# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### XGBoost Model
#### 2.2.2 Deep Learning
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm** & **Core Principle** & **Main Applications** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{CNN} & Extracts spatial features through convolutional layers, reduces dimensionality with pooling layers, and classifies using fully connected layers & Computer vision (image classification, object detection, semantic segmentation) & Spatial invariance, parameter sharing reduces computation & Requires large datasets, struggles with sequential data \\ \hline \multirow{4}{*}{RNN} & Maintains sequential dependencies through recurrent connections and classifies using fully connected layers & Natural language processing, time-series forecasting & Suitable for sequential data, capable of learning long-term dependencies & Training difficulty (vanishing/exploding gradient), inefficient parallelization \\ \hline \multirow{4}{*}{LSTM} & Introduces gating mechanisms (forget, input, and output gates) to improve RNNs & Machine translation, speech recognition & Solves vanishing gradient issue, retains long-term dependencies & High computational complexity, slower training compared to RNNs \\ \hline \multirow{4}{*}{GRU} & A simplified version of LSTM with merged forget and input gates & Speech recognition, text generation & Lower computational cost than LSTM, similar performance & Slightly less expressive power than LSTM \\ \hline \multirow{4}{*}{Transformer} & Utilizes self-attention mechanisms and feedforward networks, independent of sequential order & Machine translation, conversational AI, large models (e.g., GPT, BERT) & Highly parallelizable, strong long-range dependency learning & High computational complexity, resource-intensive for long sequences \\ \hline \multirow{4}{*}{GAN} & Composed of a generator and a discriminator in an adversarial setup, learning to generate realistic data & Image generation, data augmentation, super-resolution & Strong generative capability, useful for data augmentation & Unstable training, prone to mode collapse \\ \hline \multirow{4}{*}{VAE} & Uses probabilistic modeling to generate data, with a latent variable controlling the generator & Image generation, anomaly detection & Smooth latent space, stable training & Generated images may be blurry, lacks fine details compared to GANs \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of deep learning algorithms. Cross-Entropy--is computed based on the ground truth labels. The backpropagation algorithm is then employed to compute gradients and optimize the network parameters. The MLP model architecture is illustrated in Figure 6d. (2) CNN Model  
CNN is a deep feedforward neural network characterized by local connectivity and weight sharing, specifically designed to process grid-structured data such as images and audio [97]. Its fundamental architecture consists of three primary layers: the convolutional layer, the pooling layer, and the fully connected layer. The CNN grid structure is shown in Figure 6f. 1. Convolutional Layer: As the core component of CNNs, the convolutional layer applies convolutional filters (kernels) that slide over the input data (e.g., image pixel matrices) to perform convolution operations. Different kernels can extract distinct features, such as edges, textures, or higher-level semantic information [98]. Furthermore, convolution operations exhibit weight sharing, meaning that the same kernel is applied across different regions of the input data using identical parameters. This significantly reduces the number of trainable parameters and enhances computational efficiency [99]. 2. Pooling Layer: The pooling layer is responsible for dimensionality reduction while preserving essential features, thereby reducing computational complexity and improving model robustness. The most common pooling techniques are max pooling and average pooling. Max pooling retains the maximum value in a local region, whereas average pooling computes the mean value.