Application of Inverse Neural Networks for Optimal Pretension of Absorbable Mini Plate and Screw System

Pimentel-Mendoza, Alex Bernardo; Rico-Pérez, Lázaro; Solis, Manuel Javier Rosel; Villarreal-Gómez, Luis Jesús; Vega, Yuridia; Ramírez, José Omar Dávalos

doi:10.3390/app11031350

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…However, one hidden layer is commonly used for simple predictions [43]. The quantity of neurons of input and output layers is equal to the number of input and output variables, respectively [44]. In the case of the backpropagation ANN prediction model in this study, the quantity of neurons of input and output layers was one, since there was only one variable.…”

Section: Ann Prediction Modelmentioning

confidence: 99%

Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data

Jierula

Wang

et al. 2021

Applied Sciences

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Accuracy metrics have been widely used for the evaluation of predictions in machine learning. However, the selection of an appropriate accuracy metric for the evaluation of a specific prediction has not yet been specified. In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics. Test results showed that the training algorithm of “TRAINGLM” exhibited the best performance for predicting damage locations in deep piles. Subsequently, the artificial neural networks were trained using three different datasets collected from three acoustic emission sensor groups, and the prediction accuracies of three models were evaluated with the seven different accuracy metrics. The test results showed that the dataset collected from the pile body-installed sensors group exhibited the highest accuracy for predicting damage locations in deep piles. Subsequently, the correlations between the seven accuracy metrics and the sensitivity of each accuracy metrics were discussed based on the analysis results. Eventually, a novel selection method for an appropriate accuracy metric to evaluate the accuracy of specific predictions was proposed. This novel method is useful to select an appropriate accuracy metric for wide predictions, especially in the engineering field.

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Section: Ann Prediction Modelmentioning

confidence: 99%

Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data

Jierula

Wang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

Artificial Intelligence, Machine Learning, and Neural Network

Choi

2023

Tissue Repair and Reconstruction

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Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

Akiner,

Kartal,

Guzeler

et al. 2024

Earth Sci Inform

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The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

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Application of Inverse Neural Networks for Optimal Pretension of Absorbable Mini Plate and Screw System

Cited by 4 publications

References 27 publications

Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data

Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data

Artificial Intelligence, Machine Learning, and Neural Network

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

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