[HTML][HTML] Performance and limitation of machine learning algorithms for diabetic retinopathy screening: meta-analysis
Background Diabetic retinopathy (DR), whose standard diagnosis is performed by human
experts, has high prevalence and requires a more efficient screening method. Although …
experts, has high prevalence and requires a more efficient screening method. Although …
Feature selection techniques in the context of big data: taxonomy and analysis
Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …
production of big data, as enormous volumes of data with high dimensional features grow …
Review of swarm intelligence-based feature selection methods
In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
A deep learning ensemble approach for diabetic retinopathy detection
Diabetic Retinopathy (DR) is an ophthalmic disease that damages retinal blood vessels. DR
causes impaired vision and may even lead to blindness if it is not diagnosed in early stages …
causes impaired vision and may even lead to blindness if it is not diagnosed in early stages …
Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding
the chemical, physical, and biological functions of the soil. This study proposes machine …
the chemical, physical, and biological functions of the soil. This study proposes machine …
A novel community detection based genetic algorithm for feature selection
The feature selection is an essential data preprocessing stage in data mining. The core
principle of feature selection seems to be to pick a subset of possible features by excluding …
principle of feature selection seems to be to pick a subset of possible features by excluding …
Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification
JD Bodapati, NS Shaik, V Naralasetti - Journal of Ambient Intelligence and …, 2021 - Springer
Diabetic Retinopathy (DR) is a micro vascular complication caused by long-term diabetes
mellitus. Unidentified diabetic retinopathy leads to permanent blindness. Early identification …
mellitus. Unidentified diabetic retinopathy leads to permanent blindness. Early identification …
Stream water quality prediction using boosted regression tree and random forest models
Reliable water quality prediction can improve environmental flow monitoring and the
sustainability of the stream ecosystem. In this study, we compared two machine learning …
sustainability of the stream ecosystem. In this study, we compared two machine learning …
Blended multi-modal deep convnet features for diabetic retinopathy severity prediction
Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness
across the world. It is usually found in patients who suffer from diabetes for a long period …
across the world. It is usually found in patients who suffer from diabetes for a long period …
A two-layer feature selection method using genetic algorithm and elastic net
F Amini, G Hu - Expert Systems with Applications, 2021 - Elsevier
Feature selection, as a critical pre-processing step for machine learning, aims at determining
representative predictors from a high-dimensional feature space dataset to improve the …
representative predictors from a high-dimensional feature space dataset to improve the …