Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate
the progress of epilepsy diagnosis. Current seizure detection methods based on deep …

Traditional research and development (R&D) on biomedical materials depends heavily on
the trial and error process, thereby leading to huge economic and time burden. Most …

This paper presents a deep learning-based analysis and classification of cold speech
observed when a person is diagnosed with the common cold. The common cold is a viral …

Machine learning algorithms for seizure detection have shown great diagnostic potential,
with recent reported accuracies reaching 100%. However, few published algorithms have …

Canopy height is a crucial indicator for assessing the structure and function of the forest
ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and …

Among people who suffer from epilepsy, seizure detection is crucial to improving their
wellbeing. Through the integration of sophisticated feature selection and a reliable …

Epilepsy is a persistent health condition marked by unusual and highly synchronized
electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a …

Abstract Convolutional Neural Networks (CNNs) have become increasingly popular in
seizure detection and prediction research. While traditional CNNs are effective in image …

Epilepsy is a brain disorder that affects nearly 50 millions people around the world.
However, despite its high prevalence, the diagnosis of epilepsy is complex and usually …

In most network management systems, the Intrusion Detection System (IDS) is utilized to
detect and prevent suspicious activity. IDS fundamental concept is to use feature values from …