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Sökning: WFRF:(Shankar Meena)

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1.
  • Haghighi, Mona, et al. (författare)
  • A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
  • 2016
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
  • Tidskriftsartikel (refereegranskat)abstract
    • Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
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2.
  • Shankar, Amit, et al. (författare)
  • Exploring enablers and inhibitors of AI-enabled drones for manufacturing process audits : A mixed-method approach
  • 2024
  • Ingår i: Business Strategy and the Environment. - : John Wiley & Sons. - 0964-4733 .- 1099-0836.
  • Tidskriftsartikel (refereegranskat)abstract
    • The objective of this study is to explore the enablers and inhibitors of AI-enabled drone adoption for manufacturing process audit using a mixed-method design. A qualitative study was performed to explore the enablers and inhibitors. Further, based on the findings of the qualitative studies, a framework was proposed, and proposed hypotheses were examined using a survey-based study. The results indicated that function, environmental, and epistemic values are major enablers, whereas vulnerability and sunk cost barriers are major inhibitors to adoption intention. The initial trust and inertia were crucial mediators, and organizations' technological innovativeness played a crucial moderating role. This study enriches the literature on technological adoption for sustainability and helps audit service providers design strategies to enhance AI-enabled drone adoption for process audits. 
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