Few-shot learning is a fundamental task in computer vision that carries the promise of
alleviating the need for exhaustively labeled data. Most few-shot learning approaches to …

In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a
central role. Controlled-environment agriculture, or CEA, is a special form of urban and …

A fundamental problem in functional data analysis is to classify a functional observation
based on training data. The application of functional data classification has gained immense …

Plastic waste monitoring technology based on Earth observation satellites is one approach
that is currently under development in various studies. The complexity of land cover and the …

Automatic classification of pests and plants (both healthy and diseased) is of paramount
importance in agriculture to improve yield. Conventional deep learning models based on …

In the past twenty-five years, structural health monitoring (SHM) has become an increasingly
significant topic of investigation in the civil and structural engineering research community …

Bayes classifiers for functional data pose a challenge. One difficulty is that probability
density functions do not exist for functional data, so the classical Bayes classifier using …

Inferring brain connectivity and structure in-vivo requires accurate estimation of the
orientation distribution function (ODF), which encodes key local tissue properties. However …

Many machine learning and data mining tasks are based on distance measures, so a large
amount of literature addresses this aspect somehow. Due to the broad scope of the topic …

While the safety concerns of lithium-ion batteries have garnered increasing attention due to
the frequent accidents in electric vehicles and energy storage stations. Fault diagnosis …