In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain dif...
Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade si...
With the rapid growth of digital computing, communication, and storage devices applied in various real-world scenarios, more and more data have been collected and stored to drive the development of machine learning techniques. It is also noted that t...
Multi-label data usually carries a complex structural class imbalance, which significantly affects the overall predictive performance of multi-label learning models. Although many studies have investigated this problem, most existing methods rely on...
With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label super...
Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely...
Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the...
In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k-nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the...
Background: Breast cancer brain metastases (BCBMs) exhibit notable receptor discordance between primary tumors and metastases, yet obtaining intracranial biopsies is rarely practical. We created an interpretable deep learning radiogenomic model to pr...
This paper proposes a novel Ordinal Regression Multi-Task Learning (OR-MTL) framework to address challenges in multi-task diagnosis of PD in Gas-Insulated Switchgear (GIS). GIS PD diagnosis typically involves tasks such as discharge-type identificati...
Legal element recognition, which identifies discrete factual elements in Chinese court judgments to support judicial analysis and case retrieval, faces a severe long-tail challenge: head-to-tail label-frequency ratios exceed 100:1, and over 60% of se...
Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidit...
In intensive care units, large-scale clinical time-series data are continuously accumulated through electronic medical records and bedside monitoring systems. However, direct utilization of such data for clinical decision-making remains challenging d...
Food supplements of plant origin for weight control are increasingly being demanded by consumers as a way to promote good health. Among them, those based on Garcinia cambogia (GCFS) are widely commercialized considering their bioactive properties, ma...
The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving ma...
Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable...
Reliable osteosarcoma tissue characterization combines radiographic evidence of bone-level structural change with histopathological assessment of cellular morphology. In rare cancers, however, patient-matched multimodal datasets are rarely available...
Foot-mounted inertial pedestrian navigation is an attractive infrastructure-free solution for pedestrian positioning in environments where Global Navigation Satellite System (GNSS) signals are unavailable, degraded, or intentionally excluded. However...
Burn injuries are a common traumatic condition, and the early diagnosis of burn depth is crucial for reducing treatment costs and improving survival rates. In recent years, image-based deep learning techniques have been utilized to realize the automa...
Time-reversed Young (TRY) interferometry reconstructs interference from a fixed detector by reading out a programmable source-label distribution. This work formulates the architecture as a source-coded Bayesian response sensor. For a perturbation par...
Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is...
To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation infor...
Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mo...
The optimal computing ability of spiking neural networks (SNNs) mainly depends on the connection weights of their synapses and the thresholds that control the spiking. In order to realize the optimization calculation of different objective functions,...
Unmanned aerial vehicles (UAVs) integrated with computer vision technology have emerged as an effective method for information acquisition in various applications. However, due to the small proportion of target pixels and susceptibility to background...
Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the diffic...
The increasing realism of deepfake videos has intensified the need for reliable video-level detection systems, but benchmark performance must be interpreted together with generalization, temporal-modeling, and efficiency limits. This study presents a...
With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadba...
In synthetic aperture radar (SAR) images, pixel-level Ground Truth (GT) is a scarce resource compared to Bounding Box (BBox) annotations. Therefore, exploring the use of unsupervised instance segmentation methods to convert BBox-level annotations int...
Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contra...
Tracking highly maneuverable UAVs in complex maritime environments faces multiple challenges: dynamic sea surface interference and low-altitude occlusion make UAV motion trajectories difficult to predict; the strong maneuvering behavior of UAVs impos...
Background/Objectives: Osteoporosis and osteopenia remain underdiagnosed despite routine CT examinations containing potentially useful bone information. This study evaluated the feasibility of patient-level three-class classification of Normal, Osteo...
Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliab...
Federated learning (FL) has emerged as a promising paradigm for enabling distributed intelligence in Internet of Things (IoT) environments while preserving data privacy and reducing the need for centralized data collection. However, the practical dep...