Applying Machine Learning for diagnosis in a Cloud Computing context

This topic is in the context of data classification and regression. Classification suffers from several problems such as the problem of missing data. The management of missing data is a vast field. Missing data cannot be ignored during an industrial diagnosis. Ignoring missing data in a diagnostic system can lead to a loss of accuracy. There are several types of solutions to solve this problem. The missing data can either be imputed values or by developing algorithms that can be used to diagnose missing values. The first objective of this work is to present a state of the art of the different types of existing Cloud Computing in the literature based on the main deployment models: Private Cloud, Public Cloud, Community Cloud, and Hybrid Cloud. The second objective of the proposed work is to study the causes of missing data. Based on these causes, the student must develop a classification model. the third objective of this work is to present the different processing methods. Finally, the student must propose a new approach that combines one technique among the different existing imputation methods (simple or multiple) and a machine learning technique and apply it to a real example.