Abstract: Semi-supervised hyperspectral image (HSI) classification methods focus on exploring the spectral and spatial information of unlabeled samples. However, existing methods generally follow the ...

Abstract: This study aims to develop a novel deep learningbased approach to support the automated mushroom growth monitoring using an object tracking algorithm in conjunction with instance ...

Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of ...

Abstract: Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) ...

Abstract: Ensuring secure and user-friendly authentication is important as mobile devices increasingly handle sensitive data. Traditional methods like PINs, fingerprints, and facial recognition have ...

Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...

Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...

Abstract: Especially with cancers like melanoma, automatic and accurate skin disease classification using dermatoscopic images can significantly enhance clinical decision-making and early management.

(CNN) — Mississippi’s Senate primaries set up a general election showdown between an incumbent and a challenger she blocked from federal judgeship. CNN projected Tuesday that Republican Sen. Cindy ...

Abstract: Waste sorting serves as a highly effective approach to reduce waste at the source and promote resource recycling. It can significantly decrease the amount of waste needing final disposal ...