The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.
Network slicing allows heterogeneous applications can be launched across different domain using virtualized resources. The virtualized resources are created on the physical infrastructure. The orchestrator is essential for coordination of network slice management. The overhead in the slice orchestrator is reduced by distributed approach. The slice template act as a design speci cation template for the creation of network slices. This template can be predicted using federated learning, in which local models are trained with the generated data and global model is trained with local model parameters. Then the global parameters are updated in the local model for further learning. The federated model uses the SDN capability to learn the local model data distribution and hence enhance global SDN federated controller prediction accuracy for network slices. This process can be automated with the help of slice template with the predicted pattern. The parameters of the slice template are directly proportional to the performance of the slice orchestrator and prediction of future slice demands. The edge devices with its local model communicate with the global SDN federated model to satisfy the requirement of dynamic network slicing. The request on-demand services can be provided as virtual network function using network function virtualization. The optimal resource allocation for the requested slice can be done with statistical modeling of observed tra c and autoscaling can be carried out. Experimental studies reveal that the proposed network slicing with federated approach minimal response time with maximal orchestrator scalability.
Network slicing allows heterogeneous applications can be launched across different domain using virtualized resources. The virtualized resources are created on the physical infrastructure. The orchestrator is essential for coordination of network slice management. The overhead in the slice orchestrator is reduced by distributed approach. The slice template act as a design specification template for the creation of network slices. This template can be predicted using federated learning, in which local models are trained with the generated data and global model is trained with local model parameters. Then the global parameters are updated in the local model for further learning. The federated model uses the SDN capability to learn the local model data distribution and hence enhance global SDN federated controller prediction accuracy for network slices. This process can be automated with the help of slice template with the predicted pattern. The parameters of the slice template are directly proportional to the performance of the slice orchestrator and prediction of future slice demands. The edge devices with its local model communicate with the global SDN federated model to satisfy the requirement of dynamic network slicing. The request on-demand services can be provided as virtual network function using network function virtualization. The optimal resource allocation for the requested slice can be done with statistical modeling of observed traffic and autoscaling can be carried out. Experimental studies reveal that the proposed network slicing with federated approach minimal response time with maximal orchestrator scalability.
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