Optimization of Antibacterial, Structures, and Thermal Properties of Alginate-ZrO2 Bionanocomposite by the Taguchi Method

Safaei, Mohsen; Moradpoor, Hedaiat; Mobarakeh, Mohammad Salmani; Fallahnia, Nima

doi:10.1155/2022/7406168

Cited by 2 publications

(1 citation statement)

References 32 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tseng et al (2022) used the Taguchi method with an indigenous polymethyl methacrylate (PMMA) slit gauge to optimize the image quality of brain gray and white matter [31]. Safaei et al (2022) applied the Taguchi method to test the antimicrobial properties of an alginate/zirconia bionanocomposite [32]. Lagzian et al (2022) used the Taguchi method and a SN ratio to identify key characteristics and optimize their agentbased model for the accurate analysis of cancer stem cells and tumor growth, with migration, tumor location, and cell senescence identified as the most important features [33].…”

Section: Taguchi Methodsmentioning

confidence: 99%

Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk

Lin,

Lin

2023

Bioengineering

View full text Add to dashboard Cite

The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TSTO) method to boost predictive accuracy in an artificial neural network (ANN) model while minimizing computational costs. In the first stage, optimal hyperparameter levels and trends were identified. The second stage determined the best settings for the ANN model’s hyperparameters. In this study, we applied the proposed TSTO method with a personal computer to the Kaggle Cardiovascular Disease dataset. Subsequently, we identified the best setting for the hyperparameters of the ANN model, setting the hidden layer to 4, activation function to tanh, optimizer to SGD, learning rate to 0.25, momentum rate to 0.85, and hidden nodes to 10. This setting led to a state-of-the-art accuracy of 74.14% in predicting the risk of cardiovascular disease. Moreover, the proposed TSTO method significantly reduced the number of experiments by a factor of 40.5 compared to the traditional grid search method. The TSTO method accurately predicts cardiovascular risk and conserves computational resources. It is adaptable for low-power devices, aiding the goal of POCT.

show abstract