Abstract: Unsupervised domain adaptation (UDA) is becoming a prominent solution for the domain-shift problem in many time-series classification tasks. With sequence properties, time-series data ...

Although virus ecogenomics has expanded access to and understanding of the virosphere, existing classification tools lack taxonomic resolution and are unable to scale to modern discovery-based ...

Abstract: This study introduces the hierarchical pixel-wavelength fusion network (HPWF-Net), a novel hyperspectral reconstruction framework designed to address the challenges of tree species ...

Image Classification is the process of assigning a label to an image based on its content, such as identifying whether an image contains a cat or a dog.

Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild ...

This research introduces an innovative approach to image classification, by making use of Vision Transformer (ViT) architecture. In fact, Vision Transformers (ViT) have emerged as a promising option ...

Identification of leaf diseases plays an important role in the growing process of different types of plants. Current studies focusing on the detection and categorization of leaf diseases have achieved ...

Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer ...

Machine learning models have revolutionized the way we approach and solve complex problems in various domains. However, with the rise of big data, traditional machine learning algorithms have become ...

When modeling groundwater systems in Quaternary formations, one of the first steps is to construct a geological and petrophysical model. This is often cumbersome because it requires multiple manual ...