An Artificial Intelligence Approach Based on Multi-layer Perceptron Neural Network and Random Forest for Predicting Maximum Dry Density and Optimum Moisture Content of Soil Material in Quang Ninh Province, Vietnam

Duc, Manh Nguyen; Sy, An Ho; Ngoc, Truong Nguyen; Thi, Thuy Linh Hoang

doi:10.1007/978-981-16-7160-9_176

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…The multilayer perceptron (MLP) is a neural network classification algorithm that learns a function

using a training dataset in which m and o are the numbers of dimensions for the input and the output, respectively [ 46 ]. The MLP procedure is divided into forward and backward propagations using the backpropagation algorithm and is used to generalize a nonlinear function demonstrated in Equation (6) [ 47 ]:

…”

Section: Methodsmentioning

confidence: 99%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

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“…The multilayer perceptron (MLP) is a neural network classification algorithm that learns a function

…”

Section: Methodsmentioning

confidence: 99%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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show abstract

“…When Random Forest receives an (x) input data point, it includes the amounts of the various input features probed for a given training area, which creates K regression trees and the averages of their results. The RF regression predictor, after such K -trees { T(x) } 1 K have been trained, is [ 27 ]:

…”

Section: Methodsmentioning

confidence: 99%

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug

et al. 2022

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The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10−1 7.573 × 10−2, and 7.119 × 10−2, respectively. According to these facts, GBRT was considered as the primary model in this paper. Using this method, the optimal amounts are calculated as: P = 380.88 bar, T = 333.01 K, Y = 0.001073.

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Accurate cooling load estimation using multi-layer perceptron machine learning models

Yang

2024

SIViP

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An Artificial Intelligence Approach Based on Multi-layer Perceptron Neural Network and Random Forest for Predicting Maximum Dry Density and Optimum Moisture Content of Soil Material in Quang Ninh Province, Vietnam

Cited by 7 publications

References 15 publications

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug

Accurate cooling load estimation using multi-layer perceptron machine learning models

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