Abstract: The agriculture industry faces significant challenges in maintaining sustainable plant growth while combating diseases that threaten crops. Traditional disease prevention methods rely on ...
Abstract: In remote sensing classification problems, high visual similarity between scenes reduces the classification performance of traditional methods. Therefore, advanced deep neural network models ...
Abstract: This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The ...
Abstract: Inverse Synthetic Aperture Radar (ISAR) imaging is crucial for radar target recognition and surveillance, but ISAR images are often corrupted by noise that varies with the distance between ...
Abstract: Being a major contributor to rice production worldwide, rice leaf diseases need to be detected early and correctly to achieve maximum output and reduce losses. Processes based on deep ...
Abstract: Milk spoilage is a significant concern in food safety and public health, particularly in regions lacking reliable refrigeration or quality monitoring systems. This paper presents a real-time ...
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