Publications

Journals

  1. Herzog N. and Magoulas G.D., Convolutional Neural Networks-Based Framework for Early Diagnosis of Dementia Using MRI of Brain Asymmetry, International Journal of Neural Systems, 32 (12), 2250053 (2022).

  2. Herzog N. and Magoulas G.D., Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia, Sensors, 21 (3), 778, 2021. https://doi.org/10.3390/s21030778 .

  3. Sikora T. and Magoulas G.D., Neural Adaptive Admission Control Framework: SLA-Driven Action Termination for Real-Time Application Service Management, Enterprise Information Systems, 15(2), 133-173, 2021 . https://doi.org/10.1080/17517575.2019.1585578 .

  4. Kohli M., Magoulas G.D. , Thomas M.S.C., Evolving Connectionist Models to Capture Population Variability Across Language Development: Modelling Children’s Past Tense Formation, Artificial Life, 26 (2), 217-241, 2020. https://doi.org/10.1162/artl_a_00316.

  5. Mosca A. and Magoulas G.D., Customised Ensemble Methodologies for Deep Learning: Boosted Residual Networks and Related Approaches, Neural Computing and Applications, 31, 1713–1731, 2019. https://doi.org/10.1007/s00521-018-3922-2.

  6. Stamate, C., Magoulas, G.D., Kueppers, S., Nomikou, E., Daskalopoulos, I., Jha, A., Pons, J.S., Rothwell, J., Luchini, M.U., Moussouri, T., Iannone, M. and Roussos, G., The cloudUPDRS app: A Medical Device for the Clinical Assessment of Parkinson's Disease, Pervasive and Mobile Computing, 43, 146-166, January 2018.

  7. Mosca A. and Magoulas G.D., Hardening against adversarial examples with the Smooth Gradient Method Soft Computing, 22, 3203–3213, 2018. https://doi.org/10.1007/s00500-017-2998-4.

  8. Haq A. ul , Magoulas G.D., Jamal A., Majeed A., Sloan D., Users Perceptions of E-learning Environments and Services Effectiveness: The Emergence of the Concept Functionality Model, Journal of Enterprise Information Management, 31(1), 89 - 111, 2018.

  9. Cocea M. and Magoulas G.D., Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation, IEEE Transactions Learning Technologies, 02 February 2017, DOI: 10.1109/TLT.2017.2661310.

  10. Adam S.P., Magoulas G.D., Karras D.A., Vrahatis M.N, Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach, Journal of Machine Learning Research, 17(169), 1−40, 2016.

  11. Karoudis K. and Magoulas G.D., Ubiquitous Learning Architecture to Enable Learning Path Design across the Cumulative Learning Continuum, Informatics 2016, 3(4), 19; doi:10.3390/informatics3040019

  12. Papanikolaou K.A., Makrh K., Magoulas G.D., Chinou D., Georgalas A., Roussos P., Synthesizing Technological and Pedagogical Knowledge in Learning Design: a Case Study in Teacher Training on Technology Enhanced Learning, International Journal of Digital Literacy and Digital Competence, 7 (1), 19-32, January-March 2016.

  13. Sikora T. and Magoulas G.D., Evolutionary Approaches to Signal Decomposition in an Application Service Management System, Soft Computing, 20 (8), 3063-3084, 2016.

  14. Cocea M. and Magoulas G.D., Participatory Learner Modelling Design: a Methodology for Iterative Learner Models Development, Information Sciences, 321, 48–70, November 2015.

  15. Adam S.P., Karras D.A., Magoulas G.D., Vrahatis M.N, Solving the linear interval tolerance problem for weight initialization of neural networks, Neural Networks, 54, 17–37, June 2014.

  16. Sikora T. and Magoulas G.D. Neural adaptive control in application service management environment, Evolving Systems Journal, 4(4), 267-287, 2013.

  17. Laurillard, D., Charlton, P., Craft, B., Dimakopoulos, D., Ljubojevic, D., Magoulas, G., Masterman, E., Pujadas, R., Whitley, E.A., Whittlestone, K., A constructionist learning environment for teachers to model learning designs, Journal of Computer Assisted Learning, 29(1), 15–30, 2013.

  18. Gutierrez-Santos S., Mavrikis M., and Magoulas G.D., A Separation of Concerns for Engineering Intelligent Support for Exploratory Learning Environments, Journal of Research and Practice in Information Technology, 44(3), 347-360, 2012.

  19. Charlton P., Magoulas G. and Laurillard D., Enabling Creative Learning Design through Semantic Technologies, Technology, Pedagogy and Education, 21(2), 231-253, 2012.

  20. Cocea M. and Magoulas G.D., User Behaviour-driven Group Formation through Case-based Reasoning and Clustering, Expert Systems with Applications, 39(10), 8756-8768, 2012.

  21. Noss R., Poulovassilis A., Geraniou E., Gutierrez-Santos S., Hoyles C., Kahn K., Magoulas G.D., Mavrikis M., The design of a system to support exploratory learning of algebraic generalisation, Computers and Education, 59(1), 63–81, 2012.

  22. Peng C.-C. and Magoulas G.D., Nonmonotone Levenberg-Marquardt Training of Recurrent Neural Architectures for Processing Symbolic Sequences, Neural Computing and Applications, 20(6), 897-908, 2011.

  23. Peng C.-C. and Magoulas G.D., Nonmonotone BFGS-trained Recurrent Neural Networks for Temporal Sequence Processing, Applied Mathematics and Computation, 217(12), 5421-5441, 2011.

  24. de Freitas S., Rebolledo-Mendez G., Liarokapis F., Magoulas G., Poulovassilis A., Learning as immersive experiences: Using the four-dimensional framework for designing and evaluating immersive learning experiences in a virtual world, British Journal of Educational Technology, 41(1), 69-85, 2010.

  25. Cocea M. and Magoulas G.D., Hybrid Model for Learner Modelling and Feedback Prioritisation in Exploratory Learning, International Journal of Hybrid Intelligent Systems, 6(4), 211-230, 2009.

  26. Dimakopoulos D.N. and Magoulas G. D., Interface design and evaluation of a personal information space for mobile learners, International Journal of Mobile Learning and Organisation, vol.3(4), 440 – 463, 2009.

  27. Peng C.-C. and Magoulas G.D., Advanced Adaptive Nonmonotone Conjugate Gradient Training Algorithm for Recurrent Neural Networks, International Journal of Artificial Intelligence Tools, vol. 17(5), 963-984, 2008.

  28. Anastasiadis A.D. and Magoulas G.D., Particle Swarms and Nonextensive Statistics for Nonlinear Optimisation, The Open Cybernetics and Systemics Journal, vol. 2, 173-179, 2008.

  29. de Freitas S., Harrison I., Magoulas G.D., Mee A., Mohamad F., Oliver M., Papamarkos G., Poulovassilis A., The Development of a System for Supporting the Lifelong Learner, British Journal of Educational Technology, 37(6), pp 867-880, 2006.

  30. O'Neill P.D., Magoulas G.D., Liu X. Applying Wave Processing Techniques to Clustering of Gene Expressions, Journal of Intelligent Systems, vol. 15(1-4), 107–128, 2006.

  31. Anastasiadis A. and Magoulas G.D., Analysing the Localisation Sites of Proteins through Neural Networks Ensembles, Neural Computing & Applications, vol. 15(3), 277 – 288, 2006.

  32. Anastasiadis A., Magoulas G.D., Vrahatis M.N, Improved sign-based learning algorithm derived by the composite nonlinear Jacobi process, Journal of Computational and Applied Mathematics, vol. 191, 166 – 178, 2006.

  33. Anastasiadis A. and Magoulas G.D., Evolving Stochastic Learning Algorithm based on Tsallis Entropic index, The European Physical Journal B, vol. 50, 277–283, 2006.

  34. Frias-Martinez E., Magoulas G. D., Chen S. Y., Macredie R. D., Automated User Modeling for Personalized Digital Libraries, International Journal of Information Management, vol. 26(3), 179-260, 2006.

  35. Magoulas G.D. and Anastasiadis A.D., Approaches to Adaptive Stochastic Search Based on the Nonextensive q-Distribution, International Journal of Bifurcation and Chaos, Vol. 16, No. 7, 2081-2091, 2006.

  36. Magoulas G. D., Neuronal networks and textural descriptors for automated tissue classification in endoscopy, Oncology Reports, vol. 15, 997-1000, 2006.

  37. Magoulas G. and Vrahatis M.N., Adaptive Algorithms for Neural Network Supervised Learning: A Deterministic Optimization Approach, International Journal of Bifurcation and Chaos, vol. 16(7), 1929–1950, 2006.

  38. Plagianakos, V. P., Magoulas G. D., Vrahatis M. N., Evolutionary training of hardware realizable multilayer perceptrons, Neural Computing & Applications, vol. 15(1), 33-40, 2006.

