In the task of classifying images of cracks in underwater dams, symmetry serves as a crucial geometric feature that aids in distinguishing cracks from other structural elements. Nevertheless, the asymmetry in the distribution of positive and negative...
The global agriculture industry is encountering challenges due to labor shortages and the demand for increased efficiency. Currently, fruit yield estimation in guava orchards primarily depends on manual counting. Machine vision is an essential techno...
In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining productio...
Aiming to overcome the challenges associated with the unmanned aerial vehicle (UAV)-based inspection of transmission line insulators, including the tiny scale of defect regions, irregular morphologies, and low contrast with complex backgrounds, we pr...
Prostate cancer (PCa) is one of the most common tumors diagnosed in men worldwide, with approximately 1.7 million new cases expected by 2030. Most cancerous lesions in PCa are located in the peripheral zone (PZ); therefore, accurate identification of...
(1) Background: With the continuous development of intelligent education, classroom behavior recognition has become increasingly important in teaching evaluation and learning analytics. In response to challenges such as occlusion, scale differences,...
Changes in soybean flower and pod numbers are important factors affecting soybean yields. Obtaining the number of flowers and pods, as well as fallen flowers and pods, quickly and accurately is crucial for soybean variety breeding and high-quality an...
Timely identification of crop conditions is relevant for informed decision-making in precision agriculture. The initial step in determining the conditions that crops require involves isolating the components that constitute them, including the leaves...
In order to enhance the efficiency and precision of grab-type planting operations for disordered stacked sugarcane, and to achieve rapid deployment of the visual detection model on automatic sugarcane seed-cane planters, this study proposes a sugarca...
The integrity of urban water supply pipelines, an essential element of municipal infrastructure, is frequently undermined by internal defects such as corrosion, tuberculation, and foreign matter. Traditional inspection methods relying on CCTV are tim...
Areca (Areca catechu L.) is an important economic crop in tropical regions, but excessive nitrogen application leads to low nitrogen fertilizer utilization efficiency (approximately 30%). Vanilla (Vanilla planifolia Andrews) can be intercropped with...
Extracting instantaneous waterlines on tidal flats is challenging because spectral confusion among turbid water, wet bare tidal flats, and residual channel water can cause misclassification and boundary displacement. This study proposes a Topography-...
Nowadays, Artificial Intelligence (AI) has revolutionized many fields and the medical field is no exception. Thanks to technological advancements and the emergence of Deep Learning (DL) techniques AI has brought new possibilities and significant impr...
Deep learning models have been used in several domains, however, adjusting is still required to be applied in sensitive areas such as medical imaging. As the use of technology in the medical domain is needed because of the time limit, the level of ac...
Background/Objectives: Heart disease (HD) is recently becoming the foremost cause of death worldwide, underlining the importance of early and correct diagnosis to improve patient outcomes. Although Internet of Things (IoT)-enabled machine learning ap...
Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks....
Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extrac...
Training under extreme class imbalance (>1:100) remains an open problem in weakly supervised learning. The standard remedy—loss-level reweighting (focal loss, asymmetric loss, class-balanced loss)—is widely adopted, yet its behavior at...
The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models...
The current deep convolutional neural networks for very-high-resolution (VHR) remote-sensing image land-cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually...
Coronary heart disease is the primary cause of death worldwide. Among these, ischemic heart disease and stroke are the most common diseases induced by coronary stenosis. This study presents a Lightweight Residual Squeeze-and-Excitation Network (LRSE-...
Federated learning supports collaborative satellite-image segmentation without centralizing source imagery, but non-IID data distributions and information leakage through model updates remain important challenges. We evaluate centralized U-Net, vanil...
Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been propo...
Existing neural stochastic differential equation models, such as SDE-Net, can quantify the uncertainties of deep neural networks (DNNs) from a dynamical system perspective. SDE-Net is either dominated by its drift net with in-distribution (ID) data t...
Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intellig...
Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the heal...
Accurate radionuclide identification is critical for nuclear safety and environmental radiation monitoring. To integrate the guiding effect of peak morphology features and the nonlinear fitting capability of data-driven models, this paper proposes a...
Lightweight local feature extractors are essential for real-time SLAM. However, they frequently struggle with perceptual aliasing and low localization accuracy in texture-sparse or repetitive environments. This paper introduces the Geometric-Contextu...
Problem: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. Motivation: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such expla...
Blood vessel segmentation in X-ray coronary angiography (XCA) plays a crucial role in diagnosing cardiovascular diseases, enabling a precise assessment of arterial structures. However, segmentation is challenging due to a low signal-to-noise ratio, i...
Drivers’ facial expression recognition systems play a pivotal role in Advanced Driver Assistance Systems (ADASs) by monitoring emotional states and detecting fatigue or distractions in real time. However, deploying such systems in resource-cons...
Lately, deep learning methods have greatly improved the accuracy of brain-tumor segmentation, yet slice-wise inconsistencies still limit reliable use in clinical practice. While volume-aware 3D convolutional networks achieve high accuracy, their memo...
Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to tak...
As a technique for accelerating and stabilizing training, the batch normalization (BN) is widely used in deep learning. However, BN cannot effectively estimate the mean and the variance of samples when training/fine-tuning with small batches of data...
Accurate delineation of landslide boundaries from remote sensing imagery remains challenging because landslides exhibit irregular geometry, substantial scale variation, and strong background interference. We propose DCA-UNet, a U-Net-style segmentati...
Accurate early-stage monitoring of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically hi...
Vehicle logo recognition has become an important part of object recognition in recent years because of its usage in surveillance applications. In order to achieve a higher recognition rates, several methods are proposed, such as Scale Invariant Featu...
Unsupervised anomaly detection has been widely applied to industrial scenarios. Recently, transformer-based methods have also been developed and have produced good performance. Although the global dependencies in anomaly images are considered, the ty...
Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called micr...
Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, especially in IoT scenarios where devices ha...
Accurate segmentation of retinal vessels is an essential prerequisite for the subsequent analysis of fundus images. Recently, a number of methods based on deep learning have been proposed and shown to demonstrate promising segmentation performance, e...
Mitochondria–endoplasmic reticulum contact sites (MERCs) are known as the specialized areas that are involved in a large number of intracellular signaling pathways that regulate Ca2+ homeostasis, lipid transport, mitochondrial dynamics, cell de...
Natural Language Inference (NLI) deals with the problem of logical entailment between two sentences. It is evidenced that apart from interpretation of words and syntactic structures, use of external relevant knowledge is also required. This paper inv...
In current industries, sampling inspections of the quality of powders, such as superabsorbent polymers (SAPs) still are conducted via visual inspection. The size of samples and foreign matter are around 500 μm, making them difficult for humans to...
Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Du...
Automated cotton-percentage identification underpins sustainable textile recycling, but established near-infrared and ATR-FTIR spectroscopy systems cost USD 10,000–25,000 per unit and remain inaccessible to small recyclers. We address this on t...
Reliable local three-dimensional (3D) wind estimates are important for fixed-wing UAV flight under wind disturbances, but low-cost platforms lack direct 3D flow sensing. We propose PIRNN-AKF, which combines a physics-informed gated recurrent unit (PI...
Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This w...
Owing to the high volatility, non-stationarity, and complexity of financial time-series data, stock market trend prediction remains a crucial but difficult endeavor. To address this, we present a novel Multi-Perspective Fused Attention model (SGR-Net...
The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual...
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