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Discussion of interdisciplinary approach and framework used in this study This project featured an interdisciplinary approach in consultation with numismatic experts.
This approach features automated and autonomous decision-making.
However, this approach features some caveats as well.
In this study, we approached the NNA using a new feature selection algorithm called mRMR that can identify optimal features with minimum redundancy.
This study discusses the analytical features by comparing the intermediate approach with the radial and non-radial ones.
Therefore, in this study, the mRMR-IFS feature selection approach and the RF algorithm are combined to construct the prediction model.
In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA).
In this study, sequential approaches for damage detection in beams using time-modal features and artificial neural networks are proposed.
Thus, the OE-approach was the method of choice in this study, given its favourable features in this respect.
In this study, two different approaches were proposed for feature selection: FDAFSA and RTPFSGA.
This study has 4 salient features.
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