An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Rockbursts are typical dynamic disasters in underground coal mines. Their formation and occurrence are influenced by multiple factors, including geological conditions, mining disturbances, and microseismic activities. Machine learning models can support early warning by forecasting high‐energy microseismic (MS) activity associated with elevated rockburst risk. However, many of these models are black boxes that are difficult for humans to understand, which may diminish their credibility and constrain their practical applicability. To establish interpretable models for short‐term rockburst prediction considering multiple factors, we assess five tree‐based models (decision tree, random forest, LightGBM, XGBoost, and CatBoost) using field databases from Gao Jiapu coal mine and utilize the model explanation method SHAP to interpret the models. A day is labeled as rockburst risk if its daily maximum MS energy exceeds 1 × 10⁵ J, otherwise no risk. The results revealed that CatBoost exhibited the highest overall performance, followed by LightGBM, and both the random tree and XGBoost performed better than the decision tree. When considering the influence of time window length, all models displayed optimal performance under a 3‐day time window. Specifically, CatBoost achieved F1 scores of 0.856, 0.778, and 0.807 for the 3‐day, 5‐day, and 10‐day time windows, respectively. Global feature importance analysis showed that the average energy, energy deviation, and cumulative energy were the three most important predictors. Furthermore, local SHAP values were drawn to reveal the complex underlying relationships between the 12 factors and model predictions. The results show that factors such as energy‐related factors, coal depth, and working face advance positively contribute to rockburst risk prediction. Conversely, fold distance, total frequency, and source concentration degree negatively impact the model outcomes. The findings suggest that incorporating CatBoost and the SHAP method holds great potential in developing interpretable models for accurate rockburst risk prediction.

Parity Space-Based Fault Detection by Minimum Error Minimax Probability Machine

In order to cure the crux of low accuracy of traditional PG transient stability prediction (PGTSP) methods, a PGTSP method based on improved Convolutional Neural Network(CNN) is proposed under big data. First, the theoretical framework of state assessment of Power Grid (PG) is constructed according to analysis of historical big data in PG. Based on data source, big data classification, big data cleaning and processing, PG status assessment is realized. Then, by selecting a variety of PG traits as the multi-input trait space of CNN model. By using multi-channel idea to independently analyze and fuse various features, a multi-channel multi-trait fusion CNN(MC-MF-FCNN) model is constructed and the accurate prediction of grid transient stability is achieved. Finally, the root mean square error(RMSE), false alarm rate, false alarm rate and accuracy rate of the proposed algorithm and the other two algorithms under the same conditions are compared and analyzed through simulation experiments. The results show that RMSE, false alarm rate and miss alarm rate of the proposed algorithm are the smallest and the accuracy is the highest. The highest accuracy rate within 9 cycles after the fault is 96.83 %, and the minimum RMSE, missed alarm rate and false alarm rate are 0.196, 2.15% and 1.32%, respectively. The performance is better than the other two comparison algorithms.

In the pipeline leakage detection and location, what we need to know is whether the pipe is leaky and where the leakage point is. In addition, we are very concerned about the extent of leakage, false alarm rate and missing alarm rate. Both correlation analysis method and sequential probability ratio test method can be used in the pipeline leakage detection and location. Correlation analysis method can get the extent of leakage but it cannot limit false alarm rate and missing alarm rate. Sequential probability ratio test method can reflect the two rates, but it cannot get the extent of leakage. In this paper, the two method are fuzzily fused by means of fuzzy variable weight method, thus we can arrive at a decision about the extent and location of leakage. We verify this method in simulated pipeline in lab, and the results show that the fusion method has better effects than single method.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Rockburst is a common and huge hazard in underground engineering, and the scientific prediction of rockburst disasters can reduce the risks caused by rockburst. At present, developing an accurate and reliable rockburst risk prediction model remains a great challenge due to the difficulty of integrating fusion algorithms to complement each other’s strengths. In order to effectively predict rockburst risk, firstly, 410 sets of valid rockburst data were collected as the original data set in this paper, which was used to process these rockburst cases by the SMOTE oversampling method. Then, four integrated algorithms and eight basic algorithms were selected, which were optimized by hyperparameters and five-fold cross-validation and combined with the random search grid method, thus improving the classification performance of these algorithms. Third, the stacking integration algorithm, which was combined with the principles of various machine learning algorithms and the characteristics of the rockburst cases, integrated the optimization of rockburst algorithms with reference to four combinatorial strategies. Further, we adopted the voting integration algorithm, chose multiple combination schemes, and referred to the weighted fusion of accuracy, F1 score, recall, precision, and cv-mean as the weight values, and the optimal model for rockburst risk prediction was obtained. Finally, using the 35 generated stacking integration algorithms and 18 voting integration algorithms, the optimal model in the fusion strategy was selected and the traditional integration algorithm model was analyzed on the basis of different sample combinations of the models. The results showed that the prediction performance of stacking and voting integration algorithms was mostly better than the ordinary machine-learning performance, and the selection of appropriate fusion strategies could effectively improve the performance of rockburst prediction for ensemble learning algorithms.

