# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning  
ML [18; 19; 20] is a branch of artificial intelligence (AI). [PERSON] was among the first researchers who applied machine learning to teach a computer to improve playing checkers based on training with a human counterpart in 1959. The increase in the amount of data made the ML algorithms popular in the 1990s. Nowadays, it is an emerging area that attracts the attention of academia and industry; Google, Apple, Facebook, Netflix, and Amazon are investing in ML and using it in their products [21]. Its effectiveness has been validated in different application scenarios, i.e., healthcare, computer vision, autonomous driving, recommendation systems, network traffic management, etc. It addresses the question of how to build a computer system that improves automatically through experience [19]. ML algorithms try to automate the process of knowledge extraction from training data to make predictions of unseen data. For example, historical traffic data are used to improve traffic classification and reduce congestion. In other words, ML models generally proceed in two phases: (1) in the training process, a training set is used to construct a model; (2) this model is then applied to predict or classify the unseen data. Therefore, the main idea of ML is to generalize beyond the examples in the training set and can be thought of as \"programming by example\". Hence, it is an intelligent method used to automatically improve performance through experience. [PERSON] [20] defined ML by saying, _\"A computer program is said to learn from experience \(E\) with respect to some class of tasks \(T\) and performance measure \(P\), if its performance at tasks in \(T\), as measured by \(P\), improves with experience \(E\)\"_. The adoption of ML and DL approaches increased thanks to the computer's evolution, which has provided us with processing capabilities needed for training/testing their models, the availability of large datasets, and the cost-effectiveness of storage capabilities. In addition, the availability of open-source libraries and frameworks causes rapid diffusion within the research community and in our daily life. ML/DL algorithms can \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline SDN & Software-defined networking & MLP & Multi-layer perceptron \\ SDWSN & Software-defined wireless sensor networks & CNN & Convolutional neural network \\ ML & Machine learning & LSTM & Long short-term memory \\ DL & Deep learning & MVNO & Mobile virtual network operator \\ KDN & Knowledge-defined networking & AE & Autodnocoder \\ AI & Artificial intelligence & DR & Dimensionality reduction \\ QoS & Quality of service & QoE & Quality of experience \\ ANN & Artificial neural network & RL & Reinforcement learning \\ ONF & Open network foundation & OF & OpenFlow \\ NFV & Network function virtualisation & FL & Federated learning \\ DRN & Deep belief network & DRL & Deep Reinforcement Learning \\ GRU & Gated recurrent units & NGMN & Next Generation Mobile Networks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of abbreviations. Figure 1: Conceptual map of survey. be implemented in various platforms and languages such as Weka (does not include DL algorithms), R, and Python. Python is one of the most popular environments for ML/DL as it provides various libraries to developers. Additionally, its simplicity helps developers to save more time, as they only require concentrating on solving the ML/DL problems rather than focusing on the technicality of language. The most commonly used tool for classical machine learning implementation is scikit-learn [22]. Scikit-learn is an open-source ML library (e.g., Matplotlib, Pandas) and is not specific to DL. It covers all data mining steps from preprocessing to classification, regression, and clustering. Within the field of machine learning, a broad distinction could be made between supervised, unsupervised, semi-supervised, and reinforcement learning (RL).