Precise segmentation of colorectal polyps in colonoscopy images is essential for timely cancer diagnosis and prevention. Nevertheless, current segmentation methods contend with intrinsic variability in polyp appearance, differences in size, shape, and texture, while preserving computational efficiency necessary for clinical implementation. In this paper, we present a novel segmentation architecture that integrates scale-specific attention mechanisms within a hybrid CNN-Transformer backbone to address these challenges. Our model employs Coordinate Attention for high-resolution feature maps to preserve spatial details essential for boundary delineation, and Channel Attention for deep semantic... More >

Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection a... More >

Sentiment analysis is a crucial component of intelligent information processing systems, enabling machines to understand and categorize human opinions expressed in text. While extensively studied for high-resource languages such as English and Chinese, it remains underexplored for low-resource languages like Urdu. This paper presents an intelligent multilingual sentiment analysis framework for Urdu text by integrating supervised machine learning techniques with a transformer-based model. We manually annotated and preprocessed a dataset collected from various Urdu blog websites, categorizing sentiments into positive, neutral, and negative classes. Four machine learning classifiers—Support V... More >