## 3 Proposed w-SMNO
### Weight Configuration Learning Model  
The proposed approach utilizes the machine learning technique to dynamically provide the weights for parameters. In this method of learning weights' values, a neural network architecture consists of input, hidden, and output layers for which weights are learned during the training process. These weights represent the parameters that the network adjusts to make predictions or learn patterns from the input data. The representation of a neural network defines basic layers for input and output along with the hidden layer. The input layer receives the input features of vehicles, such as the direction, speed, neighbors, and others. Each node in this layer represents a feature for a node as the input data. The computation for weight configuration is performed via hidden intermediate layers between the input and output layers. Each node in a hidden layer performs a weighted sum of inputs and applies an activation function. Finally, the output layer produces the final output of the network. The values of weights in a neural network are initialized randomly at the beginning of training and are updated throughout the training process to improve the network's ability to make accurate predictions. These updates are performed using the gradients of the loss function with respect to the weights. For dynamic values of weights, during the training phase of the neural network, these weights are adjusted iteratively using backpropagation with gradient descent optimization techniques to minimize the error or loss between the predicted and actual outputs. The vehicles begin to analyze the data to identify patterns and develop logic according to the pattern, after which a leader is chosen. Nearly 80% of the data were entered into the learning process to find the dynamic weight values' moving patterns. The previous observations were kept. The testing procedure was then run with 20% of the data currently being received because the training set discovered absolute patterns through a large number of inputs collected via the simulation, as shown in Figure 7.  
In the backpropagation with gradient descent is a key algorithm used to train neural networks by adjusting the weights and biases to minimize the error between predicted and actual outputs. Below is the explanation for the method with the involved equations in the process of an updated weight. Figure 6: Qth LL group. Forward propagation: During forward propagation, the input data (\(I\)) are passed through the network to make predictions (\(p\)) for biases (\(b\)) in the predicted output (\(O^{\prime}\)) for the actual output (\(O\)). In our method, we consider a simple neural network with one hidden layer. \(D^{[1]}\) and \(D^{[2]}\) are weights of the hidden and output layers, respectively, for any activation function (\(\alpha\)). Equations (10) to (13) represent the forward propagation for the weight of the dynamic prediction output through the activated hidden layer. \[p^{[1]}=D^{[1]}\cdot I+b^{[1]} \tag{10}\]  
\[a^{[1]}=\alpha\cdot(p^{[1]}) \tag{11}\]  
\[p^{[2]}=D^{[2]}\cdot a^{[1]}+b^{[2]} \tag{12}\]  
\[O^{\prime[1]}=\alpha\cdot p^{[2]} \tag{13}\]  
Calculating loss: The difference between the predicted output, \(O^{\prime}\), and the actual output, \(O\), is computed using a mean squared error loss function (\(G\)) for learning loss computation and defined below in Equation (14). \[Reduction\gets G(O^{\prime},O) \tag{14}\]  
Backpropagation (gradient computation): Backpropagation involves calculating the gradients of the loss function with respect to the weights and biases, which are used to update the parameters, as shown in Equations (15) and (16). Using gradient descent, the gradients are computed by taking partial derivatives of the loss function with respect to each parameter.