A comprehensive review of machine learning approaches for dyslexia diagnosis
Electroencephalography (EEG) is the commonly employed electro-biological imaging
technique for diagnosing brain functioning. The EEG signals are used to determine head …
technique for diagnosing brain functioning. The EEG signals are used to determine head …
Automatic detection of COVID-19 infection using chest X-ray images through transfer learning
EF Ohata, GM Bezerra, JVS das Chagas… - IEEE/CAA Journal of …, 2020 - ieeexplore.ieee.org
The new coronavirus (COVID-19), declared by the World Health Organization as a
pandemic, has infected more than 1 million people and killed more than 50 thousand. An …
pandemic, has infected more than 1 million people and killed more than 50 thousand. An …
Classification of alcoholic EEG signals using wavelet scattering transform-based features
Following the research question and the relevant dataset, feature extraction is the most
important component of machine learning and data science pipelines. The wavelet …
important component of machine learning and data science pipelines. The wavelet …
Predicting the risk of alcohol use disorder using machine learning: a systematic literature review
The number of deaths caused by alcohol-related diseases may be reduced by predicting
alcohol use disorder (AUD). Many researchers have worked on AUD prediction using …
alcohol use disorder (AUD). Many researchers have worked on AUD prediction using …
Deep convolutional neural network regularization for alcoholism detection using EEG signals
Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the
disturbance of the neuronal system in the human brain. This results in certain malfunctioning …
disturbance of the neuronal system in the human brain. This results in certain malfunctioning …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
Bi-dimensional approach based on transfer learning for alcoholism pre-disposition classification via EEG signals
H Zhang, FHS Silva, EF Ohata, AG Medeiros… - Frontiers in Human …, 2020 - frontiersin.org
Recent statistics have shown that the main difficulty in detecting alcoholism is the
unreliability of the information presented by patients with alcoholism; this factor confusing …
unreliability of the information presented by patients with alcoholism; this factor confusing …
Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems
Social participation of people with disabilities is tried to be increased with state-supported
projects recently. However, even in neuromuscular diseases such as Motor Neurone …
projects recently. However, even in neuromuscular diseases such as Motor Neurone …
Complexity analysis of EEG in patients with social anxiety disorder using fuzzy entropy and machine learning techniques
The diagnosis of social anxiety disorder (SAD) is of great consequence not only due to its
impacts on the individual and society but also the expenditures to the national health …
impacts on the individual and society but also the expenditures to the national health …
EEG classification of normal and alcoholic by deep learning
H Li, L Wu - Brain Sciences, 2022 - mdpi.com
Alcohol dependence is a common mental disease worldwide. Excessive alcohol
consumption may lead to alcoholism and many complications. In severe cases, it will lead to …
consumption may lead to alcoholism and many complications. In severe cases, it will lead to …