## 4 Discussion  
The in vitro shoot regeneration in wheat has been widely studied. Establishing wheat in vitro shoot regeneration systems were associated with obstacles such as chimeric callogenesis consisting of both non-embryonic and embryonic calli, low efficiency of shoot regeneration, and genotype-dependency [3; 28; 29; 30; 31; 32; 33; 34]. Computational approaches may help in reducing trial and errors in the process of optimizing regeneration systems. Artificial intelligence (AI) models can be considered as one method to develop and optimize in vitro shoot regeneration protocols. Although there are no reports to use AI models in in vitro culture of wheat, several studies have previously proved the reliability and accuracy of AI methodology to predict and optimize different in vitro culture processes such as in vitro sterilization [22; 35], callogenesis [36; 37; 38], cell growth and protoplast culture [39; 40], somatic embryogenesis [37; 41; 42], shoot regeneration [43; 44; 45; 46], androgenesis [9], hairy root culture [47; 48], and rhizogenesis [49] in other plants. In the current study, GRNN was used, for the first time in wheat, to develop a suitable model for in vitro shoot regeneration. According to our results, GRNN was a promising and powerful tool for modeling and predicting the system. Although there is no report regarding the application of the GRNN model in plant tissue culture studies, in line with our results, studies in other fields revealed the good performance of the GRNN model [50; 51]. One of the weaknesses of using AI models is that it is hard to obtain an optimized solution [52; 53; 54; 55; 56; 57]. To tackle this problem, several studies [21; 22; 44; 52; 57; 58] used GA and NSGA-II to optimize in vitro culture conditions. In the current study, GA was linked to the GRNN model for the optimization process. Based on our results, a hybrid GRNN and GA can be considered as an efficient computational methodology for predicting and optimizing in vitro shoot regeneration of wheat. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to 2,4-D, explant, genotype, and zeatin, and less sensitive to NAA. Previous studies [3; 28; 29; 30; 31; 32; 33; 34] have demonstrated \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type of** & **BAP** & **Kin** & **2,4-D** & **IAA** & **IBA** & **NAA** & **Zeatin** & **CuSO4** & **Shoot Regeneration** \\
**Explant** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **Frequency (\%)** \\ \hline \hline
\begin{tabular}{c} Immutance \\ embryo \\ \end{tabular} & 0.15 & 0.73 & 0.17 & 0.37 & 0.04 & 0.01 & 4.51 & 13.08 & 97.63 \\ \hline \hline \end{tabular}  
* BAP: 6-benzylaminopurine; Kin: kinetin; 2,4-D: 2,4-dichlorophenoxyaceae acid; IAA: indole-3-acetic acid; IBA: indole-3-butyric acid; NAA: 1-napthaleneacetic acid.  
\end{table} 
Table 2: The optimized solution for shoot regeneration achieved by using GRNN-GA in wheat. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Item** & **Genotype** & **Explant** & **BAP** & **Kin** & **2,4-D** & **IAA** & **IBA** & **NAA** & **Zeatin** & **CuSO4** \\ \hline \hline Variable sensitivity ratio (VSR) & 1.35 & 1.83 & 1.22 & 1.08 & 1.97 & 1.17 & 0.93 & 0.54 & 1.29 & 1.02 \\ Rank & 3 & 2 & 5 & 7 & 1 & 6 & 9 & 10 & 4 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of sensitivity analysis on the developed GRNN model to rank the importance of factors involved in in vitro shoot regeneration of wheat. that shoot regeneration of wheat is changed by the type and concentration of phytohormones and also by the type of explants and genotype. [PERSON], [PERSON], [PERSON] and [PERSON] [3] showed that 2,4-D was the best auxin, among other auxins, for in vitro shoot regeneration of wheat. Also, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [34] reported that better results were achieved by using 2,4-D rather than NAA or IBA.