An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion

Wang, Delu; Tong, Xian; Wang, Yadong

doi:10.1016/j.resourpol.2020.101593

Cited by 22 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Resource risks can be expressed as the impact of the uncertainty of the estimated proved resource quantity on economic benefits. Countries with abundant resources and great potential have high investment value (Wang et al, 2020).…”

Section: Oil and Gas Investment Environment Assessment Index Systemmentioning

confidence: 99%

Dynamic Risk Assessment of the Overseas Oil and Gas Investment Environment in the Big Data Era

et al. 2021

View full text Add to dashboard Cite

Our research aims to analyze how the uncertainties and risks of the overseas oil & gas investment environment change over time and reveal the specific occurrence probabilities of risk on different levels. In the process of long-drawn overseas oil & gas investment that can last for 30 years or longer, it is difficult for investment decision-makers to grasp the occurrence probabilities and trends of some specific risks accurately and in a timely manner. The overseas risk assessment system has made great progress; however, it has remained elusive due to the challenge of too many complex and interweaved factors. With the advent of big data and artificial intelligence, more precise and specific risk evaluations can be conducted. Our research selects 25 indicators from six dimensions and applies a Cloud parameter Bayesian network algorithm to dynamically assess the oil and gas overseas investment risk of 10 countries. The results reveal how risk dynamics have changed over the past two decades. Our research may serve as a reference in future overseas oil & gas investment risk decision-making, and is also significant to outbound investing, engineering, and service projects. The proper use of risk assessment results can be conducive to potential investors who may invest in potential countries in the future.

show abstract

Section: Oil and Gas Investment Environment Assessment Index Systemmentioning

confidence: 99%

Dynamic Risk Assessment of the Overseas Oil and Gas Investment Environment in the Big Data Era

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Due to this reason, we observed the restricted applications of existing multiple classifiers. We have found in [51] that multiple classification models do not outperform the single classifier. The authors in the lateral mentioned study proved their claim by using statistical analysis of multiple classification and binary classification.…”

Section: Introductionmentioning

confidence: 96%

“…Although multiple classification has an extensive background, but studies with regards to multiple web services instances classification are relatively scarce [50], [51]. Existing studies on the multiple classifications show that classifiers used for multiple classification are relatively low in performance accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

View full text Add to dashboard Cite

To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

show abstract

“…In order to reduce innovation risks, in the work of Delu Wang et al [14] it is proposed to combine the coefficient of variation method, system clustering and combine multiclassifiers for early risk warning with 20 indicators in three dimensions with a decrease in the indicator. The significance of the system-forming property of a selforganizing intelligent data analyzer is substantiated in comparison with traditional singleclassification models (logistic regression, machine-like systems, neural network, decision tree) and six commonly used methods of merging multiclassifiers (such as expert judgment, Bayesian method and genetic algorithm).…”

Section: Introductionmentioning

confidence: 99%

Triad model: simulation - functional tensometry - information database in the assessment of the reliability of technological machines

Sevryugina

Kapyrin

2021

E3S Web Conf.

View full text Add to dashboard Cite

The concept of a multidisciplinary approach to assessing the resource of individual components of the machine by combining database information based on simulation techniques and functional tensomethration is proposed. Simulations determine the reperative points of the tendometric sensors. The creation of a diagnostic model using basic concepts of information theory has allowed the development of a synergistic model for the recognition of the area of displacement of areas of uncertainty, which will ensure the identification of the defect (risk-denial). The formation of an electronic database of parametric data on the nature of loads as a diagnostic indicator of the change in the accuracy of pairing in machine systems is justified. Experimental studies were conducted on the model of the quick-capler. Hierarchical structuring of the machine to the level of mating parts with digital control of the criticality of the magnitude of external and internal loads ensures reliability control throughout the entire service life of the machine. When disposing of machines, this data allows you to obtain information about the residual resources of the elements for their reuse or the feasibility of restoration. This, in turn, will ensure the environmental friendliness and economy of the process.

show abstract

An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion

Cited by 22 publications

References 37 publications

Dynamic Risk Assessment of the Overseas Oil and Gas Investment Environment in the Big Data Era

Dynamic Risk Assessment of the Overseas Oil and Gas Investment Environment in the Big Data Era

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Triad model: simulation - functional tensometry - information database in the assessment of the reliability of technological machines

Contact Info

Product

Resources

About