Analysis on the Influencing Factors of Yunnan Province’s Fiscal Revenue Based on Elastic Net

2021 ◽  
Vol 10 (03) ◽  
pp. 415-419
Author(s):  
秀君 于
Keyword(s):  
Influencing Factors ◽  
Elastic Net ◽  
Fiscal Revenue

scholarly journals Exploring Influencing Factors of Chronic Obstructive Pulmonary Disease Based on Elastic Net and Bayesian Network

2022 ◽  
Author(s):  
Dichen Quan ◽  
Jiahui Ren ◽  
Hao Ren ◽  
Liqin Linghu ◽  
Xuchun Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Family History ◽  
Influencing Factors ◽  
Smoking Status ◽  
Elastic Net ◽  
Household Air Pollution ◽  
Shanxi Province ◽  
Chronic Obstructive ◽  
Targeted Prevention ◽  
Network Relationship

Abstract This study aimed to construct Bayesian networks(BNs) to analyze the network relationship between those influencing factors and COPD, and to explore their intensity of effect on COPD through network reasoning. Elastic Net and Max-Min Hill-Climbing(MMHC) hybrid algorithm were adopted to screen the variables on the monitoring data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. After variables selection by Elastic Net, 10 variables closely related to COPD were selected finally. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients’ cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network relationship between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.

scholarly journals Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

2021 ◽  
Vol 13 (21) ◽  
pp. 12262
Author(s):  
Mingyu Li ◽  
Dongxiao Niu ◽  
Zhengsen Ji ◽  
Xiwen Cui ◽  
Lijie Sun
Keyword(s):  
Random Forest ◽  
Wind Power ◽  
Influencing Factors ◽  
Clean Energy ◽  
Forecast Model ◽  
Elastic Net ◽  
Offshore Wind ◽  
Low Carbon ◽  
Offshore Wind Power ◽  
Low Carbon Energy

Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.

POSSIBLE INFLUENCING FACTORS OF BOWEL CLEANLINESS DURING COLONOSCOPY

2018 ◽  
Author(s):  
I Iozsef ◽  
O Ilyés ◽  
P Miheller ◽  
AV Patai
Keyword(s):  
Influencing Factors

Reliability-Based Assessment of the Truck Safe Following Distance Considering Multiple Influencing Factors

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Bowen Dong ◽  
Wenjun Du ◽  
Feng Chen ◽  
Qi Deng ◽  
Xiaodong Pan
Keyword(s):  
Influencing Factors

Entrepreneurial Mindset

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Nusa FAIN ◽  
Michel ROD ◽  
Erik BOHEMIA
Keyword(s):  
Influencing Factors ◽  
Future Study ◽  
Engineering Students ◽  
Initial Study ◽  
Longitudinal Survey ◽  
Teaching Approaches ◽  
Approaches To Teaching ◽  
Design Students ◽  
Commerce Students ◽  
Case Based

This paper explores the influence of teaching approaches on entrepreneurial mindset of commerce, design and engineering students across 3 universities. The research presented in this paper is an initial study within a larger project looking into building ‘entrepreneurial mindsets’ of students, and how this might be influenced by their disciplinary studies. The longitudinal survey will measure the entrepreneurial mindset of students at the start of a course and at the end. Three different approaches to teaching the courses were employed – lecture and case based, blended online and class based and fully project-based course. The entrepreneurial mindset growth was surprisingly strongest within the engineering cohort, but was closely followed by the commerce students, whereas the design students were slightly more conservative in their assessments. Future study will focus on establishing what other influencing factors beyond the teaching approaches may relate to the observed change.

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