Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning
Purpose: In conventional diagnosis, the visual inspection of the malaria parasite
Plasmodium falciparum in infected red blood cells under a microscope, is done manually by …
Plasmodium falciparum in infected red blood cells under a microscope, is done manually by …
[HTML][HTML] Deep learning algorithms for efficient analysis of ecg signals to detect heart disorders
Electrocardiography (ECG) has been a reliable method for monitoring the proper functioning
of the cardiovascular system for decades. Recently, there has been a lot of research …
of the cardiovascular system for decades. Recently, there has been a lot of research …
Analysis for Predicting Respiratory Diseases from Air Quality Attributes Using Recurrent Neural Networks and Other Deep Learning Techniques.
The primary objective of this investigation is to establish a clear correlation between air
quality and the prevalence of respiratory conditions, this is done by employing deep learning …
quality and the prevalence of respiratory conditions, this is done by employing deep learning …
[HTML][HTML] Learning Rate Tuner with Relative Adaptation (LRT-RA): Road to Sustainable Computing
Optimizing learning rates (LRs) in deep learning (DL) has long been challenging. Previous
solutions, such as learning rate scheduling (LRS) and adaptive learning rate (ALR) …
solutions, such as learning rate scheduling (LRS) and adaptive learning rate (ALR) …
Artificial intelligence and data science in the detection, diagnosis, and control of COVID-19: a systematic mapping study
V Tintín, H Florez - Computational Science and Its Applications–ICCSA …, 2021 - Springer
Abstract On March 11 2020, the World Health Organization (WHO) announced that the new
COVID-19 disease, caused by the SARS-CoV2 could be considered a pandemic. Both this …
COVID-19 disease, caused by the SARS-CoV2 could be considered a pandemic. Both this …
Relative Learning Rate Adaptation on Loss Feedback
In the realm of Deep Learning (DL), optimization of hyperparameters like learning rate has
been one of the well-known challenges. To address this problem, previous works have …
been one of the well-known challenges. To address this problem, previous works have …