Sports video classification has become vital for automated sports analytics, highlight generation, and content recommendation. Traditional approaches have relied on handcrafted features or ...
Abstract: Recent improvements in Convolution Neural Networks (CNN) have demonstrated extraordinary performance in solving real-world problems. However, the performance of CNN depends purely on its ...
The collaborative infrastructure innovation delivers nearly half a million Trainium2 chips in record time, with Anthropic scaling to more than one million chips by the end of 2025. Project Rainier, ...
Abstract: In this paper, a hybrid deep learning model based on Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are introduced for the automated detection of lung cancer ...
Abstract: The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed ...
Abstract: Graph convolutional networks (GCNs) have emerged as a prominent research focus for hyperspectral image classification (HSIC). However, existing GCN-based HSIC methods still face the ...
Abstract: This study aims to compare the performance of two classification methods—Support Vector Machine (SVM) and Convolutional Neural Network (CNN)—in identifying music genres based on audio data ...
Abstract: Hyperspectral images (HSIs) are pivotal in remote sensing, providing rich spectral and spatial information for applications such as agriculture, environmental monitoring, and mineral ...
Abstract: When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can significantly affect image visual quality and ...
Designed with Amazon’s front-line employees in mind, these innovations reduce repetitive tasks, improve safety, and boost productivity—while speeding up delivery. Every time we innovate across ...