HISHAM KHALAF ALLAHEM

Machine learning techniques have recently been used to predict and detect premature labour. Recent studies have used machine learning classifiers such as Random Forest and Decision Tree to categorize and recognize electrohysterography contractions with a high accuracy rate. In addition, deep learning models such as artificial neural networks, similar to machine learning techniques, have been designed to mimic the human brain to analyze and extract complex relationships between data.

Radio Frequency Identification (RFID) systems are rapidly becoming popular in a variety of applications such as supply chain management, storage management and healthcare. Such a system consists of a tag with a unique identifier, a tag reader and a backend server. Due to the system's limited computational resources, it can be subject to various types of attacks. This can exacerbate when the reader itself is mobile. The objective of this thesis is to propose a mutual authentication scheme for mobile RFID systems.