# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Artificial Intelligence
#### 2.2.1 Machine Learning  
Machine learning [75] is a discipline that explores how computers can simulate human learning to acquire knowledge, enhance skills, and optimize system performance [76]. Based on different learning paradigms, ML can be categorized into four main types: supervised learning, which relies on labeled data to train models for predicting unknown data; unsupervised learning, which processes unlabeled data for pattern recognition and data clustering; semi-supervised learning, which leverages a small amount of labeled data alongside a large volume of unlabeled data to reduce annotation costs and improve model performance [77]; and reinforcement learning, which enables an agent to interact with its environment through a reward mechanism to learn optimal decision-making strategies [78].  
As shown in Table 8, supervised learning relies on labeled data for classification and regression tasks, ensuring high accuracy but requiring extensive data annotation. Unsupervised learning uncovers hidden patterns in unlabeled data, commonly used for clustering and dimensionality reduction, though its results may lack interpretability. Semi-supervised learning combines labeled and unlabeled data to improve performance while reducing labeling costs [79]. Reinforcement learning, driven by interaction with the environment, excels in sequential decision-making but demands high computational resources and extensive training. These paradigms are applied across various domains, from image recognition and anomaly detection to autonomous systems and robotics. We will briefly introduce several commonly used machine learning models and algorithms in blockchain applications.  
(1) Decision Tree Model  
The decision tree model [80] is an intuitive and efficient data mining tool widely used in classification and regression problems. This method constructs a tree-like structure that decomposes complex decision processes into a series of simple judgments, thereby enabling effective data partitioning and prediction.  
The basic components of the model include the root node, internal decision nodes, and terminal nodes (leaf nodes), which correspond to the overall dataset, splitting conditions, and final decision outcomes, respectively [81].  
This Figure 5(a) shows the boosting algorithm of ensemble learning. It starts with training the first weak learner with the original dataset D1. Then, the sample weights are iteratively updated based on the prediction results of each weak learner to increase the weight of the misclassified samples. The new dataset Dn is used to train new weak learners. Finally, the results of multiple weak learners are integrated into a strong learner to give the final prediction. The model performance is improved by focusing on difficult-to-classify samples and iterative integration.  
During the construction process, commonly used evaluation metrics include information entropy, information gain, and the Gini coefficient, which measure the change in data purity before and after partitioning. For example, information entropy can be expressed as follows:  
\[Entropy(S)=-\sum_{i=1}^{n}p_{i}\log_{2}p_{i} \tag{1}\]where \(S\) represents the current dataset, and \(p_{i}\) represents the proportion of samples belonging to class \(i\) is denoted. Information gain reflects the reduction in uncertainty brought about by a particular feature in the dataset partitioning, and its formula is given by the following:  
\[Gain(S,A)=Entropy(S)-\sum_{v\in Values(A)}\frac{|S_{v}|}{|S|}Entropy(S_{v}) \tag{2}\]  
Here, \(A\) is the candidate feature, \(S_{v}\) is the feature, and the subset corresponding to the value v of feature \(A\) is denoted.  
Figure 6: (**a**) Schematic diagram of the decision tree boosting model. (**b**) SVM core concept diagram. (**c**) LSTM unit structure diagram. (**d**) MLP feedforward neural network structure diagram. (**e**) Schematic diagram of XGBoost model for incremental construction of decision tree. (**f**) CNN network architecture diagram.