Improving Accuracy in 5G Network Using Novel Neural Network Conjugate Gradient and Comparison With Stepwise Linear Regression

Our goal is to enhance the accuracy of 5G networks using our novel neural network conjugate gradient comparison with stepwise linear regression. The Approach and Medications Used: The analysis was carried out by twenty individuals from each group. A pre-test power of 80% and an alpha of 0.001 were used with forty samples. We were able to predict the network load efficiency rate with a 90% success rate using stepwise linear regression and a neural network conjugate gradient. The t-tests for independent samples showed that the two algorithms' accuracy levels were substantially different from each other, with a 2 tailed significance value of 0.001 (p <0.05). In terms of statistical performance, the results show that the new neural network conjugate gradient is superior to linear regression.