21 hours ago · A subset of machine learning that uses artificial neural networks. Features, Individual measurable properties of observed data that serve as input variables ...
22 hours ago · It generates wave propagation maps [3] by using ultra-fast A-mode ultrasound imaging (over 2,000 frames per second) to form M-mode images as illustrated in Fig.
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15 hours ago · Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach - Download as a PDF or view online for free.
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21 hours ago · Our approach simplifies this by using a type of deep learning model known as a convolutional neural network (CNN), specifically designed to handle the data ...
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12 hours ago · To address this challenge, we propose MAP-ADAPT, a real-time method for quality-adaptive semantic 3D reconstruction using RGBD frames. MAP-ADAPT is the first ...
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11 hours ago · An adaptive ensemble transfer CNN (AETC) is used to identify the pests after it has been detected. DenseNet, MobileNet, and ResNet are the three models that ...
19 hours ago · Estimating atmospheric visibility using deep learning involves leveraging advanced models and techniques to predict visibility accurately.
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21 hours ago · A GAN-based method to generate haze-free images, which consider the color cast, haze effect, and low contrast. It uses U-Net to create the enhanced images.
14 hours ago · We design Underwater Adaptive ViT. Encoder to incorporate underwater visual prompts into the network via adapters, and Salient Feature Prompter. Generator to ...
6 hours ago · Unsupervised high-resolution depth learning from videos with dual networks. ... Learning depth from single monocular images using deep convolutional neural fields ...