Design of High Temperature Complex Dielectric Properties Measuring System Based on XGBoost Algorithm.

To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.

In this study, the performances of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms in agricultural crop classification were compared using multi-temporal Sentinel-2 images. The study area is located within an area of approximately 45* 45 km2 within the borders of Çukurova Plain. Within the scope of the study, agricultural crop pattern classification including corn, cotton, wheat, sunflower, sunflower, watermelon, peanut, and citrus trees, as well as double crop corn, soya, and cotton crops planted after wheat using RF, ANN, SVM, XGBoost algorithms with multi-time Sentinel-2 images of 2021. The study used parcels registered in the Farmer Registration System (FRS) as reference data. Before defining the parcels that are called Farmer Declaration Parcels (FDP) as ground truth data, pre-editing and rule-based deletion operations were performed, afterwards false and incorrect declarations were eliminated.10 bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12) of Sentinel-2 imagery from 11 different dates and Enhanced Vegetation Index (EVI) were used. All classification results showed a high overall accuracy (OA) ranging from 85% to 92%. As a result of the classification using a total of 121 different features, the highest overall accuracy OA (92.14%) belongs to the XGBoost algorithm. XGBoost algorithm was followed by RF (89.15%), SVM (86.14%) and ANN (85.48%). In all algorithms, although cotton, maize and wheat gave the highest accuracy in the classification results, double crops (wheat-maize, wheat-soybean, wheat-cotton) especially those with very close phenological periods, could not reach very high accuracy values. As a result of the study, it was observed that crops at different phenological stages achieved high classification accuracy using all algorithms, while crops at very similar phenological stages lower classification results. With the methodology applied in this study, a large number of ground truth data were generated from FDP plots and used for classification.

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence.

An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.

Brain-computer interface is a framework which provides a communicating pathway between the human brain and neuroprosthetic devices. In this work, we have performed signal smoothing to fix unregulated electroencephalogram (EEG) fluctuations and to effectively collect hidden patterns in corresponding EEG spectra. To do it, we have applied a Savitzky Golay function because of its ability to preserve spectral properties without distorting it much. For feature selection, we have applied the Filter Bank Common Spatial Pattern (FBCSP) algorithm with the Principle Component Analysis (PCA). FBCSP creates 11352 features from EEG signals and PCA gradually reduces the features to 185 most significant features. These features are utilized in the classification process by the eXtreme Gradient Boosting (XGBoost) algorithm with suitable node split criteria to manage optimal tree height. A five-fold cross-validation approach concludes the superior performance of XGBoost in terms of minimizing execution time (3.7 times faster in the training phase) and providing improved accuracy as compared to existing results. Our approach enhances classification accuracy (88.80%) by approximately 10% over regularized common spatial patterns (78.01% accuracy), 15% over shift variance approximation (73.84% accuracy) and 15% over Riemannian approach (74.77% accuracy). It also concludes that the pre-consideration of the noise level in EEG spectra provides a better approximation.

Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning.Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset.Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83).Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.

Under adverse sea conditions, propeller ventilation caused by in-and-out water can decrease the reliability of the ship power grid and the lifespan of the propulsion shaft system. Predicting the development of propeller ventilation severity while identifying it can contribute to improving propeller ventilation control. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm combined with a ship dynamics/control model is proposed as a propeller ventilation identification and prediction method. Meanwhile, the Pelican optimization algorithm (POA), particle swarm optimization (PSO), and genetic algorithm (GA) are applied to determine the optimal hyperparameters of the XGBoost algorithm. The results indicate that the method can effectively identify the current propeller ventilation state and predict whether a full ventilation state will occur after experiencing a partial propeller ventilation state. The comparison results indicate that the POA has a better optimization effect on the XGBoost algorithm for propeller ventilation identification and prediction. The method proposed in this study provides crucial technical support for the effective switching of propulsion control strategies for ship electric propulsion systems under adverse sea conditions.

Renewable energy power prediction is crucial to economic dispatch and reliable operation of power systems. This paper proposes a wind power forecasting approach based on the Extreme Gradient Boosting (XGBoost) algorithm. XGBoost is not only an effective feature selection method but also an accurate forecasting approach. In order to avoid excessive manual interventions for hyperparameter tuning, the Tree-Structured Parzen Estimator (TPE) model is presented to optimize the hyperparameters of XGBoost. This forecasting strategy has been tested in a real wind farm in Spain, compared with Persistence and Support Vector Regression (SVR). The results show that the XGBoost algorithm has higher accuracy and is a novel effective approach for very short-term wind power prediction.

Background:Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients.Methods:From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score).Results:Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI]: 0.41%–1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI: 0.65–0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI: 0.62–0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI: 8.7%–15.7%).Conclusion:In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making.Trial Registration:www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).

YouTube, as the largest video platform in Indonesia, faces significant challenges due to spam in the comment sections, which can reduce the quality of discussions. This study aims to develop a spam detection model for YouTube comments using the Extreme Gradient Boosting (XGBoost) algorithm, optimized through Randomized Search CV. This model is compared with several other algorithms, such as Random Forest, Naïve Bayes, and Support Vector Machine (SVM). The methodology used in this study involves developing a machine learning-based model with a primary focus on the XGBoost algorithm. The dataset used is the YouTube Spam Collection, which underwent preprocessing using Natural Language Processing (NLP) techniques such as tokenization and lemmatization to extract important features from the comments. The model was tested using various test data ratios (20%, 25%, 30%, and 35%) and optimized using Randomized Search CV to determine the best parameter combinations.The results indicate that Random Forest achieved the highest accuracy of 92.34% at a 30% test data ratio, while XGBoost demonstrated stable accuracy across different test data ratios, reaching 91.70%. Compared to previous studies, the accuracy of 92.34% is still lower than the previous results of 96.94%. Nevertheless, this study provides important insights by demonstrating that hyperparameter optimization can significantly improve the performance of machine learning algorithms. Based on these findings, it can be concluded that Random Forest and XGBoost are effective models for detecting spam in YouTube comments, with Random Forest excelling in accuracy and XGBoost offering consistent performance stability.

Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques.

The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories.

TECX-TCN: Prediction of ionospheric total electron content at different latitudes in China based on XGBoost algorithm and temporal convolution network

Intrusion detection is a significant technique for detecting various attacks in Wireless Mesh Networks (WMNs). Intrusion Detection Systems (IDS) powered by machine learning algorithms play a vital role in enhancing security by actively monitoring data traffic in real-time. The performance of attack detection in IDS is reduced by the existence of noisy and unwanted features in the network data. Hence, extracting Informative Features (IF) is essential for enhancing the effectiveness of the IDS. The proposed approach optimally selects IF from the network dataset, which facilitates in accurately detecting intrusions. Existing methods lack in efficient feature selection, leading to higher computational complexity and lower detection performance. This research aims to enhance IDS accuracy by leveraging the eXtreme Gradient Boosting (XGBoost) algorithm to classify the intrusions in the mesh network of smart grid and the optimal feature selection carried out with the help of Golden Eagle Optimization Algorithm (GEOA). This novel hybrid IDS model enhances the attack detection in WMNs. The proposed IDS’s performance is improved by using a feature selection method based on the GEOA, and then obtained results are compared against evolutionary algorithms-based features selection method. The proposed model of IDS using XGBoost algorithm is tested on a smart grid dataset, and its performance is compared with existing literature works.

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.