  39. Plagianakos, V. P., Magoulas G. D. , Vrahatis M. N., Distributed Computing Methodology for Training Neural Networks in an Image-guided Diagnostic Application, Computer Methods and Programs in Biomedicine, vol. 81(3), 228-235, 2006.

  40. Anastasiadis A., Magoulas G.D., Vrahatis M.N., New Globally Convergent Training Scheme Based on the Resilient Propagation Algorithm, Neurocomputing, vol. 64, 253-270, March, 2005.

  41. Anastasiadis A., Magoulas G. D., Vrahatis M.N, Sign-based Learning Schemes for Pattern Classification, Pattern Recognition Letters, vol. 26, 1926–1936, 2005.

  42. Chen S.Y., Magoulas G.D., Dimakopoulos D., A Flexible Interface Design for Web Directories to Accommodate Different Cognitive Styles, Journal of the American Society for Information Science and Technology, vol. 56(1), 70-83, 2005.

  43. Frias-Martinez E., Magoulas G., Chen S., Macredie R. , Modeling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Techniques, Expert Systems with Applications, vol. 29(2), 320–329, 2005.

  44. Ghinea G., Magoulas G.D., Siamitros C., Multi-criteria Decision Making for Enhanced Perception-based Multimedia Communication, IEEE Tr. Systems, Man and Cybernetics: part A, vol. 35(6), 855-866, 2005.

  45. Ghinea G., Magoulas G.D., Siamitros C., Intelligent Synthesis Mechanism for Deriving Streaming Priorities of Multimedia Content, IEEE Tr. Multimedia, vol. 7(6), 1047-1053, 2005.

  46. Ghinea G., Thomas J. P., Magoulas G.D., Heravi S., Adaptation as a premise for perceptual-based multimedia communications, Int. J. Information Technology and Management, vol. 4(4), 405-422, 2005.

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

  48. Anastasiadis A., and Magoulas G.D., Nonextensive statistical mechanics for hybrid learning of neural networks, Physica A: Statistical Mechanics and its Applications, vol. 344, 372-382, 2004.

  49. Chen S., Magoulas G.D., Macredie R. Cognitive Styles and Users’ Reponses to Structured Information Representation, International Journal on Digital Libraries, vol. 4(2), 93-107, 2004.

  50. Ghinea G., Magoulas G. D. , Frank A.O. Intelligent protocol adaptation in a medical e-collaboration environment, International Journal of Artificial Intelligence Tools, Vol. 13(1), 199-218, 2004.

  51. Ghinea G., Magoulas G. D., Frank A. O., Intelligent Multimedia Communication for Enhanced Medical e-Collaboration in Back Pain Treatment, Transactions of Institute Measurement Control, vol. 26(3), 223-244, 2004.

  52. Magoulas G.D., Karkanis S.A., Karras D.A., Vrahatis M.N., Evaluation of texture-based schemes in neural classifiers training, WSEAS Transactions on Computers, vol. 3(6), 1729-1735, December 2004.

  53. Magoulas G.D., Plagianakos V.P., Vrahatis M.N., Neural Network-based Colonoscopic Diagnosis Using On-line Learning and Differential Evolution, Applied Soft Computing, Vol. 4(4), 369-379, 2004.

  54. Hossain S., Pouloudi A., Magoulas G.D. and Grigoriadou M., IT Adoption in British and Greek Secondary Education: Issues and Reflections, Themes in Education, vol. 4(2), 123-154, 2003.

  55. Magoulas G.D., Papanikolaou K.A., and Grigoriadou M., Adaptive web-based learning: accommodating individual differences through system’s adaptation, British Journal of Educational Technology, vol. 34(4), 511 – 527, 2003.

  56. O’Neill P., Magoulas G. D., and Liu X., Improved Processing of Microarray Data using Image Reconstruction Techniques, IEEE Tr. Nanobioscience, vol. 2(4), 176-183, 2003.

  57. 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.

  58. Vrahatis M.N., Magoulas G.D. and Plagianakos V.P., From linear to nonlinear iterative methods, Applied Numerical Mathematics, vol. 45(1), 59 - 77, 2003.

  59. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N., Globally convergent algorithms with local learning rates, IEEE Tr. Neural Networks, vol. 13(3), 774-779, 2002.

  60. Papanikolaou K., Grigoriadou M., Magoulas G.D., and Kornilakis H., Towards New Forms of Knowledge Communication: the Adaptive Dimension of a Web-based Learning Environment, Computers and Education, vol. 39, 333-360, 2002.

  61. Plagianakos V. P., Magoulas G.D., and Vrahatis M.N., Deterministic Nonmonotone Strategies for Effective Training of Multi-layer Perceptrons, IEEE Tr. Neural Networks, vol. 13(6), 1268-1284, 2002.

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

  63. Magoulas G.D., Plagianakos G.D., Androulakis G.S. and Vrahatis M.N., A Framework for the Development of Globally Convergent Adaptive Learning Rate Algorithms, International Journal of Computer Research, vol. 10(1), 1-10, 2001.

  64. Magoulas G.D., Plagianakos V.P. and Vrahatis M.N., Adaptive stepsize algorithms for on-line training of neural networks, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3425-3430, 2001.

  65. 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.

  66. Plagianakos V.P. , Magoulas G.D. and Vrahatis M.N. , Learning in multilayer perceptrons using global optimization strategies, Nonlinear Analysis: Theory, Methods and Applications, vol. 47, 3431-3436, 2001.

  67. 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(3-4), 225-230, August 2000.

  68. Magoulas G.D. and Vrahatis M.N., A Class of Adaptive Learning Rate Algorithms Derived by One-Dimensional Subminimization Methods, Neural, Parallel and Scientific Computations, vol. 8, 147-168, 2000.

  69. Pouloudi A. and Magoulas G.D. , Neural Expert Systems in Medical Image Interpretation: Development, Use and Ethical Issues, Journal of Intelligent Systems, vol.10 (5-6), 451-471, 2000.

  70. Vrahatis M.N., Androulakis G.S., Lambrinos J.N. and Magoulas G.D., A class of gradient unconstrained minimisation algorithms with adaptive stepsize, Journal of Computational and Applied Mathematics, vol. 114, 367-386, 2000.

  71. Vrahatis M.N., Magoulas G.D. and Plagianakos V.P., Globally convergent modification of the Qprop method, Neural Processing Letters, vol. 12(2), 159-170, October 2000.

  72. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., Improving the convergence of the back-propagation algorithm using learning rate adaptation methods, Neural Computation, vol. 11, 1769-1796, 1999.

  73. Androulakis G.S., Magoulas G.D. and Vrahatis M.N., Geometry of learning: visualizing the performance of neural network supervised training methods, Nonlinear Analysis: Theory, Methods and Applications, vol. 30, 4539-4544, 1997.

  74. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., Effective back-propagation training with variable stepsize, Neural Networks, vol.10, 69-82, 1997.

  75. 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, vol. 30, 4545-4550, 1997.

  76. Vrahatis M.N., Androulakis G.S. and Magoulas G.D., On the acceleration of the back-propagation training algorithm, Nonlinear Analysis: Theory, Methods and Applications, vol. 30, 4551-4554, 1997.

  77. King R.E., Magoulas G.D. and Stathaki A.A., Multivariable fuzzy controller design, Control Engineering Practice, vol.2, 431-437, 1993.

  78. Magoulas G.D., King R.E. and Stathaki A.A., Design of industrial multivariable fuzzy controllers, Studies in Informatics and Control, vol.2, 253-261, 1993.

Referred articles and chapters in edited volumes

  1. Herzog N.J., Magoulas G.D., Transfer learning and magnetic resonance imaging techniques for deep neural network-based diagnosis of early cognitive decline and dementia. In: Davide Chicco, Angelo Facchiano, Margherita Mutarelli (eds) International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB2021), Lecture Notes in Bioinformatics, vol. 13483, pp 53–66, Springer, 2022. https://doi.org/10.1007/978-3-031-20837-9_5

  2. Herzog N.J., Magoulas G.D., Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia. In: Hassanien A.E., Bhatnagar R., Snášel V., Yasin Shams M. (eds) Medical Informatics and Bioimaging Using Artificial Intelligence. Studies in Computational Intelligence, vol 1005, pp. 29-52. Springer, Cham, 2022. https://doi.org/10.1007/978-3-030-91103-4_3

  3. Haq A. ul, Majeed A., Magoulas GD., Jamal A., Transformative Power of Smart Technologies Enabled by Advances in AI: Changing Landscape for Digital Marketing, in Sumesh Singh Dadwal (ed), the Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer, pp. 1-17, IGI Global, 2020.

  4. Karoudis K. and Magoulas G.D., User Model Interoperability in Education: Sharing Learner Data Using the Experience API and Distributed Ledger Technology, in Badrul Khan, Joseph Rene Corbeil, and Maria Elena Corbeil (eds), Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making, Routledge, Taylor & Francis, 2018.

