Risk Identification and Evaluation and Machine Learning in Manufacturing Innovation

2021 ◽  
pp. 280-285
Author(s):  
Xianke Li
Keyword(s):  
Machine Learning ◽  
Risk Identification ◽  
Manufacturing Innovation

Trade risk identification and prediction using experts’ knowledge and machine learning

2021 ◽  
Author(s):  
Ieva Dunduliene ◽  
Robertas Alzbutas ◽  
Kamile Baltrusone
Keyword(s):  
Machine Learning ◽  
Risk Identification

scholarly journals Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 172
Author(s):  
Debalina Banerjee Chattapadhyay ◽  
Jagadeesh Putta ◽  
Rama Mohan Rao P
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Risk Management ◽  
High Risk ◽  
Risk Prediction ◽  
Learning Model ◽  
Risk Identification ◽  
Large Set ◽  
High Risk Factors ◽  
Machine Learning Model

Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA–K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success.

scholarly journals Student risk identification learning model using machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 3872
Author(s):  
Susheelamma K. H. ◽  
K. M. Ravikumar
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Learning Model ◽  
Risk Identification ◽  
Learning Approach ◽  
Applied Machine Learning ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Significant Performance ◽  
Student Risk

<p class="Abstract">Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall.</p>

Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification

2020 ◽  
Author(s):  
Eugene Mangortey ◽  
Dylan Monteiro ◽  
Jamey Ackley ◽  
Zhenyu Gao ◽  
Tejas G. Puranik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Identification ◽  
Parameter Selection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection For
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