Abstract: Local spectral features and global spatial context are essential for hyperspectral image (HSI) classification. However, existing methods based on convolutional neural networks (CNNs), graph ...
As members of the inaugural graduating class in Ohio University’s artificial intelligence program, three students share what ...
Deep neural networks (DNNs) have achieved significant advancements in hyperspectral image (HSI) classification, enabling critical applications in environmental monitoring, medical imaging, and ...
MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance ...
Artificial intelligence (AI) is emerging as a powerful tool to predict food consumption patterns and guide policy decisions, ...
Researchers have examined the challenge of detecting and classifying dynamic road obstacles for autonomous driving systems and presented a deep learning-driven convolutional neural network approach ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
A comprehensive machine learning project for classifying images of fruits and vegetables using Convolutional Neural Networks (CNNs). This project includes both a training pipeline and a web ...
ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...
Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a ...
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