Student Projects

 

Iím willing to supervise student projects in the area of my research interests. Below is a list of indicative project topics. Undergraduate and postgraduate students are encouraged to email me to arrange an appointment in order to discuss projects of the list, or their own proposal.

 

1. Nonlinear optimisation and local minima

 

Local minimisation methods and have no mechanism that allows them to escape the influence of a local minimum. In this project global search methods will be investigated. These methods are expected to lead to "optimal" or near-optimal parameter configurations by allowing the search to escape from local minima. Specifically, the practical part of the project will be focused on the software implementation of a method of this type and its application on a notorious for its local minima problem.

 

Indicative literature

1.  Gori, IEEE Trans.Pattern Analysis and Machine Intelligence, vol. 14, pp. 76-85, 1992

2. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., On the alleviation of the problem of local minima in back-propagation, Nonlinear Analysis: Theory, Methods and Applications, 30, 4545-4550, 1997.

3. Parsopoulos K.E. , Plagianakos V.P. , Magoulas G.D. and Vrahatis M.N., Objective function ``stretching'' to alleviate convergence to local minima, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3419-3424, 2001.

 

Search keywords: local minima, global search. global optimisation, backpropagation

 

 

2. The use of neural networks in medical imaging

 

Intelligent systems, particularly those for medical imaging, cover a major application area providing significant assistance in medical diagnosis. In most cases, the development of these systems leads to valuable diagnostic tools that may largely assist physicians in the identification of tumours or malignant formations by means of non-invasive or minimally invasive imaging procedures (e.g., computed tomography, ultrasonography, endoscopy, confocal microscopy, computed radiography, and magnetic resonance imaging). The aim of the project is to provide a survey of neural network-based intelligent systems in this area and to implement a neural network-based system for identifying abnormal tissue regions in endoscopic images.

 

Indicative literature

1.  Pouloudi A. and Magoulas G.D. , Neural Expert Systems in Medical Image Interpretation: Development, Use and Ethical Issues, Journal of Intelligent Systems, vol.10, No. 5-6, 451-471, 2000.

2. Karkanis S., Magoulas G.D. and Theofanous N., Image Recognition and Neuronal Networks: Intelligent Systems for the Improvement of Imaging Information, Minimally Invasive Therapy and Allied Technologies,  vol. 9, No. 3-4, 225-230, August 2000.

 

Relevant articles in journals as IEEE Transactions on Medical Imaging, IEEE Transactions on Biomedical Engineering, Computers in Biology and Medicine, Computer Methods and Programs in Biomedicine.

 

 

3. Texture recognition and classification using neural networks

 

Texture plays an important role for the characterisation of regions in digital images. Texture carries information about the micro-structure of the regions and the distribution of the grey levels. A scheme for the recognition of regions based on the texture information should be capable of encoding the properties of the texture using a number of parameters, named descriptors. These descriptors are usually represented by sets of statistical measures defining by this way the vectors to be used, consequently, for the recognition and can be very useful for recognition and classification. Usually, the approach followed has two major processing stages. The first stage consists of all the processing procedures that will be performed on an image to extract all the identifiable features, which will form the feature vectors. The second processing stage decides how to incorporate obtained from the first stage together with background and prior information, such as temporal data, relationships about features, etc., in order to draw inferences. The project will focus on the second stage of processing by investigating the use of neural networks in the recognition and classification of images by texture. A review of neural network models for texture recognition and classification should be provided and the performance of a neural network will be investigated by means of a software implementation.

 

Indicative literature

1.  Panden, IEEE Tr. Pattern Analysis and Machine Intelligence, vol. 24, pp. 291-310, 1999; http://www.ux.his.no/~tranden/.

2. Karkanis S., Magoulas G.D. and Theofanous N., Image Recognition and Neuronal Networks: Intelligent Systems for the Improvement of Imaging Information, Minimally Invasive Therapy and Allied Technologies,  vol. 9, No. 3-4, 225-230, August 2000.

 

Search keywords: texture, backpropagation, neural networks, feature extraction

 

 

4. Knowledge-based neurocomputing in user-adaptive systems

 

In Neural expert systems or knowledge-based neurocomputing, as it is the name that is used now, the emphasis is on the use and representation of knowledge about an application within the neurocomputing paradigm. Despite the powerful processing capabilities of a neurocomputing system, explicit modelling of the knowledge represented by that system remains a major research topic. The aim of this project is to address this issue from various perspectives, present state-of-the-art in knowledge-based neurocomputing in an easily accessible form, and implement a knowledge-based neural network for user modelling.

 

Search keywords: hybrid systems, neuro-fuzzy systems, knowledge-based neurocmputing

 

Indicative literature

1. Stathacopoulou  R., Magoulas G. D.,  Grigoriadou M. and Samarakou M., Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis, Information Sciences, 170, 2, 273-307, 2005.

2. Frias-Martinez E., Magoulas G.D., Chen S., and Macredie R. Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia. In Paul De Bra, Wolfgang Nejdl (eds), Adaptive Hypermedia and adaptive web-based systems, Proceedings of 3rd Int Conf Adaptive Hypermedia-AH 2004, Eindhoven, The Netherlands, Aug. 2004, Lecture Notes in Computer Science, vol. 3137, Springer, 104-113.

3. Magoulas G. D. , Papanikolaou K. and Grigoriadou M., Towards a computationally intelligent lesson adaptation for a distance learning course, in Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, 5-11, Chicago, November 1999.

4. Magoulas G.D. , Papanikolaou K.A., and Grigoriadou M. Neuro-fuzzy Synergism for Planning the Content in a Web-based Course, Informatica, vol. 25, 39-48, 2001.

 

Relevant articles in the journals IEEE Tr. Neural Networks, Neurocomputing, Neural Computing and Applications, Neural Networks, and in the ACM Digital Library.

 

 

5. Hybrid Genetic Algorithms in User-adaptive systems

 

GAs may be crossed with various problem-specific search techniques that exploit the global perspective of the GA and the convergence of the problem-specific technique. There are a number of ways to hybridise GAs and still maintain a fairly modular program structure. The aim of this project is to address this issue and to present state-of-the-art in hybrid GAs in an easily accessible form.

 

Search keywords: global search, global optimisation, Darwinian strategies, Lamarkian strategies,

 

Indicative literature

1. Zacharis N, and Panayiotopoulos T. (2001). Web search using genetic algorithms, IEEE Internet Computing, March-April, 18-26.

2. http://wwwis.win.tue.nl/ah/

3. Frias-Martinez E., Magoulas G.D., Chen S., and Macredie R. Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia. In Paul De Bra, Wolfgang Nejdl (eds), Adaptive Hypermedia and adaptive web-based systems, Proceedings of 3rd Int Conf Adaptive Hypermedia-AH 2004, Eindhoven, The Netherlands, Aug. 2004, Lecture Notes in Computer Science, vol. 3137, Springer, 104-113.

 

Relevant articles in the journals IEEE Tr. Neural Networks, IEEE Tr. Evolutionary Computation, Neurocomputing, Neural Computing and Applications, Neural Networks, Natural Computing, IEEE Intelligent Systems, IEEE Internet Computing, and in the ACM Digital Library.

 

 

6. Data Mining in Personalisation

 

Personalised web-based systems make use of intelligent techniques to provide individual users with content tailored to their needs. A number of data mining techniques can be used to support personalisation and they can be classified into supervised learning (i.e. classification) and unsupervised learning (i.e. clustering). These techniques can increase the effectiveness of personalisation, but they still have some limitations. The project can take different forms. For example, it can focus on either (a) the comparison between supervised learning and unsupervised learning, or (b) the differences between data mining and traditional techniques in the support of personalisation, or (c) the effectiveness and limitations of a particular technique in personalisation, e.g. neural network, genetic algorithms. 

 

Search keywords: adaptive web-based systems, adaptive hypermedia, personalisation, data mining, machine learning, user modelling, neural networks, genetic algorithms.

 

Indicative literature

1. Adomavicius G. and Tuzhilin A. (2001). Using data mining methods to build customer profiles, Computer, February, 74-82.

2. Changchien S and Lu T (2001). Mining association rules procedure to support online recommendation by customers and product fragmentation. Expert Systems with application, 20, 4, 325-335.

3. Hui S., and Jha G. (2000). Data mining for customer service support, Information and Management, 38, 1-13.

 

 

7. Personalisation in e-commerce, e-health, digital libraries, e-learning, e-museums, TV, mobile computing (several projects)

 

The use of the Web has proliferated in businesses, libraries, and schools. Research and development on the semantic web indicates that we must direct web technologies towards developing relevant, and, to the extent possible, complete personal information spaces, visualization methods that enable users to process vast quantities of information, and interaction paradigms that facilitate human-computer communication. The proposed set of projects aim at the development of personalised information spaces in which both e-content and navigation as well as the user interface are adapted according to the individual information and navigation requirements based on a user model, which records user actions and changes in observed user navigational and interactive behaviour depending on the task. The projects require significant programming work (usually this includes Java or ASP, Javascript, Access or SQL but Iím open to suggestions) and their output will be the development and evaluation of proof-of-concept applications.

 

Search keywords: adaptive web-based systems, adaptive hypermedia, personalisation.

 

Indicative literature

1. Ghinea G., Magoulas G. D., and Frank A. O., Intelligent Multimedia Communication for Enhanced Medical e-Collaboration in Back Pain Treatment, Transactions of Institute Measurement Control, vol. 26, No. 3, pp. 223-244, 2004.

2. Papanikolaou K., Grigoriadou M., Kornilakis H., and Magoulas G.D.,  Personalising the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE, User-Modeling and User-Adapted Interaction, vol. 13, 213-267, 2003.

3. Magoulas G.D., Papanikolaou K.A., and Grigoriadou M., Adaptive web-based learning: accommodating individual differences through systemís adaptation, British Journal of Educational Technology, 34(4), 511 Ė 527, 2003.

4. Chen S., Magoulas G.D. and Macredie R. Cognitive Styles and Usersí Reponses to Structured Information Representation, International Journal of Digital Libraries, vol. 4, No. 2, pp. 93-107, 2004.

5. http://www.easy-hub.org/hub/index.jsp

6. http://wwwis.win.tue.nl/ah/

7. Magoulas, G. D., Chen, S. Y., and Dimakopoulos, D. A Personalised Interface for Web Directories based on Cognitive Styles. In User-Centered Interaction Paradigms for Universal Access in the Information Society: 8th ERCIM Workshop on User Interfaces for All, Vienna, Austria, June 28-29, 2004, Revised Selected Papers. Lecture Notes in Computer Science, vol. 3196, Springer-Verlag, 159-166.

8. Magoulas, G. D., Chen, S. Y., and Papanikolaou , K. A. Integrating Layered and Heuristic Evaluation for Adaptive Learning Environments. In Proceedings of the Second Workshop on Empirical Evaluation of Adaptive Systems, 9th International Conference on User Modeling UM2003, June 22-26, 2003.

9. Papanikolaou K.A., Grigoriadou M., Kornilakis H., and Magoulas G.D. INSPIRE: an INtelligent System for Personalized Instruction in a Remote Environment. In S. Reich. M. Tzagarakis, P.M.E. De Bra, Hypermedia: Openess, Structural Awareness, and Adaptivity. Lecture Notes in Computer Science, Vol. 2266, 215-225. Berlin, Heidelberg: Springer-Verlag, 2002.

10. Magoulas G.D. and Ghinea G., Neural network-based interactive multicriteria decision making in a quality of perception-oriented management scheme, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington DC, 14-19 July 2001, USA.