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Plithogenic Machine Learning Solutions to Material Selection in Renewable Energy Systems

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Decision models optimize materials selection for renewable energy systems, highlighting plithogenic methods and machine learning.

Plithogenic-based decision models improved material selection for renewable energy systems, emphasizing optimization.
Using random forest classifier, the study identified crucial criteria for selecting sustainable materials across various types.
Implementation of TOPSIS enabled effective ranking of materials, demonstrating the advantages of the integrated decisioning approach.
Sensitivity analysis showcased the efficacy of the proposed model, though it has limitations regarding material variety.

Martin et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Decision models optimize materials selection for renewable energy systems, highlighting plithogenic methods and machine learning.

Plithogenic-based decision models improved material selection for renewable energy systems, emphasizing optimization.
Using random forest classifier, the study identified crucial criteria for selecting sustainable materials across various types.
Implementation of TOPSIS enabled effective ranking of materials, demonstrating the advantages of the integrated decisioning approach.
Sensitivity analysis showcased the efficacy of the proposed model, though it has limitations regarding material variety.

Martin et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark