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Sökning: WFRF:(Giabbanelli Philippe J.)

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1.
  • Jordan, Rebecca, et al. (författare)
  • Twelve Questions for the Participatory Modeling Community
  • 2018
  • Ingår i: Earth's Future. - : American Geophysical Union (AGU). - 2328-4277. ; 6:8, s. 1046-1057
  • Tidskriftsartikel (refereegranskat)abstract
    • Participatory modeling engages the implicit and explicit knowledge of stakeholders to create formalized and shared representations of reality and has evolved into a field of study as well as a practice. Participatory modeling researchers and practitioners who focus specifically on environmental resources met at the National Socio-Environmental Synthesis Center (SESYNC) in Annapolis, Maryland, over the course of 2 years to discuss the state of the field and future directions for participatory modeling. What follows is a description of 12 overarching groups of questions that could guide future inquiry.
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2.
  • Mkhitaryan, Samvel, et al. (författare)
  • How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions : a case study on fruit intake
  • 2023
  • Ingår i: BMC Public Health. - : Springer Nature. - 1471-2458. ; 23:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.
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3.
  • Wozniak, Maciej K., et al. (författare)
  • A New Application of Machine Learning : Detecting Errors in Network Simulations
  • 2022
  • Ingår i: Proceedings of the 2022 Winter Simulation Conference, WSC 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 653-664
  • Konferensbidrag (refereegranskat)abstract
    • After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.
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  • Resultat 1-3 av 3

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