The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

Contrastive learning models based on Siamese structure have demonstrated remarkable
performance in self-supervised learning. Such a success of contrastive learning relies on …

Crop management plays a crucial role in determining crop yield economic profitability and
environmental sustainability. Despite the availability of management guidelines optimizing …

Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The
current predictive models of yield and soil–crop dynamics during the crop growing season …

Representation learning stands as one of the critical machine learning techniques across
various domains. Through the acquisition of high-quality features, pre-trained embeddings …

Classifying and segmenting patterns from a limited number of examples is a significant
challenge in remote sensing and earth observation due to the difficulty in acquiring …

In this study, we address the challenging task of biomedical text document classification of
Cancer Doc Classification, specifically focusing on lengthy research papers related to …

In this study, we uncover the unexpected efficacy of residual-based large language models
(LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of …

Alzheimer's Disease (AD) is a neurodegenerative condition affecting predominantly elderly
individuals, repre-senting the most common cause of dementia. Early clinical manifestations …