The Urdu version was able to discriminate between the study groups. The mean difficulties score was higher in the case group (mean = 21.7) than the compari... L Samad,C Hollis,M Prince,... - 《International Journal of Methods in Psychiatric Research》 被引量: 65发表: 2010年 Familism, ...
Due to their complex history, plastids possess proteins encoded in the nuclear and plastid genome. Moreover, these proteins localize to various subplastid compartments. Since protein localization is associated with its function, prediction of subplastid
𝔼E represents the mean square error (MSE) between observed values and actual values at a given layer. 𝛽𝑙𝑘(𝑝+1)=𝛽𝑙𝑘(𝑝)−𝜂∂𝔼∂𝛽𝑙𝑘(𝑝)βkl(p+1)=βkl(p)−η∂E∂βkl(p) (13) This is followed by a pooling layer, i.e., a ...
There is a huge gap in-between the columns and with other columns. The mean value of column ‘t’ is 1938.056124 and the standard deviation is 7453.591519, while column ‘I’ has a mean value of 0.146325 and a standard deviation of 0.159337. Thus, for this purpose, a standard scaling ...
This slight variation in feedback time is not expected to significantly impact the value of the model’s predictive results. In addition, the models were quantitatively evaluated using two loss functions that represent regression errors: mean absolute percentage error (MAPE) and root mean square ...
The Mn values of the investigated CBPDS specimens are predicted using the new analytical model and compared with those obtained from the corresponding test results in Table 1 and Figure 18. A lower estimate was achieved using this developed model with a sufficient mean value and standard deviation...
This constant U, similarly to the machining case in Reference [18], can be called specific forming energy: U = Fz ·∆z V , V = S·t, (2) where S is the affected punch-sheet area, and t is the mean thickness beneath the punch. When knowing S and t at a specific position ...
4.2. Prediction of Moisture Content Using NIR Infrared Spectra Raw spectra were selected as an input to build the final PLSR model because this resulted in the lowest number of latent variables, the highest R2 and lowest root mean square error compared to other pre-processing methods (Table 2)...