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    Developing effective optimized machine learning approaches for settlement prediction of shallow foundation
    (2024-09-15)
    Khajehzadeh, Mohammad
    ;
    Keawsawasvong, Suraparb
    ;
    Kamchoom, Viroon
    ;
    Shi, Chao
    ;
    Khajehzadeh, Alimorad
    The precise assessment of shallow foundation settlement on cohesionless soils is a challenging geotechnical issue, primarily due to the significant uncertainties related to the factors influencing the settlement. This study aims to create an advanced hybrid machine learning methodology for accurately estimating shallow foundations' settlement (Sm). The initial contribution of the current research is developing and validating a robust hybrid optimization methodology based on an artificial electric field and single candidate optimizer (AEFSCO). This approach is thoroughly tested using various benchmark functions. AEFSCO will also be used to optimize three useful machine learning methods: long short-term memory (LSTM), support vector regression (SVR), and multilayer perceptron neural network (MLPNN) by adjusting their hyperparameters for predicting the settlement of shallow foundations. A database consisting of 189 individual case histories, conducted through various investigations, was used for training and testing the models. The database includes five input parameters and one output. These factors encompassed both the geometric characteristics of the foundation and the properties of the sandy soil. The results demonstrate that employing effective optimization strategies to adjust the ML models’ hyperparameters can significantly improve the accuracy of predicted results. The AEFSCO has increased the coefficient of determination (R<sup>2</sup>) value of the MLPNN model by 9.3 %, the SVR model by 8 %, and the LSTM model by 22 %. Also, the LSTM-AEFSCO model is more accurate than the SVR-AEFSCO and MLPNN-AEFSCO models. This is shown by the fact that R<sup>2</sup> went from 0.9494 to 0.9290 to 0.9903, which is an increase of 4.5 % and 6 %.
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    A deep learning model for predicting buy and sell recommendations in stock exchange of Thailand using long short-term memory
    (2019-02-01)
    Sanboon, Thaloengpattarakoon
    ;
    Keatruangkamala, Kamol
    ;
    Jaiyen, Saichon
    Nowadays, the stock price prediction has been one of the most challenging problem to the AI research community. Most prediction techniques concentrate on forecasting the future prices of stocks based on conventional Machine learning techniques. However, these techniques cannot capture long term dependencies in stock price data. Therefore, they cannot consider the relation between the current predicted data and the previous data in stock data. This research adopts deep learning techniques for predicting buy and sell recommendations in Stock Exchange of Thailand using Long Short-Term Memory. The proposed model can capture long term dependencies in stock price data in order to enhance the prediction accuracy. The accuracy of the proposed model is evaluated on five Stock Exchange of Thailand (SET) stocks, between 5 January 2015 and 29 December 2017, and compared the results with support vector Machine, multilayer perceptron, decision tree, random forest, logistic regression and k-nearest neighbors. The experimental results signify that the proposed model can outperform all comparative models.