Cataract grading method based on deep convolutional neural networks and stacking ensemble learning
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22722
Abstract: The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In… read more here.
Keywords: neural networks; ensemble learning; stacking ensemble; fundus images ... See more keywords
Ensemble Learning for Early‐Response Prediction of Antidepressant Treatment in Major Depressive Disorder
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Magnetic Resonance Imaging"
DOI: 10.1002/jmri.27029
Abstract: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Together with machine‐learning methods, prediction models have proved to be valuable for baseline prediction. read more here.
Keywords: response prediction; early response; treatment; learning early ... See more keywords
Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the science of food and agriculture"
DOI: 10.1002/jsfa.12376
Abstract: BACKGROUND Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily… read more here.
Keywords: stacking blending; ensemble learning; rape leaves; rape ... See more keywords
Multi-matrices entropy discriminant ensemble learning for imbalanced problem
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-019-04306-6
Abstract: The objective of this paper is to make an improvement on ensemble learning for imbalanced problem. Multi-matrices approach and nearest entropy are introduced into model of base classifier for the sake of utilizing spatial information… read more here.
Keywords: learning imbalanced; matrices entropy; imbalanced problem; multi matrices ... See more keywords
A stacked ensemble learning model for intrusion detection in wireless network
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04986-5
Abstract: Intrusion detection pretended to be a major technique for revealing the attacks and guarantee the security on the network. As the data increases tremendously every year on the Internet, a single algorithm is not sufficient… read more here.
Keywords: ensemble learning; intrusion detection; network; stacked ensemble ... See more keywords
Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05172-3
Abstract: Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of… read more here.
Keywords: machine learning; ensemble learning; streamflow forecasting; super ensemble ... See more keywords
Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Intelligent Information Systems"
DOI: 10.1007/s10844-019-00550-3
Abstract: This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster… read more here.
Keywords: time series; electricity; learning methods; unsupervised ensemble ... See more keywords
Research of SVM ensembles in medical examination scheduling
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Combinatorial Optimization"
DOI: 10.1007/s10878-019-00510-1
Abstract: In order to solve the problem of deterioration of the generalization ability caused by support vector machine (SVM), this paper proposes a regression prediction method based on SVM ensemble learning. The grid search method is… read more here.
Keywords: research; svm; examination scheduling; ensemble learning ... See more keywords
An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Medical Systems"
DOI: 10.1007/s10916-019-1259-8
Abstract: Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public… read more here.
Keywords: control level; classifier supervised; based classifier; ensemble learning ... See more keywords
An evolutionary generation method of deep neural network sets combined with Gaussian random field
Sign Up to like & getrecommendations! Published in 2021 at "Wireless Networks"
DOI: 10.1007/s11276-021-02677-0
Abstract: As a research hotspot in the field of machine learning, ensemble learning improved the prediction accuracy of the final model by constructing and combining multiple basic models. In recent years, many experts and scholars are… read more here.
Keywords: neural network; field; ensemble learning; deep networks ... See more keywords
Improved hepatocellular carcinoma fatality prognosis using ensemble learning approach
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-021-03256-z
Abstract: Hepatocellular Carcinoma (HCC) is the most common type of liver cancer which accounts for around 75% of all liver cancer cases. From statistical data, it has been found that fatality due to liver cancer is… read more here.
Keywords: ensemble learning; model; prediction; fatality ... See more keywords