Process monitoring of wastewater treatment plant (WWTP) is a challenging industrial problem, due to its exposure to the hostile working environment and significant disturbances. This paper proposed a novel fault diagnosis method, termed as optimization forecast components-support vector machine (OFC-SVM). The method firstly improved the forecastable component analysis (ForeCA) for feature extraction. Secondly, in order to further enhance the method, the quadratic Grid Search (GS) algorithm is utilized to optimize the parameters of the proposed method. Thirdly, to properly evaluate the method performance, a new evaluation index is proposed, named Pre Alarm Rate (PAR), aiming to achieve the quantitative trade-off between false alarm rate (FAR) and missed alarm rate(MAR). Then, the new ROC curve can be further derived by PAR. Finally, the performance of OFC-SVM is strictly compared with other five methods as well as validated by a Monte Carlo model and a full-scale WWTP.

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes.

Aiming at the challenges such as the difficulty in identifying new variant viruses in computer virus detection, the slow convergence speed of traditional algorithms, and the limited edge computing resources, a new virus detection paradigm integrating quantum computing and improved CSA is proposed. Firstly, the global search ability of the algorithm is enhanced through dynamic affinity calculation and a two-stage clone amplification strategy. The quantum superposition state mutation operator and the quantum tunneling effect adaptive mutation rate adjustment mechanism are introduced to break through the local optimal bottleneck of the classical algorithm in the high-dimensional solution space. Secondly, the storage structure of the immune memory bank is optimized by utilizing the characteristics of quantum entanglement to achieve parallel retrieval and distributed update of the historical optimal solution, reducing the memory occupation on edge devices to 1/5 of the traditional method. Experiments show that although CAS is slightly inferior to the CNN algorithm in terms of detection accuracy rate, false alarm rate and missed alarm rate, it has certain advantages in detection speed and is significantly better than FCM. The research results have broken through the limitations of traditional detection technologies and achieved effective detection of unknown viruses.

Designing alarm system using modified generalized delay-timer

Performance analysis of up/down counters in alarm design

In the face of the growing network security, intrusion detection system, and the traditional shortcomings of more and more prominent. It is the false alarm rate, missed alarm rate, and the problem of poor real-time has not been resolved. In this paper, the lack of it, will make a concerted genetic algorithm combined with intrusion detection systems, designed a co-evolutionary genetic algorithm-based intrusion detection system model, it is great to make up for the lack of intrusion detection system now. I use a specific simulation experiments show the usefulness of this model in the final.

To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed — the MLCψfits into a mine line pattern with fixed mine spacing; random -the MLCψfits into a mine line pattern with random mine spacing; or false alarm-ψ-the MLCψdoes not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-spaced mine lines. Extensive Matlab simulations characterize the algorithm's performance across a wide range of operating environments and simulated minefield configurations. In total, ten replications each of 1,367 different configurations have been evaluated. These configurations include three different water depths (10 ft., 6 ft. and 200 ft.), six false notional alarm rates (1, 2,5,10,15 and 20 false alarms per sq. NM), three false alarm distributions (uniform, sloped, and striped), three sensor probabilities of detections (0.4, 0.6 and 0.9), three target confidence scores (0.6,0.8 and 1.0) and four false alarm confidence scores (0.5, 0.6, 0.8 and 1.0). The range of simulation parameters was selected in part as an attempt to push the algorithm past its acceptable performance limits. However, the algorithm performed remarkably well even under the worst conditions. For MLC sets with false alarm rates as high as 20 per square nautical mile, the algorithm correctly classifies over 98% of the MLCs as mines and detects 97% of the mine lines. Furthermore, it reduces the false alarm rate from 20 to 0.2 per square nautical mile with a false mine line rate of 0.14 false lines per square nautical mile. The algorithm achieves even lower false alarm rates when the initial false alarm rate is less than 20. The automated mine line detection discovers many obscure lines that are virtually undetectable to the naked eye.

In recent years, cardiovascular disease (CVD) has emerged as a leading cause of mortality worldwide, highlighting the urgent need for reliable predictive models to support early diagnosis and preventive care. This study conducts a comparative analysis of various machine learning models to assess CVD risk, focusing on their ability to accurately identify individuals at high risk based on clinical and demographic data. We evaluate multiple supervised learning algorithms, including logistic regression, random forests, support vector classifiers, K-nearest neighbors, gradient boosting, and AdaBoost, comparing their predictive performance in terms of accuracy, precision, recall, and F1-score. Additionally, we propose a novel hybrid model that combines Random Forest (RF) for feature selection and Deep Neural Networks (DNN) for classification, aiming to leverage the strengths of both approaches for more accurate CVD prediction. Our findings demonstrate that ensemble models such as random forests and gradient boosting achieve superior performance, with high accuracy (0.99) and balanced precision and recall values, outperforming simpler models like logistic regression and support vector classifiers. The hybrid model further enhances prediction accuracy, achieving 92.4% accuracy, 91.7% precision, 93.0% recall, and an AUC-ROC score of 96.0%. The analysis also underscores the importance of data preprocessing techniques, including normalization and handling of missing values, in optimizing model accuracy and stability. Notably, K-nearest neighbors also performed exceptionally well with a high F1-score across classes, highlighting its robustness for this task. This study provides a detailed examination of each model's strengths and limitations, including the proposed hybrid model, offering valuable insights for healthcare practitioners and data scientists in selecting effective machine learning models for CVD risk prediction. By integrating these models into healthcare systems, real-time risk prediction can be enhanced, ultimately supporting clinical decision-making and advancing personalized care in cardiovascular health. Keywords— cardiovascular disease, machine learning, risk assessment, predictive models, supervised learning, healthcare