## 3 Materials and Proposed Method
### Hyperparameter Optimization (HPO)  
The structural organization and learning techniques of neural networks are governed by a set of hyperparameters known as structural and algorithm hyperparameters [50]. The structure and topology are characterized by structural hyperparameters, which include the number of network layers, the number of neurons in each layer, the degree of connection, the transfer function, etc. As the network's structure changes, they have an impact on its effectiveness and computational complexity. The size of the training dataset, the training method, momentum, learning rate, and other algorithm parameters all affect the learning process. Although hyperparameters are not part of the neural network model and have no impact on its performance, they do have an impact on the training stage's speed and performance. Figure 5: Proposed Image Data augmentation with the model’s architecture. Figure 6: Proposed decision support models’ hyperparameter-optimizer architecture. The HPO settings for machine learning or deep learning algorithms are a collection of resolutions that have a true impact on the training procedure and the classification results, which reflect how a model performs appropriately. The procedure of training a model to classify human pose images in the training dataset and apprehend the output based on the image patterns is referred to as model training. Aside from hyperparameter selection, model design, which describes a model, directly influences how long it takes to train, validate, and test a model. The setting has arisen as an important and problematic subject in the application of deep learning algorithms because of their impact on model performance and the fact that the ideal collection of values is unknown. Hyperparameters can be adjusted in a variety of methods in the literature. The procedures for optimizing these hyperparameters are presented as follows:  
* When the scholar has a firm understanding of neural network structure and learning data, the manual search calculates the hyperparameter value based on the scholar's perception or knowledge. However, the criteria for setting hyperparameters are ambiguous, demanding multiple experiments. * Random search is a method to train a model that selects random combinations of hyperparameters. We utilize the best combinations of random hyperparameters. Random search resembles grid search in several ways. The fact that we do not give a list of feasible values for each hyperparameter is a critical distinction. Instead, for each hyperparameter, we sample values from a statistical distribution. For each hyperparameter, a sample distribution is constructed to perform a random search. This method allows us to limit the number of hyperparameter combinations that are attempted. In contrast to grid search, which attempts every conceivable combination, random search allows us to define the number of models to train. Our search iterations might be based on our computational resources or the time spent per iteration. An experiment is a set of tests designed to identify the factors that have the greatest impact on a response variable [51]. The major goal of the Design-of-Experiments (DOE) methodology is to maximize this response variable after these components are found. To discover the link between factors and the response variable, these studies need a careful selection of variables, their ranges, and the number of experiments run. The influence of factors on the response variable has traditionally been examined by changing the amounts of one component at a time while keeping the other factors constant. However, this method is inefficient and leaves out information about prospective interactions. Table 1 provides an overview of all hyperparameters that were fine-tuned for all models. The most significant parameters of neural networks are the initial learning rate, the learning rate decay factor, the number of hidden neurons, and regularization strength.