A survey on unbalanced classification: How can evolutionary computation help?
Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …
widespread attention from both the academic and industrial communities due mainly to its …
COVID-19 image classification using deep features and fractional-order marine predators algorithm
Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-
19, which causes dangerous symptoms to humans and animals, its complications may lead …
19, which causes dangerous symptoms to humans and animals, its complications may lead …
Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm
Recently, research has shown an increased spread of non-communicable diseases such as
cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in …
cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in …
bSSA: binary salp swarm algorithm with hybrid data transformation for feature selection
Feature selection is a technique commonly used in Data Mining and Machine Learning.
Traditional feature selection methods, when applied to large datasets, generate a large …
Traditional feature selection methods, when applied to large datasets, generate a large …
Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm
Abstract In the Internet of Things (IoT), the data that are sent via devices are sometimes
unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks …
unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks …
Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach
In medical practice, all decisions, as for example the diagnosis based on the classification of
images, must be made reliably and effectively. The possibility of having automatic tools …
images, must be made reliably and effectively. The possibility of having automatic tools …
Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse …
PS Chen, ZY Zeng - Applied Soft Computing, 2020 - Elsevier
Many researchers have studied optimization problems with soft and hard constraints, such
as school timetabling, nurse rostering, vehicle routing with soft time window, and …
as school timetabling, nurse rostering, vehicle routing with soft time window, and …
An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer
The performance of neural network is largely dependent on their capability to extract very
discriminant features supporting the characterization of abnormalities in the medical image …
discriminant features supporting the characterization of abnormalities in the medical image …
An effective neural network model for lung nodule detection in CT images with optimal fuzzy model
BKJ Veronica - Multimedia Tools and Applications, 2020 - Springer
Cancer disease is assumed as a gathering of diseases which is initiated because of
uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the …
uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the …
[PDF][PDF] Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering
SSM Ali, AH Alsaeedi, D Al-Shammary… - Indones. J. Electr. Eng …, 2021 - academia.edu
This paper proposes efficient models to help diagnose respiratory (SARSCOVID19)
infections by developing new data descriptors for standard machine learning algorithms …
infections by developing new data descriptors for standard machine learning algorithms …