 | | |  This review explores the critical role of image-processing technologies in structural health monitoring (SHM) for civil infrastructures. It highlights the integration of artificial intelligence (AI) and machine learning (ML) to enhance SHM automation and accuracy. Various imaging modalities, including drones, thermography, LiDAR, and satellite imagery, are discussed for damage detection, crack identification, and deformation monitoring. | | | | |  This article explores the evolution, principles, applications, and future prospects of quantum computing processors. | | | | |  Researchers conducted a noise audit on human-labeled benchmarks for machine commonsense reasoning (CSR), revealing significant levels of noise across different experimental conditions and datasets. The study emphasized the impact of noise on performance estimates of CSR systems, challenging the reliance on single ground truths in AI benchmarking practices and advocating for more nuanced evaluation methodologies. | | | | |  Researchers introduced RST-Net, a novel deep learning model for plant disease prediction, combining residual convolutional networks and Swin transformers. Testing on a benchmark dataset showed superior performance over state-of-the-art models, with potential applications in smart agriculture and precision farming. | | | | |  Researchers developed an explainable machine learning (ML) model using NHANES data to predict high-risk metabolic dysfunction-associated steatohepatitis (MASH). Their ensemble-based XGBoost model outperformed traditional biomarkers, offering a promising tool for early identification of high-risk MASH patients. | | | | |  Researchers introduced SCB-YOLOv5, integrating ShuffleNet V2 and convolutional block attention modules (CBAM) into YOLOv5 for detecting standardized gymnast movements. SCB-YOLOv5 showed enhanced precision, recall, and mean average precision (mAP), making it effective for on-site athlete action detection. Extensive experiments validated its effectiveness, highlighting its potential for practical sports education in resource-limited settings. | | | | |  Researchers integrated gradient quantization (GQ) into DenseNet architecture to improve image recognition (IR). By optimizing feature reuse and introducing GQ for parallel training, they achieved superior accuracy and accelerated training speed, overcoming communication bottlenecks. | |
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