## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Edge-Enable Network Slicing
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Application Area** & **Objective** & **Methodology** & **Outcome (Compared to SOTA Methods)** \\ \hline
[47] & Network Slicing & Resource allocation for vehicles & Convolutional Long Short-Term Memory Naive Bayes /SVM /Neural Network /RF SVM /K-means & Reduced transmission and waiting delay by 15.33 ms \\
[45] & Network Slicing & Comparison of ML algorithms for network slicing & /Neural Network /RF SVM /K-means & Classification accuracy of different ML methods \\
[51] & Network Slicing & Optimize network load balancing problems & SVM /K-means and power efficiency & Improved latency, load balancing, and power efficiency \\
[50] & Network Dynamic slicing and resource allocation for Industrial IoT & Online Gaussian Mixture Model & Decreased energy and packet error rate \\
[46] & Network Slicing & Auto-configure radio resources & Regression Model & Increased throughput \\
[42] & Network Slicing & Enhance performance of network slicing & Regression Model & Increased system capacity \\
[48] & Network Slicing & Efficient communication btwn the vehicle and charging station & Unsupervised Learning & Increase the throughput and decreased latency \\
[41] & Network Slicing & Optimize network load and resources resources & Deep Neural Network & Predicts the most accurate network slice for an application \\
[49] & Network Slicing & Comparison of network slicing methods for V2X services & Deep Reinforcement & Improved packet latency and Block \\
[40] & Network Slicing & Automated resource optimization and E2E service reliability & Learning & Error Rate \\
[44] & Network Slicing & Predict a new network slice request can be fulfilled or not & Deep Learning & Dynamically allocated resources within QoS requirements \\
[43] & Network Slicing & Classify slices as “eMBB, mMTC, or URLLC” & Deep Learning & Reduced the number of false positive allocations by a 75\%. \\ \hline
[55] & Edge Computing & Smart healthcare applications & K-nearest neighbours /Naive Bayes /SVM & Increased energy consumption and computation time \\
[54] & Edge Computing & Efficiently operate self-driving vehicles & Naive Bayes /SVM & Decreased task failure rate and service time \\
[59] & Edge Computing & Reducing network traffic and latency & ML & Reduced network traffic up to 80\% \\
[53] & Edge Computing & Increase service accuracy & Transfer Learning & Serve more traffic up to 10\% \\
[58] & Edge Computing & Improve communications security & Deep Learning & Improved classification accuracy by 6\% \\
[52] & Edge Computing & Offloading mechanism for edge devices & Deep Neural Network & Improved accuracy and latency \\
[56] & Edge Computing & Develop Intelligent Cognitive Assistants & Reinforcement Learning & Reduced task processing time (22\%) and energy consumption (10\%) \\
[57] & Edge Computing & Minimize the computational and transmission delay & Reinforcement Learning & Reduced maximal delay up to 11.1\% \\ \hline
[61] & Edge-enabled slicing & Minimize power consumption and delay for aerial vehicles & Reinforcement Learning & Improved performance, scalability, compatibility \\
[60] & Enabled slicing & E2E resource orchestration slicing & Deep Reinforcement Learning & Improved performance, scalability, compatibility \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of studies that combine ML, network slicing, and edge computing techniques mentioned in Section 3.