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The prediction time of deep learning model is lower than the time required by the best conventional ML approach - Random Forest.
From the results we conclude that both methods are essentially consistent, but the reliability index calculated by the traditional model is lower than that from the catastrophic model.
In addition, the critical hunting speed evaluated via the heuristic nonlinear creep model is lower than that derived using a linear creep model.
The bottom rock average temperature in five-orifice model is lower than for the four-orifice model due to more drastic thermal and kinetic transfer between the hydrothermal jet and the cooling water.
The adhesion work Wad of γ-Fe/MC interface model is larger than that of γ-Fe/M2C one, while the interfacial energy of γ-Fe/MC interface model is lower than that of γ-Fe/M2C one.
The RMSE error index (0.019) of the ANFIS-DE/SVD model is lower than the ANFIS model (0.027), but both models outperform the best available model (CKM, Cao and Knight, 1998) (RMSE = 0.120).
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A Turing-style model is higher-level, whereas a neural network model is lower-level.
The embodied environmental impacts from the previous model were lower than those from the developed model by 4.6 25.2%.
The flexural frequencies predicted by the present model are lower than the corresponding estimates from the CPT, HSDT and the earlier model [1, 2].
The correlation coefficient (R2) of the BP model was lower than that of ELM model.
The equivalent stress values of the fixation model were lower at all loading sites than the contact model (Figure 12a).
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