Hybrid of AdaBoost and Optimized LSTM Using Modified Bald Eagle Search in Predicting Concrete Compressive Strength

Nowadays, many machine learning techniques are widely integrated into human life sectors such as transportation, industries, and others. LSTM has been vastly used to forecast sequential problems. Hybridization between several models has started to emerge recently to improve the model performance due...

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Bibliographic Details
Published inInternational Journal on Electrical Engineering and Informatics Vol. 15; no. 4; pp. 527 - 553
Main Authors Christine, Herny, Mandala, Rila
Format Journal Article
LanguageEnglish
Published Bandung School of Electrical Engineering and Informatics, Bandung Institute of Techonolgy, Indonesia 01.12.2023
Subjects
Accuracy
Algorithms
Compressive strength
Concrete properties
Crude oil
Crude oil prices
Datasets
Decomposition
Forecasting
Foreign exchange rates
Hybridization
Machine learning
Mathematical models
Neural networks
Optimization
Parameters
Regression analysis
Search algorithms
Time series
Wind power
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Summary:Nowadays, many machine learning techniques are widely integrated into human life sectors such as transportation, industries, and others. LSTM has been vastly used to forecast sequential problems. Hybridization between several models has started to emerge recently to improve the model performance due to its capabilities compared to single model. Latest researches have applied hybridization of LSTM model with other algorithms such as LSTMAdaBoost, LSTM-BES, and others. However, the optimization and exploration in selecting the learning parameters of the model can be subsequently improved to increase the accuracy. Therefore, this paper aims to propose a hybrid model of AdaBoost and optimized LSTM using modified Bald Eagle Search algorithm to predict concrete compressive strength as the dataset. Modified Bald Eagle Search algorithm is incorporated to find the optimum solution and parameters of LSTM model. By combining the behaviour of AdaBoost into the model and using Quasi opposition-based learning to enhance the performance of Bald Eagle Search algorithm, thus, it can further improve the accuracy in predicting concrete compressive strength. The research results have shown that the proposed model has obtained better MSE, MAE, and MAPE compared to LSTM, LSTM-BES, and LSTM-MBES model.
ISSN:2085-6830
2087-5886
DOI:10.15676/ijeei.2023.15.4.2