ΠΕΡΙΛΗΨΗΗ παρούσα διπλωματική εργασία εξετάζει το ρόλο της κβαντικής μηχανικής μάθησης στη βελτίωση της φαρμακευτικής έρευνας.Στόχος της είναι η διερεύνηση καινοτόμων τεχνολογιών, με το συνδυασμό της μηχανικής μάθησης και της κβαντικής υπολογιστικής, που μπορο…
Quantum machine learning methods, including quantum kernels and variational quantum algorithms (VQAs), are poised to demonstrate quantum advantages in machine learning, chemistry, condensed matter physics, and materials science. However, these methods face cri…
Machine learning quantique appliqué à des cas d'usage industriels Cette thèse explore le développement de méthodes en Apprentissage Automatique Quantique (Quantum Machine Learning, QML) destinées aux applications industrielles, en mettant l'accent sur la jonct…
The adoption of network virtualization and cloud computing has introduced new complexities and attack vectors, which demands more intelligent and scalable security solutions. In such cases, traditional Intrusion Detection Systems (IDS) often have struggle to p…
With the increasing growth of Internet of Things (IoT) devices, network infrastructures have become more vulnerable to cyber-attacks. Traditional network security measures often fall short in detecting sophisticated intrusion patterns in real-time, highlightin…
The increasing reliance on Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems has raised critical concerns regarding their vulnerability to cyberattacks. While machine learning (ML) methods have emerged as effective t…
With the complexity of the cyber threats, the issue of developing more intelligent and flexible systems that can detect the known and unknown intrusions in real-time has become a burning matter. The paper suggests a hybrid threat detection scheme that combines…
The growing connectivity of Industrial Control Systems (ICS) has increased their exposure to cyber threats, posing serious risks to both critical infrastructure (e.g., power plants, water systems) and industrial operations. Although Machine Learning (ML)-based…
In this study, efficient machine learning algorithms to detect network traffic anomalies are developed to enhance the current cybersecurity systems. The conventional rule-based intrusion detection is not able to detect the changing and unknown attacks, making…
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