# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 4 Results and Discussion
Further, the result is fitting and reasonable due to the ultimate MSE being small; besides, the NN-RBFN converges and cuts off training once the error rate becomes constant after a set number of iterations. Figure 12: Training performance of the proposed NN-RBFN framework. Figure 13: Training data of the proposed NN-RBFN framework. Figure 13 displays a three-layer training performance of the proposed NN-RBFN framework using the backpropagation gradient on each iteration in logarithmic scale. Therefore, at each iteration, both the weights and biases become updated. Monitoring the value of gradient coefficient, as well as the value of mu are substantial to indicate the performance of the network. Further, mu is an adaptation parameter that is used in the Levenberg optimization to train the NN-RBFN when calculating and updating the parameters. The Mu value ranges between 0 and 1, where at the gradient coefficient and mu lowest minimum levels, this means better training and testing of the NN-RBFN network. However, validation checks denote the number of consecutive epochs, where the validation value collapses to decrease. Training stops when the validation parameter reaches the highest number of validations of 6 at epoch 49 with the lowermost gradient coefficient and Mu values. Inclusive performance displays that quite accurate predictions have been attained, that is because there is no overfitting happening before its best validation performance happens. Figure 14 displays that regression plots targets against training, validation, and test sets. It is widely known that data should tumble along 45 degrees, where the outputs are equal to the targets to accomplish the seamless fit. Targets refer to the ratio among the seamless result and the actual outputs. The solid lines denote the finest fit between targets and outputs, which are called \"R value\". The dashed lines denote the targets. For this NN,  
Figure 14: Regression plots of the proposed NN-RBFN framework. the fit is practically good for most of the data sets, with R-values in our case at 0.99, being very close to 1. Overall, the R-values are satisfactory and represent the best levels of fitness. Figures 12-14 confirm that the proposed NN-RBFN framework display a satisfactory level of performance, where it comes not only in terms of training, testing, and validating the data, but also in terms of giving more details about the best propagation model at which altitude suits the selected area. Therefore, at 30 m altitude of the drone, the five models give a reasonable performance based on the outcomes of the proposed model with its assigned weights of the parameters. Moreover, the NN-RBFN helps in selecting the propagation model that guarantees a robust channel with high data rate, and a large cover footprint without requiring a lot of transmission power: this model is the ATG model. Unsurprisingly, this propagation model is reportedly preferred for UAVs, since it uses a closed-form method between a UAV and terrestrial receivers based on LoS and NLoS conditions. Table 2 summarizes the outcome of the NN-RBFN framework in terms of the five models and the link budget parameters along with their assigned weights. Clearly, out of the five models, we can see model number 5 shows noticeably the best overall performance. This is because the Two-Ray model (M5) has the advantage of high accuracy performance due to its precise correction factors at such low altitudes; besides its inclusion of the elevation angle in calculating the coverage radius instead of the traditional method, which leads to better wireless connectivity. The overall results of the best model indicate reasonable improvements that would help in livestreaming, HD aerial imaging, as well as a reduction in the required transmission power, hence, increasing network lifetime. Complexity analysis is presented here to analyze the execution time of the proposed NN-RBFN framework against Particle Swarm Optimization (PSO) and Self Organizing Map (SOM) optimization approaches.