## 4 Materials and Methods
### Methods
Similar to grid search, this approach involves predetermining the hyperparameter ranges based on prior knowledge of the problem. However, instead of testing every value within these ranges, random values are selected and evaluated until the best-performing hyperparameter group is discovered or a desired performance level is achieved [36]. In addition to the techniques mentioned, Figure 3 illustrates a comparison of search algorithms, where the axes represent different hyperparameters. Each dot corresponds to a specific combination of hyperparameters evaluated by each method. In this visual representation, the different exploration strategies employed by each method can be observed, as well as how the hyperparameter space is navigated by them. While random search and grid search are widely used methods, recent advancements like Hyperband have emerged to address the need for more efficient exploration of the hyperparameter space. Hyperband is a hyperparameter optimization technique that aims to efficiently search the hyperparameter space to find the optimal configuration for a machine learning model. It is designed to balance the trade-off between exploration and exploitation during the hyperparameter tuning process. The Hyperband algorithm works by iteratively allocating resources to a set of candidate hyperparameter configurations and then eliminating the poorly performing configurations based on their initial performance. It consists of two main components: random search and successive halving. Hyperband starts with a random sampling of hyperparameter configurations. Each configuration is evaluated using a predetermined amount of computational resources, such as the training time or epochs. This initial random search phase helps identify promising configurations to explore further. Then, Hyperband employs a successive halving strategy to efficiently allocate resources to the most promising configurations. This involves dividing the set of configurations into smaller subsets, or \"brackets\", and allocating more resources to the configurations with the highest performance in each bracket. The configurations with worse performance are eliminated at each stage, allowing more resources to be focused on the most promising candidates. By iteratively applying random search and successive halving, Hyperband aims to quickly identify the best-performing hyperparameter configuration with minimal computational resources. It efficiently balances the exploration of the hyperparameter space with the exploitation of promising configurations, making it a popular choice for hyperparameter optimization tasks [37]. In the CNN model proposed in this study, after every Conv1D layer, there were max pooling layers and dropout layers. Max pooling layers help with reducing the spatial dimensions of the input data, thereby reducing the computational complexity of the model and extracting the most salient features from the data. This helps with capturing the essential information while discarding redundant or less important features, leading to better generalization and improved performance of the model. The dropout layers, on the other hand, were added to prevent overfitting of the CNN model to the training data. Overfitting occurs when the model learns to memorize the training data instead of generalizing patterns, leading to poor performance on unseen data. By randomly deactivating a fraction of the neurons during training, the dropout layers forced the model  
Figure 3: Comparison of search algorithms. to learn more robust and generalized representations, thereby improving its ability to generalize to unseen data. In the context of hyperparameter optimization, the \"search space\" refers to the range or set of possible values that each hyperparameter can take. Essentially, it encompasses all the potential options that the optimization algorithm explores when seeking the best-performing combination of hyperparameters.