Introduction to Finite-element-model Updating

doi:10.1007/978-1-84996-323-7_1

Cited by 2 publications

(1 citation statement)

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“…Among several training algorithms, the scaled conjugate gradient was selected due to its computationally efficient. 64 The Sigmoid activation function was employed for the hidden layer, while the softmax activation function was utilized for the output layer. Bayesian hyperparameter optimization was adapted to find the best possible number of neurons in the hidden layer and the network's learning rate.…”

Section: Development Of Ann Modelmentioning

confidence: 99%

Predicting sun protection measures against skin diseases using machine learning approaches

Sultana

2021

J of Cosmetic Dermatology

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Background The substantial growth rate of skin cancer has necessitated adequate protection from solar radiation. Consequently, analyzing sun protection practices is an imperative research area in dermatology and pharmacology. Aims This paper aims to analyze public sun‐protection manners in the Arabian Peninsula regions. Methods A simple random survey was conducted to assess public sun protection manners. Artificial neural network (ANN) and support vector machine (SVM) were selected from several machine learning algorithms to create the models for predicting public sun protection measures based on the prediction accuracy. Model performances were evaluated based on several performance indicators depending on the confusion matrices and receiver operating characteristic curves. Results 51% of the respondents have a low level, and 49% have a high level of sun protection practices. The results showed that the SVM performed considerably amended than the ANN for predicting the response. The relative importance of the predictors for the best predictive SVM model was also analyzed. The predictors are ranked as: the number of times having sunburnt >gender > use seat belt while driving/riding a vehicle >considers the UV index for personal sun exposure >income based on the expenses >sports/exercise activities >consciousness of the chance for having sunburnt on extended exposure to the sun >age > having any skin problem >nationality > skin type. Conclusion These identified significant predictors might be considered for developing an effective policy to increase public consciousness using proper protection from solar radiation's detrimental effect to rule out skin diseases.

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Section: Development Of Ann Modelmentioning

confidence: 99%