  5. Mosca A. and Magoulas G.D., Distillation of Deep Learning Ensembles as a Regularisation method in Hatzilygeroudis I., Palade, V. (eds.), Advances in Hybridization of Intelligent Methods, Smart Innovation, Systems and Technologies, vol. 85, Extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016, Springer, 2018.

  6. Stamate C., Magoulas G.D., Thomas M.S.C. (2018) Initialising Deep Neural Networks: An Approach Based on Linear Interval Tolerance. In: Bi Y., Kapoor S., Bhatia R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_36.

  7. Mosca A. and Magoulas G.D., Learning Input Features Representations in Deep Learning, Angelov P., Gegov A., Jayne C., Shen Q.(eds.), Advances in Intelligent Systems and Computing, vol. 513, Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK, pp. 433-445, Springer, 2016.

  8. Sikora T.D., Magoulas G. D., Finding Relevant Dimensions in Application Service Management Control. Liming Chen, Supriya Kapoor, Rahul Bhatia (Eds.), Intelligent Systems for Science and Information, Extended and Selected Results from the Science and Information Conference, Studies in Computational Intelligence, vol. 542, pp 335-353, 2014.

  9. Charlton P. & Magoulas G.D. Context-aware Framework for Supporting Personalisation and Adaptation in Creation of Learning Designs. S. Graf, F. Lin, Kinshuk & R. McGreal (Eds.) Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers. Hershey, PA: IGI Global, 2011.

  10. Peng C-C and Magoulas G.D., Nonmonotone Learning of Recurrent Neural Networks in Symbolic Sequence Processing Applications, Palmer-Brown, D., Draganova, Ch., Pimenidis, E., Mouratidis, H. (Eds.), Engineering Applications of Neural Networks, Communications in Computer and Information Science Series, Springer Berlin Heidelberg, vol. 43, pp. 325-335, 2009, ISBN: 978-3-642-03969-0.

  11. Charlton, P. and Magoulas, G. D. Next Generation Environments for Context-Aware Learning Design, Hatzilygeroudis, I. and Prentzas, J. (eds.), Combinations of Intelligent Methods and Applications, vol. 8, Smart Innovation, Systems and Technologies Series, Springer Berlin Heidelberg, pp. 125-143, 2011, ISBN: 978-3-642-19618-8.

  12. Van Labeke N., Magoulas G.D. and Poulovassilis A., Searching for “People Like Me” in a Lifelong Learning System, Learning in the Synergy of Multiple Disciplines, Lecture Notes in Computer Science, Volume 5794, Proceedings of the 4th European Conference on Technology Enhanced Learning (EC-TEL 2009) Nice, France, Sept 29–Oct 2, 2009, U. Cress, V. Dimitrova, M. Specht (Eds.), Springer, pp. 106-111, 2009.

  13. Dimakopoulos D. and Magoulas G.D., An architecture for a personalised mobile environment to facilitate contextual lifelong learning, chapter 12 in H. Ryu and D. Parsons (eds.), Innovative Mobile Learning, 2009.

  14. Peng C.-C. and Magoulas G.D., Sequence Processing with Recurrent Neural Networks, J. R. R. Dopico, J. Dorado, and A. Pazos (eds), Encyclopedia of Artificial Intelligence, Information Science Reference, ISBN: 978-1-59904-849-9, 2008.

  15. Van Labeke N., Poulovassilis A. and Magoulas G.D., Using Similarity Metrics for Matching Lifelong Learners, Intelligent Tutoring Systems, Lecture Notes in Computer Science, vol. 5091, Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008), B. P.Woolf, E. Aïmeur, R. Nkambou, S. Lajoie (Eds.), Springer, pp. 142-151, 2008.

  16. de Freitas S., Harrison I., Magoulas G.D., Papamarkos G., Poulovassilis A., Van Labeke N., Mee A., and Oliver M., L4All: a Web-Service Based System for Lifelong Learners, S. Salerno, M. Gaeta, P. Ritrovato, N. Capuano, F. Orciuoli, S. Miranda and A. Pierri (eds.), The Learning Grid Handbook: Concepts, Technologies and Applications, Volume 2: The Future of Learning, IOS Press, 2008, ISBN: 978-1-58603-829-8.

  17. Magoulas G.D., User Modeling in Information Portals, Encyclopedia of Portal Technologies and Applications, Arthur Tatnall (ed.), vol II, Information Science Reference, ISBN: 978-1-59140-989-2, 2007.

  18. Peng C.-C. and Magoulas G.D., Adaptive Self-scaling Non-monotone BFGS Training Algorithm for Recurrent Neural Networks, Lecture Notes in Computer Science vol. 4668, Artificial Neural Networks Part I – J. Marques de Sá et al. (eds.). Presented as a full paper at the 17th International Conference Artificial Neural Networks, pp. 259–268, 2007.

  19. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Improved learning of neural nets through global search, chapter 15 in Global Optimization - Scientific and Engineering Case Studies, János D. Pintér (ed.), Series: Nonconvex Optimization and Its Applications, vol. 85, Springer-Verlag New York Inc, (ISBN: 0-387-30408-8), pp. 361- 388, 2006.

  20. Magoulas G.D, Web-based instructional systems. Encyclopaedia of Human Computer Interaction, Claude Ghaoui (ed.), IDEA publishing (ISBN: 1-59140-562-9), pp. 729-738, 2005.

  21. Magoulas G.D. and Vrahatis M.N., Parameter optimization algorithm with improved convergence properties for adaptive learning. Lecture Series on Computer and Computational Sciences, vol. 3, Frontiers of Computational Science, G. Maroulis and Th. Simos (eds.), Brill Academic Publishers, Leiden, The Netherlands (ISBN 90-6764-442-0), pp.384-398, 2005.

  22. Frias-Martinez E., Magoulas G.D., Chen S., and Macredie R. Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia. Lecture Notes in Computer Science, vol. 3137, Adaptive Hypermedia and adaptive web-based systems, Paul De Bra, Wolfgang Nejdl (eds), Springer, pp. 104-113, 2004. Presented as a full paper at 3rd Int Conf Adaptive Hypermedia.

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

  24. Stathacopoulou R., Grigoriadou M., Samarakou M., Magoulas G.D., Using Simulated Students for Machine Learning. Lecture Notes in Computer Science, vol. 3220, James C. Lester, Rosa Maria Vicari, Fabio Paraguau (eds.), Springer, pp. 889-891, 2004. Presented as a short paper at the 7th International Conference on Intelligent Tutoring Systems (ITS 2004).

  25. Anastasiadis A.D., Magoulas G.D., and Liu X. Classification of Protein Localisation Patterns via Supervised Neural Network Learning. Lecture Notes in Computer Science, vol. 2810, Advances in Intelligent Data Analysis V, M. Berthold, H.-J. Lenz, E. Bradley et al. (eds.), Berlin: Springer-Verlag, pp. 430-439, 2003. Presented as a short paper at the 5th International Symposium on Intelligent Data Analysis.

  26. O’Neill P., Magoulas G. D., and Liu X. Obtaining Quality Microarray Data via Image Reconstruction. Lecture Notes in Computer Science, vol. 2810, Advances in Intelligent Data Analysis V, M. Berthold, H.-J. Lenz, E. Bradley et al. (eds.), Berlin: Springer-Verlag, pp. 364-375, 2003. Presented as a full paper at the 5th International Symposium on Intelligent Data Analysis.

  27. Stathacopoulou R., Grigoriadou M., Magoulas G. D. and Mitropoulos D., A Neuro-Fuzzy Approach in Student Modeling, Lecture Notes in Computer Science (LNCS) Vol. 2702, Springer-Verlag Heidelberg, pp. 337-341. Presented as poster at 9th International Conference on User Modeling (UM2003).

  28. Grigoriadou, M., Kornilakis, H., Papanikolaou, K.A., and Magoulas, G.D. Fuzzy Inference for Student Diagnosis in Adaptive Educational Systems. Lecture Notes in Artificial Intelligence, vol. 2308, Methods and Applications of Artificial Intelligence: Vlahavas and C.D. Spyropoulos (eds.), Berlin: Springer-Verlag, pp. 191-202, 2002. Presented as a full paper at the 2nd Hellenic Conference on AI, SETN2002.

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

  30. Magoulas G.D. and Prentza A., Machine learning in medical applications. Lecture Notes in Artificial Intelligence, vol. 2049, Machine Learning and its Applications: Advanced Lectures, G. Paliouras, V. Karkaletsis and C.D. Spyropoulos (Eds.), Springer-Verlag, pp. 300-307, 2001.

  31. Parsopoulos, K., Plagianakos, V.P., Magoulas, G.D., and Vrahatis M.N., Improving the particle swarm optimizer by function “stretching”. Advances in convex analysis and global optimization vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 28, pp.445-457, 2001.

