Prediction model for classification of 5G network slicing

5G networks have had a major impact on the communication industry in recent years. With its high data transfer rates and improved latency, 5G networks have enabled a range of services such as autonomous vehicles, virtual reality, and the Internet of Things (IoT). As a result of these applications, a massive amount of data is generated every minute. This has created significant issues and considerably impacted network slicing performance. To provide end users with customized network services and improved user experience of 5G network slicing technology, an efficient prediction model is needed for the classification of different types of 5G network slicing. In this paper, we propose a model for predicting the different classes of 5G network slicing. The model utilizes machine algorithms to classify the 5G network into four different slices, eMBB (enhanced mobile broadband), mMTC (massive machine type communications), URLLC (ultra-reliable low-latency communication) and V2X (vehicle-to-everything) slicing. The results obtained indicate that the proposed model is capable of accurately classifying the 5G network slices with an average precision of 0.94% and an average recall of 0.96%. This showcases the effectiveness of our approach. The experimental results indicated that the proposed model could significantly influence the delivery of accurate 5G network slicing services.