## 3 Analysis of AI Techniques in SVAD
### Learning
#### 3.1.1 Supervised Learning  
SL acquires knowledge from pre-existing labeled datasets or \"the training set\", then compares the predicted output to the known labels. A high-level training set is always required to build a model that works effectively, but more is needed to ensure that the final product will be satisfactory; the training procedure is also a crucial element in creating a reliable predictor. A classifier model is first developed in SL through training, and after that, it can forecast either discrete or continuous outputs. The ASL model's performance, such as accuracy, is typically validated before prediction to demonstrate its dependability. Additionally, classification and regression techniques can be used to categorize SL tasks [25].  
The training data are first divided into separate categories in the classification technique. It then calculates the probability of test samples falling into each category and chooses the category with the most votes [26]. This probability represents the likelihood that a sample is a class member. Credit scoring and medical imaging are examples of typical applications. The regression technique uses input factors such as temperature changes or variations in electricity demand to forecast continuous responses, often in quantity [27]. Forecasting power load and algorithmic trading are examples of typical applications. While the regression model can calculate the root-mean-squared error, the classification model can quantify the percentage of accurate predictions. Nevertheless, a discrepancy between the expected and actual values is acceptable since the output data are continuous.  
Several works have been performed with SL. One of the suggestions in this area is presented by the study [28]. They proposed a unique way to identify fights or violent acts based on learning the temporal and spatial information from consecutive video frames that are evenly spaced. Using the proposed feature fusion approach, features with many levels for two sequential frames are retrieved from the first and last layers of the Convolutional Neural Network (CNN) and fused to consider the action knowledge. They also suggested a \"Wide-Dense Residual Block\" to learn the unified spatial data from the two input frames. These learned characteristics are subsequently consolidated and delivered to long-term memory components to store temporal dependencies. Using the domain adaptation strategy, the network may learn to efficiently merge features from the input frames, improving the results' accuracy. They evaluated their experiments by using four public datasets, namely HockeyFight, Movies, ViolentFlow, and BEHAVE, to show the performance of their model, which was compared with the existing models. There are several important learning techniques in SL, such the Hidden Markov Model (HMM) [29], Support Vector Machine (SVM) [30], Gaussian Regression (GR) [31], CNN [32], Multiple Instance Learning (MIL) [33], and Long Short-Term Memory (LSTM) [34]. It is clear that each technique has advantages and disadvantages in anomaly detection, and it is impossible to say that one technique can solve all problems efficiently.