  32. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Supervised training using global search methods. Advances in convex analysis and global optimization, vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 26, pp.421-432, 2001.

  33. Plagianakos V.P., Magoulas G.D. and Vrahatis M.N., Learning rate adaptation in stochastic gradient descent. Advances in convex analysis and global optimization, vol. 54, Noncovex Optimization and its Applications, Hadjisavvas N. and Pardalos P. (ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (ISBN 0-7923-6942-4), Chapter 27, pp.433-444, 2001.

  34. Papanikolaou K., Magoulas G.D., and Grigoriadou M., A connectionist approach for supporting personalized learning in a web-based learning environment. Lecture Notes in Computer Science, vol. 1892, Springer, pp. 189-201, 2000. Presented as a full paper at International Conference on Adaptive Hypermedia and Adaptive Web-based System.

  35. Magoulas G.D., Plagianakos V.P., Androulakis G.S. and Vrahatis M.N., A framework for the development of globally convergent adaptive learning rate algorithms. Advances in Intelligent Systems and Computer Science, N.E. Mastorakis ed., World Scientific and Engineering Society Press, pp.207-212, 1999.

  36. Plagianakos V.P., Magoulas G.D., Androulakis G.S. and Vrahatis M.N., Global search methods for neural network training. Advances in Intelligent Systems and Computer Science, N.E. Mastorakis ed., World Scientific and Engineering Society Press, pp.47-52, 1999.

  37. Magoulas G.D. and Vrahatis M.N., A model for local convergence analysis of batch-type training algorithms with adaptive learning rates. Recent Advances in Circuits and Systems, Mastorakis, N. E. (ed.), World Scientific, pp. 321-326, 1998.

  38. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., A training method for discrete multilayer neural networks. Mathematics of Neural Networks: Models, Algorithms & Applications, Ellacot, S. W., Mason J. C. and I. J. Anderson (eds.), Kluwer Academic Publishers, Operations Research/Computer Science Interfaces series, chapter 41, pp. 245-249, 1997.

  39. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., Neural network supervised training based on a dimension reducing method. Mathematics of Neural Networks: Models, Algorithms & Applications, Ellacot, S. W., Mason, J. C. and Anderson, I. J. (eds.), Kluwer Academic Publishers, Operations Research/Computer Science Interfaces series, chapter 42, pp.250-254, 1997.

  40. Androulakis G.S., Magoulas G.D. and Vrahatis M.N., Minimization techniques in neural network supervised training. Selected Works of the 6th International Colloquium on Differential Equations, VSP International Science Publishers, pp. 9-16, 1996.

Refereed articles in conference proceedings

  1. P. Lagias, G.D. Magoulas, Y. Prifti, A. Provetti, Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark. In: 23rd International Conference on Engineering Applications of Neural Networks-EANN 2022, 17 – 20 June, 2022, Crete, Greece.

  2. N. Herzog, and G.D. Magoulas, Deep transfer learning for DTI- and MRI- based early diagnosis of cognitive decline and dementia. In: 17th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics-CIBB 2021, 15-17 November 2021, pp. 1-6.

  3. C. Stamate, G.D. Magoulas, M.S.C. Thomas, Deep learning topology-preserving EEG-based images for autism detection in infants, in Iliadis L., Macintyre J., Jayne C., Pimenidis E. (eds), Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN2021), part of the Proceedings of the International Neural Networks Society book series, vol 3. Springer, pp 71-82, 2021.

  4. N. Herzog, and G.D. Magoulas, Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia, in in Iliadis L., Macintyre J., Jayne C., Pimenidis E. (eds), Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN2021), part of the Proceedings of the International Neural Networks Society book series, vol 3. Springer, pp 57-70, 2021.

  5. M. Ermaliuc, D. Stamate, G.D. Magoulas, I. Pu, Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification, in Iliadis L., Macintyre J., Jayne C., Pimenidis E. (eds), Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN2021), part of the Proceedings of the International Neural Networks Society book series, vol 3. Springer, pp 13-23, 2021.

  6. D. Celik, G.D. Magoulas, Challenging the Alignment of Learning Design Tools with HE Lecturers’ Learning Design Practice, in the Proceedings of the European Conference on Technology Enhanced Learning (EC-TEL 2019), pp 142-15, Springer, LNCS, volume 11722.

  7. A. Fassbinder, E.F. Barbosa, G. Magoulas, Massive Open Online Courses in Software Engineering Education at the 47th Annual Frontiers in Education (FIE 2017) Conference.

  8. A. Fassbinder, E.F. Barbosa, G.Magoulas, Developing an Educational Design Pattern Language for MOOCs. In: XXVIII Simpósio Brasileiro de Informática na Educação (SBIE). Recife, Pernambuco, Brasil, Novembro, 2017.

  9. A. Fassbinder, E.F. Barbosa, G. Magoulas Towards and Educational Design Pattern Language for Massive Open Online Courses (MOOCs). In: 24th Conference on Pattern Languages of Programs (PLoP). Vancouver, Canadá, Outubro, 2017.

  10. Mosca A., and Magoulas G.D., Boosted Residual Networks, Proceedings of the 18th International Conference on Engineering Applications of Neural Networks (EANN 2017), Athens, Greece, August 25–27, 2017, Giacomo Boracchi, Lazaros Iliadis, Chrisina Jayne, Aristidis Likas (eds), Communications in Computer and Information Science book series (CCIS), vol. 744, pp. 137-148, Springer.

  11. Grawemeyer B., Karoudis K., Magoulas G.D., Pinto M., Poulovassilis A., Design and Evaluation of Adaptive Feedback to Foster ICT Information Processing Skills in Young Adults at DigiLEarn track, WWW Conference 2017.

  12. Mosca A., and Magoulas G.D., Training Convolutional Networks with Weight–wise Adaptive Learning Rates, Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), Bruges, Belgium, 26-28 April 2017.

  13. Stamate C., Magoulas G.D. , Kueppers S. , Nomikou E., Daskalopoulos I. , Luchini M.U., Moussouri T., and Roussos G., Deep Learning Parkinson’s from Smartphone Data, Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2017), March 13- 17, 2017, Island of Hawai’i (a.k.a. The Big Island), USA.

  14. Mosca A., and Magoulas G.D. Deep Incremental Boosting, Proceedings of the Global Conference on Artificial Intelligence (GCAI 2016), Freie Universität Berlin, Germany, September 29-October 2, 2016, Christoph Benzmüller, Geoff Sutcliffe and Raul Rojas (eds), EPiC Series in Computing, vol. 41, pp 293–302, 2016, Berlin.

  15. Stamate C., Magoulas G.D., Thomas M.S.C., Initialising Deep Neural Networks: an Approach based on Linear Interval Tolerance, Proceedings of IEEE SAI Intelligent Systems Conference, September 21-22, pp 477-485, 2016, London, UK.

  16. Mosca A. and Magoulas G.D., Learning Input Features Representations in Deep Learning, Proceedings of the 16th UKCI 2016, Angelov P., Gegov A., Jayne C., Shen Q. (eds), Advances in Intelligent Systems and Computing, vol. 513, Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK, pp. 433-445, Springer.

  17. Celik D., Magoulas G.D., A Review, Timeline, and Categorization of Learning Design Tools, Proceedings of the International Conference on Web-based learning-ICWL 2016, Rome, Italy, October 26-29, M. Spaniol, M. Temperini, D.K.W. Chiu, I. Marenzi, U. Nanni (eds.), Advances in Web-Based Learning, Springer.

  18. Celik D., Magoulas G.D., Approaches to Design for Learning, Proceedings of the International Conference on Web-based learning-ICWL 2016, Rome, Italy, October 26-29, M. Spaniol, M. Temperini, D.K.W. Chiu, I. Marenzi, U. Nanni (eds.), Advances in Web-Based Learning, Springer.

  19. Karagkiozoglou, K., Magoulas, G., An Architecture for Smart Lifelong Learning Design. In Proceedings of the 3rd International Conference on Smart Learning Environments, September 28-30, 2016, Tunis, Tunisia. Popescu, E., Kinshuk et al. (eds.) Innovations in Smart Learning. Springer-Verlag Berlin Heidelberg, 2016.

  20. Wells M., Wollenschlaeger A, Lefevre D., Magoulas G.D., Poulovassilis A., Analysing engagement in an online management programme and implications for course design, In Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK' 16), Edinburgh, UK, April 25-29, 2016, D. Gašević, G. Lynch, S. Dawson, H. Drachsler, and C. Penstein Rosé (eds.), ACM, pp. 236-240, 2016.

  21. Mosca A., and Magoulas G.D. Adapting Resilient Propagation for Deep Learning, Proceedings of the 15th UK Workshop on Computational Intelligence, 7-9h September 2015, Exeter, UK.

