Diagnosis Failure Cause of complex Pharmaceutical System by Bayes Learning for Decision Support
Ngoc-Hoang Tran

Ngoc-Hoang Tran*, The University of Danang – University of Technology and Education, 48 Cao Thang, Danang, Vietnam.

Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 5-8 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9796038620/2020©BEIESP | DOI: 10.35940/ijrte.F9796.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This work proposes a real application of diagnosis protocol for complex pharmaceutical process drifts. Main challenge is to identify and classify failure causes of production process. The model which we have proposed is structured in the causal graph form, named “Hierarchical Naïve Bayes” (HNB) formalism. Our contribution is the presentation of a methodology that allows developing flexibility in particular complex pharmaceutical production context. A data extraction and processing prototype is performed in this paper from real pharmacy company to build Bayesian model. Diagnosis results are decision support elements that built based on HNB probabilities. Furthermore, this work can be applied in order to improve production quality in businesses competition. 
Keywords: Modeling and identification, Pharmaceutical production system, Data learning, Equipment diagnosis, Bayes Networks.
Scope of the Article: Next Generation Internet & Web Architectures.