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 …

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 …

This research aims to develop a Machine Learning model for predicting soil moisture levels,
which may be used to construct smart irrigation systems. The model was evaluated and …

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 …

Large language models (LLMs) have reached human-like proficiency in generating diverse
textual content, underscoring the necessity for effective fake text detection to avoid potential …

Robust crop yield projections under future climates are fundamental prerequisites for
reliable policy formation. Both process-based crop models and statistical models are …

Protagonists allege that artificial intelligence (AI) is revolutionising contemporaneous
mindscapes. Here, we authoritatively review the status quo of AI and machine learning …

As the aging population grows, particularly for the baby boomer generation, the United
States is witnessing a significant increase in the elderly population experiencing …