## 5 Conclusions  
This article discusses the integration of simulation, measurement, development of the RLC equivalent circuit model, and applying machine learning approaches to evaluate the performance of the proposed antenna. In terms of frequency, the designed antenna supports the whole X-band as well as some portions of the Ku-band. The prototype was built and analyzed to confirm the intended performance. In addition, the RLC equivalent model of the proposed MPA designed with the ADS Agilent software yields resonance frequencies nearly identical to those generated by simulation (with CST) and measurement. Furthermore, six machine learning and one deep learning (CNN) algorithm have been developed to determine the resonant frequency of the MPA. When the predicted and simulated resonant frequencies are compared, it is observed that they are almost identical. Different performance metrics, such as MAE, MSE, RMSE, and variance scores, are calculated to validate the prediction using the learning algorithms. These metrics are obtained through the process of computing the results of the prediction. The predicted results show that the error performances of the decision tree regression model are comparatively better than other models. The MAE, MSE, RMSE, and variance scores (in percentage) of the DTR model are 5.63, 0.71, 8.42, and 99.68, respectively. The XGB model (MAE = 7.03%, MSE = 1.06%, RMSE = 10.27%, and var score = 99.54%) performs better than the other learning models that were introduced in this study, except DTR. The performance of the deep learning model (CNN) is slightly lower than the presented regression models, which may have occurred due to the inadequate number of data samples for the CNN model. Despite the fact that the proposed MPA has two resonant frequencies, we have only predicted one (9 GHz) using ML models. In addition, the designed MPA has a lower gain of 4.06 dB at 9 GHz and 3.46 dB at 13 GHz. The measured resonance frequency range (7.90 GHz to 14.6 GHz) does not quite correspond to the predicted resonance frequency range (8.35 GHz to 14.25 GHz). In the future, we will generate an adequate number of data samples to achieve better results using DL models, such as CNN, and predict the multiple frequencies for a multiband antenna. Furthermore, we will develop the ML models to predict the return loss, gain, length, and width of the proposed antenna. Moreover, we will ensure better impedance matching between the proposed MPA and the SMA connector so that the simulated and measured frequencies are completely matched. Finally, it can be concluded that the simulated, measured, and predicted results ensure the reliability of the proposed antenna in the whole X-band and part of Ku-band applications. [PERSON] and [PERSON] presented the idea, performed the experiments, prepared the manuscript and visualization, N.S.S.S. prepared funding acquisition. [PERSON] and [PERSON] prepared the data curation and formal analysis, [PERSON] and M.I. described the investigation and methodology. [PERSON] contributed to the supervision of the research work. [PERSON] and A.H.S. contributions  
Figure 15: Comparative bar chart for seven algorithms. include software development, resources, and validation. [PERSON] and [PERSON] contributed to writing and reviewing. All authors have read and agreed to the published version of the manuscript. Regarding software simulation, this research was sponsored by INTI International University, Malaysia, and they sponsored this research work from the beginning till publication. Not applicable. The author wishes to extend his sincere thanks to the support of the Centre of Graduate Studies, University Teknologi, PETRONAS, Malaysia, and the Faculty of Data Science and Information Technology at INTI International University, Malaysia, for providing the state-of-the-art research facilities to carry on this work.