(1) Background: In the future Internet era, clarity and structural rationality are important factors in image inpainting. Currently, image inpainting techniques based on generative adversarial networks have made great progress; however, in practical...
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...
Existing image super-resolution methods still suffer from limitations in edge-structure restoration, high-frequency texture preservation, and artifact suppression, which may lead to blurred contours and unnatural textures. To address these issues, th...
An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep le...
Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate ne...
Building footprint extraction from remotely sensed imagery is a critical task in the field of illegal building discovery, urban dynamic monitoring, and disaster emergency response. Recent research has made significant progress in this area by utilizi...
Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflage...
Pedestrian detection is a challenging task, mainly owing to the numerous appearances of human bodies. Modern detectors extract representative features via the deep neural network; however, they usually require a large training set and high-performanc...
Hyperspectral remote sensing images, with their continuous, narrow, and rich spectra, hold distinct significance in the precise classification of land cover. Deep convolutional neural networks (CNNs) and their variants are increasingly utilized for h...
Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationsh...
This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper...
At present, a prevalent approach to speaker diarization is clustering based on speaker embeddings. However, this method encounters two primary issues. Firstly, it cannot directly minimize the diarization error during the training process; secondly, t...
Object detection in remote sensing images is a challenge because remote sensing targets have characteristics such as small geometries, an unfixed direction and multiple poses. Recent studies have shown that the accuracy of object detection can be imp...
Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance...
With the rapid development of deep learning, object detection in remote sensing images has attracted extensive attention. However, remote sensing images typically exhibit the following characteristics: significant variations in object scales, dense s...
This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is buil...
To address the limitations of unimodal visual detection in complex scenarios involving low illumination, occlusion, and texture-sparse environments, this paper proposes an improved YOLOv11-based dual-branch RGB-D fusion framework. The symmetric archi...
Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above probl...
Video super-resolution aims to generate high-resolution video sequences with realistic details from existing low-resolution video sequences. However, most existing video super-resolution models require substantial computational power and are not suit...
Mechanochromic polymers are defined as materials that are able to detect a mechanical stress through an optical output. This feature has evoked a growing interest in the last decades, thanks to the progress of chromogenic molecules whose optical char...
Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images....
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, car...
Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95...
In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduc...
Real-time object detection in Unmanned Aerial Vehicle (UAV) imagery presents unique challenges, primarily characterized by extreme scale variations and intense background clutter. Existing detectors often suffer from spectral homogenization in which...
Processing-in-memory (PIM) architectures have emerged as promising alternatives to conventional von Neumann architectures for accelerating memory-bound applications, such as Graph Neural Networks (GNNs), by reducing data movement between memory and p...
Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The...
The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By co...
Accurate identification of the combustion state in municipal solid waste incineration (MSWI) processes is crucial for achieving efficient, low-emission, and safe operation. However, existing methods often struggle with stable and reliable recognition...
To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrat...
Small-object representations in unmanned aerial vehicle (UAV) imagery are easily degraded by repeated downsampling, crowded occlusion, and background clutter. We present MF-YOLO, an accuracy-oriented detector based on YOLO11n. It introduces a stride-...
Unmanned aerial vehicles (UAVs) play an essential role in various applications, such as transportation and intelligent environmental sensing. However, due to camera motion and complex environments, it can be difficult to recognize the UAV from its su...
Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easi...
Intrinsically disordered proteins play a central role in dynamic regulatory and assembly processes in the cell. Recently, a human κ-casein proteolytic fragment called lactaptin (8.6 kDa) was found to induce apoptosis of human breast adenocarcin...
In this study, a brush-like polymer with aggregation-induced emission (AIE) features was synthesized for drug delivery and intracellular drug tracking. The polymer consisting of tetraphenylethene (TPE) chain-end as well as oligo-poly (ethylene glycol...
With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantitie...
Network traffic classification models, an essential part of intrusion detection systems, need to be as simple as possible due to the high speed of network transmission. One of the fastest approaches is based on decision trees, where the classificatio...
Underwater marine debris detection remains challenging because light attenuation, scattering, low contrast, blurred object boundaries, and complex seabed backgrounds degrade feature representation and hinder the reliable detection of small, sparsely...
Deep learning methods based on convolutional neural networks (CNNs) and Mamba have advanced medical image segmentation, yet two challenges remain: (1) trade-off in feature extraction, where CNNs capture local details but miss global context, and Mamb...
Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-tumor heterogeneity, epigenetics, and progression of cancer. Most of the existing methods for tissue phenotyping often rely on time-consuming and error...
For financial institutions, credit card fraud detection is a critical activity where the accuracy and efficiency of detection models are important. Traditional methods often use standard feature selection techniques that may ignore refined patterns i...
Recently, point-based networks have begun to prevail because they retain more original geometric information from point clouds than other deep learning-based methods. However, we observe that: (1) the set abstraction design for local aggregation in p...
Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of c...
With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the l...
The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based f...
Factory extraction from satellite images is a key step in urban factory planning, and plays a crucial role in ecological protection and land-use optimization. However, factory extraction is greatly underexplored in the existing literature due to the...
Most existing video action recognition methods mainly rely on high-level semantic information from convolutional neural networks (CNNs) but ignore the discrepancies of different information streams. However, it does not normally consider both long-di...
Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. We propose the Weighted Importance Score and Frequency Count (WISFC)...
Pest infestation poses significant threats to grain storage due to pests’ behaviors of feeding, respiration, excretion, and reproduction. Efficient pest detection and control are essential to mitigate these risks. However, accurate detection of...
Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large propo...
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