Ensemble super learner based genotoxicity prediction of multi-walled carbon nanotubes

Latha, B.; Pravin, Sheena Christabel; Saranya, J.; Manikandan, E.

doi:10.1016/j.comtox.2022.100244

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…The aggregation of learners evolved from the stacked generalization model [16]. Further experimentation demonstrates the capability of stacking predictors for meta-learning [6], [14], [15], [17]- [24], with variations extending eSL functions for a specific set of tasks. eSL solves some of the bottlenecks common with individual models, such as an expectation space that is overly large for the quantity of available training data, an analytical challenge which guarantees a global optimum, and an individual model that lacks a well-defined approximation for model distribution outcomes.…”

Section: E(y |X)mentioning

confidence: 99%

“…eSL solves some of the bottlenecks common with individual models, such as an expectation space that is overly large for the quantity of available training data, an analytical challenge which guarantees a global optimum, and an individual model that lacks a well-defined approximation for model distribution outcomes. This study focuses on stacked eSL for load-shedding task [6], [17]. Details of the schema are established in section II.…”

Section: E(y |X)mentioning

confidence: 99%

“…According to Latha, Pravin, Saranya, et al [17], the super learner approach was reliable for compressive value forecasting in high-performance concrete. In another study, Lee, Nguyen, Karamanli, et al [14] executed heterogeneous combinations to predict the genotoxic description for different Multi-Walled Carbon Nano Tubes.…”

Section: Ensemble Super Learnermentioning

confidence: 99%

“…where X(X ∈ χ) and Y are the input parameters and the result of interest, respectively. The outcome of the regression is described as the minimization of the predicted loss E[ι(Obj, ψ)] and expressed in (17),…”

Section: Ensemble Super Learnermentioning

confidence: 99%

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Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Akinola,

Olukanmi,

Wang

et al. 2024

IEEE Access

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load-shedding is vital for managing electrical power shortages and avoiding grid collapse. However, excessive electricity demand poses an imminent threat to the overall stability of power grid system (PGS) and its ability to run safely and reliably. Load-shedding strategies can be complicated and inadequate to manage electrical power system efficiently. The study proposed a data-driven load-shedding time series classification (TSC) technique employing a heterogeneous ensemble super learner (eSL) to categorize loadshedding based on contributing features. The model investigated challenges with binary classification while using a multidimensional time series for South Africa's hourly load-shedding stages in MW collected from PGS data. Considering that load-shedding is planned and predicted based on contributing features, we use these features as strong indicators to classify expected outcomes for load-shedding or no load-shedding. Validation tests for the suggested technique included the precision recall curve, the confusion matrix, the class likelihood ratio, the Brier skill scores and critical difference factor (CDF). Logistic regression (LR) produced the highest CDF average score, while support vector classifier (SVC) had the highest balanced precision (90.694%). The recursive feature elimination (RFE) model exhibited the most significant true negative and true positive counts, at 50.59% and 40.84%, respectively, and the highest proportion of valid classifications.INDEX TERMS ensemble, super learner, recursive feature elimination, and time series classification. NOMENCLATURE

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Section: E(y |X)mentioning

confidence: 99%

Section: E(y |X)mentioning

confidence: 99%

Section: Ensemble Super Learnermentioning

confidence: 99%

Section: Ensemble Super Learnermentioning

confidence: 99%

See 2 more Smart Citations

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Akinola,

Olukanmi,

Wang

et al. 2024

IEEE Access

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“…Performance metrics of efficient DenseNet model Table15, the proposed efficient DenseNet model performs well in unique feature extraction for accurate classification of the severity levels of DR, and it has enhanced the efficacy of DR screening. Moreover, the computational complexity[24][25][26][27][28][29] has been reduced compared with the baseline models. The metrics such as precision, recall, and F1 score are used to monitor the grading of DR by the efficient DenseNet model as depicted in Table16, along with the trainable parameters in Table17.…”

mentioning

confidence: 99%

An Efficient DenseNet for Diabetic Retinopathy Screening

Pravin

Sabapathy²,

Selvakumar³

et al. 2023

Int. j. eng. technol. innov.

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This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%.

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Unveiling the potential activities of myriad carbonaceous nanomaterials in scaling and fabrication of miniaturized, sensitive, and economical medical diagnostic kits

Chen,

Lai,

Murugan

et al. 2024

Carbon-Based Nanomaterials in Biosystems

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Ensemble super learner based genotoxicity prediction of multi-walled carbon nanotubes

Cited by 5 publications

References 17 publications

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

An Efficient DenseNet for Diabetic Retinopathy Screening

Unveiling the potential activities of myriad carbonaceous nanomaterials in scaling and fabrication of miniaturized, sensitive, and economical medical diagnostic kits

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