J
Joseph Ekong
Researcher at Auburn University
Publications - 5
Citations - 118
Joseph Ekong is an academic researcher from Auburn University. The author has contributed to research in topics: Data envelopment analysis & Fuzzy logic. The author has an hindex of 2, co-authored 4 publications receiving 71 citations. Previous affiliations of Joseph Ekong include Ohio Northern University & Western New England University.
Papers
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Journal ArticleDOI
Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach
TL;DR: A DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance and the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model is presented.
Journal ArticleDOI
Design and Manufacturing of High Surface Area 3D‐Printed Media for Moving Bed Bioreactors for Wastewater Treatment
Olivia Elliott,Stephanie Gray,Michael McClay,Bakr Nassief,Ann Nunnelley,Eric Vogt,Joseph Ekong,Kamran Kardel,Ali Khoshkhoo,Gabriel Proaño,David M. Blersch,Andres L. Carrano +11 more
TL;DR: In this article, mathematical models and 3D printing technologies were used to design and fabricate complex media designs that provide high specific surface area and refugia to protect biofilm from premature sloughing.
Journal ArticleDOI
Influence of three-dimensional features of a woven-fabric substrate on benthic algal biomass production
TL;DR: In this article, the authors investigated the effect of 3D substrate features on algal biomass cultivation and found that the introduction of vertical structures in three-dimensional substrata to support algal attachment and colonization has a significant effect on biomass productivity, with productivity increasing by as much as 300% with optimum strand spacing.
Book ChapterDOI
A Data Scientific Approach to Measure Hospital Productivity
TL;DR: A fuzzy logic-based multi-criteria decision-making model is proposed so as to enhance business performance and presents uniqueness in that it helps make the most suitable decision with the consideration of the weights determined by the data from the hybrid model.
Journal ArticleDOI
Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology
Gopal Nath,Austin Coursey,Joseph Ekong,Elham Rastegari,Saptarshi Sengupta,Asli Z. Dag,Dursun Delen +6 more
TL;DR: In this article , the temporal effects of factors that are important for brain and central nervous system (BCNS) cancer survival, by proposing a machine learning methodology, have been investigated from the perspective of biomedical sciences.