  22. Stamate C. , Magoulas G.D., Thomas M.S.C. Transfer learning approach for financial applications, Proceedings of the 15th UK Workshop on Computational Intelligence, 7-9h September 2015, Exeter, UK.

  23. Adam S., Karras D., Magoulas G.D. and Vrahatis M., Reliable estimation of a neural network's domain of validity through interval analysis based inversion, Proceedings of the International Joint Conference Neural Networks 2015, forthcoming.

  24. Sikora, T.D.; Magoulas, G.D., Search-guided activity signals extraction in application service management control, Proceedings of the 14th UK Workshop on Computational Intelligence (UKCI), 8-10 Sept., pp. 1 - 8, 2014.

  25. Maitrei K.i, Magoulas G.D., Thomas M.S.C., Transfer learning across heterogeneous tasks using behavioural genetic principles, Proceedings of the 13th UK Workshop on Computational Intelligence, pp.151-158, 2013.

  26. Sikora, T.D.; Magoulas, G.D., Finding relevant dimensions in Application Service Management control: A features selection approach, IEEE Science and Information Conference (SAI), 2013 , pp.387,395, 7-9 Oct. 2013.

  27. Sikora T. and Magoulas G.D., Neural Adaptive Control in Application Service Management Environment, In Proc. of the 13th International Conference on Engineering Applications of Neural Networks, 20-23 September 2012, London, C. Jayne, S. Yue, and L. Iliadis (eds.), Springer CCIS 311, pp. 223–233, 2012.

  28. Adam S.P., Magoulas G.D., and Vrahatis M.N., Direct Zero-Norm Minimization for Neural Network Pruning and Training, In Proc. of the 13th International Conference on Engineering Applications of Neural Networks, 20-23 September 2012, London, C. Jayne, S. Yue, and L. Iliadis (eds.), Springer CCIS 311, pp. 295–304, 2012.

  29. Kohli M., Magoulas G.D., and Thomas M., Hybrid Computational Model for Producing English Past Tense Verbs, In Proc of the 13th International Conference on Engineering Applications of Neural Networks (EANN), 20-23 September 2012, London, C. Jayne, S. Yue, and L. Iliadis (eds.), Springer CCIS 311, pp. 315–324, 2012.

  30. Cocea M. and Magoulas G.D., Learning Task-related Strategies from User Data through Clustering, In Proc of 12th IEEE International Conference on Advanced Learning Technologies, 400-404, 2012.

  31. Cocea M. and Magoulas G.D., Context-dependent Feedback Prioritisation in Exploratory Learning Revisited, In Proc of User Modeling, Adaptation and Personalization (UMAP) Conference 2011, Girona, Spain, 11-15 July 2011. Joseph A. Konstan et al. (Eds.): UMAP 2011, LNCS 6787, pp. 62–74, 2011.

  32. Charlton P. and Magoulas G.D., Autonomic Computing and Ontologies to Enable Context-aware Learning Design. Proceedings of the 22nd IEEE International Conference Tools with AI, vol. 2, pp. 286-291, 2010.

  33. Cocea M. and Magoulas G.D., Group Formation for Collaboration in Exploratory Learning Using Group Technology Techniques, In Proc. 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2010), 8-10 September 2010 Cardiff, Wales, UK, Rossitza Setchi, Ivan Jordanov, Robert J. Howlett and Lakhmi C. Jain (eds), Lecture Notes in Computer Science, vol. 6277, pp. 103-113.

  34. Cocea M., Gutierrez-Santos S. and Magoulas G.D., Adaptive Modelling of Users’ Strategies in Exploratory Learning Using Case-Based Reasoning. In Proc. 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2010), 8-10 September 2010 Cardiff, Wales, UK, Rossitza Setchi, Ivan Jordanov, Robert J. Howlett and Lakhmi C. Jain (eds), Lecture Notes in Computer Science, vol. 6277, pp. 124-134.

  35. Voulgaris Z. and Magoulas G.D., Discernibility-based Algorithms for Classification. In Proc. Conf. Numerical Analysis (NumAn2010), Chania, Crete, Greece, pp. 266-272 (ISBN 978-960-8475-14-4).

  36. Gutiérrez Santos S., Mavrikis M. and Magoulas G.D., Sequence Detection for Adaptive Feedback Generation in an Exploratory Environment for Mathematical Generalisation. In Proc. 14th International Conference on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2010), Varna, Bulgaria, September 8-10. 2010, Darina Dicheva and Danail Dochev (eds), Lecture Notes in Computer Science, vol. 6304, 2010,pp 181-190.

  37. Gutierrez-Santos S., Mavrikis M., and Magoulas G.D., Layered development and evaluation for Intelligent Support in Exploratory Environments: the case of microworlds, Proceedings of the International Conference on Intelligent Tutoring Systems 2010, vol. 1, pp. 105-114.

  38. Gutierrez-Santos S., Cocea M., and Magoulas G.D., A Case-Based Reasoning Approach to Provide Adaptive Feedback in Microworlds, Proceedings of the International Conference on Intelligent Tutoring Systems 2010, vol. 2, pp. 330-333.

  39. Charlton P. and Magoulas G.D., Self-configurable Framework for Enabling Context-aware Learning Design, Proceedings of the IEEE Intelligent Systems Conference 2010, pp. 1-6.

  40. Cocea, M. and Magoulas, G.D. Identifying User Strategies in Exploratory Learning with Evolving Task Modelling, Proceedings of the IEEE Intelligent Systems Conference 2010, pp. 13-18.

  41. Lewis T.E. and Magoulas G.D. Tweaking a Tower of Blocks Leads to a TMBL: Pursuing Long Term Fitness Growth in Program Evolution, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2010), pp. 1 - 8.

  42. Cocea M. and Magoulas G.D., Context-dependent Personalised Feedback Prioritisation in Exploratory Learning for Mathematical Generalisation. User Modelling, Adaptation and Personalisation Conference (UMAP 2009).

  43. Cocea, M. and Magoulas, G.D. Identifying strategies in users exploratory learning behaviour for mathematical generalisation. The 14th International Conference on Artificial Intelligence in Education (AIED 2009), Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, vol. 200 Frontiers in Artificial Intelligence and Applications, V. Dimitrova, R. Mizoguchi, B. Du Boulay and A. Graesser (eds.), July 2009, pp 626-628.

  44. de Freitas, S. Rebolledo-Mendez, G., Liarokapis, F., Magoulas, G.D, and Poulovassilis, A. Developing an evaluation methodology for immersive learning experiences in a virtual world. In Rebolledo-Mendez, G., Liarokapis, F., de Freitas, S. (Eds) Proceedings of 2009 Conference in Games and Virtual Worlds for Serious Applications, IEEE, pp 43-50.

  45. Lewis T.E. and Magoulas G.D. Strategies to Minimise the Total Run Time of Cyclic Graph Based Genetic Programming with GPUs, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2009), pp. 1379-1386.

  46. Voulgaris Z. and Magoulas G.D., Extensions of the k Nearest Neighbour Methods for Classification Problems, Proc. of the 26th IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, Innsbruck, Austria, February 11 – 13, 2008, pp. 23-28.

  47. Lewis T. E and Magoulas G.D., TREAD: A New Genetic Programming Representation Aimed at Research of Long Term Complexity Growth, GECCO’08, July 12–16, 2008, Atlanta, Georgia, USA.

  48. Voulgaris Z. and Magoulas G. D., A discernibility-based approach to feature selection for microarray data, CD Proceedings of IEEE International Conference of Intelligent Systems, Varna, Bulgaria, 6-8 Sept. 2008.

  49. Voulgaris Z. and Magoulas G. D., Dimensionality reduction for feature and pattern selection in classification problems. Proceeding of The Third International Multi-Conference on Computing in the Global Information Technology, Athens, Greece, July 2008, pp. 160-165.

  50. Baajour H., Magoulas G. D., and Poulovassilis A., Modelling the lifelong learner in a services-based environment, Proceedings of the 2nd International Conference on Internet Technologies and Applications (ITA 07), Wrexham, North East Wales, UK 4-7 September 2007, pp. 191-201.

  51. Anastasiadis A.D., Georgoulas G., Magoulas G.D., and Tzes A., Adaptive Particle Swarm Optimizer with Nonextensive Schedule, Proceedings of the Genetic and Evolutionary Computation Conference 2007 (GECCO’07), July 7–11, 2007, London, UK, pp. 168. Presented as poster.

  52. Anastasiadis A.D., Magoulas, G.D., Georgoulas G., and Tzes A., Nonextensive Particle Swarm Optimization Methods, Proceedings of the Conference in Numerical Analysis (NumAn2007), September 3-7, 2007, Kalamata, pp. 15-18.

  53. Peng C.-C., and Magoulas G.D. Effective Modification of the BFGS Method for Training Recurrent Neural Networks, Proceedings of the Conference in Numerical Analysis (NumAn2007), September 3-7, 2007, Kalamata, pp. 113-117.

  54. Peng C.-C. and Magoulas G.D., Adaptive Nonmonotone Conjugate Gradient Training Algorithm for Recurrent Neural Networks, Proc. 19th IEEE International Conference on Tools with Artificial Intelligence 2007 (ICTAI’07), 29-31 October 2007, Patras, Greece, pp. 374-381.

  55. Magoulas, G.D. and Dimakopoulos, D. An Adaptive Fuzzy Model for Personalization with Evolvable User Profiles, Proceedings of IEEE 2nd International Symposium on Evolving Fuzzy Systems, September 7-9, 2006, Ambelside, Lake District, UK, 336-341.

  56. Dimakopoulos, D.N. and Magoulas, G.D. A personalised mobile environment for lifelong learners, Proceedings of IADIS International Conference on WWW/Internet 2006, October 5-8, 2006, Murcia, Spain, 31-38.

  57. Magoulas G.D. and Anastasiadis A., A nonextensive probabilistic model for global exploration of the search space. In T. Simos, G. Psihoyios, G. Tsitouras, Proceedings of International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), 16-20 September 2005, Rhodes, Greece, Wiley-Vch, 878-881 (ISBN: 3-527-40652-2).

  58. Magoulas, G.D. and Dimakopoulos, D. Personalisation in e-learning: an approach based on services, Proceedings of IADIS International Conference on WWW/Internet 2005, October 19-22, 2005, Lisbon, Portugal, pp. 312-316.

  59. Anastasiadis A.D. and Magoulas G.D., Nonextensive Entropy and Regularization for Adaptive Learning, Proc. of the IEEE International Joint Conference on Neural Networks (IJCNN-04), Budapest, Hungary, 25-29 July, 2004, vol. 2, 1067-1072.

  60. Anastasiadis A.D., Magoulas G.D., and Vrahatis M.N., A New Learning Rates Adaptation Strategy for the Resilient Propagation Algorithm. In M. Verleysen (ed.), Proceedings of the 12th European Symposium on Neural Networks (ESANN-04), April 28-30, Bruges, Belgium, D-side Publications: Evere, 1-6, 2004.

  61. Magoulas G.D., Plagianakos V.P., Tasoulis D.K., and Vrahatis M.N., Tumor detection in colonoscopy using the unsupervised k-windows clustering algorithm and neural networks. In Proceedings of the Fourth European Symposium on Biomedical Engineering, Session 3, June 25-27, 2004, Patras, Greece.

  62. Ghinea G. and Magoulas G. Integrating Perceptech Requirements through Intelligent Computation of Priorities in Multimedia Streaming, Lecture Series on Computer and Computational Sciences, Vol. 1, Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004), VSP International Science Publishers, Zeist, The Netherlands, 2004, pp.856-859.

  63. Anastasiadis A.D., Magoulas G.D. and Vrahatis M.N., A globally convergent Jacobi-bisection method for neural network training, Lecture Series on Computer and Computational Sciences, Vol. 1, Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004), VSP International Science Publishers, Zeist, The Netherlands, 2004, pp.843-848.

  64. Stathacopoulou R., Samarakou M., Grigoriadou M., and Magoulas G.D., A Neuro-Fuzzy Approach to Detect Student's Motivation. In Kinshuk, Chee-Kit Looi, Erkki Sutinen, Demetrios G. Sampson, Ignacio Aedo, Lorna Uden and Esko Kahkonen, Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2004), 30 August-1 September 2004, Joensuu, Finland, pp. 71-75, IEEE Computer Society.

  65. O'Neill P., Magoulas G.D., and Liu X., Quality Processing of Microarray Image Data through Image Inpainting and Texture Synthesis. In Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2004), Arlington, VA, USA, 15-18 April 2004, vol. 1, pp. 117-120.

  66. Stathacopoulou R., Grigoriadou M., Magoulas G. D., Samarakou M., and Mitropoulos D., Neuro-Fuzzy Student Modeling in an Exploratory Learning Environment. Proceedings of the 6th Hellenic European Conference on Computer Mathematics & its Applications (HERCMA 2003). Sept. 25-27, 2003, Athens, Creece,.vol. 1, p. 340-345.

  67. Ghinea G., Magoulas G. D. and Frank A.O. Intelligent Protocol Adaptation for Enhanced Medical e-Collaboration. In Proceedings of the International FLAIRS Conference, May 12-14, 2003 St. Augustine, Florida.

  68. Ghinea G., Magoulas G. D. and Thomas J.P., Intelligent Management of QoS requirements for Perceptual Benefit. In Proceedings 3rd Conference on Intelligent Systems Design and Applications, pp. 437-446, Tulsa, USA, 2003.

  69. Anastasiadis A. and Magoulas G.D. Neural Network-based Prediction of Proteins Localisation Sites. In Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, 10-12 July 2003, Oulu, Finland, 318 – 325.

  70. Chen, S. Y. and Magoulas, G. D. The Relationships between Cognitive Styles and Information Representation in Web Directories. In Proceedings of the LIDA Conference 2003, Libraries in the Digital Age, May 26-30, 2003.

  71. Plagianakos V .P .,Magoulas G .D .and Vrahatis M .N ., On-line neural network training (in Greek), Order and Chaos in Nonlinear Dynamical Systems Vol .8, Proc. of the 9th Panhellenic Conference /14th Summer School on Non -linear dynamics chaos and complexity, Patras , July 23 –August 2, 2001, T .Bountis S .Ichtiaroglou and S .Pnevmatikos (eds.).,K . Sfakianaki Editions, Thessaloniki, pp .329 –340, 2003.[SET 960-7258-16-9 ][ISBN 960-87136-2-5 ].

  72. Vrahatis M .N ., Magoulas G .D .and Plagianakos V .P ., Introduction to artificial neural networks (in Greek), Order and Chaos in Nonlinear Dynamical Systems Vol .7, Proceedings of the 8th Panhellenic Conference /13th Summer School on Non-linear dynamics chaos and complexity, Patras July 17 –28, 2000, T .Bountis D .Ellinas and I .Grispolakis (eds.), Pnevmatikos publications Athens pp .225 –247, 2002.

  73. Magoulas, G.D., Eldabi, T., and Paul R.J., Adaptive Stochastic Search Methods for Parameter Adaptation of Simulation Models, in Proceedings of the IEEE International Symposium on Intelligent Systems, Varna, Bulgaria, Sept. 10-12, 2002, vol. 2, 23-27.

  74. Ghinea G., Magoulas G.D., and Frank A.O., Intelligent Multimedia Transmission for Back Pain Treatment, in Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems (EUNITE 2002), Session "Intelligent E-health Applications in Medicine", 19-21 September 2002, Albufeira, Portugal, 309-316.

  75. Magoulas G.D., Eldabi T., and Paul R.J., Global search strategies for simulation optimization, in E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, eds., Proceedings of the Winter Simulation Conference, December 8-11, 2002, San Diego, California, vol. 2, 1978-1985.

  76. Plagianakos V .P .,Magoulas G .D .and Vrahatis M .N., Tumor detection in colonoscopic images using hybrid methods for on –line neural network training, Proc. Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED 2001), G .M .Papadourakis (ed.), Technological Educational Institute of Crete Heraklion 2001, pp .59 –64 [ISBN 9608531659].

  77. Ghinea G. and Magoulas G. D., A novel application of the analytic hierarchy process in “perceived” quality of service management, in Proceedings of IASTED International Conference on Applied Informatics, Innsbruck, Austria, February 19-22, 2001, pp. 43-47.

  78. Grigoriadou M., Papanikolaou K., Kornilakis H., and Magoulas G., Towards new forms of communication of knowledge in educational hypermedia systems, in Proceedings of the Computer-Aided Learning Conference (CAL2001), April 2-4, 2001, University of Warwick, Coventry, UK.

  79. Stathacopoulou R., Magoulas G.D., Grigoriadou M., and Mitropoulos D., Neural network-based fuzzy modeling of the diagnostic process, in J.D. Moore et al (eds), Proceedings of the 10th International Conference on Artificial Intelligence in Education (AI-ED 2001), San Antonio, Texas, May 19-23 2001, USA, pp.476-487, IOS Press.

  80. 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, 15-19 July 2001, USA, vol. 4, 2536-2541.

  81. Ghinea G. and Magoulas G. D., Perceptual considerations for quality of service management: an integrated architecture. Proceedings of the User Modeling Conference, 234-236, 2001.

  82. Plagianakos V.P., Magoulas G.D., Nousis N.K., and Vrahatis M.N., Training multilayer networks with discrete activation functions, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington DC, 15-19 July 2001, USA, vol. 4, 2805-2810.

  83. Plagianakos V.P., Magoulas G.D., Nousis N.K., and Vrahatis M.N., PVM-based training of large neural architectures, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington DC, 15-19 July 2001, USA, vol. 4, 2584-2589.

