## 5 Data Processing and Artificial Intelligence Methods
### Systematic Review on Cattle Bolus Artificial Intelligence Methods  
In a systematic literature search, we searched the Google Scholar and ScienceDirect databases for the keyword combination \"cattle\" and \"bolus\" and \"artificial intelligence\" and \"cattle\" and \"bolus\" and \"Machine learning\". As a result of the search, we obtained Table 2. The acronym AI could not be used in the search because it is already included in animal husbandry jargon.  
It is clear that the number of hits increases over time, indicating the importance of this area.  
The article by [PERSON] et al. [68] used AI solutions at the third level of analysis in the study of epidemic spread. In the article, in fact, the bovine rumen bolus occurs only at the mentioned level [83].  
The article by [PERSON] et al. [69] used machine-learning algorithms in the second level of analysis. Authors investigated the nutritional characteristics of goats using an sEMG sensor. The bovine rumen bolus appears in the paper as a mention, but the authors recognized that AI algorithms should be used for classification in sensor-based analyses.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Google Scholar** & **Science Direct** \\ \hline
2016 & 12 & 1 [83] \\ \hline
2017 & 18 & 0 \\ \hline
2018 & 34 & 1 [6] \\ \hline
2019 & 42 & 3 [3; 7; 16] \\ \hline
2020 & 60 & 2 [1; 70] \\ \hline
2021–2022 & 132 & 6 [13; 62; 65; 67; 76; 77] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Search and relevant result of keywords: “cattle” and “bolus” and “Artificial Intelligence” and “cattle” and “bolus” and “Machine learning”.  
The paper includes a comparison of K nearest neighbor (KNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), multilayer perceptron neural network (MLP-NN), and radial base function (RBF) methods. It notes that the highest classification accuracy in the given analysis could be achieved with the MLP-NN model [6].  
Experimental rumen bolus temperature measurement A: M. [PERSON] (2019), mostly using classical statistical methods for analysis, only mentions AI algorithms without the extent of their use [16]. Another paper specifically discusses IoT sensors used on animal farms and applicable AI solutions [3]. One article deals with a rumen bolus that can be used to detect rumination, using SVM classification in the analysis [7]. Rumination was detected via neck-mounted accelerometers and a RumiWatch halter [84].  
Analysis of bovine behavior was conducted by [PERSON] et al. (2020) using data from a video system and using a deep neural network. The methodology used is an alternative to the rumen bolus technique, and the referenced publication can be used to recognize and subdivide 15 different types of hierarchical behaviors [70]. [PERSON]'s study also includes an analysis of video data that examines the identification of bovine lameness. The study uses a convolutional neural network with an accuracy of over 90%.  
[PERSON]'s analysis (2020) is a review [1] of sensor techniques; this study also considers rumen boluses to be a forward-looking approach and includes an analysis of the significance of the artificial intelligence solutions that can be used, without detailing the specific uses of each solution. [PERSON]'s analysis deals with the subject in a similar way [4].  
The year 2021 stood out in integrative data analysis. [PERSON] et al. [13] highlighted the importance of the continuous e-monitoring of livestock, mentioning rumen bolus applications and artificial intelligence methods in modern animal husbandry among modern technologies. [PERSON] similarly analyzes the possibilities of data analysis and data integration in the precision dairy sector in a systematic literature search. The paper specifically addresses the machine learning algorithms used in holistic data analysis [67].