Athlete’s physiological parameter monitoring system based on K-means and MTLS-SVM algorithm

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

In order to overcome the problems of low accuracy of current system tourism route prediction and poor accuracy of route recommendation, this paper proposes a tourism route recommendation system design based on fast Spark artificial intelligence architecture. In the hardware part, the search process structure of tourist route, web page information classification and extraction module and tourist document classification process structure are designed. For the software part, firstly the problem is defined for the tourist route recommendation, secondly the Markov model is constructed, and finally the tourist route is mined. By ranking the transfer probability of tourist attractions, the tourist route can be obtained, and finally the tourist route recommendation is realised. The experimental results show that the designed system has a prediction accuracy of more than 70% and a recommendation accuracy of more than 80%, with high prediction accuracy and route recommendation accuracy.

The influence of vegetation cover on the spectroscopic estimation of soil properties

Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.

The end-point carbon content and temperature in the steelmaking process of AOD furnace are important factors affecting product quality, and the traditional algorithms have the problems of slow convergence, being easy to fall into the local optimal solution and lack a unified parameter selection criterion, which leads to the problems of slow convergence and low prediction accuracy. To solve the above problems, the research group adopts the improved arithmetic optimization algorithm and deep stochastic configuration networks (LAOA-DeepSCNs) to predict the end-point carbon content and temperature of the AOD furnace. First, correlation analysis was performed with SPSS to identify the seven factors as model inputs. Second, to verify the prediction effect of the model, the algorithm is compared with three typical algorithms: BP, RBF and SCN. The results show that LAOA-DeepSCNs have the fastest convergence speed, the highest prediction accuracy, and the strongest generalization ability. Finally, the model was applied to the actual production of a steel mill, and the results showed that the hit rate is 90.8%, 86.4%; and 92.6%, 88.1% for refining end-point carbon content and end-point temperature error within ±0.015%, ±0.01%; and ±10 °C, ±5 °C, respectively. Which can well meet the practical needs of a steel mill. It also provides theoretical guidance for the control of carbon content and temperature at the end-point of the AOD furnace.

Due to lack of efficient measuring means, it is not possible to realize the accurate measurement for the photosynthetic rate of greenhouse crops, and it affects the reliability of optimization and control based on photosynthesis demands for greenhouse environment. Therefore, basing on Least Squares Support Vector Machine (LSSVM), a photosynthetic rate prediction model is established in this paper. Through the multi-factor experiment on tomato seedlings, the photosynthetic rate data is obtained under different temperature, CO2 concentration and photosynthetic photon flux densities (PPFD) conditions. Basing on the obtained data, the photosynthetic rate prediction model is established by training LSSVM. Aiming at the influence of the empirical values of parameters (the penalty parameter γ and kernel constant σ ) on the model prediction performance, Particle Swarm Optimization (PSO) algorithm is used for optimizing and determining the two parameters values. Compared with the commonly used BP model and SVM model, the proposed model has obvious advantage on the prediction accuracy and performance. In addition, the Maximum Relative Error (MRE) of the proposed prediction model is 0.0224, and the Root Mean Square Error (RMSE) is 0.2324, which indicates that the proposed model has the higher prediction accuracy and provides a good model basis for optimization and control of greenhouse environment.

Accuracy of Intraocular Lens Calculation Formulas in Patients Undergoing Combined Phakic Intraocular Lens Removal and Cataract Surgery

Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.

How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data

Collapse pressure is an important part of calculating the lower limit of the safe mud density window, which is crucial for optimizing the well trajectory, designing the drilling fluid density, and ensuring drilling safety. However, collapse pressure prediction methods based on physical mechanisms are theoretically complex, computationally intensive, and slow. Data mining-based prediction methods often rely on conventional machine learning models, which suffer from low prediction accuracy, high data demand, and poor interpretability. In this paper, a novel hybrid-driven model combining mechanistic knowledge and machine learning methods is proposed, which has a faster computational speed in collapse pressure prediction compared with the traditional analytical model, and a better performance compared with the existing data-driven models. The model incorporates the stress transformation, rock strength criteria, and other knowledge to ensure the robustness and interpretability of this prediction model. The neural network structure and model hyperparameters are optimized using a Bayesian optimization algorithm. To consider the influence of uncertainty of input parameters on collapse pressure, the Monte Carlo method is used to quantify the influence of uncertainty of input parameters on collapse pressure prediction results based on the hybrid-driven prediction model, and the sensitivity of different input parameters to the outcomes is determined. The proposed model, tested on a test dataset, demonstrated high prediction accuracy and prediction stability with an average absolute error of 0.0037 g/cm3 and a root mean square error of 0.0104 g/cm3 for the collapse pressure equivalent density. Furthermore, a horizontal well was selected for validation, with predicted results exhibiting an average absolute error of only 0.0045 g/cm3 compared to the logging interpretation results, and a computational speed nearly 100 times faster than traditional analytical models. Three points with different stress conditions were selected on this well and their equivalent density of collapse pressure hemispheric projection maps were predicted, and the results were consistent with the actual results, indicating that the model can accurately capture the variation of collapse pressure with well inclination and azimuth. To quantify the effect of input parameter uncertainty on wellbore stability, the influence of input parameter uncertainty on the equivalent density of collapse pressure is simulated based on the above prediction model in combination with the Monte Carlo method, and the corresponding confidence intervals are given. The results found that the effect of uncertainty in ground stress on collapse pressure is relatively significant. In conclusion, the hybrid-driven model effectively integrates physical knowledge, enabling rapid and accurate prediction of collapse pressure in horizontal and inclined wells, offering an innovative approach for intelligent wellbore stability assessment and uncertainty analysis.

