[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Mental health is a basic need for a sustainable and develo** society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …
financial burden of mental illness have increased globally, and especially in response to …
A comprehensive analysis towards exploring the promises of AI-related approaches in autism research
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents
challenges in communication, social interaction, repetitive behaviour, and limited interests …
challenges in communication, social interaction, repetitive behaviour, and limited interests …
[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
Automatic and efficient framework for identifying multiple neurological disorders from EEG signals
The burden of neurological disorders is huge on global health and recognized as major
causes of death and disability worldwide. There are more than 600 neurological diseases …
causes of death and disability worldwide. There are more than 600 neurological diseases …
An efficient LSTM neural network-based framework for vessel location forecasting
Forecasting vessel locations is of major importance in the maritime domain, with
applications in safety, logistics, etc. Nowadays, vessel tracking has become possible largely …
applications in safety, logistics, etc. Nowadays, vessel tracking has become possible largely …
Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram
The early diagnosis of neurodevelopmental conditions such as autism spectrum disorder
(ASD), is an unmet need. One difficulty is the identification of a biological signal that relates …
(ASD), is an unmet need. One difficulty is the identification of a biological signal that relates …
Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG …
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by
challenges in social communication, limited interests, and repetitive, stereotyped …
challenges in social communication, limited interests, and repetitive, stereotyped …
Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the …
Purpose Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD)
are conditions that similarly alter cognitive functioning ability and challenge the social …
are conditions that similarly alter cognitive functioning ability and challenge the social …
Classification of low-functioning and high-functioning autism using task-based EEG signals
Autism is a neurodevelopmental disorder marked by a lack of interpersonal, social, and
communication skills, and repetitive and limited behavioral patterns. Autism exhibits a …
communication skills, and repetitive and limited behavioral patterns. Autism exhibits a …
CNN-FEBAC: A framework for attention measurement of autistic individuals
Electroencephalogram (EEG) signals are a cost-effective and efficient method to measure
and analyse neurological data and brain-related ailments. Autism Spectrum Disorder (ASD) …
and analyse neurological data and brain-related ailments. Autism Spectrum Disorder (ASD) …