# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 3 Materials and Methods
### Hyperparameter Optimization  
The learning process and the structural structure are controlled by several hyperparameters, which may be classified as either structural or algorithmic hyperparameters [55]. The structure and topology are characterized by structural hyperparameters, which include the number of layers of the network, the number of neurons in each layer, the degree of connection, the neuron transfer function, and others. They alter the network's structure, which affects the effectiveness and computational complexity. The learning process is driven by algorithmic parameters, including the size of the training set, the training method, the learning rate, and other factors. Although these variables do not belong to the neural network model and do not affect how well it performs, they do affect how quickly and effectively the training step goes.  
A machine learning model's hyperparameter settings are a predetermined set of choices that directly affect the learning process and the output of the prediction, which shows how well the model works. Model training is the process of instructing a model to find patterns in training data and predict the outcome of incoming data based on these patterns. In addition to the hyperparameter selections, model architecture, which reflects the model's complexity, has a direct bearing on how long it takes to train and test a model. The setting has become a crucial and challenging problem in the application of ML algorithms due to their influence on model performance and the fact that the ideal collection of values is unknown. In the literature, there are several methods to adjust the hyperparameters.  
Manual search is one way to improve these hyperparameters. This may be used when the researcher has a solid understanding of neural network structure and learning data since it determines the hyperparameter value based on the researcher's intuition or skill. However, the standards for choosing hyperparameters are ambiguous and require several experiments.  
To choose the ideal hyperparameter values for ML algorithms, designs of experiment (DOE) methods are utilized. DOE evaluates the effects of several experimental components simultaneously, with each experiment consisting of many experimental runs at various hyperparameter values that should be evaluated collectively. The experimental data are statistically examined when the tests are finished to ascertain how the hyperparameters affect the performance of the classifiers. In other words, a model that empirically connects classification performance to hyperparameters, such as prediction errors (as a response variable) (as predictors of classifier performance).  
In the domain of DL, it is established that a technique is trained straight from the data in an end-to-end way, meaning that time-consuming manual feature extraction is not necessary from the human (domain experts). However, the model selection procedure in deep learning requires significant human work. Discovering the hyperparameter settings that result in the optimum performance is the first step in this procedure. The best hyperparameter values can typically be found using one of three methods: manually built on previous knowledge; arbitrarily selected from a set of candidate hyperparameter values; or in-depth grid search. Based on previous research, the authors applied the manual method in this paper. The learning rate is reduced when it is noticed during the model training that there is no improvement in the validation training value. The learning rate is set to be reduced every 10 epochs.  
The proposed CNN architecture includes several hyperparameters. These hyperparameters should be carefully selected because they control the performance of the suggested technique. The details of the hyperparameter settings are given in Table 2. The study experimentally finds that these are the best suitable values of hyperparameters for the proposed Inception V3-SVM model and other pre-trained networks for this application.