## 2 Related Works
### Random-Forest  
Mu used a random forest algorithm to increase customer loyalty through investigations of customer statistics, and dynamic and enterprise service attributes. As a result, we have taken appropriate steps to improve the accuracy of our forecasts of customer loyalty and to prevent customer losses [16]. Figure 2 shows high level architecture of the proposed system. Though identifying eating-related gestures using wrist-worn devices is a viable application of the watch, the focus of our work is to explore the idea of using audio to detect eating behavior based on bites, rather than swallows as other works have done. [PERSON] et al. described signal-processing techniques for identification of chews and swallows using smart watch devices built-in microphone. In addition, the goal is to evaluate the potential of smartwatches as a platform for nutrition monitoring. Thus, signal processing technology uses random forest classifiers to classify sounds in a given environment. Random forests classify sounds based on a certain number of samples [17]. [PERSON] et al. proposed a classification algorithm based on local cluster centers (CLCC) for data sets with a few labeled training data. The experimental results on uci data sets show that CLCC achieves competitive classification accuracy as compared to other traditional and state-of-the-art algorithms, such as sequential minimal optimization (SMO), adaptive boosting (AdaBoost), random tree, random forest, and co-forest [18]. [PERSON] et al. proposed a probabilistic algorithm for segmenting time-series signals, in which window boundaries are dynamically adjusted when the probability of the correct classification is  
Figure 2: A high level architecture of the proposed system. low. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which are individually processed using statistical classifiers to recognize various activities or events. The algorithm improves the number of correctly classified instances from a baseline of 75%-94% using the random forest classifier [19]. [PERSON] et al. proposed the three data mining algorithms, namely the self-organizing map (SOM), C4.5, and random forest. They are applied on adult population data from the Ministry of National Guard Health Affairs (MNGHA), Saudi Arabia to predict diabetic patients using 18 risk factors. Health care data is often huge, complex, and heterogeneous because it contains different variable types and missing values as well. Therefore, data extraction using data mining was applied. Random forest achieved the best performance compared to other data mining classifiers [20]. [PERSON] et al. proposes a semi-supervised multi-layered clustering model (SMLC) for network intrusion detection and prevention tasks. SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of the supervised Machine Learning (ML)-based intrusion detection and prevention system (IDPS). The performance of the SMLC is compared with a well-known semi-supervised model (i.e., tri-training) and supervised ensemble ML models, namely, random forest, bagging, and AdaboostM1 on two benchmark network intrusion datasets, the NSL and Kyoto 2006+. In addition, SMLC demonstrates detection accuracy comparable to that of the supervised ensemble models [21]. [PERSON] et al. evaluates the performance of a set of classifier algorithms of rules (JRIP, decision table, PART, and OneR) and trees (J48, random forest, REP Tree, and NB Tree). Based on the evaluation results, the best algorithms for each attack category are chosen and two classifier algorithm selection models are proposed. The classification models used the data collected from knowledge discovery databases (KDD) for intrusion detection. The trained models were then used for predicting the risk of the attacks in a web server environment or by any network administrator or any security experts [22]. [PERSON] et al.