An artificial intelligence model capable of reading and interpreting animal behavior like language has been developed by ...

DeepDrug is a deep learning framework, using residual graph convolutional networks (RGCNs) and convolutional networks (CNNs) to learn the comprehensive structural and ...

Abstract: In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. Deep learning has been widely used in the recognition of ...

The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the ...

As the simplest type of time series data, univariate time series provides a reasonably good starting point to study the temporal signals. The representation learning and classification research has ...

Abstract: In this letter, we introduce deep active learning (AL) for multi-label image classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several ...

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater ...

Composed of nodes and edges, graph structured data are organized in the non-Euclidean geometric space and ubiquitous especially in chemical compounds, proteins, etc. They usually contain rich ...