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Conference Papers Year : 2020

Augmented Learning and Data Filtering: Knowledge Management and Discovery

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Cyrus F. Nourani
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  • PersonId : 1120227
Eunika Mercier-Laurent
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Ever since the authors’ publications on frontiers of decision trees, forecasting, and business ecosystems, over a decade the techniques for enterprise business systems planning and design with predictive models have become an attention focus. Heuristics on predictive analytics are developed with novel applications to decision trees. Augmented world and priority-based decision trees are new applications for machine learning and big data filtering. The areas addressed include designing predictive modeling with strategic decision systems with applications to analytics, enterprise modeling, and cognitive social media business interfaces. The areas further explored range from plan goal decision tree satisfiability with competitive business models to predictive analytics models that accomplish goals on 3-tier glimpse to business systems. Example decision support application for AI KM with applications is presented. Augmented learning decisions is how AI enhances the decision-making process with more comprehensive cognitive views to business models and infrastructures.
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Dates and versions

hal-03478012 , version 1 (13-12-2021)


Attribution - CC BY 4.0



Cyrus F. Nourani, Eunika Mercier-Laurent. Augmented Learning and Data Filtering: Knowledge Management and Discovery. 6th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), Jul 2018, Stockholm, Sweden. pp.91-106, ⟨10.1007/978-3-030-52903-1_8⟩. ⟨hal-03478012⟩
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