AML_VUCA_2020: Applied Machine Learning & Management of Volatility, Uncertainty, Complexity and Ambiguity (V.U.C.A) |
Website | https://www.iospress.nl/journal/journal-of-intelligent-fuzzy-systems/ |
Submission link | https://easychair.org/conferences/?conf=aml-vuca-2020 |
Submission deadline | January 31, 2020 |
This special issue shall cover various Machine Learning techniques applied to the problems associated with the risk factors, such as Volatility
(rapid rise and fall in the responses), Uncertainty (unpredictable situations/conditions), Complexity (difficulties in problem understanding) and Ambiguity (confusion in situations/ surrounding conditions).
Submission Guidelines
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B.Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).
All papers are refereed through a peer review process.
List of Topics
The following are the topics to be coved but not limited to:
- Machine Learning Problems: Classification, Regression, Recognition, and Prediction.
- Machine Learning Methods: Supervised and unsupervised learning, decision and regression trees, probabilistic networks, inductive logic programming, ensemble methods, clustering, Reinforcement learning.
- Soft Computing: Fuzzy, Neural computing, Soft Computing, Expert Systems, GPU Computing for Machine Learning, Advanced Soft Computing.
- Application of ML Techniques for V.U.C.A. Management: Application of Linear Regression, Application of Logistic Regression, Application of Decision Tree, Application of Random Forest, Roll of SVM, Application of Naïve Bayes, Application of kNN (k- Nearest Neighbors), Roll of k-Means.
- New Machine Learning algorithms with empirical, theoretical justification.
- Experimental and/or theoretical studies yielding new insight into the design and behavior of business or industrial application.
- New learning tasks in the context of industrial or business applications and of the methods for assessing performance on those tasks.
- Development of new analytical frameworks that advance theoretical studies of practical learning methods.
Contact
Guest Editor
Prof. Srikanta Patnaik,
Department of Comoputer Science and Engineering,
SOA University, Bhubaneswar India
Email: patnaik_srikanta@yahoo.co.in