Edge AI for early detection of chronic diseases and the spread of infectious diseases: opportunities, challenges, and future directions
E Badidi - Future Internet, 2023 - mdpi.com
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge
devices, is quickly becoming a critical component in early health prediction. Edge AI …
devices, is quickly becoming a critical component in early health prediction. Edge AI …
[HTML][HTML] A robust predictive diagnosis model for diabetes mellitus using Shapley-incorporated machine learning algorithms
With the rapid advancement and integration of Artificial Intelligence (AI) in medicine, the
need for new developments and accuracy in Machine Learning (ML) models and algorithms …
need for new developments and accuracy in Machine Learning (ML) models and algorithms …
[HTML][HTML] A risk assessment and prediction framework for diabetes mellitus using machine learning algorithms
Diabetes disease seriously threatens people's health and is becoming more common
nowadays. Diabetes Mellitus (DM) is a condition caused by high blood sugar levels …
nowadays. Diabetes Mellitus (DM) is a condition caused by high blood sugar levels …
[HTML][HTML] The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification
Breast cancer is one of the most common causes of death among women, and early
diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used …
diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used …
A federated learning-inspired evolutionary algorithm: Application to glucose prediction
In this paper, we propose an innovative Federated Learning-inspired evolutionary
framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is …
framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is …
[HTML][HTML] A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients
Dengue is a common viral disease in tropical and subtropical countries. The clinical
manifestation of dengue has a wide spectrum, from asymptomatic seroconversion to severe …
manifestation of dengue has a wide spectrum, from asymptomatic seroconversion to severe …
[HTML][HTML] Modelo de inteligencia artificial para la detección temprana de diabetes
W Hoyos, K Hoyos, R Ruiz-Pérez - Biomédica, 2023 - scielo.org.co
Objetivo. Desarrollar un modelo basado en la inteligencia artificial para apoyar la toma de
decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó …
decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó …
Classification, Regression, and Survival Rule Induction with Complex and M-of-N Elementary Conditions
Most rule induction algorithms generate rules with simple logical conditions based on
equality or inequality relations. This feature limits their ability to discover complex …
equality or inequality relations. This feature limits their ability to discover complex …
Bayesian optimization for sparse neural networks with trainable activation functions
M Fakhfakh, L Chaari - IEEE Transactions on Pattern Analysis …, 2024 - ieeexplore.ieee.org
In the literature on deep neural networks, there is considerable interest in developing
activation functions that can enhance neural network performance. In recent years, there has …
activation functions that can enhance neural network performance. In recent years, there has …
[HTML][HTML] An investigation of the COVID-19 impact on liver cancer using exploratory and predictive analytics
This study presents the influence of COVID-19 and the pandemic on individuals diagnosed
with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common …
with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common …