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Accurate delineation of debris-flow-endangered areas (e.g., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. However, existing machine-learning models are generally developed based on a very limited number of observation data, which may result in the predictive model overfitting or underfitting. How to develop a robust model for accurate forecasting of debris-flow-endangered areas still remains a difficult task. This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. The proposed method takes the calculated maximum runout distance obtained from the empirical model as supplementary inputs to increase the amount of training data to construct hybrid machine-learning models. Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. Then, these three machine-learning models are combined with an empirical relationship developed using the same training datasets to generate corresponding hybrid models. Finally, the performance metrics (i.e., coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE)) of the proposed hybrid models are comprehensively investigated and compared with the single predictive model (i.e., MARS, RF, SVM, and the empirical model) under fivefold cross-validation. The proposed method is illustrated using 134 channelized debris-flow events in Sichuan province, China. Results show that compared with the three individual machine-learning models, hybridization of machine-learning algorithms and the empirical model results in R2, RMSE, and MAE improved by 70.5%, 32.9%, and 41.1%, respectively. In contrast to the empirical model, the R2, RMSE, and MAE value of the proposed hybrid models are improved by 29.6%, 22.3%, and 32.5%, respectively. The proposed hybrid models generally perform better than the single machine-learning and the empirical model, providing a promising tool for accurate forecasting of a debris-flow-endangered area. [ABSTRACT FROM AUTHOR]

In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China's financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability. [ABSTRACT FROM AUTHOR]

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China's sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China's educational methods in the future. [ABSTRACT FROM AUTHOR]

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China's Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map. [ABSTRACT FROM AUTHOR]

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity. [ABSTRACT FROM AUTHOR]

In the past 30 years, a substance that can prevent burning has played an important role in reducing the loss of life and property caused by fire. At present, the total amount of antibaking agent is second only to plasticizer and various plastic additives in the world. With an average annual growth rate of 0.5% from 2019 to 2021. The fire protection industry is a regulatory industry and a globally competitive industry. Therefore, the entry into force and gradual improvement of relevant laws and regulations at home and abroad will affect the pattern of the whole fire protection industry. China's "Twelfth Five-Year Plan" will bring a substance that can prevent burning into key development industries and form a strategic alliance for technological innovation of green substance that can prevent burning industries. It provides a policy platform for the development of a substance that can prevent burning in industry. Firstly, we introduce several common a substance that can prevent burnings, then we use machine learning method to establish prediction model and to analyze the performance of composite combustibles. And draw the conclusion: composite material is better than the other two composites; among the six machine learning algorithms, the gradient boosting regression (GBR) model has the best prediction ability, followed by the extra tree regressor (ETR) model and the random forest regressor (RFR) model. Compared with the above three integrated algorithms, Ridge, Ada Boost regressor (ABR), and Lasso regression algorithms have relatively poor prediction results. [ABSTRACT FROM AUTHOR]

How to promote air traveler repurchasing has become an important marketing strategy in airlines. However, because of the growing concern over user privacy, effectively and accurately delivering advertising to promote repurchasing has become more difficult. Here, we propose an effective framework based on machine learning to model the air traveler repurchasing and furthermore employ a field experiment to test the utility of a model framework. Specifically, we collected that this model framework is based on the random forest algorithm and compared with the conclusions of the other four algorithms, K-nearest neighbor, decision tree, support vector machine, and ExtraTree algorithms. The results show that the proposed model framework is better than the prediction results of the other algorithms. In addition, the proposed model framework was verified through a real case of an airline in China. This study will serve as a guide to analyze the repurchase behaviors of an air traveler and help airlines build a loyal air traveler base. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination ( R 2 ), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata. [ABSTRACT FROM AUTHOR]