# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 2 Related Work
### Learning Algorithm  
The LeNet-5 which is one of the early CNN model proposed by LeCun for use in character recognition is specialized in adaptive image processing [34]. CNN is easier to train than other types of feedforward artificial neural network techniques and has the advantage of using fewer parameters. For this reason, CNN has been applied in various fields [35; 36; 37]. The structure of a common CNN is shown in Figure 1. It consists of a series of layers. Each layer contains one or more planes. Each plane receives inputs from a small neighborhood in the plane of the previous layer. Each plane can be viewed as a feature map with a fixed feature detector that is convolved to a local window that retrieves the plane from the previous layer. Multiple layers are used on each layer to detect multiple features. The layer is called the convolutional layer. Once the feature is detected, the exact location becomes less important. As a result, the convolutional layer typically follows a different layer that typically performs local averages and sub-samples operations. These layers are referred to as sub-sample layers. Finally, the network has a fully connected network that performs cataloguing operations using features extracted from the previous layer. The network was uniformly trained by a backpropagation algorithm. The NB classifier is a supervised machine learning classifier, and it is a probabilistic classification algorithm based on the Bayes theorem. In this model, it is generally assumed that all attributes are independent of each other, given the class [38] and it is widely adopted to reduce the number of parameters. Equations (1)-(3) represent the Bayes rule, assumption of conditional independence, and classification rule for the new input data. According to [PERSON] [39], less training data is required to accurately estimate the parameters required for the classification as compared to other complex graphical models. \[P(Y=y_{k}|x_{1},\cdots X_{n})=\frac{P(Y=y_{k})P(X_{1}\cdots X_{n}|Y=y_{k})}{ \Sigma_{j}P(y=y_{k})P(X_{1}\cdots X_{n}|y=y_{j})} \tag{1}\]  
\[p(y=y_{k}|X_{1}\cdots X_{n})=\frac{p(Y=y_{k})\Pi_{i}P(x_{i}|Y=y_{k})}{\Sigma_{ j}p(Y=y_{i})\Pi_{i}p(x_{i}|Y=y_{j})} \tag{2}\]  
\[X^{new}=(X_{1}\cdots X_{n}), \tag{3}\]  
\[Y^{new}\leftarrow\underset{y_{k}}{\operatorname{argmax}}P(Y=y_{k})\prod_{i}P(x_ {i}|y=y_{k})\]  
The multinomial logistic regression (MLR) classifier was introduced by [PERSON], and it is a classification method that can be used to predict more than two possible discrete variables [40]. Similar to binary logistic regression, the probability of categorical membership is evaluated based on the maximum likelihood estimation. In addition, this model does not assume normality, linearity, or homoscedasticity [41]. Figure 1: Typical structure of the convolutional neural network (CNN). The DT method is a regression analysis or classification method depending on whether the response variable is continuous or categorical. The DT learning algorithm was developed by [PERSON] to construct decision tree from an obtained data set [42]. This model divides the space of the predictive variables and predicts the values of the dependent variables in the divided parts. The formation process of this model mainly consists of growth, pruning, feasibility assessment, interpretation, and forecasting. This algorithm forms a tree-like structure based on separation criterion concerning an impurity index such as entropy and the Gini index. According to [PERSON] [43], this model consists of three types of nodes, which are the decision, chance, and end nodes. An example of a typical structure of this model is presented in Figure 2.  
The NN is a statistical learning algorithm that is inspired by neural networks of the brain, which is an animal's central nervous system [44]. To realize an acceptable performance, this model requires the use of a large number of interconnected neurons [45]. Generally, models of NNs are divided into feed forward NNs and recurrent NNs in terms of signaling schemes.