[HTML][HTML] Recent advances in electrochemical biosensors: Applications, challenges, and future scope
A Singh, A Sharma, A Ahmed, AK Sundramoorthy… - Biosensors, 2021 - mdpi.com
The electrochemical biosensors are a class of biosensors which convert biological
information such as analyte concentration that is a biological recognition element …
information such as analyte concentration that is a biological recognition element …
[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 developing 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 …
Advancing biosensors with machine learning
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis.
Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved …
Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved …
An experimental review on deep learning architectures for time series forecasting
P Lara-Benítez, M Carranza-García… - International journal of …, 2021 - World Scientific
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …
machine learning tasks. Deep neural networks have successfully been applied to address …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
[PDF][PDF] Detection of coronavirus disease (covid-19) based on deep features
The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner.
The coronavirus spread so quickly between people and approaches 100,000 people …
The coronavirus spread so quickly between people and approaches 100,000 people …
Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review
AG Sreedevi, TN Harshitha, V Sugumaran… - Information Processing & …, 2022 - Elsevier
Human Intelligence is considered superior compared to Artificial Intelligence (AI) because of
its ability to adapt faster to changes. Due to increasing data deluge, it is cumbersome for …
its ability to adapt faster to changes. Due to increasing data deluge, it is cumbersome for …
Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
[HTML][HTML] Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …