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The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate of households and extract meaningful insights that can enhance it. For this purpose, we use LightGBM (Light Gradient Boosting Machine)-, SHAP (SHapley Additive exPlanations)-, and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. First, we use LightGBM to predict the ESSR of households and identify the key features that impact the prediction model. By using LightGBM, we demonstrated that the key features are the housing type, average monthly electricity bill, presence of floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs). Furthermore, we adopted the LightGBM classifier with ℓ 1 regularization to extract the most significant features and established a statistical correlation between these features and the electricity self-sufficiency rate. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. The LightGBM-based model offers a global view of feature importance but lacks detailed explanations for individual predictions. For this purpose, we used SHAP analysis to identify the impact-wise order of key features that influence the electricity self-sufficiency rate (ESSR) and evaluated the contribution of each feature to the model's predictions. A heatmap is also used to analyze the correlation among household variables and the ESSR. To evaluate the performance of the classification model, we used a confusion matrix showing a good F1 score (Weighted Avg) of 0.90. The findings discussed in this article offer valuable insights for energy policymakers to achieve the objective of developing energy-self-sufficient houses. [ABSTRACT FROM AUTHOR]

Connected components in automobiles face cybersecurity threats if not adequately protected. The Controller Area Network (CAN), the primary communication protocol in vehicular systems, lacks essential security mechanisms, necessitating heightened cybersecurity awareness in the automotive industry. This paper presents a methodology to enhance cybersecurity within the CAN network, including a comparative study with benchmark results. Our research focuses on improving multiclass Intrusion Detection Systems (IDSs) for the CAN network. We introduce a hybrid deep learning model tailored for precise multiclass classification of attacks, an Artificial Neural Network (ANN)-based Light Gradient Boosting Machine (LightGBM) IDS that incorporates a comprehensive set of features. The evaluation on diverse cyber-attacks from the Car-Hacking dataset demonstrates the model's effectiveness. This study contributes to the field by offering a brief assessment of current state-of-the-art methods and presenting a novel approach to bolster cybersecurity. The proposed IDS serves as a valuable reference for ensuring the cybersecurity of automotive systems. [ABSTRACT FROM AUTHOR]

Machine learning-based direction-of-arrival (DOA) estimation methods can have a good predictive ability even in complex scenarios. However, their estimation performance in the face of unknown data is poor because they rely on the generalization ability of the algorithm itself. Therefore, this paper proposes a DOA estimation method based on the Light Gradient Boosting Machine (LightGBM) algorithm. Using the histogram algorithm, gradient-based one-sided sampling and exclusive feature bundling measures, the LightGBM algorithm can reduce the time to find the best segmentation point, reduce the amount of data and the number of features in the dataset, thus reducing the model training time, and achieve high prediction accuracy. Applying the LightGBM algorithm to the DOA estimation problem and using a large dataset for training can improve the estimation accuracy while reducing the training cost. Simulation and real experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Previous landslide susceptibility assessments often overlook the heterogeneity of landslides across different regions and their generalization ability. The objective of this study was to explore the landslide susceptibility model from geomorphic zones-based K-means constructed sub-regions to geomorphic label-based matching whole regions. To achieve this, we divided the study area into slope units based on curvature calculation and utilized a Light Gradient Boosting Machine (LightGBM) to construct the susceptibility model. For model training, we randomly selected fourteen townships in Fengjie County, Chongqing, China. By employing K-means clustering, we established geomorphic zones for the landslide susceptibility model, which were assigned a corresponding geomorphic label. Notably, the model divided the entire study area into four geomorphic zones, with zone 4 demonstrating the highest accuracy of 0.85. Additionally, we developed two comparative models to assess the effectiveness of our proposed approach. The first comparative model considered only ten conditioning factors, resulting in a model accuracy of 0.73. The second comparative model incorporated the classification results of geomorphic labels as explanatory variables for landslides and achieved a model accuracy of 0.75. In this study, we successfully established a model with an accuracy of 0.87 by automatically matching and generalizing the remaining townships with the existing geomorphic labels. Our findings suggest that a geomorphic label-based model, adopting a framework from sub-regions to the whole region, offers better quantification of the landslide occurrence mechanism in the region and demonstrates stronger generalization ability compared to the overall-based model. [ABSTRACT FROM AUTHOR]

Electricity load prediction plays a vital role in energy management. The necessity of combining scattered power generation channels of sustainable energy and other substitute resources into distribution grid networks is increasing because of the 4.1 percent rise in world electricity since 2021. The utilization of probability power generators and energy storage is maximized by short-term load prediction methods, which make it possible to anticipate potential energy usage. To facilitate the growth of energy systems in quite a sustainable way, urban energy planners utilize a range of methods and technological advances, including simulation, probability optimization, comprehensive improvement, and modelling techniques for predicting power requirements. Predicting energy consumption at granular time scales (e.g., hourly or sub-hourly intervals) is common practice for short-term energy load prediction. For similar time-series forecasting challenges, the authors have decided to use transfer learning (TL) with light gradient-boosting machine (LightGBM).1 This model can benefit from models trained on larger data sets through TL, which might boost prediction accuracy. Energy prediction and effective resource utilization largely support the accurate transition of energy systems. In this paper, the authors propose a hybrid model for short-term energy load prediction. Based on TL to optimize LightGBM (OLGBM) for smart grids, The proposed model first applies data pre-processing using an abnormal supplement strategy with an immediate deviation selection technique. This phase eliminates the missing values and extracts the required features.The second phase uses a TL-OLGBM with hyperparameters. The TL method helps to learn the dynamic time scale and complex data patterns, and the hyperparameters are tuned via the Bayesian optimization technique, which addresses the challenge of short-term load forecasting (STLF) and increases forecasting accuracy. Finally, an optimized LightGBM model is applied, combining the time and energy features to predict effective energy forecasting. The proposed model gained a 2.834589 mean absolute percentage error (MAPE) value and a 0.9706495 GINI index after execution on the prescribed data set. We compare the performance of the proposed model with other comparable models, and the simulation outcomes show that the model can generate the best results for MAPE, accuracy, and root-mean-square deviation (RMSE). [ABSTRACT FROM AUTHOR]

Objective: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. Methods: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. Results: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. Conclusion: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology. [ABSTRACT FROM AUTHOR]

Load recognition remains not comprehensively explored in Home Energy Management Systems (HEMSs). There are gaps in current approaches to load recognition, such as enhancing appliance identification and increasing the overall performance of the load-recognition system through more robust models. To address this issue, we propose a novel approach based on the Analysis of Variance (ANOVA) F-test combined with SelectKBest and gradient-boosting machines (GBMs) for load recognition. The proposed approach improves the feature selection and consequently aids inter-class separability. Further, we optimized GBM models, such as the histogram-based gradient-boosting machine (HistGBM), light gradient-boosting machine (LightGBM), and XGBoost (extreme gradient boosting), to create a more reliable load-recognition system. Our findings reveal that the ANOVA–GBM approach achieves greater efficiency in training time, even when compared to Principal Component Analysis (PCA) and a higher number of features. ANOVA–XGBoost is approximately 4.31 times faster than PCA–XGBoost, ANOVA–LightGBM is about 5.15 times faster than PCA–LightGBM, and ANOVA–HistGBM is 2.27 times faster than PCA–HistGBM. The general performance results expose the impact on the overall performance of the load-recognition system. Some of the key results show that the ANOVA–LightGBM pair reached 96.42% accuracy, 96.27% F1, and a Kappa index of 0.9404; the ANOVA–HistGBM combination achieved 96.64% accuracy, 96.48% F1, and a Kappa index of 0.9434; and the ANOVA–XGBoost pair attained 96.75% accuracy, 96.64% F1, and a Kappa index of 0.9452; such findings overcome rival methods from the literature. In addition, the accuracy gain of the proposed approach is prominent when compared straight to its competitors. The higher accuracy gains were 13.09, 13.31, and 13.42 percentage points (pp) for the pairs ANOVA–LightGBM, ANOVA–HistGBM, and ANOVA–XGBoost, respectively. These significant improvements highlight the effectiveness and refinement of the proposed approach. [ABSTRACT FROM AUTHOR]

Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents' safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese local cities needs to be deepened. Therefore, this paper chooses Chaoyang District in Beijing as the research area. Firstly, the network safety perception distribution of Chaoyang District is calculated and presented through the CNN model trained based on the perception dataset constructed by Chinese local cities. Then, the street view elements are extracted from the street view images using image semantic segmentation and target detection technology. Finally, the street view elements that affect the road safety perception are identified and analyzed based on LightGBM and SHAP interpretation framework. The results show the following: (1) the overall safety perception level of Chaoyang District in Beijing is high; (2) the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks are the four factors that have the greatest impact on environmental safety perception; (3) there is an interaction between different street view elements on safety perception, and the proportion and number of street view elements have interaction on safety perception; (4) in the sections with the lowest, moderate, and highest levels of safety perception, the influence of street view elements on safety perception is inconsistent. Finally, this paper summarizes the results and points out the shortcomings of the research. [ABSTRACT FROM AUTHOR]

In response to the deficiencies in visible-near infrared spectral identification of coal gangue under different working conditions, this paper employs multiple algorithms to construct local and global recognition models and conducts analysis. Simulating five different working conditions in the laboratory, including different detection angles, illumination angles, detection distances, coal powder content, and moisture content. Each working condition is further divided into four levels. Collecting spectral data of coal and gangue samples. Constructing global and local datasets, which are respectively derived from all levels and single levels under certain working conditions. Training local and global models to identify samples based on machine learning algorithms such as Lightgbm and 1DCNN. The results of the experiment indicate that there are variations in the sample spectra and recognition accuracy based on different working conditions. Furthermore, the local model exhibits lower recognition accuracy and inadequate robustness for other level samples under the same working condition. The global model is more robust and practical, as it achieves higher or equal recognition accuracy for local data compared to the local model. Among the global models, Lightgbm has the best overall performance, with an average F1-score of 99.34% and 97.9% for local and global datasets, respectively. It also has fast training time of 0.75s and recognition time of 1 ms. The Lightgbm global model can accurately identify coal and gangue in various working conditions, making it a reliable solution. [ABSTRACT FROM AUTHOR]

Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which is widely used as a classification problem solving method) to determine when the fluctuation exceeds the tolerable limit and what the acceptable range is. We also apply this to clarify the tolerable limit of fluctuation in the "Kurume Kasuri" pattern, which is unique to the Chikugo region of Japan, and devise a method to evaluate the fluctuation in real time when weaving the Kasuri and keep it within the acceptable range. This study proposes a method for maintaining a unique faded pattern of woven textiles by utilizing EfficientNet for classification, fine-tuned with Optuna, and LightGBM for predicting subtle misalignments. Our experiments show that EfficientNet achieves high performance in classifying the quality of unique faded patterns in woven textiles. Additionally, LightGBM demonstrates near-perfect accuracy in predicting subtle misalignments within the acceptable range for high-quality faded patterns by controlling the weaving thread tension. Consequently, this method effectively maintains the quality of Kurume Kasuri patterns within the desired criteria. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the small signal stability analysis and correction of power system based on Light Gradient Boosting Machine (LightGBM). Taking the load power, branch power, and generator power as inputs, the minimum damping ratio is output to build the mapping relationship between input and output. A small signal stability analysis model is established to assess the minimum damping ratio of the system, and the influence of noise on the accuracy of assessment results is also considered. The damping ratio sensitivity of generator is estimated based on LightGBM method, and the approximate sensitivity optimization model of the generator damping ratio is established when the system is weakly damped. The outputs of generators are adjusted according to the optimization model to correct the minimum damping ratio to enhance the system's stability and finally estimate the modified minimum damping ratio by LightGBM algorithm. Test results on 3-machine 9-node and 10-machine 39-node systems indicate that the proposed method is hardly prone to over-fitting under noise interference with better robustness and could maintain better performance in assessing and correcting the small signal stability of the power system. [ABSTRACT FROM AUTHOR]

This study emphasizes the explainability of machine learning (ML) models in predicting the seismic response of rigid rocking structures, specifically using the LightGBM algorithm. By employing SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), and accumulated local effects (ALE), a comprehensive feature importance analysis has been performed. This revealed that ground motion parameters, particularly peak ground acceleration (PGA), are critical for predicting small rotations, while structural parameters like slenderness and frequency are more significant for larger rotations. Utilizing an extensive dataset generated from nonlinear time history analyses, the trained LightGBM model demonstrated high accuracy in estimating the maximum rotation angle of rigid blocks under natural ground motions. The study also examined the sensitivity of model performance to lower bound thresholds of the target variable, revealing that reduced feature sets can maintain predictive performance effectively. These findings advance ML-based modeling of seismic rocking responses, providing interpretable and accurate models that enhance our understanding of rocking structures' dynamic behavior, which is crucial for designing resilient structures and improving seismic risk assessments. Future research will focus on incorporating additional parameters and exploring advanced ML techniques to further refine these models. [ABSTRACT FROM AUTHOR]

Conventional liquid lubricants prove inadequate for effective lubrication in conditions characterized by high temperatures and high vacuum environments. In such extreme scenarios, powder lubricants emerge as a more viable solution. The present study is to conduct a series of experiments using a reciprocating wear test setup and evaluate the capability of four different machine learning models in analysing the tribological attributes of metals when lubricated with three distinct powder types: zirconium dioxide, copper oxide, and molybdenum disulfide, specifically under conditions of elevated contact pressures and dry environments. The experiments were systematically carried out encompassing a range of load and temperature combinations. Four different machine learning models (MLP, KNN, extreme gradient boosting and light gradient‐boosting machine) were used for predicting the coefficient of friction of metals lubricated with different powders. Extreme gradient boosting machine learning model gives better result than the other models with mean absolute error, root mean squared error, R2 value and average absolute deviation percentage of 0.0215, 0.0278, 0.9962 and respectively. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's. The significant number of individuals afflicted with this illness makes it essential to develop a method to diagnose the conditions in their early phases. PD is typically identified from motor symptoms or via other Neuroimaging techniques. Expensive, time-consuming, and unavailable to the general public, these methods are not very accurate. Another issue to be addressed is the black-box nature of machine learning methods that needs interpretation. These issues encourage us to develop a novel technique using Shapley additive explanations (SHAP) and Hard Voting Ensemble Method based on voice signals to diagnose PD more accurately. Another purpose of this study is to interpret the output of the model and determine the most important features in diagnosing PD. The present article uses Pearson Correlation Coefficients to understand the relationship between input features and the output. Input features with high correlation are selected and then classified by the Extreme Gradient Boosting, Light Gradient Boosting Machine, Gradient Boosting, and Bagging. Moreover, the weights in Hard Voting Ensemble Method are determined based on the performance of the mentioned classifiers. At the final stage, it uses SHAP to determine the most important features in PD diagnosis. The effectiveness of the proposed method is validated using 'Parkinson Dataset with Replicated Acoustic Features' from the UCI machine learning repository. It has achieved an accuracy of 85.42%. The findings demonstrate that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases. [ABSTRACT FROM AUTHOR]

AbstractOur research described in this manuscript investigated the dielectric properties and structural characteristics of Bisphenol-A epoxy resin composites infused with various concentrations (5 wt.%, 10 wt.%, and 15 wt.%) of hybrid nanofillers, namely alumina (Al2O3) and zinc oxide (ZnO). Ultrasonic dispersion was utilized to integrate the nanofillers into the resin matrix. Structural properties were assessed using X-ray diffraction (XRD), which confirmed the presence of the Al2O3 and ZnO nanoparticles within the epoxy matrix. Dielectric properties were measured over a frequency range of 104 Hz to 2 MHz. The results provide new insights into the polarization mechanisms and structural characteristics of these composites, highlighting their potential for enhanced dielectric performance in high-frequency applications. To further understand and predict these dielectric properties, the CatBoost and LightGBM regression models were employed to predict the dielectric constant (ε'), loss tangent (tan δ), and AC conductivity (σac) of these composites. The models demonstrated strong predictive accuracy, with performance metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2), indicating the robustness and accuracy of the models in predicting the dielectric properties of the composites. The study’s findings underscore the significant potential of Bisphenol-A epoxy resin composites with hybrid nanofillers for high-frequency dielectric applications. [ABSTRACT FROM AUTHOR]

Accurately predicting daily production trends for individual wells in oilfield operations is crucial, but it poses a significant challenge due to the complex nature of oil well production conditions. A production model was developed based on multivariate time series data. Deep features were extracted using CNN-GRU (convolutional neural network-gate recurrent unit) for time series prediction, and predictions were also made using LightGBM ( light gradient boosting machine) framework from a regression perspective. To further enhance production prediction accuracy, the results of both approaches were integrated. Additionally, a method called the advanced parameter recursive prediction strategy was proposed, which allows for accurate production prediction even without known input features. This strategy involves forecasting important features that affect production in advance and applying these predicted features to simulate production prediction tests. The simulation results demonstrate that the model established in this study, combined with the advanced parameter recursive strategy, achieves the highest prediction accuracy on the test set. It significantly improves prediction accuracy compared to single-variable time series prediction and regression prediction models. [ABSTRACT FROM AUTHOR]

The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess zeros. To address this, actuarial science researchers have employed ‘zero-inflated’ models that combine a traditional count model and a binary model. This paper investigates the use of boosting algorithms to process insurance claim data, including zero-inflated telematics data, to construct claim frequency models. Three popular gradient boosting libraries - XGBoost, LightGBM, and CatBoost -- are evaluated and compared to determine the most suitable library for training insurance claim data and fitting actuarial frequency models. Through a comprehensive analysis of two distinct datasets, it is determined that CatBoost is the best for developing auto claim frequency models based on predictive performance. Furthermore, we propose a new zero-inflated Poisson boosted tree model, with variation in the assumption about the relationship between inflation probability p and distribution mean μ, and find that it outperforms others depending on the characteristics of the data. This model enables us to take advantage of particular CatBoost tools, which makes it easier and more convenient to investigate the effects and interactions of various risk features on the frequency model when using telematics data. [ABSTRACT FROM AUTHOR]

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO 2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications. [Display omitted] • Effective connectionist tools are used to assess H 2 storage with MOF. • LightGBM model exhibits a better prediction accuracy, compared to RF. • H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy. • Increased surface area and pressure lead to higher H 2 uptake. • Data pre-processing steps considerably improve the model predictive performance. [ABSTRACT FROM AUTHOR]

Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality. [ABSTRACT FROM AUTHOR]

Background: Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. Result: The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS–FD CASR–RF and MWS–FD chemical characteristic band RF, respectively. Their respective R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. Conclusions: This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long time and low positioning accuracy of indoor positioning algorithm, an indoor positioning algorithm based on improved LightGBM (light gradient boosting machine) algorithm was proposed. The algorithm first preprocessed the data in the fingerprint database, and removed outliers and outliers through KNN (K-nearest neighborhood ) algorithm to reduce environmental noise interference and improve data reliability. Next, the algorithm divided the sample set into training set and test set, and used LightGBM algorithm to model. At the same time, genetic algorithm was used to adjust the parameters in LightGBM algorithm, and the optimal parameters were found according to the fitness function to obtain the LightGBM + GA (genetic algorithm) coordinate prediction model. Finally, a prediction model was established according to the optimized parameters to realize coordinate prediction. The experimental results show that the algorithm improves the accuracy of WiFi positioning by 0. 1 m compared with XGBoost (extreme gradient boosting,XGBoost) algorithm and 0. 19 m compared with GBDT algorithm. In terms of positioning time, LightGBM + GA algorithm is 5. 10 s faster than GBDT (gradient boosting decision tree) algorithm and 5. 97 s faster than XGBoost algorithm, which has good practicability. [ABSTRACT FROM AUTHOR]

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10–13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP–LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality. [ABSTRACT FROM AUTHOR]

This study proposes using the Light Gradient Boosting Machine (LightGBM) to improve the defrosting control strategy in frost-free refrigerators. By analyzing data and optimizing control parameters, the aim is to enhance defrosting performance and reduce energy consumption using the time–temperature difference (t–dT) approach. The research involves analyzing performance and reliability data for two control strategies (time–temperature (t–T) and t–dT), creating three feature sets (FS1, FS2, and FS3) based on correlation analysis, and employing LightGBM models to forecast datasets. The control parameter threshold (Δ T op,s) is optimized using the LightGBM-based data. The key findings indicate that the t–dT strategy with a fixed threshold (7.8 °C) outperforms the t–T method in efficiency at an ambient temperature of 38 °C. At 10 °C, the performance tests show no significant difference, but the t–T method performs better in reliability tests. The FS1-based data from the t–dT strategy in the reliability test at 10 °C are considered ideal input, and the LightGBM models generate FS2-based and FS3-based data for evaluation. The optimized t–dT defrosting strategy achieves favorable refrigeration conditions with minimal power consumption and optimal cooling capacity. The ideal Δ T op,s threshold, based on data for the idealized frosting condition, is determined to be 8.3 °C. [ABSTRACT FROM AUTHOR]

Background: The incidence of graft failure following liver transplantation (LTx) is consistent. While traditional risk scores for LTx have limited accuracy, the potential of machine learning (ML) in this area remains uncertain, despite its promise in other transplant domains. This study aims to determine ML's predictive limitations in LTx by replicating methods used in previous heart transplant research. Methods: This study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2010 and 2020. Gradient boosting models (XGBoost and LightGBM) were used to predict 14, 30, and 90‐day graft failure compared to conventional logistic regression model. Models were evaluated using both shuffled and rolling cross‐validation (CV) methodologies. Model performance was assessed using the AUC across validation iterations. Results: In a study comparing predictive models for 14‐day, 30‐day and 90‐day graft survival, LightGBM consistently outperformed other models, achieving the highest AUC of.740,.722, and.700 in shuffled CV methods. However, in rolling CV the accuracy of the model declined across every ML algorithm. The analysis revealed influential factors for graft survival prediction across all models, including total bilirubin, medical condition, recipient age, and donor AST, among others. Several features like donor age and recipient diabetes history were important in two out of three models. Conclusions: LightGBM enhances short‐term graft survival predictions post‐LTx. However, due to changing medical practices and selection criteria, continuous model evaluation is essential. Future studies should focus on temporal variations, clinical implications, and ensure model transparency for broader medical utility. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of sodium-ion batteries is of significant importance for large-scale energy storage systems in renewable energy applications. In this study, a feature selection-based LightGBM method is proposed to predict the RUL of sodium-ion batteries. By combining Pearson correlation coefficient and grey relational degree, four optimal features with high correlation to battery life and low autocorrelation between different features are selected. The LightGBM model is employed, and its hyperparameters are optimized using the grid search cross-validation (GridSearchCV) algorithm to achieve the best prediction performance. The superiority of the prediction model is validated using sodium-ion battery data and compared with the GBRD and RF models with GridSearchCV algorithm under the same conditions. The results demonstrate that this method significantly speeds up the model computation, and it exhibits higher reliability and better prediction performance compared to traditional algorithms. The maximum MAE, MSE, and RMSE of the predictions do not exceed 3. 0,17. 7, and 4. 2, respectively. [ABSTRACT FROM AUTHOR]

The database obtained from the rp5 data archive is provided by the LLC ”Weather Schedule” and represents the collected information about weather conditions in all parts of the world, describing temperature, clouds, and precipitation, and is available for analysis to everyone. Data is collected every 3 hours, which provides valuable data from day to day. Many of these features are optional and dependent on time, like maximum temperature during night time, also vision range during night always equals zero, etc. The purpose of this study is to create a solution, that can deal with missing data, and to find an important machine-learning combination for weather prediction. It needs to be mentioned that a study should be done on open data since it doesn’t have any presumptions inside. With work on raw and open data, we can try to establish new rules in weather prediction modeling and find meaningful solutions for Kazakhstani society. For this research, algorithms for implementing prediction models were used from the scikitlearn Python library. It contains Gradient Boosting Regressor, XGBoost, CatBoost, Linear Regression, Bayesian Ridge, etc. Applied machine learning algorithms were evaluated based on different approaches: from various data preprocessing ideas to selecting best best-performing model with better results and optimizing it to achieve the possible maximum in predictions. The key course of this study is to help find a way to the optimal approach to weather prediction problems and analysis by the way, which current tendencies can look at it. [ABSTRACT FROM AUTHOR]

The identification of human-herpesvirus protein–protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/. [ABSTRACT FROM AUTHOR]

Globally, lung and colon cancers are among the most prevalent and lethal tumors. Early cancer identification is essential to increase the likelihood of survival. Histopathological images are considered an appropriate tool for diagnosing cancer, which is tedious and error-prone if done manually. Recently, machine learning methods based on feature engineering have gained prominence in automatic histopathological image classification. Furthermore, these methods are more interpretable than deep learning, which operates in a "black box" manner. In the medical profession, the interpretability of a technique is critical to gaining the trust of end users to adopt it. In view of the above, this work aims to create an accurate and interpretable machine-learning technique for the automated classification of lung and colon cancers from histopathology images. In the proposed approach, following the preprocessing steps, texture and color features are retrieved by utilizing the Haralick and Color histogram feature extraction algorithms, respectively. The obtained features are concatenated to form a single feature set. The three feature sets (texture, color, and combined features) are passed into the Light Gradient Boosting Machine (LightGBM) classifier for classification. And their performance is evaluated on the LC25000 dataset using hold-out and stratified 10-fold cross-validation (Stratified 10-FCV) techniques. With a test/hold-out set, the LightGBM with texture, color, and combined features classifies the lung and colon cancer images with 97.72%, 99.92%, and 100% accuracy respectively. In addition, a stratified 10-fold cross-validation method also revealed that LightGBM's combined or color features performed well, with an excellent mean auc_mu score and a low mean multi_logloss value. Thus, this proposed technique can help histologists detect and classify lung and colon histopathology images more efficiently, effectively, and economically, resulting in more productivity. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. [ABSTRACT FROM AUTHOR]

Background: Clusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning. Results: This paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster. Conclusions: The experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas. [ABSTRACT FROM AUTHOR]

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method's effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Human–machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses. [ABSTRACT FROM AUTHOR]

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA). [ABSTRACT FROM AUTHOR]

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. [ABSTRACT FROM AUTHOR]

A sudden increase in electrical activity in the brain is a defining feature of one of the severe neurological diseases known as epilepsy. This abnormality appears as a seizure, and identifying seizures is an important field of research. An essential technique for examining the features of neurological issues brain activities, and epileptic seizures is electroencephalography (EEG). In EEG data, analyzing epileptic irregularities visually requires a lot of time from neurologists. For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children's Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations. [ABSTRACT FROM AUTHOR]

Ground subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city. [ABSTRACT FROM AUTHOR]

The study of clouds and their characteristics provides important information for understanding climate change and its impacts as it provides information on weather conditions and forecasting. In this study, Earth observation (EO) data from the FY4A AGRI and Himawari-8 CLP products were used to classify and identify distinct cloud types in southeastern China. To reduce the impact of parallax between geostationary satellites, we proposed adopting a sliding detection method for quality control of cloud-type data. Additionally, the Bayesian optimization method was employed herein to tune the hyperparameters of the LightGBM model. Our study results demonstrated that Bayesian optimization significantly increased model performance, resulting in successful cloud-type classification and identification. The simultaneous use of visible and shortwave infrared channels, and brightness temperature difference channels, enhanced the model's classification performance. Those channels accounted for 43.79% and 21.84% of the overall features, respectively. Certainly, the model in this study outperformed compared with the traditional thresholding method (TT), support vector machine (SVM), and random forest (RF). Results showed a model prediction accuracy of 97.54%, which was higher than that of TT (51.06%), SVM (96.47%), and RF (97.49%). Additionally, the Kappa coefficient of the model was 0.951, indicating the model's classification results were consistent with the true values. Notably, this performance also surpassed TT (0.351), SVM (0.929), and RF (0.950). [ABSTRACT FROM AUTHOR]

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height. [ABSTRACT FROM AUTHOR]

Aiming at the problem of single data recognition in pantograph-catenary arc recognition method, a pantograph-catenary arc recognition method based on improved multi-scale convolutional network ( GInception ) combined with LightGBM is proposed. The original signal is denoised and reconstructed by using the combination of complete ensemble empirical mode decomposition ( CEEMDAN ) and wavelet soft threshold. The reconstructed signal is broadcasted and transformed into a multi-dimensional signal, which is input into the GInception network for feature extraction, and then the features extracted by the GInception network are imported into the lightweight gradient hoist for recognition. The results show that the recognition accuracy of pantograph-catenary arc reaches 96.3 % under five different working conditions. The research conclusions can provide reference for the identification of pantograph-catenary arc in trams. [ABSTRACT FROM AUTHOR]

Accurate streamflow estimation and assessing the significant parameters are crucial for effective water resource management. In this research, the SWAT model was used to determine streamflow in the Ponnaiyar River Basin, achieving satisfactory accuracy with NSE and R2 of 0.67, KGE of 0.73, and RMSE of 9.257 during calibration. The correlated parameters were established using Pearson Correlation Analysis from the calibrated SWAT-generated parameters. Streamflow prediction was performed with Principal Component Analysis-Multiple Linear Regression (PCA-MLR) using these correlated parameters, resulting in an accuracy of NSE and R2 = 0.67, KGE = 0.69, and RMSE = 9.577 during training, and NSE and R2 = 0.47, KGE = 0.49, and RMSE = 13.624 during testing. Since PCA-MLR exhibited reduced accuracy during testing, this study proposed the combined Soil and Water Assessment Tool-eXtreme Gradient Boosting (SWAT-XGBoost) model, which outperformed the leading-edge models such as SWAT-Categorical Boosting (SWAT-CatBoost) and SWAT-Light Gradient Boosting Machine (SWAT-LightGBM) while maintaining the same correlated parameters. The SWAT-XGBoost model achieved enhanced accuracy with NSE and R2 = 0.83, KGE = 0.85, and RMSE = 2.226 during training, and NSE and R2 = 0.67, KGE = 0.69, and RMSE = 9.805 during testing. The most influential parameters were determined for accurate streamflow prediction using XGBoost's built-in feature importance. The XGBoost model was developed, considering only these influential parameters among the correlated ones, maintaining the same accuracy during training but exhibiting increased accuracy of NSE and R2 = 0.71, KGE = 0.72, and RMSE = 8.516 during testing. Additionally, SHapley Additive exPlanations (SHAP) impact analysis was conducted on the SWAT-XGBoost model to explain the interactions between these influential parameters. Based on the results of the SHAP impact analysis, an XGBoost model was constructed, incorporating positive impact features, negative impact features, and a combination of both. The XGBoost model, built with combined positive and negative impact features, exhibited superior accuracy during training and testing compared to SWAT-XGBoost, which focused primarily on the most influential parameters. This study provides valuable guidance for researchers and policymakers working with limited data availability using integrated model development techniques to enhance streamflow prediction. [ABSTRACT FROM AUTHOR]

Prostate cancer is a common malignant tumor in men and early diagnosis and treatment are crucial for the survival rate of patients. This study aimed to establish a machine learning-based prostate cancer prediction model to help physicians accurately identify high-risk patients. This study performed a retrospective analysis using prostate cancer patient data from the MIMIC-IV database. First, the data was cleaned and preprocessed, including imputing missing values, handling outliers, and feature selection. Then, prediction models were established using machine learning algorithms (including logistic regression, support vector machine, deep neural networks, XGBoost, LightGBM and CatBoost) and evaluated using cross-validation and ROC curve analysis. We screened out 1975 patients diagnosed with PC and 11,745 patients diagnosed with BPH based on ICD codes. However, among the BPH patients, 467 were also diagnosed with PC, so we excluded these patients. The LightGBM machine learning model outperformed the other models in distinguishing patients with PC [LightGBM vs. CatBoost vs. XGBoost vs. DNN vs. SVM vs. LR; area under the curve (AUC): 0.93 vs. 0.91 vs. 0.89 vs. 0.86 vs. 0.70 vs. 0.68, respectively]. The LightGBM model had a sensitivity of 86%, specificity of 85% at the best cut-off value. The model was capable of predicting whether a patient has prostate cancer based on their clinical features (including age, Laboratory test, etc.) and had a high level of accuracy and stability. The machine learning-based prostate cancer prediction model established in this study has some clinical application value and can help physicians accurately identify high-risk patients, providing more precise prevention and treatment plans for patients. [ABSTRACT FROM AUTHOR]

The atmospheric environment is of great importance to human health. However, its influencing factors are complex and variable. An efficient technique is required to more precisely estimate PM2.5 concentration values. In this paper, an enhanced Sparrow Search Algorithm (LASSA)-optimized Light Gradient Boosting Machine (LightGBM) is proposed for PM2.5 concentration prediction. This approach can provide accurate predictions while also reducing potential losses resulting from unexpected events. LightGBM is regarded as an outstanding machine learning approach; however, it includes hyperparameters that must be optimally mixed in order to achieve the desired results. We update the Sparrow Search Algorithm (SSA) and utilize it to identify the optimal combination of the most crucial parameters, using cross-validation to increase the reliability. Using limited air quality data and meteorological data as inputs, PM2.5 concentration values were predicted. The LASSA-LGB's output was compared to normal LGB, SSA-LGB and ISSA-LGB. The findings demonstrate that LASSA-LGB outperforms the other models in terms of prediction accuracy. The RMSE and MAPE error indices were lowered from 3% to 16%. The concordance correlation coefficient is not less than 0.91, and the R2 reached 0.96. This indicates that the proposed model has potential advantages in the field of PM2.5 concentration prediction. [ABSTRACT FROM AUTHOR]

In the Kuche piedmont structure in the Tarim Basin where complex geological conditions prevail, frequent mud losses into the salt/gypsum formations and the target zones cause huge economic losses. To identity the types of the mud losses, a judgement model is established using the LightGBM algorithm. The LightGBM model, with good discriminative performance, has average recall rate of 85%, precision of 91% and F1-Score of 86.7%. In analyzing the types of mud losses, the interpretable machine learning techniques based on SHAP values are adopted to analyze a single mud loss event and all mud loss events as a whole. The SHAP value method, which is based on Cooperative Game Theory, breaks down the occurrence of mud loss events into contribution values of different features, and explains the effects of each feature on the mud loss event. Studies show that the main factors affecting mud losses include the difference between the mud density and the equivalent density calculated from the fracture pressure of the formation, the flow rate of mud, the well depth and the formation drilled. For the geology of the salt/gypsum formations and the target zones in the Kuche piedmont structure, the effects of the formation geology and the vertical distribution of the interlayer are in depth analyzed. This study enables the field engineers to fast and accurately determine the types of mud losses, and provides a strong support to the design of measures for preventing and controlling mud losses. [ABSTRACT FROM AUTHOR]

Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs. [ABSTRACT FROM AUTHOR]

A classification forecast method based on Light Gradient Boosting Machine (LightGBM) was utilized in this study to predict low visibility weather, using the coastal fusion observations and EC-thin model products of Zhangzhou from March 2020 to July 2021. The experiment was divided into four groups, including the new feature construction and model fusion schemes. The Bootstrap Aggregating (Bagging) technology and Area Under Curve (AUC) score were used to diminish the negative effect of extreme imbalance of samples, and the benchmark experiment employed the Logistics Regression (LR) method. The results showed that: (1) The most significant feature for estimating the possibility of low visibility weather was the 2 m dew point, followed by the temperature difference between 2m and 1000 hPa. (2) All model schemes exhibited improvement in comparison to the original forecast from the numerical model to varying degrees. In terms of metrics, the LightGBM model performed better than the LR model, largely due to its lower false alarm rate. (3) The skills of reasonable feature construction and model fusion contributed to optimizing the prediction performance and achieving higher scores on the test set. The impact of reasonable feature construction was particularly noteworthy. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of preventing front-end failures in forklifts by addressing the center of gravity, accurate prediction of the remaining useful life (RUL), and efficient fault diagnosis through alarm rules. The study's significance lies in offering a comprehensive approach to enhancing forklift operational reliability. To achieve this goal, acceleration signals from the forklift's front-end were collected and processed. Time-domain statistical features were extracted from one-second windows, subsequently refined through an exponentially weighted moving average to mitigate noise. Data augmentation techniques, including AWGN and LSTM autoencoders, were employed. Based on the augmented data, random forest and lightGBM models were used to develop classification models for the weight centers of heavy objects carried by a forklift. Additionally, contextual diagnosis was performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models. The results indicated that the random forest achieved an accuracy of 0.9563, while lightGBM achieved an accuracy of 0.9566. The acceleration data were collected through experiments to predict forklift failure and RUL, particularly due to repeated forklift use when the centers of heavy objects carried by the forklift were skewed to the right. Time-domain statistical features of the acceleration signals were extracted and used as variables by applying a 20 s window. Subsequently, logistic regression and random forest models were employed to classify the failure stages of the forklifts. The F1 scores (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. Moreover, random forest probabilities for each stage were combined and averaged to generate a degradation curve and determine the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and an RUL prediction model was developed to predict the failure point. Furthermore, the SHAP algorithm was utilized to identify significant features for classifying the stages. Fault diagnosis using alarm rules was conducted by establishing a threshold derived from the significant features within the normal stage. [ABSTRACT FROM AUTHOR]

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring. [ABSTRACT FROM AUTHOR]

Climate change, uneven distribution of water resources and anthropogenic impact have led to salinization and land degradation in the southern regions of Kazakhstan. Identification of saline lands and their mapping is a laborious process associated with a complex of ground measurements. Data from remote sensing are widely used to solve this problem. In this paper, the problem of assessing the salinity of the lands of the South Kazakhstan region using remote sensing data is considered. The aim of the study is to analyze the applicability of machine learning methods to assess the salinity of agricultural lands in southern Kazakhstan based on remote sensing. The authors present a salinity dataset obtained from field studies and containing more than 200 laboratory measurements of soil salinity. Moreover, the authors describe the results of applying several regression reconstruction algorithms (XGBoost, LightGBM, random forest, Support vector machines, Elastic net, etc.), where synthetic aperture radar (SAR) data from the Sentinel-1 satellite and optical data in the form of spectral salinity indices are used as input data. The obtained results show that, in general, these input data can be used to estimate salinity of the wetted arable land. XGBoost regressor ( R 2 = 0.282) showed the best results. Supplementing the radar data with the values of salinity spectral index improves the result significantly ( R 2 = 0.356). For the local datasets, the best result shown by the model is R 2 = 0.473 (SAR) and R 2 = 0.654 (SAR with spectral indexes), respectively. The study also revealed a number of problems that justify the need for a broader range of ground surveys and consideration of multi-year factors affecting soil salinity. Key results of the article: (i) a set of salinity data for different geographical zones of southern Kazakhstan is presented for the first time; (ii) a method is proposed for determining soil salinity on the basis of synthetic aperture radar supplemented with optical data, and this resulted in the improved prediction of the results for the region under consideration; (iii) a comparison of several types of machine learning models was made and it was found that boosted models give, on average, the best prediction result; (iv) a method for optimizing the number of model input parameters using explainable machine learning is proposed; (v) it is shown that the results obtained in this work are in better agreement with ground-based measurements of electrical conductivity than the results of the previously proposed global model. [ABSTRACT FROM AUTHOR]

The mechanical parameters of the surrounding rock are the critical factors considered in the design and construction of tunnels, especially soft rock tunnels. The accuracy of obtained mechanical parameters of the surrounding rock will directly affect the stability evaluation of the tunnel and the rationality of the support structure design. Thus, this study proposes a hybrid model—LightGBM with an equilibrium optimizer (EO-LightGBM)—to invert the mechanical parameters of the surrounding rock based on the displacement values of the soft rock tunnel. And then, the EO-LightGBM model is used to invert the mechanical parameters of the actual engineering surrounding rock, and the mechanical parameters obtained by the inversion are input into the FLAC3D program for analysis and calculation. The calculated results are highly consistent with the actual deformation values, and the maximum average error is 6.03%, verifying the rationality and accuracy of the EO-LightGBM model further. In addition, this study discusses the applicability of the equivalent parameter calculation method for different support structures in tunnel support simulation. In summary, the study's findings can provide a reference for the support optimization design and inversion of surrounding rock mechanical parameters in soft rock tunnels. Highlights: Developed a hybrid model EO-LightGBM that can accurately invert the mechanical parameters of soft rock tunnel surrounding rock. The design method of surrounding rock mechanics parameters is comprehensive, and the parameter values are more reasonable. The reliability of the EO-LightGBM inversion model was further verified using actual tunnel engineering as an example. The equivalent parameter calculation methods of various supporting structures were discussed for tunnel engineering. [ABSTRACT FROM AUTHOR]

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset. [ABSTRACT FROM AUTHOR]