A review on transfer learning in EEG signal analysis
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
apnea may last for a few seconds and happen for many while sleeping. This reduction in …
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
Abstract The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on
global health and the daily life of people still living in more than two hundred countries. The …
global health and the daily life of people still living in more than two hundred countries. The …
An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump
S Tang, Y Zhu, S Yuan - Advanced Engineering Informatics, 2021 - Elsevier
Hydraulic piston pump is a vital component of hydraulic transmission system and plays a
critical role in some modern industrials. On account of the deficiencies of traditional fault …
critical role in some modern industrials. On account of the deficiencies of traditional fault …
Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network
The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …
bearing fault classification accuracy using deep learning models. However, implementing a …
[HTML][HTML] A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent
impairments in social interaction, speech and nonverbal communication, and restricted or …
impairments in social interaction, speech and nonverbal communication, and restricted or …
Hand gesture classification using time–frequency images and transfer learning based on CNN
Hand gesture-based systems are one of the most effective technological advances and
continue to develop with improvements in the field of human–computer interaction. Surface …
continue to develop with improvements in the field of human–computer interaction. Surface …
[HTML][HTML] Automated detection of sleep stages using deep learning techniques: A systematic review of the last decade (2010–2020)
Sleep is vital for one's general well-being, but it is often neglected, which has led to an
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …
Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning
The occurrence of multiple faults is a practical problem in the bearings of rotating machines,
and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0 …
and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0 …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …