# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Deep Learning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Learning Model** & **Description** & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{MLP} & \multirow{4}{*}{Supervised, unsupervised} & MLP is a simple artificial neural network (ANN) & \multirow{4}{*}{Easy to implement.} & \multirow{4}{*}{Model performance, slow convergence, occupies a large amount of memory.} \\  & & which consists of three layers. The first layer is the input layer. The second layer is used to extract features from the input. The last layer is the output layer. The layers are composed of several neurons. & \\ \hline \multirow{4}{*}{AE} & \multirow{4}{*}{Unsupervised} & Autoencoder consists of three parts: (i) encoder, (ii) code, and (iii) decoder blocks. The encoder converts the input features into an abstraction, known as a code. Using the code, the decoder tries to reconstruct the input features. It uses some non-linear hidden layers to reduce the input features. & Works with big and unlabeled data, suitable for feature extraction and used in place of manual engineering. & The quality of features depends model architecture and its hyperparameters, hard to find the code layer size. \\ \hline \multirow{4}{*}{CNN} & \multirow{4}{*}{Supervised, Unsupervised} & CNN is a class of DL which consists of a number of convolution and pooling (subsampling) layers followed by a fully connected layers. Pooling and convolution layers are used to reduce the dimensions of features and find useful patterns. & Weight sharing, extracts relevant features, high competitive performance. & High computational cost, requires large training dataset and high number of hyperparameter tuning to achieve optimal features. \\ \hline \multirow{4}{*}{LSTM} & \multirow{4}{*}{Supervised} & LSTM is an extension of recurrent neural network (RNNs) and was created as the solution to short-term memory. It has internal mechanisms called gates (forget gate, input gate, and output gate) that can learn which data in a sequence is important to keep or throw away. Therefore, it chooses which information is relevant to remember or forget during sequence processing. & Good for sequential information, works well with long sequences. & High model complexity, high computational cost. \\ \hline \multirow{4}{*}{GRU} & \multirow{4}{*}{Supervised} & GRU was proposed in 2014. It is similar to LSTM but has fewer parameters. It works well with sequential data, as does LSTM. However, unlike LSTM, GRU has two gates, which are the update gate and reset gate; hence, it is less complex. & Computationally more efficient than LSTM. & Less efficient in accuracy than LSTM. \\ \hline \multirow{2}{*}{DRL} & \multirow{2}{*}{Reinforcement} & DRL takes advantage of both DL and RL to be applied to larger problems. In others word, DL enables RL to scale to decision-making problems that were previously intractable. & Scalable (i.e., can learn a more complex environment). & Slow in terms of training. \\ \hline \multirow{4}{*}{DBN} & \multirow{4}{*}{Unsupervised, supervised} & DBN is stacked by several restricted Boltzmann machines. It takes advantage of the greedy learning process to initialize the model parameters and then fine-tunes the whole model using the label. & Training is unsupervised, which removes the necessity of labelling data for training or properly initializing the network, which can avoid the local optima, extracting robust features. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of different deep learning models used in SDN. More specifically, the neuron receives a vector \(x\) as input and uses it to compute an output signal, which is transferred to other neurons. It is parameterized through {\(W\), \(b\)}, where \(W\) is a weight vector, \(b\) is the bias, and \(f\) is referred to as an activation function. In modern networks, activation functions are referred to as non-linear functions. This non-linearity enables the DL model to learn complex patterns and avoid constraints associated with linear functions.