## 4 Result and Discussions  
The performance metrics of each model resulting from a combination of non-image features merged with features from one or two feature extraction techniques applied to the images are shown in Table 3. Amongst the hand-crafted features, GLCM had the lowest error metrics and the highest coefficient of determination, but its accuracy is below the case in which no image features were used. The combination of GLCM features with other hand-crafted features further decreased the accuracy below that of GLCM features. The addition of hand-crafted features led to a reduction in accuracy when compared to using only non-image features, as shown by the performance metrics as well as the last column of Table 3, indicating a percentage improvement over using only non-image features. Negative values were observed as percentage improvements, indicating a reduction in performance. However, even with the reduction in performance in models with hand-crafted features, they are expected to perform better when tested with data from a site not used in model training. This is because the model trained with the hand-crafted features has some knowledge of the environments. The performance when CNN-extracted features were used was better than when features extracted based on any of the hand-crafted techniques were used. A combination of CNN features with any hand-crafted feature extraction methods decreased the accuracy compared to when CNN features were used alone, except in the case of LBP and GLCM, where an increase was observed. A combination of CNN-extracted features with GLCM features (CNN + GLCM) had the highest overall accuracy with an \(R^{2}\) of 0.9372, seconded by a combination of CNN with LBP (CNN + LBP) with an \(R^{2}\) of 0.9364 and were ahead of CNN features \(\left(R^{2}=0.9357\right)\). The RMSE value of 3.6682 dB when CNN + GLCM features were used is an indication that the model's performance is acceptable as it is below the maximum allowed value. The acceptable RMSE for a path loss model is a value less than 7 dB in an urban environment and any value less than 15 dB in suburban and rural environments [65]. The RMSE value achieved is an improvement over the results in [11] with an RMSE of 8.27 dB, [17], 6.8944 dB, [12], 4.4286 dB, [29], 7.61 dB, and [66], 3.89 dB. The results indicated that the hand-crafted features and their combinations could not compete with CNN for feature extraction. As such, the requirement of training a CNN that needs a large volume of data and training time still holds as CNN-extracted features cannot be replaced by hand-crafted features due to the low accuracy of the hand-crafted features. However, the addition of LBP or GLCM features to the CNN features improved model accuracy over CNN features alone. However, since a combination of CNNand GLCM features had the highest improvement; they are recommended for use in this application. Meanwhile, the architecture of the CNN used resulted in an 18-fold speed over the ResNet50V2 used in [16] as it trained in 29.8242 min while the ResNet50V2 trained in 546.5685 min. In addition, a reduction in the RMSE value of 0.7604 dB was observed compared to the results in [16]. Compared to models based on diffraction that require the complete path profile from transmitter to receiver, such as the Walfisch Ikegami and Walfisch Bertoni, the proposed model is cost-effective and saves time as it does not require the additional measurement of parameters such as building heights and street width. In addition, ML models have accuracies better than such models, as seen in [6; 67; 68] because ML models do capture non-linear relationships. Deterministic models, which also use the complete profile from transmitter to receiver, as well as the principles of wave transmission, such as reflection, refraction, diffraction, and shadowing, have high computational complexity and are very slow. With the proposed method, predictions are made instantly from the trained model, and the model can be used for similar environments.