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London, United Kingdom
I conduct interdisciplinary research that seeks to enable systems to exhibit different levels of intelligence, learn from data and transfer knowledge to new contexts. My latest projects are in deep learning for psychophysiological data modelling and classification, and in intelligent learning environments. I design and develop innovative learning algorithms, system components that employ machine learning, sometimes combined with knowledge engineering, learner models and intelligent tutors. I was educated at the University of Patras, Greece (BEng/MEng, Dr. Eng), and hold a PGCE (Brunel University, UK). Before joining academia I held R&D positions in the cement and automotive industries working on embedded systems that used soft computing and machine learning methods. My research received best paper awards from the IEEE (2000 and 2008), the European Network on Intelligent Technologies for Smart Adaptive Systems (2001 and 2004), the International Association for Development of the Information Society (2006), the ACM (2009) and KES International (2010). I am a Fellow of the Higher Education Academy, and a Member of the EPSRC College, UK.

Refereed articles in conference proceedings



    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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).
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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).
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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).
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. 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. 
    60. 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.
    61. 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 ].
    62. 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.
    63. 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.
    64. 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.
    65. 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.
    66. 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].
    67. 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.
    68. 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.
    69. 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.
    70. 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.
    71. 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. 
    72. 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.
    73. 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.
    74. 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.
    75. 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.
    76. 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.
    77. 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 ].
    78. 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.
    79. 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.
    80. 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.
    81. 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.
    82. 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.
    83. 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.
    84. 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.
    85. 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.
    86. 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.
    87. 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.
    88. 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.
    89. 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.
    90. 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.
    91. 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.
    92. 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.
    93. 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.
    94. 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.
    95. 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.
    96. 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.
    97. 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.
    98. 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.
    99. 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.
    100. 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.
    101. 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.
    102. 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.
    103. 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.
    104. 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.
    105. 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].
    106. 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.
    107. 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.
    108. 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.
    109. 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.
    110. 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.
    111. 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.
    112. 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.
    113. 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.