A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …

The capacity of a neural network to absorb information is limited by its number of
parameters. Conditional computation, where parts of the network are active on a per …

Previous research on empathetic dialogue systems has mostly focused on generating
responses given certain emotions. However, being empathetic not only requires the ability of …

There has been considerable recent research into the connection between Parkinson's
disease (PD) and speech impairment. Recently, a wide range of speech signal processing …

In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM
(KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest
neighbor (FKNN) for Parkinson's disease (PD) diagnosis. The proposed FKNN-based …

The recently proposed Skip-gram model is a powerful method for learning high-dimensional
word representations that capture rich semantic relationships between words. However …

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most
common neurodegenerative disorders due to the loss of dopamine-producing brain cells …