Blood pressure detection using CNN-LSTM model
Blood pressure (BP) is a key indication that needs to be checked on a regular basis. For
maintaining a healthy life normal blood pressure is essential, and a continuous change can …
maintaining a healthy life normal blood pressure is essential, and a continuous change can …
A convolutional neural network approach for diabetic retinopathy classification
Diabetic Retinopathy (DR) is a kind of problem which affects diabetic patients, particularly
those at their age of working, and can result in vision impairment and possibly irreversible …
those at their age of working, and can result in vision impairment and possibly irreversible …
Liver disease prediction using machine learning classification techniques
Machine Learning is a process which is used to discover patterns in huge data/large data
set to enable decision, thereby allowing machines to go through a learning process (ie …
set to enable decision, thereby allowing machines to go through a learning process (ie …
[HTML][HTML] An inception V3 approach for malware classification using machine learning and transfer learning
Malware instances have been extremely used for illegitimate purposes, and new variants of
malware are observed every day. Machine learning in network security is one of the prime …
malware are observed every day. Machine learning in network security is one of the prime …
Automated Seizure Detection using Theta Band
The EEG signal is made up of numerous frequency bands that characterize human
behaviours like emotion, attentiveness, and sleep status, among others. In order to detect …
behaviours like emotion, attentiveness, and sleep status, among others. In order to detect …
A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
Artificial neural network model using short-term fourier transform for epilepsy seizure detection
Epilepsy is a neurological illness that can strike anyone at any time in their life. However, a
person with epilepsy will experience frequent to uncommon seizures, resulting in death …
person with epilepsy will experience frequent to uncommon seizures, resulting in death …
Deep DR: detection of diabetic retinopathy using a convolutional neural network
N Nasir, P Oswald, O Alshaltone… - 2022 Advances in …, 2022 - ieeexplore.ieee.org
Diabetic retinopathy (DR) is a consequence of diabetes that affects the back of the eye due
to excessive blood sugar levels. If left misdiagnosed and untreated, it might result in …
to excessive blood sugar levels. If left misdiagnosed and untreated, it might result in …
Automatic epileptic seizure detection using LSTM networks
KS Shekokar, S Dour - World Journal of Engineering, 2022 - emerald.com
Purpose The purpose of this work is to make a computer aided detection system for epileptic
seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more …
seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more …
A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique
Early detection and proper treatment of epilepsy seizure is essential and meaningful to
those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing …
those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing …