This topic focuses on using machine learning techniques to identify abnormal patterns and deviations in business processes, enabling early detection of inefficiencies, fraud, or system failures to improve operational reliability and performance.

This topic focuses on leveraging blockchain technologies to enhance transparency, security, and trust in financial technology (FinTech) systems, enabling decentralized, tamper-proof transactions and improved accountability in financial services.

This topic explores the use of blockchain technology to ensure transparency, traceability, and trust in business process workflows by providing immutable records of transactions and improving accountability across organizational operations.

This topic focuses on designing and modeling enterprise architectures to optimize and align business processes with organizational goals, improving efficiency, scalability, and system integration within complex enterprise environments.

This topic explores the use of AI and machine learning techniques to analyze, optimize, and automate business process workflows, improving efficiency, decision-making, and adaptability in organizational operations.

This topic focuses on applying Explainable Artificial Intelligence, blockchain, and machine learning techniques in financial services to improve transparency, trust, and decision-making in areas such as risk analysis, fraud detection, and automated financial systems.

This topic explores the practical applications of blockchain technology across various domains, focusing on decentralized, secure, and transparent systems for data management, transactions, and digital trust

This research area focuses on designing secure, transparent, and trustless digital systems using blockchain technology. It includes the development of smart contracts (eg, using Ethereum and Solidity) to automate agreements, and the exploration of decentralized finance (DeFi) and peer-to-peer (P2P) lending platforms that eliminate intermediaries. Applications also extend to real estate and rental agreements, enabling tamper-proof and automated transactions, as well as healthcare systems, where blockchain enhances trust, data integrity, and transparency in sensitive processes such as organ donation.

This research area focuses on the design and application of machine learning and AI techniques to solve complex real-world problems. It includes developing ML models for cybersecurity, such as intrusion detection and SIEM systems for threat classification, as well as AI-driven debugging and code repair using large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent systems. It also covers medical diagnosis applications, including predictive models for diseases (e.g., stroke) and analysis of medical images (e.g., X-rays), and AI solutions for sustainability, such as ingredient-based recipe recommendation systems aimed at reducing food waste.

This research area focuses on enhancing digital security through intelligent and data-driven approaches. It includes the development of machine learning–based SIEM systems for real-time monitoring and analysis, as well as advanced threat detection and classification techniques to identify and mitigate cyber attacks. It also covers the design of phishing detection tools and lightweight browser-based security solutions, aiming to provide fast, accurate, and user-friendly protection against online threats.

This research area focuses on modern software development practices and tools for building scalable, maintainable, and high-quality applications. It includes full-stack development using technologies such as Node.js, React.js, and Django, alongside the application of clean code principles and software quality standards. It also emphasizes Continuous Integration and Git-based workflows to support collaborative and efficient development, as well as the design of IDE-integrated intelligent environments that leverage automation and AI to improve coding, debugging, and developer productivity.

This research area explores the application of game theory and artificial intelligence to model, analyze, and optimize strategic interactions in games. It includes strategy optimization, algorithmic game design, and the development of AI-based decision-making systems for competitive and adaptive gameplay.

It is closely integrated with teaching activities, including Software Technology and Advanced Software Technology courses at both BSc and MSc levels, where students design and develop games, web applications, and mobile applications, applying modern software engineering practices and AI-driven techniques in real-world projects.

This research area focuses on the design and optimization of large-scale, data-driven enterprise systems. It includes the integration of AI into ERP systems to enhance automation and intelligence, the development of real-time decision support systems for improved organizational responsiveness, and the design of data-driven enterprise architectures that enable efficient, scalable, and informed business processes.

this research area explores the use of technology to support social and cultural needs in digital environments. It includes developing technology-driven solutions for diaspora identity preservation, as well as designing digital platforms for cultural engagement that foster community connection, cultural exchange, and the maintenance of identity across geographically distributed populations.