Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and
cognitive content of neural processing based on noninvasively measured brain activity …

Background Obesity is a leading cause of preventable death worldwide. Artificial
intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has …

Justifiably, while big data is the primary interest of research and public discourse, it is
essential to acknowledge that small data remains prevalent. The same technological and …

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …

Handwritten signature is a common biometric trait, widely used for confirming the presence
or the consent of a person. Offline Signature Verification (OSV) is the task of verifying the …

Sleep posture monitoring is an essential assessment for obstructive sleep apnea (OSA)
patients. The objective of this study is to develop a machine learning-based sleep posture …

Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate;
therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging …

Small samples are prone to overfitting in the neural network training process. This paper
proposes an optimization approach based on L2 and dropout regularization called a hybrid …

Emerging sleep health technologies will have an impact on monitoring patients with sleep
disorders. This study proposes a new deep learning model architecture that improves the …

Classification problems in the small data regime (with small data statistic T and relatively
large feature space dimension D) impose challenges for the common machine learning (ML) …