of the Tibetan Plateau’s influence on the Asian climate. This study employs eight machine learning models (support vector regression (SVR), k-nearest neighbors (...
Wafer defect analysis is important for semiconductor manufacturing, but labeled data are limited, and class distributions are highly imbalanced. We present a semi-supervised framework with two lightweight hybrid CNN–Transformer models for wafer...
Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Re...
Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transforme...
With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the sta...
Concrete dams are essential components in hydraulic engineering, and the detection of surface cracks remains a major concern in structural health monitoring. This study presents CCT Net, a segmentation model designed to enable the automated detection...
Dermatopathology has rapidly advanced due to the implementation of deep learning models and artificial intelligence (AI). From convolutional neural networks (CNNs) to transformer-based foundation models, these systems are now capable of accurate whol...
Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constr...
Phishing and malicious websites are a serious threat on the internet. Attackers use fake links to trick users and steal their private information. Detecting these links is difficult because attackers change their tricks often. Many old methods cannot...
Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfu...
Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced...
Methyltransferases (MTs) are a functionally diverse class of enzymes that catalyse methyl group transfer from S-adenosyl-L-methionine (SAM) to DNA, RNA, and proteins, thereby regulating epigenetic programming, gene expression, and numerous disease pr...
Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to f...
Background: Accurate recognition of promoter sequences in Escherichia coli is fundamental for understanding gene regulation and engineering synthetic biological systems. However, existing computational methods struggle to simultaneously model long-ra...
Accurate predictions of the temperature of battery energy storage systems (BESSs) are crucial for ensuring their efficient and safe operation. Effectively addressing both the long-term historical periodic features embedded within long look-back windo...
Background/Objectives: Lung and colon cancers remain among the most prevalent and fatal diseases worldwide, and their early detection is a serious challenge. The data used in this study was obtained from the Lung and Colon Cancer Histopathological Im...
The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the ab...
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