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A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China's county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods' accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops. [ABSTRACT FROM AUTHOR]

The public's support for emergency infrastructure projects, which will affect the government's credibility, social stability, and development, is very important. However, there are few systematic research findings on public support for emergency infrastructure projects. In order to explore the factors influencing the public's support and the degree of influence of each factor on the public's support, this paper employs K-Nearest Neighbors (KNN), a learning curve with m-fold cross-validation, grid search, and random forest to study the public's support for emergency infrastructure projects and its influencing factors. In this paper, a prediction model of the public's support for emergency infrastructure projects is developed based on KNN from data drawn from a questionnaire survey of 445 local residents concerning Wuhan Leishenshan Hospital, China. Two optimization algorithms, the learning curve with m-fold cross-validation and the grid search algorithm, are proposed to optimize the key parameters of the KNN predictive model. Additionally, quantitative analysis is conducted by using the random forest algorithm to assess the importance of various factors influencing public support. The results show that the prediction accuracy and model stability of the KNN prediction model based on the grid search algorithm are better than those using a learning curve with m-fold cross-validation. Furthermore, the random forest algorithm quantitative analysis shows that the most important factor influencing the public's support is government attention. The conclusions drawn from this paper provide a theoretical reference and practical guidance for decision making and the sustainable development of emergency infrastructure projects in China. [ABSTRACT FROM AUTHOR]

Poplar (Poplus spp.) is an important forest species widely distributed in China of great significance in identifying factors that clearly influence its growth rate in order to achieve effective control of poplar growth. In this study, we selected 16 factors, including tree size, competition, climate, location, topography, and soil characteristics, to construct linear regression (LR), multilayer perceptron (MLP), k-nearest neighbor regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN) models based on the poplar growth rate. Using model fusion methods, the fitting accuracy and estimation capability were improved. The relative importance of each variable in estimating the poplar growth rate was analyzed using the permutation importance evaluation. The results showed the following: (1) the model fusion approach significantly improved the estimation accuracy of the poplar growth rate model with an R2 of 0.893; (2) the temperature and precipitation exhibited the highest importance in poplar growth; (3) the forest stand density, precipitation, elevation, and temperature had significant variations in growth rates among different-sized poplar trees within different ranges; (4) low-forest stand density, high-precipitation, low-elevation, and high-temperature environments significantly increased the poplar growth rate and had a larger proportion of large-sized individuals with high growth rates. In conclusion, environmental factors significantly influence poplar growth, and corresponding planting and protection measures should be tailored to different growth environments to effectively enhance the growth of poplar plantations. [ABSTRACT FROM AUTHOR]

Flash floods are a frequent and highly destructive natural hazard in China. In order to prevent and manage these disasters, it is crucial for decision-makers to create GIS-based flash flood susceptibility maps. In this study, we present an improved Blending approach, RF-Blending (Reserve Feature Blending), which differs from the Blending approach in that it preserves the original feature dataset during meta-learner training. Our objectives were to demonstrate the performance improvement of the RF-Blending approach and to produce flash flood susceptibility maps for all catchments in Jiangxi Province using the RF-Blending approach. The Blending approach employs a double-layer structure consisting of support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) as base learners for level-0, and the output of level-0 is utilized as the meta-feature dataset for the meta-learner in level-1, which is logistic regression (LR). RF-Blending employs the output of level-0 along with the original feature dataset for meta-learner training. To develop flood susceptibility maps, we utilized these approaches in conjunction with historical flash flood points and catchment-based factors. Our results indicate that the RF-Blending approach outperformed the other approaches. These can significantly aid catchment-based flash flood susceptibility mapping and assist managers in controlling and remediating induced damages. [ABSTRACT FROM AUTHOR]

A rapid identification method for flue-cured tobacco quality was proposed based on Raman spectroscopy. Considering the critical quality factors of flue-cured tobacco-like oil content, softness, and glossiness, four statistical methods, random forest, K-nearest neighbor, logistic regression, and partial least squares, can effectively improve the accuracy of quality identification. We randomly collected 149 flue-cured tobacco samples from multiple producing areas in China. After Raman spectroscopy analysis, Savitzky–Golay convolution smoothing and multi-scatter correction were done. The functional groups were analyzed to select characteristic peaks as features for discriminant analysis. The results show that the Raman spectroscopic information can distinguish the quality of flue-cured tobacco with an accuracy greater than 95%, whereas the partial least-squares approach delivers an accuracy of 100%. We conclude that Raman spectroscopy can be considered a vital avenue for identifying the quality of flue-cured tobacco. [ABSTRACT FROM AUTHOR]

Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method. [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]

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]

Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China's film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model. [ABSTRACT FROM AUTHOR]

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods. [ABSTRACT FROM AUTHOR]