## 7 Conclusions  
The primary aim of this study was to develop an intelligent, robust decision-making model for suitable requirement elicitation technique selection. Our model uses deep learning technology to automate the elicitation technique selection operation and reduce current human intervention errors. This improves software and IS project practices and reduces the risk of failure. The deep learning model in Figure 1 begins by categorizing the technique selection attributes (Tables 1 and 2) in an effort to identify the key decision-making factors. Next, we performed a comprehensive data collection process from various data sources to maximize the existing datasets. Moreover, scaling and mapping the collected dataset as part of the preprocessing phase was carried out to ensure the integrity of the data. Next, the neural network training session phase (Figure 2) was initialized with a configuration of 3 hidden layers, each containing 100 neurons. As a result, the model produced a prediction accuracy of 82%. Moreover, it had a precision and recall score of 0.83 and F1 score of 0.82. These findings show the model's ability to return more relevant results than irrelevant ones and return most of the relevant results. The model validation phase computed the loss curve and the area under the ROC curve (AUC), evaluation metrics which are used to validate deep learning model prediction. In Figure 3, the loss curve highlighted the decreasing cost value of every iteration, indicating increased performance over time for the model. Moreover, 10-fold cross-validation was used. Each fold is used as a testing set in the evaluation process. Furthermore, the area under the ROC curve (AUC) was computed to evaluate the model prediction accuracy. Table 4 illustrates One-vs-One ROC AUC scores of 0.74 and One-vs-Rest ROC AUC scores of 0.75 for each label. Moreover, Figure 5 showing the multiclass ROC curves (AUC) highlighted the model's ability to distinguish between the elicitation technique. These results confirm the model's ability to produce accurate predictions. However, two limitations still exist in this study. The first is that the model prediction is based on elicitation case scenario parameter rather than project parameters. The second limitation is the scale of the model's deployment. Overall, the proposed model is able to select a suitable elicitation technique that best fits the elicitation scenario, thus lowering the risks of project failure and improving the elicitation industry practice. Future studies should aim to increase the data collection sample size to accommodate further IS project environments. Furthermore, investigations should be conducted to broaden the model suitability feature to include IS project as an elicitation entity. Moreover, in the future, representation learning and label learning [28; 29] should be examined to automate the elicitation technique selections operation. The implications of this study could be used to promote the automatization of the requirement elicitation process, thus increasing the potential for enhancing the produced systems designs. Conceptualization, H.D. and M.A. ([PERSON]); methodology, H.D. ; software, H.D. ; validation, H.D., M.A. ([PERSON]); formal analysis, H.D. ; resources, H.D., M.A. ([PERSON]), A.A. ([PERSON]), M.A. ([PERSON]); data curation, H.D., M.A. ([PERSON]); writing--original draft preparation resources, H.D., M.A. ([PERSON]); writing--review and editing, H.D., M.A. ([PERSON]), A.H., H.E., N.A., M.A. ([PERSON]), A.A. ([PERSON]), A.A. ([PERSON]);supervision, H.D., M.A. ([PERSON]); project administration, H.D., M.A. ([PERSON]); funding acquisition, H.D., M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at King Khalid University, grant number (RGP.1/250/43). Not applicable.