Application of waikato environment for knowledge analysis based artificial neural network models for wind speed forecasting

Azeem, Abdul; Kumar, Gaurav; Malik, Hasmat

doi:10.1109/poweri.2016.8077352

Cited by 18 publications

(1 citation statement)

References 16 publications

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“…Attribute importance can be relatively measured and quantified based on information obtained from the models. The advantage of measuring the importance based on built model information is that being closely tied to the model performance it incorporates the correlation structure between the predictors into the importance calculation [44]. We recorded the attribute importance of RF based on average impurity decrease (and number of nodes using that attribute) in WEKA via the information gain with respect to the outcome [45,46].…”

Section: Important Attribute Evaluationmentioning

confidence: 99%

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

Furxhi¹,

Murphy²

2020

IJMS

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The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

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Section: Important Attribute Evaluationmentioning

confidence: 99%