On-line voltage stability monitoring using an Ensemble AdaBoost classifier

Maaji, Salim S.; Cosma, Georgina; Taherkhani, Aboozar; Alani, Ali A.; McGinnity, TM

doi:10.1109/infoman.2018.8392845

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…The same algorithms are compared in [58], but this time considering also the random forest algorithm. Reference [85] compares decision trees, SVMs, core vector machines and naive Bayes models while [91] compares several ensemble methods (XGBoost, Bagging, Random Forest, and AdaBoost) with Naive Bayes, k Nearest Neighbor (kNN) and decision trees, this time for voltage stability assessment. In [59], the authors propose an automated multi-model approach for online security assessment.…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…32 XGBoost is another boosting technique implementing gradient boosted decision trees with advanced speed and performance. 33 Gradient boosting, which is a gradient descent method in function space capable of tting non parametric predictive models, have been empirically demonstrated to be accurate when applied to the tree models. 34 The parameters of AdaBoost and XGBoost were tuned experimentally: the best case was chosen among multiple cases with randomly selected parameters.…”

Section: Algorithmsmentioning

confidence: 99%

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Park

Kim

et al. 2021

Preprint

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Background: Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm.Methods: We retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root mean square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.Results: AdaBoost achieved the highest performance with an RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was pre-operative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon.Conclusions: Among the diverse machine learning algorithms, AdaBoost exhibited the highest performance in prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.

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“…Adaboost is an iterative ensemble method boosting classifier. Badly classified data take a higher weight than well-classified data (Maaji, Cosma, Taherkhani, Alani, & McGinnity, 2018). NASNet is a scalable CNN architecture that consists of a cell that combines blocks that are optimized using reinforcement learning (Zoph, Vasudevan, Shlens, & Le, 2018).…”

Section: Experimental Settingmentioning

confidence: 99%

Automatic Classification of COVID-19 using CT-Scan Images

Reis

2021

Acta Sci Technol

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Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F1- score, root mean squared error (RMSE), and receiver operating characteristic- area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated.

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On-line voltage stability monitoring using an Ensemble AdaBoost classifier

Cited by 15 publications

References 17 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Artificial Intelligence-Based Nomogram for Small-Incision Lenticule Extraction

Automatic Classification of COVID-19 using CT-Scan Images

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