## 2 Materials and Methods
### Orange Data Mining Application and Image Management  
Orange is a machine learning and data mining suite for data analysis through Python version 3.12.6 scripting and visual programming [29]. Orange is open-source data mining software version 3.37.0 for visualizing, analyzing, and modeling data. Several authors, such as [PERSON] and [PERSON] [43], have used Orange Data Mining to compare different artificial intelligence techniques. [PERSON] et al. [44] used the software in the classification method on the dataset lenses. [PERSON] and [PERSON] [45] classified sector of economy activity. Figure 3: ZENMUSE H20 thermal camera. Orange provides a graphical user interface that allows users to create data mining workflows by dragging and dropping widgets onto a palette. Widgets version 3.37.0 can include a simple data table, graph, machine learning model, or custom interface. The software can perform tasks such as data preprocessing, classification, regression, clustering, network analysis, data visualization, and more. It also supports many data file formats, including CSV, Excel, SQL, and others. The Orange Data Mining interface has widgets associated with different actions, such as data manipulation, transform, and model, which can be seen in the image analytics. The image embedding widget connects to the Test and Score widget to obtain results of testing classification algorithms. The widget does two things. First, it shows a table with different classifier performance measures, such as classification accuracy and area under the curve. Second, it outputs evaluation results, which can be used by other widgets for analyzing the performance of classifiers, as shown in Figure 4.  
It is necessary to use models such as logistic regression, which is a widely used statistical method, for modeling the probability of a binary outcome based on one or more predictor variables. Unlike linear regression, which predicts a continuous outcome, logistic regression is used when the dependent variable is categorical and typically binary (0 or 1, success or failure, respectively) [46]. The core idea of logistic regression is to model the log-odds of the probability of an event occurring as a linear combination of the predictor variables. The log-odds, also known as the logit, is defined as in Equation (3). \[logit(P)=log\bigg{(}\frac{P}{1-P}\bigg{)} \tag{3}\]  
Figure 4: Connecting to the Test and Score widget. where \(P\) is the probability of the event occurring. The logistic regression model can be expressed as Equation (4). \[logit(P)=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\ldots+\beta_{n}X_{n} \tag{4}\]  
Here, \(\beta_{0}\) is the intercept, and \(\beta_{1}\), \(\beta_{2}\), \(\ldots\), \(\beta_{n}\) are the coefficients of the predictor variables \(X_{1}\), \(X_{2}\), \(\ldots\), \(X_{n}\), respectively. The probability \(P\) that the event occurs can be derived from the logit model by applying the inverse of the logit function, which is the logistic function, Equation (5). \[P=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\ldots+\beta_{n}X_{ n})}} \tag{5}\]  
This transformation ensures that the predicted probabilities lie within the \((0,1)\) interval. The coefficients \(\beta_{i}\) are typically estimated using maximum likelihood estimation (MLE), which finds the values that maximize the likelihood of the observed data given the model. According to [PERSON] et al. (2017), tree-based models, such as decision trees, are powerful and interpretable methods for both classification and regression tasks. These models partition the feature space into a set of rectangles (for regression) or regions (for classification) based on the values of input variables, leading to predictions that are easily interpretable and often robust to outliers. A decision tree model is constructed by recursively splitting the data into subsets based on a feature that maximizes a specific criterion. For a classification problem, the most common criteria are Gini impurity and entropy. The Gini impurity for a node \(t\) is given by Equation (6).