  84. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N., Hybrid methods using evolutionary algorithms for on-line training, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington DC, 15-19 July 2001, USA, vol. 3, 2218-2223.

  85. Ghinea G. and Magoulas G.D., Quality of Service for Perceptual Considerations: An Integrated Perspective, in Proceedings of 2001 IEEE International Conf. on Multimedia & Expo (ICME2001), 22-25 August 2001, Tokyo, Japan, 571-574.

  86. Karkanis S.A., Magoulas G.D., Iakovidis D.K., Karras D.A. and Maroulis D.E., Evaluation of textural feature extraction schemes for neural network-based interpetation of regions in medical images, in Proceedings of IEEE International Conference on Image Processing (ICIP-2001), October 7-10, 2001, Thessaloniki, Greece, vol. 1, 281-284.

  87. Magoulas G.D., Plagianakos V.P. and Vrahatis M.N., Improved Neural Network-based Interpretation of Colonoscopy Images Through On-line Learning and Evolution, in Proceedings of European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems (EUNITE 2001), 12-14 December 2001, Tenerife, Spain, 402-407. Also in Adaptive Systems and Hybrid Computational Intelligence in Medicine G.D. Dounias and D.A. Linkens (eds.), European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems Published by the University of the Aegean Chios Greece 2001,pp .38 –43, [ISBN 960-7475-19-4 ].

  88. Magoulas, G.D., Plagianakos, V.P., and Vrahatis, M.N., Global learning rate adaptation in on-line neural network training, in Proceedings of the 2nd International ICSC Symposium on Neural Computation, May 23-26, 2000, Technical University of Berlin, Germany.

  89. Vrahatis, M.N. and Magoulas, G.D., and Plagianakos, V.P., Neural network supervised training as minimization problem (in Greek), Dymamical Systems Vol. 6, Proc. of the 7th Panhellenic Conference/12th Summer School on Non-linear dynamics, chaos and complexity, Patras, July 14-24, 1999, Pnevmatikos publications, Athens, pp. 243-262, 2000.

  90. Papanikolaou K., Magoulas G.D., and Grigoriadou M., Computational intelligence in adaptive educational hypermedia, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, 24-27 July 2000, Como, Italy, vol. 6, 629-634.

  91. Magoulas, G.D., Plagianakos, V.P., and Vrahatis, M.N., Development and convergence analysis of training algorithms with local learning rate adaptation, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, 24-27 July 2000, Como, Italy, vol. 1, 21-26.

  92. Karkanis, S.A., Magoulas, G.D., Iakovidis, D.K., Maroulis, D.E., and Schurr, M.O., On the importance of feature descriptors for the characterisation of texure, in Proceedings of the World Multi-conference on Systemics, Cybernetics and Informatics, July 23-26, 2000, Orlando, Florida, U.S.A.

  93. Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Magoulas, G.D., and Theofanous, N.G., Tumor recognition in endoscopic video images using artificial neural network architectures, Proceedings of the 26th Euromicro Conference, 5-7 September, 2000, Maastricht, the Netherlands, vol. 2, 423-429.

  94. Hossain S., Pouloudi A., and Magoulas G. D., Issues of IT adoption in schools, in Proceedings of the Business Information Technology Conference- BIT 2000, November 1-2, 2000, Manchester, U.K.

  95. Vrahatis M .N .,Magoulas G .D .,Parsopoulos K .E .and Plagianakos V .P ., Introduction to artificial neural network training and applications, Proceedings of the 15th Annual Conference of Hellenic Society for Neuroscience (Neuroscience 2000), October 27 –29, 2000, Patras Greece.

  96. Magoulas G. D., Papanikolaou K. and Grigoriadou M., Adaptive lesson presentation based on connectionist knowledge representation, in Proceedings of abstracts of the International Conference in Technology and Education, Edinbrough, March 1999.

  97. Grigoriadou M., Magoulas G. D. and Panagiotou M., A hybrid decision making model for intelligent tutoring systems, in Proceedings of the 5th International Conference of the Decision Sciences Institute, 195-197, Athens, Greece, July 1999.

  98. Magoulas G.D. and Vrahatis M.N., Analysis and synthesis of a class of neural network training algorithms derived by one-dimensional subminimization methods, in Integrating Technology and Human Decisions: Global Bridges into the 21 ST Century, Proceedings of the 5th International Conference of the Decision Sciences Institute, D.K .Despotis and C. Zopounidis eds., Athens, Greece, July 1999, vol. 1, pp .512 –514. 512-514, 1999.

  99. Magoulas G.D., A new sign-method in neural network training for embedded control applications, Proceedings of the 5th International Conference of the Decision Sciences Institute, 2001-2003, Athens, Greece, July 1999.

  100. Stathacopoulou R. , Magoulas G.D. and Grigoriadou M., Neural network-based fuzzy modeling of the student in intelligent tutoring systems, Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington, U.S.A., 10-16 July 1999, vol. 5, 3517-3521.

  101. Papanikolaou K., Magoulas G.D., and Grigoriadou M., A connectionist approach for adaptive lesson presentation in a distance learning course, Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington, U.S.A., 10-16 July 1999, vol. 5, 3522-3526.

  102. Magoulas G. D., Plagianakos V., and Vrahatis M. N., Sign-methods for training with imprecise error function and gradient values, Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington, U.S.A., 10-16 July 1999, vol. 3, 1768-1773.

  103. Plagianakos V., Vrahatis M. N. and Magoulas G. D., Nonmonotone methods for backpropagation training with adaptive learning rate, Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington, U.S.A., 10-16 July 1999, vol. 3, 1762-1767.

  104. Vrahatis M. N., Magoulas G. D., and Plagianakos V., Convergence analysis of the quickprop method, in Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington, U.S.A., 10-16 July 1999, vol. 2, 1209-1214.

  105. Plagianakos V., Magoulas G. D. and Vrahatis M. N., Nonmonotone learning rules for backpropagation networks, Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems, vol. 1, 291-294, Paphos, Cyprus, 5-8 September 1999.

  106. Magoulas G. D., Plagianakos V., and Vrahatis M. N., Effective neural network training with a different learning rate for each weight, Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems, Paphos, Cyprus, 5-8 September 1999, vol. 1, 591-594, 1999.

  107. Karkanis, S., Magoulas, G.D., Karras, D. and Grigoriadou, M., Neural network-based textural labeling of images in multimedia applications, in Proceedings of the 25th Euromicro Conference, 8-10 September 1999, Milan, Italy, vol. 2, 392-396.

  108. Plagianakos, V.P., Magoulas, G.D., Androulakis, G.S., and Vrahatis, M.N., Global search methods for neural network training, in Proceedings of the 3rd IEEE-IMACS World Multiconference on Circuits, Systems, Communications and Computers, vol. 1, 3651-3656, Athens, Greece, July 1999. Also in Advances in Intelligent Systems and Computer Science N.E. Mastorakis ed .,World Scientific and Engineering Society Press, pp .47 –52, 1999.

  109. Magoulas, G.D., Plagianakos, V.P., Androulakis, G.S., and Vrahatis, M.N., A framework for the development of globally convergent batch training algorithms, in Proceedings of the 3rd IEEE-IMACS World Multiconference on Circuits, Systems, Communications and Computers, vol. 1, 3641-3646, Athens, Greece, July 1999. Also in Advances in Intelligent Systems and Computer Science N .E .Mastorakis ed .,World Scientific and Engineering Society Press, pp. 207 –212, 1999.

  110. Magoulas, G.D., Karkanis, S., Karras, D. and Vrahatis, M.N., Comparison study of textural descriptors for training neural network classifiers, in Proceedings of the 3rd IEEE-IMACS World Multiconference on Circuits, Systems, Communications and Computers, vol. 1, 6221-6226, Athens, Greece, July 1999. Also in Computers and Computational Engineering in Control N .E .Mastorakis (ed.),World Scientific and Engineering Society Press 1999, pp.193 –198.

  111. Plagianakos, V.P., Magoulas, G.D., and Vrahatis, M.N., Optimization strategies and backpropagation neural networks, in Proceedings of the 7th Hellenic Conference on Informatics, D .I .Fotiadis and S .D .Nikolopoulos (eds.), Ioannina Greece August 26 –29,1999, pp.V .88 –V .95.

  112. Magoulas G. D. and Pouloudi, A., Ethical issues in the use of neural network-based methodologies for image interpretation in medicine, in Proceedings of ETHICOMP99 - The 5thInternational Conference on the Social and Ethical Impacts of Information and Communication Technologies, LUISS Guido Carli University, Rome, Italy, October 1999.

  113. Magoulas G. D., Papanikolaou K. and Grigoriadou M., Towards a computationally intelligent lesson adaptation for a distance learning course, in Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, Chicago, 9-11 November 1999, pp. 5-11.

