Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

High-resolution remotely sensed images pose challenges to traditional semantic
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …

The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Classification of land use and land cover from remote sensing images has been widely used
in natural resources and urban information management. The variability and complex …

Medical image segmentation has seen significant improvements with transformer models,
which excel in gras** far-reaching contexts and global contextual information. However …

Computer vision (CV) combined with a deep convolutional neural network (CNN) has
emerged as a reliable analytical method to effectively characterize and quantify high …

The farmers' welfare and its interlinkages to energy efficiency and farm sustainability has
attracted global scientific interest within the last few decades. This study examines the …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Recent advancements in natural language processing have given rise to numerous pre-
training language models in question-answering systems. However, with the constant …