Stacking strategy-assisted random forest algorithm and its application

Article Engineering, Multidisciplinary

Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

Yongliang Yuan et al.

Summary: In this paper, a COVID-19 prevention-inspired bionic optimization algorithm called Coronavirus Mask Protection Algorithm (CMPA) is proposed. The CMPA is based on the infection and immunity process of COVID-19 and simulates self-protection behavior mathematically to offer an optimization algorithm. The performance of CMPA is evaluated and compared to other metaheuristic optimizers, demonstrating its competitiveness. Furthermore, CMPA is applied to identify parameters of a gantry crane's main girder, resulting in improvements in mass and deflection.

JOURNAL OF BIONIC ENGINEERING (2023)

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Article Computer Science, Artificial Intelligence

Genetic algorithm-assisted an improved AdaBoost double-layer for oil temperature prediction of TBM

Jianji Ren et al.

Summary: In this study, a method based on genetic algorithm and improved AdaBoost double-layer learner (GA-ADA-RF) is proposed to predict the oil temperature of TBM. The GA-ADA-RF constructs a double-level learner by introducing random forests as weak learners of AdaBoost and using random sampling with replacement. The fitness function is established using the classification evaluation of the algorithm. Compared to other algorithms, the GA-ADA-RF has better prediction performance and has shown potential in complex projects similar to TBM.

ADVANCED ENGINEERING INFORMATICS (2022)

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Article Computer Science, Interdisciplinary Applications

Alpine skiing optimization: A new bio-inspired optimization algorithm

Yongliang Yuan et al.

Summary: A novel swarm intelligence optimization algorithm called Alpine Skiing Optimization (ASO) is proposed, inspired by the behaviors of skiers competing for the championship. The ASO algorithm simulates and develops the winning behavior of skiers from a mathematical perspective and demonstrates competitive results compared to other optimization algorithms.

ADVANCES IN ENGINEERING SOFTWARE (2022)

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Article Computer Science, Artificial Intelligence

Optimization of an auto drum fashioned brake using the elite opposition-based learning and chaotic k-best gravitational search strategy based grey wolf optimizer algorithm

Yongliang Yuan et al.

Summary: This paper proposes a novel assisted optimization strategy, EOCS, for tackling highly non-linear optimization problems. By combining the elite opposition-based learning strategy and chaotic k-best gravitational search strategy, the proposed method demonstrates competitive accuracy and robustness, ranking first among the compared optimization algorithms.

APPLIED SOFT COMPUTING (2022)

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Article Nanoscience & Nanotechnology

Remaining useful life prediction for equipment based on RF-BiLSTM

Zhiqiang Wu et al.

Summary: The prediction technology of remaining useful life plays an important role in ensuring the reliability and stability of complex mechanical equipment. Machine learning, with its ability to handle large-scale, non-linear, and high-dimensional monitoring data, shows great potential in the field of life prediction. This study proposes a RF-BiLSTM algorithm for remaining useful life prediction, which combines random forest and bi-directional long short-term memory neural network. Experimental results demonstrate that RF-BiLSTM outperforms other algorithms in terms of prediction accuracy, highlighting its application value.

AIP ADVANCES (2022)

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Article Thermodynamics

Electrical load-temperature CNN for residential load forecasting

Maryam Imani

Summary: A new nonlinear relationship extraction method is proposed in this work, using convolutional neural network and support vector regression for load forecasting, showing superior performance compared to several outstanding forecasters.

ENERGY (2021)

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Article Thermodynamics

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

Haixiang Zang et al.

Summary: This paper proposes a novel day-ahead residential load forecasting method based on feature engineering, pooling, and a hybrid deep learning model. Case studies on a practical dataset demonstrate the effectiveness and superiority of the proposed method.

ENERGY (2021)

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Article Business, Finance

Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms

Jaewon Park et al.

Summary: The study aims to identify important variables representing the BIS capital adequacy ratio using machine learning techniques and found that the BRNN model predicted the ratio more accurately than the OLS model based on past 12 years of data from all domestic banks in South Korea.

RISKS (2021)

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Article Computer Science, Information Systems