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- W3173977017 abstract "Heterogeneity in healthcare data is a cause of concern for the medical professionals. With the increased application of robots in the medical field, data heterogeneity requires to be ad- dressed for improved classification accuracy. In this work, we leverage the Nelder–Mead (NM) optimization method for mitigating data heterogeneity. The NM method is applied on the raw healthcare data to acquire the heterogeneity mitigated data. We classify the electrocardiogram signals from two heterogeneous datasets as normal and abnormal using LSTM (Long Short Term Memory)-based deep learning technique. On mitigating the heterogeneity, the classification accuracies get improved. Therefore the reliability of the robotic healthcare systems increases." @default.
- W3173977017 created "2021-07-05" @default.
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- W3173977017 date "2021-01-01" @default.
- W3173977017 modified "2023-09-25" @default.
- W3173977017 title "Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder–Mead method" @default.
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- W3173977017 doi "https://doi.org/10.1016/b978-0-323-85498-6.00012-5" @default.
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