## 3 Proposed Methodology
### AWRF (ANN Weighted Random Forest) for Classification  
Generally, ANN refers to the biologically inspired sub-field of AI modeled after the brain. Typically, ANN is a computational network relying on biological NNs (Neural  
Figure 2: Optimal selection of features with OWSA.  
Networks), which mirror human brain's structure. Like the human brain, this NN possess neurons interconnected with one another, and ANNs possess neurons that are associated with one another in several network layers. Further, RF (Random Forest) is a well-renowned ML method that relies on a supervised learning method. The RF's function relies on the idea of EL (Ensemble Learning), whereby multiple classifiers are integrated to solve complex issues, thereby enhancing the model's performance. The main intention of the current study is to propose a weight updating process that is applicable to an individual tree in the RF model, and the comprehensive evaluation of ideal parameter tuning. The proposed approach is recommended by its stability, flexibility and avoidance of over-fitting. The overall process is shown in Figure 3.  
As shown in Figure 3, the selected features are fed into RF. Based on weight updating, the RF generates optimal outcomes. RF encompasses numerous DTs for several subsets of the dataset, and by averaging, it enhances the prediction rate of a specific dataset. The RF model determines the predictions of all the DTs (Decision Trees), and the overall outcomes are predicted in accordance with the prediction that receives the most votes. Besides this, weight in the ANN indicates a parameter that possesses converted input data in the network's hidden layers. The resulting layer repeatedly tunes the input within the hidden layers to produce a desirable count in the specific range, as the less weighted value does not alter the input. However, on the contrary, high-weighted values are applied, causing significant alterations in the results. Thus, the chosen features are fed into RF. Concurrently, the ANN weight updates are fed into RF. Several input data subsets are employed for training the ML models. DT acts as the core element of the RF model. From the actual data, a set of DTs related to the bootstrap samples is generated. Usually, the bootstrapping method assists the RF with the collection of sufficient DT counts, which enhance the classification rate via the overlap-thinking concept. With the voting approach, optimal trees are chosen by bagging. The chosen features are subjected to cross-validation. These features are also fed into RF. Lastly, the ANN weight updating process assist the RF model in performing effective classification. The overall process is presented in Pseudocode 4.  
To receive effective outcomes with the contribution of the considered classifier, all the input is weighted. The solution relies on majority voting. Each of the quadrant solutions is compared with the overall solution. When there is a match, the fixed value (\(\beta_{+}\)) is added to the weight. Contrarily, when it does not match, (\(\beta_{-}\)) is taken from the classifier's weight, which minimizes the negative impact on further processes. The algorithm is thus updated with persistent weights, and hence the system can be subjected to internal and external alterations. This ensures the classifier's reliability. It is significant for pre-defining the initial weight value \(\varphi_{0i}\), wherein \(i=1,\;2\ldots N\), and \(N\) denotes the classifier count. Values have a huge influence on the evolution of the system, as the system's reliability is in accordance with the weights of ANN.  
Figure 3: Overall process of AWRF.