## 3 Materials and Methods
### Hyperparameter Optimization  
Neural networks are dependent on various hyperparameters that are used to govern the structural organization and the learning procedure, which may be classified as structural and algorithmic hyperparameters [52]. Structural hyperparameters, which are represented by the number of network layers, the number of neurons in each layer, the degree of connection, the transfer function, etc., characterize the network structure and topology of the network. They have an impact on the network's efficacy and computational complexity as they change its structure. Algorithmic parameters drive the learning process and include the size of the training set, the training algorithm, momentum, learning rate, etc. Hyper-parameters are not part of the neural network model and have no effect on its performance; nevertheless, they influence the speed and performance of the training stage. Hyperparameter settings for ML models are a predefined set of decisions that have a direct influence on the training process and the prediction output, indicating how well a ML model performs. The process of teaching a model to discover patterns in the training data and predict the output of new data based on these patterns is known as model training. Model architecture, which depicts the model's complexity, has a direct impact on the time it takes to train and test a model, in addition to hyperparameter choices. Because of their effects on model performance and the fact that the optimum set of values is unknown, the setting has emerged as an essential and difficult topic in the use of ML algorithms. There are various ways to tweak the hyperparameters in the literature. The methods for optimizing these hyperparameters are described below. * The manual search determines the hyperparameter value based on the researcher's intuition or expertise and can be employed when the researcher has a strong grasp of neural network topology and learning data. However, the criteria for setting hyperparameters are vague and necessitating several experimentations. * Grid-based search (GS) identifies the hyperparameter with the highest performance by calculating many values for each hyperparameter and combining them. GS is straightforward, easy to use, and requires minimal prior knowledge. With GS, all potential hyperparameter value combinations are explored to identify the ideal values based on the upper and lower limits of each hyperparameter and a predefined step size, which creates the hyperparameter value space. Because GS runs all potential combinations, it is considered comprehensive. The combination of required runs increases exponentially as the number increases, which is a weakness of GS. As a result, GS takes a long time and has a significant computational cost. Another disadvantage of utilizing GS is that, owing to the nature of some ML algorithms, such as ANN and  
Figure 3: Architecture of the convolutional neural network model. CNN, rerunning algorithms with the same settings will get differing prediction results and thus different performance. Designs of experiment (DOE) strategies are used to determine the best hyperparameter values for ML algorithms [53]. DOE assesses the impacts of several experimental elements at the same time, with each experiment consisting of a series of experimental runs at distinct hyperparameter values that should be assessed jointly. Following the completion of the trials, the experimental data are statistically analyzed to determine the influence of the hyperparameters on the classifiers' performance. To put it another way, an empirical model is developed that links classification performance, such as prediction errors (as a response variable), to hyperparameters (as predictors of classifier performance). Table 2 provides an overview of all hyperparameters that must be fine-tuned for the CNN model, while Table 3 shows the hyper-parameters of the ANN model to get optimal prediction results.