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] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
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 …
A Neural Network Approach For Malware Classification
Malicious program is commonly known as malware, which is purposely designed to cause
problems in networks and harm in a variety of ways. Malware's primary goal is to disrupt …
problems in networks and harm in a variety of ways. Malware's primary goal is to disrupt …
MI-EEG classification using Shannon complex wavelet and convolutional neural networks
C Wang, Y Wu, C Wang, Y Zhu, C Wang, Y Niu… - Applied Soft …, 2022 - Elsevier
Many classification methods by machine learning and convolutional neural networks (CNN)
have been proposed to recognize MI-EEG recently. However, the indescribable properties …
have been proposed to recognize MI-EEG recently. However, the indescribable properties …
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 …