Application of Support Vector Machine Based on Rough Sets to Project Risk Assessment (RS-SVM)

Author(s):  
Zhengyuan Jia ◽  
Lihua Gong ◽  
Jia Han
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Amir Farmahini Farahani ◽  
Kaveh Khalili-Damghani ◽  
Hosein Didehkhani ◽  
Amir Homayoun Sarfaraz ◽  
Mehdi Hajirezaie

2019 ◽  
Vol 7 ◽  
Author(s):  
Matej Masár ◽  
Mária Hudáková

Current trends show that education in the field of project risk management is a very actual topic. Long - term projects, which was realized in 2018, was mainly focused on R&D across the world. Short - term projects, was focused on innovation and improve manufacturing processes. Many projects failed because project managers did not manage project risks. Project managers have less knowledge and skills on how to effectively manage project risks, especially risks in the planning phase of projects. The main aim of this article is to analyze the current state of usage project risk assessment across the world, based on own empirical research, which was provided, by authors in 2018 and 2019 (mainly level of usage project risk management methods, experience and level of education). The research focused on analyzing the current state of project risk assessment among continents. The authors focused on the average level of use qualitative and quantitative project risk analysis by project managers, level of project risk management experience by project managers and complexity of learning in using of qualitative and quantitative project risk management methods and tools.  Some recommendation were established to educate project managers in the field of project risk management.


2015 ◽  
Vol 25 (2) ◽  
pp. 117-138 ◽  
Author(s):  
Petra Brinkhoff ◽  
Malin Norin ◽  
Jenny Norrman ◽  
Lars Rosén ◽  
Kristine Ek

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