  114. Magoulas G.D. and Vrahatis M.N., A model for local convergence analysis of batch-type training algorithms with adaptive learning rates, In Proceedings of the 2nd IMACS International Conference on Circuits Systems & Computers, vol. 1, 86-91, Athens, Greece, 1998. Also In Recent Advances in Circuits and Systems N.E .Mastorakis (ed.), World Scientific Publishing Co Ltd.,1998, pp. 321 –326.

  115. Magoulas G.D. and Vrahatis M.N., New optimization algorithms for efficient neural network training, in Lipitakis E.A. (ed.) Proceedings of the 4th Hellenic-European Research Conference on Computational Mathematics and Applications, Athens, Greece, Sept. 24-26, pp. 209-216, 1998 [ISBN 960-85176-7-2].

  116. Papaspyridis A., Janetis J. Berger R. and Magoulas G. D., Designing mixed fuzzy logic and PID embedded automotive control systems with FLDE Autostudio, in Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing-EUFIT'98, 1998.

  117. Androulakis G.S., Magoulas G.D. and Vrahatis M.N., Minimization techniques in neural network supervised training, In Proceedings of the 6th International Colloquium on Differential Equations, Bulgaria, 1996.

  118. Magoulas G.D., Vrahatis M.N. and Androulakis G.S., A new method in neural network supervised training with imprecision, In Proceedings of the 3rd IEEE International Conference on Electronics Circuits & Systems, vol. 1, 287-290, 13-16 October, Rodos, Greece, 1996.

  119. Michos S.E., Magoulas G.D. and Fakotakis N., A hybrid knowledge representation model in a natural language interface to MS-DOS, In Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence, 480-483, 5-8 November, Washington, U.S.A., 1995.

  120. Michos S.E. and Magoulas G.D., A hybrid approach to knowledge representation and learning in a natural language interface to operating systems, in Proceedings of the 5th Hellenic Conference on Informatics, 431-440, Athens, Greece, 1995.

  121. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., Neural network supervised training based on a dimension reducing method, in Proceedings of abstracts of the 1st International Conference on Mathematics of Neural Networks and Applications, Lady Margaret Hall, Oxford, England, 1995.

  122. Magoulas G.D., Vrahatis M.N., Grapsa T. N. and Androulakis G.S., A training method for discrete multilayer neural networks, in Proceedings of abstracts of the 1st International Conference on Mathematics of Neural Networks and Applications, Lady Margaret Hall, Oxford, England, 1995.

  123. King R.E. and Magoulas G.D., Adaptive digital laguerre filters, In Proceedings of the International Conference on Digital Signal Processing, vol.1, 46-53, 1993.

Refereed articles in workshop proceedings

  1. Adam S.P., Karras D.A., Magoulas G.D., Vrahatis M.N., Interval methods for resolving neural computation issues, Proceedings of the Tenth Summer Workshop on Interval Methods, and the Third International Symposium on Set Membership — Applications, Reliability and Theory (SWIM-SMART 2017), June 14-16, 2017, Aerospace Research Institute, University of Manchester, Manchester, UK, pp.51-54, 2017.

  2. Mosca A. and Magoulas G.D., Regularizing Deep Learning Ensembles by Distillation, Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), in conjunction with the 22nd European Conference on Artificial Intelligence (ECAI-16), August 30, 2016, The Hague, Holland, pp. 53-59.

  3. Cocea, M, Gutierrez-Santos, S. Magoulas, G.D. Enhancing Modelling of Users’ Strategies in Exploratory Learning through Case-base Maintenance. In Proceedings of 14th UK Workshop on Case-Based Reasoning, 2009, pp. 2-13.

  4. Charlton P., Magoulas G.D., and Laurillard D., Designing for Learning with Theory and Practice in Mind, in Proceedings of the Workshop on Enabling Creative Learning Design, part of the AIED 2009: 14th International Conference on Artificial Intelligence in Education, Brighton, July 2009, pp. 52-62.

  5. Cocea, M., Magoulas, G., Combining Intelligent Methods for Learner Modelling in Exploratory Learning Environments. In Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2008), in conjunction with the 18th European Conference on Artificial Intelligence (ECAI-08), pp. 13-18.

  6. Cocea, M., Gutierrez-Santos, S. and Magoulas, G., Challenges for Intelligent Support in Exploratory Learning: the case of ShapeBuilder. In Proceedings of the 1st International Workshop on Intelligent Support for Exploratory Environments (ISEE’08), in conjunction with the third European Conference on Technology-Enhanced Learning (EC-TEL '08), 16-19 September 2008, Maastricht, The Netherlands.

  7. Baajour H., Magoulas G.D., Poulovassilis A. Towards Cross-System User Model Interoperability for Personalised Lifelong Learning, Proceedings of the 6th International Workshop on Ubiquitous User Modeling with the focus on User Model Integration (UMI 2008) in conjunction with the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, July 29, 2008, Hannover, Germany, pp. 28-32.

  8. Voulgaris Z., Magoulas G. D., "Discernibility-based approach for creating ensembles in pattern classification applications". Proceedings of the UKCI Conference, Leicester U.K., Sept. 2008.

  9. Baajour H., Magoulas G. D., and Poulovassilis A., Designing services-enabled personalisation for planning of lifelong learning based on individual and group characteristics, Proceedings of the Workshop on Personalisation in E-Learning Environments at Individual and Group Level, 11th International Conference on User Modeling (UM 2007), Corfu, Greece, 25-29 June 2007, pp. 8-15.

  10. de Freitas S., Magoulas G.D., Oliver M., Papamarkos G., Poulovassilis A., Harrison I, Mee A., L4All - a web-service based system for Lifelong Learners, Proceedings of eChallenges'2006, Workshop on Next Generation in Technology Enhanced Learning, October 25-27, 2006, Barcelona, IOS Press, pp 1477-1484.

  11. Magoulas G.D., Papamarkos G., Poulovassilis A., A Services-enabled Environment for Personalising Lifelong Learning Pathways, Proceedings of Workshops held at the 4th International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, Dublin, Ireland, June 20, 2006, Lecture Notes in Learning and Teaching, Weibelzahl, S., Cristea, A., editors, Dublin: National College of Ireland, 2006. (ISSN 1649-8623), pp. 140-147.

  12. Magoulas G.D. and Dimakopoulos D.N. Designing Personalised Information Access to Structured Information Spaces, Proceedings of the Workshop on New Technologies for Personalized Information Access, 10th International conference on User Modeling, July 24-29, 2005, Edinburgh, Scotland, UK, pp. 64-73.

  13. Magoulas G.D., Building diverse neural ensembles for bioinformatics applications, Proceedings of the Workshop on Intelligent Technologies in Bioinformatics and Medicine, European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems (EUNITE 2004), Aachen, Germany, June 10-12, 2004.

  14. Anastasiadis A.D., Magoulas G.D., and Vrahatis M.N., An efficient improvement of the Rprop algorithm. In M. Gori and S. Marinai (eds.), Artificial Neural Networks in Pattern Recognition, Proceedings of the 1st Int Association of Pattern Recognition-TC3 Workshop, Florence, Italy, September 2003, pp. 197-201. Firenze: Stampa Digitale.

  15. 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.

  16. Ghinea G., Magoulas G. D. and Siamitros C., Perceptual considerations in a QoS framework: a fuzzy logic formulation, in Proceedings of the 4th IEEE Workshop on Multimedia Signal Processing, October 3-5, 2001, Cannes, France, pp. 353-358.

  17. Grigoriadou M., Papanikolaou K., Kornilakis H., and Magoulas G., INSPIRE: an INtelligent System for Personalized Instruction in a Remote Environment, in P. De Bra, P. Brusilovsky & A. Kobsa (eds), Pre-Workshop Proceedings: Third Workshop on Adaptive Hypertext and Hypermedia, 8th International Conference on User Modeling (UM2001), Sonthofen, Germany, July 13, 2001, pp. 31-40.

  18. Parsopoulos, K., Plagianakos, V.P., Magoulas, G.D., and Vrahatis M.N., Stretching technique for obtaining global minimizers through particle swarm optimization, in Proceedings of the Particle Swarm Optimization Workshop, April 6-7, 2001, Indiana, USA, pp. 22-29.

  19. Papanikolaou K., Magoulas G.D., and Grigoriadou M., The role of the educational material for personalised learning in a web-based course, in Proceedings of the 1st Research Workshop of the European Distance Education Network (EDEN-2000): Research and Innovation in Open and Distance Learning, Prague, Czech Republic, 16-17 March 2000, pp. 151-154.

  20. Karkanis, S.A., Magoulas, G.D., Grigoriadou, M. and Schurr, M., Detecting abnormalities in colonoscopic images by textural description and neural networks, in Proceedings of the Workshop Machine Learning in Medical Applications, pp. 59-62, Chania, Greece, July 1999.