scholarly journals Fire Prediction Based on CatBoost Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fangrong Zhou ◽  
Hao Pan ◽  
Zhenyu Gao ◽  
Xuyong Huang ◽  
Guochao Qian ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Prediction Model ◽  
Land Surface ◽  
Feature Selection Method ◽  
Gradient Boosting ◽  
Stable Operation ◽  
Wildfire Risk ◽  
Redundant Data ◽  
Fire Point ◽  
Auc Value

In recent years, increasingly severe wildfires have posed a significant threat to the safe and stable operation of transmission lines. Wildfire risk assessment and early warning have become an important research topic in power grid risk assessment. This study proposes a fire prediction model on the basis of the CatBoost algorithm to effectively predict the fire point. Five wildfire risk factors, including vegetation factors, meteorological factors, human factors, terrain factors, and land surface temperature, were combined using the feature selection method on the basis of the gradient boosting decision tree model and principal component analysis to achieve dimensionality reduction of redundant data and create a fire prediction model. The MODIS fire point product is used as the model evaluation data. The verification result uses the AUC value as the evaluation factor. The accuracy of the model is 0.82, and the AUC value is 0.83. The obtained fire point evaluation results are in good agreement with the actual fire points. Results show that this model can effectively predict the risk of wildfires.

Financial Risks of the Bank: Assessment and Mechanism of Neutralization

2020 ◽  
Vol 22 (1) ◽  
pp. 6-12
Author(s):  
Nelia Volkova ◽  
◽  
Alina Mukhina ◽  
Keyword(s):  
Risk Assessment ◽  
Financial Stability ◽  
Financial Risk ◽  
Stable Operation ◽  
Financial Risks ◽  
Negative Consequences ◽  
Conceptual Approach ◽  
Relevant Today ◽  
Banking Institution ◽  
The Impact

Abstract. Introduction. The issue of financial risk management of commercial banks is quite relevant today, because the activity of banks is the most risky of all. The presence of risks in banking can lead to unexpected losses, namely the loss of own resources. That’s why for the stable operation of the bank without loss the priority is to assess the financial risks, which is the basis for their further neutralization. Purpose. The purpose of the article is to develop conceptual provisions for assessment financial risks and justifying the need to neutralize them. Results. The article analyzes the impact of risks on the financial stability of a banking institution. The main methods of bank risk assessment are considered. All these include the statistical method, the analytical method, the expert method, the analogue method and the combined method. The necessity of neutralization of financial risks in order to avoid negative consequences is substantiated. Also the methods of bank risks neutralization are considered. It should be noted that these methods of neutralization can not only be used, but also supplement the list with new methods must be done, which in the future will protect the bank from the influence of undesirable factors. A conceptual approach to the assessment and neutralization of financial risks is proposed. This conceptual approach aims to ensure effective assessment of the level of risk with their subsequent neutralization Conclusions. Use of a conceptual approach will allow an effective risk assessment and decision-making to avoid or accept risk. Thanks to using this approach, the banking institution will be able to react swiftly to the presence of financial risks and to prevent the occurrence of negative consequences, which may lead to a violation of the financial stability of the bank.

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

Quick risk assessment profile (qRAP) is a prediction model for post-traumatic venous thromboembolism

Injury ◽  
2019 ◽  
Vol 50 (9) ◽  
pp. 1540-1544
Author(s):  
Jotaro Tachino ◽  
Kouji Yamamoto ◽  
Kentaro Shimizu ◽  
Ayumi Shintani ◽  
Akio Kimura ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Post Traumatic

scholarly journals Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 12302
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Bosong Chen ◽  
Jiaqi Feng
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Carbon Emission ◽  
Historical Data ◽  
Global Climate ◽  
Optimization Methods ◽  
Gradient Boosting ◽  
Target Values ◽  
Whale Optimization ◽  
Practical Implications

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

scholarly journals Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1334
Author(s):  
Hasan Symum ◽  
José Zayas-Castro
Keyword(s):  
Prediction Model ◽  
Information Exchange ◽  
High Risk Patient ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hospital Discharges ◽  
Discharge Model ◽  
Readmission Risk

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

scholarly journals EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Faris A. Almalki ◽  
Soufiene Ben Othman ◽  
Fahad A. Almalki ◽  
Hedi Sakli
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Routing Protocol ◽  
Health Care Professionals ◽  
Energy Efficient ◽  
Temporal Correlation ◽  
Sensor Nodes ◽  
Energy Efficient Routing ◽  
Battery Lifetime ◽  
Redundant Data

Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients’ data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called “MySignals HW V2.” Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay.

scholarly journals Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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