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Intelligent mining area detection is fundamental for ecological monitoring and sustainable development of mining areas. However, current remote sensing detection lacks high-quality multimineral rotated object detection d...

ABSTRACT With the advancement of satellite remote sensing technology, object detection based on high‐resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision. Although...

Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of...

Unmanned Aerial Vehicles (UAVs) have become a prevalent tool for aerial image analysis, thanks to their agility in low-altitude flight and real-time sensing. However, objects in UAV imagery typically occupy minimal pixel...

Flood disasters, with high suddenness, wide impact and severe consequences, severely threaten ecological security, human lives, property and socioeconomic development. Optical remote sensing, though advantageous in spati...

Abstract The current work on emotion recognition considering EEG signals has attracted significant attention due to its advantages in fields like mental health, neural computing, and human-computer interaction. Nonethele...

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint. The suggested system is composed of several st...

Zernike moments are well-suited for unmanned aerial vehicle scene matching because of their rotation invariance and robustness to noise. However, their high computational complexity poses challenges for meeting the real-...

Abstract Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhan...

Abstract Background Rapid and accurate pest diagnosis is essential for reducing crop losses and improving agricultural productivity. Most existing methods rely on deep learning-based object detection to classify pests in...

The inherent non-stationarity, weak periodicity, and abrupt transient mutations of wind turbine gearbox vibration signals present substantial challenges to traditional diagnostic frameworks. To improve diagnostic accurac...

Multimodal remote sensing image registration is a fundamental step for multisource data fusion and geospatial analysis. However, nonlinear radiometric, textural, and geometric discrepancies across modalities challenge in...

To address the difficulty of blasthole detection during the charging phase of drill-and-blast tunnelling, which is aggravated by dust interference and insufficient illumination, this study proposes an intelligent blastho...

To address the low detection accuracy caused by leaf occlusion, the loss of disease targets, and complex backgrounds in citrus leaf disease detection, this study proposes a leaf disease detection method termed DBG-DETR (...

Abstract Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of ...

Plant disease diagnosis in field settings is challenged by subtle symptomology, high inter-class visual similarity, and class imbalance, making automated detection particularly difficult. While deep learning models achie...

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with strong invasiveness, limited treatment options, and poor prognosis. Its early accurate diagnosis and individualized treatment are major challenges f...

To detect internal valve leakage in liquid-filled pipelines, a method using kernel principal component analysis (KPCA) is proposed to analyze acoustic emission (AE) signals for leakage detection. Time-frequency analysis ...

Synthetic Aperture Radar (SAR) is one of the primary sensing models and is broadly used in numerous remote sensing applications including environmental monitoring, maritime monitoring, and climate change research. Unfort...

Abstract As reported by the World Health Organization (WHO), Skin Cancer (SC) is a growing medical concern with millions of cases diagnosed each year. Deep Learning methods show promising results in early detection and c...

Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a...

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improv...

Skin lesions are one of the most prevalent form of diseases existing among us. Early detection and classification of potentially malignant skin lesions can give us a lead in the fight against skin cancer. There are many ...

Lung diseases such as pneumonia, tuberculosis, COVID-19, and lung cancer remain significant global health challenges that demand rapid and accurate diagnosis to improve patient outcomes. This study proposes NASNet-ViT, a...