## 5 Data Processing and Artificial Intelligence Methods
### Artificial Intelligence Methods Used in Cattle Sensor Papers  
Support Vector Machine (SVM) [63; 64]  
The SVM method separates the labeled training data into two groups according to the type of kernel function, looking for the maximum distance between the two groups to be separated. Classically, it would be used to split a hyperplane in two parts with a hyperline. \[ \begin{array}{c}\underset{f}{min}\|f\|_{K}^{2}+C\ \sum_{i=1}^{l}|1-y_{i},f(x_{i})|, \end{array}  \tag{1}\]  
\(l\) is the number of learning data, \(C\) is the regularization parameter, and the task is a minimization problem. K-Nearest Neighbor (KNN) [63]  
Similarity is measured with a distance function. The learning database is stored and when an unknown object needs to be classified, we search for the \(k\) closest points according to the distance function and classify the object into the category that occurs most often among the \(k\) neighbors (majority voting). \[\left(\hat{f}x\right)=y_{k}, \tag{2}\]  
\[\text{\emph{if}}\ x_{i}\in N_{k}(x), \tag{3}\]  
where \(k\) is the number of neighbors, \(x_{i}\) the database, and \(y\) is the output variable. Linear discriminant analysis (LDA) [63] is a supervised learning technique, which looks for an orientation that can transform high-dimensional feature vectors to a lower-dimensionality feature space, such that the projected feature vectors of a class on this lower dimensional space remain also distinguishable from the feature vectors of the other classes. LDA is a small-sample-size (SSS) problem in professional terms. Quadratic discriminant analysis (QDA) [63] is a generalization of LDA, where it cannot be supposed that the covariance matrix in all classes is equal. Radial base function (RBF) neural network [63] is a feed-forward neural network, which contains three layers (input, hidden, and output layers). It is a function approximator whose learning time is less than traditional MLP-NN. Multilayer Perceptron (MLP_NN) Neural Network [63]A fully connected multilayer neural network is called a multilayer perceptron (MLP). It consists of three types of layers, i.e., the input layer, output layer, and hidden layer. MLPs are proven to approximate any continuous function. Fuzzy solutions and ANFIS models [63]: A fuzzy inference system is based on [PERSON]'s theorem and can support complex decisions and function approximations. A fuzzy system works with membership functions and an ANFIS neuro-fuzzy system is able to develop function approximations with the help of the found membership functions. Deep Convolutional Neural Networks (CNN) [63]  
This is a special deep neural network for image processing. It contains frequently at least 20-100 neuron layers with special activation functions. Based on the literature and our own experiences, it can be stated that in the case of rumen boluses for monitoring purposes, it is worth discussing at least four levels of data processing and implementing them during development. Level 0 contains the measurement techniques and data preprocessing. The measured data are subjected to various data-cleaning and preprocessing steps, by the end of which raw sensor data are prepared for analysis. Level 1 covers first-level data processing; extraction of primary-level characteristics; and extraction of biologically interpretable features, during which primary characteristics are obtained from the obtained raw sensor data (rumen temperature, heart rate, type of locomotor activity, body position information, etc.) Level 2 means complex and integrative health, behavioral, or state assessment at the level of individual animals. It is secondary-level data processing, during which, based on the primary characteristics, by combining them and combining with other information, a result can be obtained that is interpretable for farmers at the level of the individual (estrus detection, time spent ruminating, integral value of locomotor activity, etc.) Level 3 is a higher, holistic level, representing an integrative big data solution.