This course is designed to provide an introduction to theoretical perspectives and foundations in the fundamental topics of information and communication technologies, digital systems and digitization in business processes. This course aims to help information systems postgraduate students to carry out a research in Information Systems field as well as in fields of business, information science, communications, computer science, etc. In this course, students will be able to apply and understand the strengths and weaknesses of the commonly used research methodologies in information systems, enhance their ability to theorize about information technology and learn the process of publishing information technology related research in information systems and non-IS journals.

This course is intended to explain the state art of the expert system technology. Based on the main concepts, students will learn the components of expert systems. The students will learn knowledge acquisition and validation. Moreover, they will learn different techniques of knowledge representation and the related programming languages and tools. Students will learn also the inference processes, explanation and reasoning under uncertainty. The course examines the importance of expert systems as management information systems and its role in management. After the successful completion of the course students will be able to discuss and use the expert system in different aspects of the organization and evaluate its impact on the organizational effectiveness.

This course will present an introduction to the principles of operation, design, testing, and implementation of the unimodal and multimodal biometric systems. Major and emerging biometric technologies (fingerprint, face, hand, iris, hand geometry, palmprint, keystroke, handwriting, signature, gait, voice etc.) performance and issues related to the security and privacy aspects of these systems will be addressed. Students will be introduced to a variety of methods used for processing data from various biometrics (especially signature biometric modality) and to statistical methods employed to achieve acceptable recognition performance rates (i.e. false accept rate, false reject rate, equal error rate and correct classification rates).

This course introduces the main concepts of electronic government (e-government) practices and analyses the practical implications of e-government for public organizations. The course examines the development of e-government practices, discusses and analyses main forms of e-government methods, focuses on the measurements of e-government. E-participation and e-engagement will also be examined throughout the course. Furthermore, students will also learn wider concepts such as barriers and impediments towards e-government and will be able to evaluate solution methods based on real case scenarios. The impact of innovation and technological infrastructure will also be examined within the course. After the successful completion of the course students will have an in-depth understanding of the e-government practices which are used worldwide as well as impacts on organizational effectiveness, transparency and accountability.

Introduction to cognitive science. Parallel, distributed problems. Constraint satisfaction. Liopfield model. Supervised vs. unsupervised learning. Single vs. multi-layer perceptions. Static vs. dynamic network architecture. Comparison of neural approaches with parametric and non-parametric statistical methods. Neural network applications.

The goal of this module is to introduce the students to advanced network programming concepts. They will study issues such as multitasking, multi-threading, processes, inter-process communications and network communications. Client-server communications for long term (e.g. large file transfer, multimedia streaming) and short term sessions (e.g. Web traffic) will be presented. P2P, RMI, applet-servlet, e-mail and multimedia mail will be introduced. The course will also address issues related to the programming of network equipment, including router architecture, network signaling, firewall and deep packet probes, network processors, network support for unicast, multicast and broadcast, and the use of techniques for dynamically changing router code, such as mobile agents and active networks

Image formation. Early processing: low-level vision and feature extraction. Boundary detection. Region growing. Texture. Motion. Two-dimensional and three-dimensional representation. High-level vision: learning and matching.

Design and develop applications by using the benefits of operating system and computer architecture. Threads and concurrent programming, TCP/UDP, RPC/IPC, System calls, remote procedure calls (RPC) and web services, xml and xml parsing, socket communication, logging.