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Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of easy implementation, less parameters and fast convergence. In the paper, genetic search is combined with the hybrid rice optimization algorithm (GHRO) and employed to obtain the optimal hyperparameter of the capsule network automatically, that is, a probability search technique and a hybridization strategy belong with the primary HRO. Thirteen benchmark functions are used to evaluate the performance of GHRO. Furthermore, the MNIST, Chest X-Ray (pneumonia), and Chest X-Ray (COVID-19 & pneumonia) datasets are also utilized to evaluate the capsule network learnt by GHRO. The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network, which is able to boost the performance of the capsule network on image classification. [ABSTRACT FROM AUTHOR]

In recent years, peer-to-peer (P2P) lending in China, which is a new form of unsecured financing that uses the Internet, has boomed, but the consequent credit risk problems are inevitable. A key challenge facing P2P lending platforms is accurately predicting the default probability of the borrower of each loan using the default prediction model, which effectively helps the P2P lending platform avoid credit risks. The traditional default prediction model based on machine learning and statistical learning does not meet the needs of P2P lending platforms in terms of default risk prediction because for data-driven P2P lending, credit data have a large number of missing values, are high-dimensional and have class-imbalanced problems, which makes it difficult to effectively train the default risk prediction model. To solve the above problems, this paper proposes a new default risk prediction model based on heterogeneous ensemble learning. Three individual classifiers, extreme gradient boosting (XGBoost), a deep neural network (DNN) and logistic regression (LR), are used simultaneously with a liner weight ensemble strategy. In particular, this model is able to process missing values. After generating discrete and rank features, this model adds missing values to the model for self-training. Then, the hyperparameters are optimized by the XGBoost model to improve the performance of the prediction model. Finally, compared with the benchmark model, the proposed method significantly improves the accuracy of the prediction results. In conclusion, the prediction method proposed in this paper solves the class-imbalanced problem. [ABSTRACT FROM AUTHOR]

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications. • A novel hybrid wind power forecasting system was proposed. • Solve problems of noise interference and high computing burden by data refining. • A strategy to fully extract nonlinear and linear data features was designed. • An enhanced multi-objective golden jackal algorithm was raised for optimization. • Pareto optimal solutions were theoretically proven to be achieved. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive SVR-based prediction framework for DO concentration was proposed. • Hybrid pathways to improve the SVR model using intelligent approaches were provided. • Water temperature and the CSPAR were the fixed predictors for DO in three stations. • Indiscriminate use of feature selection may cause overfitting after data denoising. • Combination of feature selection and HPO can effectively improve model robustness. Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it is still a challenging topic to accurately understand and predict the spatiotemporal variation of DO concentrations under the complex effects of different environmental factors. In this study, a practical prediction framework was proposed for DO concentrations based on the support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, and four hyperparameter optimization methods). The holistic framework was tested using a data matrix (17,532 observation data in total) of 12 indicators from three vital water quality monitoring stations of the longest inter-basin water diversion project in the world (i.e., the Middle-Route of the South-to-North Water Diversion Project of China), during the year 2017 to 2020 period. The results showed that the framework we advocated for could successfully and accurately predict DO concentration variations in different geographical locations. The model used the "wavelet analysis–LASSO regression–random search–SVR" combination of the Waihuanhe station has the best prediction performance, with the Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) values of 0.251, 0.063, 0.190, and 0.911, respectively. The combined methods using feature selection and hyperparameter optimization techniques can significantly promote the robustness and accuracy of the prediction model and can provide a new universal and practical way of investigating and understanding the environmental drivers of DO concentration variations. For the water quality management department, this proposed comprehensive framework can also identify and reveal the key parameters that should be concerned and monitored under different environmental factors change. More studies in terms of assessing potential integrated water quality risk using multi-indicators in mega water diversion projects and/or similar water bodies are required in the future. [ABSTRACT FROM AUTHOR]

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. [ABSTRACT FROM AUTHOR]