## 6 Conclusions  
In this study, six methods, namely, ANNS, ANNN, Ridge regression, Lasso regression, MLR, and Elastic Net regression, were examined to build a model that can be used to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline ANNS & 0.689 & 0.281 & 0.370 \\ ANNN & 0.623 & 0.300 & 0.388 \\ MLR & 0.674 & 0.288 & 0.364 \\ Ridge & 0.672 & 0.295 & 0.368 \\ Lasso & 0.673 & 0.291 & 0.365 \\ Elastic Net & 0.670 & 0.294 & 0.367 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of the models.  
Figure 10: BTS predicted values.  
predict BTS. Most of these methods perform variable selection, which is the process by which a reduced number of independent variables is chosen, as well as prediction. Both ordinary least squares and maximum likelihood methods were used to fit the BTS data to these models. Those are well-known methods that can provide highly accurate predictions. The limitations of those methods were investigated using 10-fold CV criteria, and the results have demonstrated all the methods are useful and competitive for use along with the other existing modeling methods. Another key limitation of this study is the size of the data. However, these samples were the averages of large sample sizes with unequal lengths. Such cases are more common when the cost of the extraction is very high or it is difficult to obtain enough samples. Prediction results from the six best models produced by the above techniques are compared by using Root Mean Square Error (RMSE), Absolute Mean Error (MAE), and the coefficient of determination (R\({}^{2}\)). Based on the results of the RMSE, the accuracy of the predictions for the BTS values obtained from all the competing models are very close, but the results of the MAE and R\({}^{2}\) have shown that the Keras sequential model outperformed the other competing models. Although this dataset indicated the simplicity and the potential superiority of the ANNS model, but ANNS is closely adapted to the training data, and exploiting its broad flexibility demands ingenuity in choosing the estimation method to achieve high accuracy prediction. Besides, Elastic Net and Lasso play an important role in the studies with small sample sizes that have large number of parameters. In such cases, those techniques, which are mainly used for the analysis of small samples, are the best candidates to be employed for the modeling and the prediction of such data types.  
Conceptualization, H.A. and M.Y.H.; methodology, M.Y.H.; software, M.Y.H.; investigation, H.A.; data acquisition, H.A. and M.Y.H.; validation, M.Y.H.; Formal Analysis, M.Y.H.; supervision, writing--original draft, writing--review and editing, H.A. and M.Y.H. All authors have read and agreed to the published version of the manuscript.  
The field and laboratory studies of this study were supported by a grant from the United Arab Emirates University, Research Affairs, under the title of UPAR 2016-31S252 program. The obtained data were used to prepare this paper.  
The authors would like to thank their research associates, technicians, and students who were involved in various stages of this study.  
The authors declare no conflict of interest.