Supervised Learning Model

Applications in 5G

Model Adoption in healthcare delivery



ML and statistical logistic regression

Self-organized LTE dense small cell deployments with dynamic frequency and bandwidth allocation

This model gives healthcare providers the chance to specifically target at-risk individuals who need a more individualized health plan to help them improve their everyday health habits.

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Support Vector Machines (SVM)

Model for predicting path loss in metropolitan areas

This model is a potential classification strategy for the healthcare industry, and in smart hospitals, it is generally used to forecast drug adherence.



Neural-Network-based approximation

Channel learning to infer unobservable CSI from an observable channel

This model is generally applied for forecasting e.g. It can be applied to forecast kidney disease.



Supervised ML Frameworks

TDD uplink-downlink configuration in XG-PON-LTE systems is adjusted to optimize network performance based on current traffic conditions in the hybrid optical-wireless network.

This model can be used as a predictive analytics tool to identify and cure illnesses before they pose a serious threat to human life.

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Artificial Neural Networks (ANN), and Multi-Layer Perceptrons (MLPs)

For next-generation wireless networks, modeling, and approximations of objective functions for link budget and propagation loss are needed.

The cost of creating medicines is decreased by using these models to identify a safe and effective medication option from a set of databases.

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