There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

A research team has now developed an intelligent, non-destructive method that integrates hyperspectral imaging (HSI) with a ...

Tested across eight lettuce types, the system successfully visualizes pigment spatial distribution from individual leaves to full canopies, offering a ...

Multimodal Learning, Deep Learning, Financial Statement Analysis, LSTM, FinBERT, Financial Text Mining, Automated ...

WiMi Hologram Cloud is studying a hybrid quantum-classical learning architecture for advanced multi-class image classification applications in artificial intelligence research worldwide.

Abstract: Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional ...

In this architecture, the training process adopts a joint optimization mechanism based on classical cross-entropy loss. WiMi treats the measurement probability distribution output by the quantum ...

Abstract: We present a novel and robust deep-learning architecture that takes into account the pathological characteristics of eye diseases on color fundus images. The proposed hybrid architecture is ...

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...

Abstract: Deep learning models have achieved impressive results across various image processing and computer vision tasks. However, they often require large datasets, lack transparency, and struggle ...

Abstract: Road segmentation is a key task in remote sensing semantic segmentation, and the existing deep learning methods still have the problems of insufficient fineness, difficulty in modeling ...