Offline constraint-based causal feature selection (OC-CFS) algorithms are essential for identifying causal relationships from observational data. However, existing methods often suffer from limitations such as low prediction accuracy or high computational cost, particularly when sample sizes vary. To address these limitations, we propose Triplet, a novel framework that leverages the HITON-MB Parents and Children (PC) strategy to identify strongly relevant PC nodes while eliminating irrelevant and redundant features. It concurrently employs the BAMB strategy to detect relevant spouses and discard irrelevant ones, and applies the STMB non-Markov Blanket (non-MB) strategy to identify and exclude non-MB descendants. Through this integration, the proposed T-OCD[Formula: see text] overcomes these limitations, accurately identifying the true MB with high prediction accuracy and reduced runtime. To validate its effectiveness, we evaluated T-OCD[Formula: see text] on benchmark Bayesian networks (BNs) and real-world datasets. Extensive experimental results demonstrate that T-OCD[Formula: see text] achieves significant improvements in both prediction accuracy and computational efficiency compared to existing methods. On small sample sizes (n=500), T-OCD[Formula: see text] achieved the highest recall in 5 out of 7 datasets, with an average improvement of over 20% compared to rivals. On large sample sizes (n=5000), it excelled in precision, achieving the top score in 4 out of 7 datasets with an average precision of 94%. Computationally, T-OCD[Formula: see text] is highly efficient, operating as the second-fastest method overall. It ran over 55% faster than half of the benchmarks and a remarkable 35% faster than the average competitor on large datasets. The source code for this research is available at the following repository: https://github.com/vickykhan89/T-OCDmb.

This study investigates the prediction of alkane solubility in carbon dioxide (CO2) using advanced machine learning methods. Alkane solubility in CO2 plays a crucial role in various industrial applications, including enhanced oil recovery, carbon capture and storage, and natural gas purification. Traditional experimental approaches to measure solubility are often time-consuming and resource-intensive, necessitating the development of robust predictive models. In this work, three machine learning techniques such as: Least Squares Support Vector Machine (LS-SVM), Multilayer Perceptron (MLP), and Gene Expression Programming (GEP) were employed to model the solubility of alkanes in CO2 under varying conditions of temperature, pressure, CO2 density, alkane molecular weight, and carbon number. A comprehensive dataset comprising experimental measurements was used to train and test the models, with 80% of the data allocated for training and 20% for testing. Key statistical metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score were used to evaluate model performance. The results reveal that all three machine learning methods achieved high prediction accuracy, with the MLP model demonstrating the best performance (R² = 0.9824). The GEP model, while slightly less accurate, provided valuable insights into the relationships between variables, offering a high degree of interpretability. The SVR method achieved competitive accuracy with the advantage of computational efficiency. This study highlights the potential of machine learning techniques as reliable tools for predicting alkane solubility in CO2, significantly reducing the need for experimental data. The findings have broad implications for optimizing industrial processes and improving the efficiency and sustainability of CO2-based applications.

A novel MP-LSTM method for ship trajectory prediction based on AIS data

The current prediction model for the system efficiency of pumping units primarily relies on a mechanistic approach. However, this approach incorporates numerous unnecessary factors, thereby, increasing the cost associated with predictions. With the improvement of the oil field database, the available information is increasing. Some scholars propose a prediction model based on a single neural network, however, such models face challenges in effectively capturing complex data, resulting in lower prediction accuracy and limited resistance to interference. To solve the above problems, the study proposes a novel stacking integrated learning prediction model, which incorporates fivefold cross‐validation. First, the magnitude of the correlation coefficient was quantified using the Pearson correlation coefficient. Second, the impact and predictive features were normalized. Final, convolutional neural network (CNN), recurrent neural network (RNN), Long Short‐Term Memory network (LSTM), gated recurrent unit (GRU), and transformer are used as the base models, and fully connected neural network (FNN) is used as the metamodel. Each base model was trained by fivefold cross‐validation, and the predicted values of each fold were stacked by rows. Next, the predicted values of each base model are stacked by columns as input variables to the metamodel and metamodel learning is performed, and the stacking integrated learning prediction model based on fivefold crossover validation is established. To validate the accuracy of the model, we selected 5,000 actual well parameters, including 26 impact features and one predictive feature, for comparative analysis. This analysis presents the maximum percentage reduction in mean square error (MSE), mean absolute error (MAE), and root‐mean‐square error (RMSE) of our proposed integrated learning model concerning a single neural network prediction model as 28.26%, 24.40%, and 15.66%, respectively. The maximum percentage improvement in R2 is 17.74%. It shows that our proposed integrated learning prediction model has high prediction accuracy.

Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model.