The existence of class imbalance in a dataset can greatly bias the classifier towards majority
classification. This discrepancy can pose a serious problem for deep learning models, which …

In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …

Abstract The Internet of Medical Things (IoMT) is increasingly replacing the traditional
healthcare systems. However, less focus has been paid to their security against cyber …

The study of generative models is a promising branch of deep learning techniques, which
has been successfully applied to different scenarios, such as Artificial Intelligence and the …

Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …

Modern machine and deep learning methods require large datasets to achieve reliable and
robust results. This requirement is often difficult to meet in the medical field, due to data …

The increasingly sophisticated and rapidly evolving application of artificial intelligence in
medicine is transforming how health care is delivered, highlighting a need for current and …

The problem is the vaccination of a large number of people in a short time period, using
minimum space and resources. The tradeoff is that this minimum number of resources must …

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …

Purpose: The aim of this study was to analyze the performance of multivariate machine
learning (ML) models applied to a speech-in-noise hearing screening test and investigate …