Search
photo of Serafeim Moustakidis

Dr Serafeim Moustakidis

Honorary Senior Visiting Fellow

School of Science and TechnologyDepartment of Engineering

Contact details

Address

Dr Serafeim Moustakidis City, University of London
Northampton Square
London EC1V 0HB
United Kingdom

About

Overview

Dr Serafeim Moustakidis received his master’s and PhD degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTh). He has a wide experience in Artificial Intelligence (AI) and data processing with more than 12 years of research experience in various AI fields.

His research focuses in developing novel algorithms for solving important existing and/or emerging problems. Serafeim has proposed novel algorithms for data/complexity reduction in demanding big data environments and hierarchical decomposition of complex classification problems. His main scientific interests cover various fields such as Big Data, Data mining, novel Machine Learning and Deep Learning algorithms applied on Healthcare, Energy, Transport and Aerospace. He has been involved in numerous National and European projects (more than 20) as senior researcher or project manager as well as in the preparation of successful proposals for European projects.

He has worked for several research organisations (researcher in Centre for Research and Technology Hellas - CERTH, research associate at National Technical University of Athens - NTUA, University of Thessaly - UTH). Dr Moustakidis has participated in two EU-funded projects of CITY (Compete and InDeal) as technical manager and project coordinator. Currently, Dr Moustakidis has been appointed as Honorary Senior Visiting Fellow Research for City University of London.

Qualifications

  1. PhD Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece
  2. MSc Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece

Publications

Publications by category

Conference papers and proceedings (13)

  • Alexos, A., Moustakidis, S., Kokkotis, C. and Tsaopoulos, D. (2020). Physical Activity as a Risk Factor in the Progression of Osteoarthritis: A Machine Learning Perspective.
  • Liakos, K.G., Georgakilas, G.K., Moustakidis, S., Karlsson, P. and Plessas, F.C. (2019). Machine Learning for Hardware Trojan Detection: A Review. 2019 Panhellenic Conference on Electronics & Telecommunications (PACET) 8-9 November.
  • de Diego, S., Goncalves, C., Lage, O., Mansell, J., Kontoulis, M., Moustakidis, S. … Liapis, A. (2019). Blockchain-Based Threat Registry Platform. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 17-19 October.
  • Christodoulou, E., Moustakidis, S., Papandrianos, N., Tsaopoulos, D. and Papageorgiou, E. (2019). Exploring deep learning capabilities in knee osteoarthritis case study for classification. 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) 15-17 July.
  • Liakos, K., Moustakidis, S., Tsiotra, G., Bartzanas, T., Bochtis, D. and Parisses, C. (2017). Machine learning based computational analysis method for cattle lameness prediction.
  • Parthipan, T., Jackson, P., Chong, A., Legg, M., Mohimi, A., Kappatos, V. … Hrissagis, K. (2014). Long Range Ultrasonic inspection of aircraft wiring. 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE) 1-4 June.
  • Budimir, M., Mohimi, A., Moustakidis, S. and Gan, T.H. (2013). High temperature transducers system for long range ultrasound non-destructive evaluation in aging power plants.
  • Moustakidis, S., Kappatos, V., Karlsson, P., Selcuk, C., Hrissagis, K. and Gan, T.-.H. (2012). An automated long range ultrasonic rail flaw detection system based on the support vector machine algorithm. COMPRAIL 2012 11-13 September.
  • Mitrakis, N.E., Moustakidis, S.P. and Theocharis, J.B. (2010). A Fuzzy Complementary Criterion for structure learning of a neuro-fuzzy classifier. 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 18-23 July.
  • Moustakidis, S.P., Theocharis, J.B. and Giakas, G. (2009). Feature extraction based on a fuzzy complementary criterion for gait recognition using GRF signals. 2009 17th Mediterranean Conference on Control and Automation (MED) 24-26 June.
  • Moustakidis, S.P., Rovithakis, G.A. and Theocharis, J.B. (2006). An Adaptive Neuro-Fuzzy Control Approach for Nonlinear Systems via Lyapunov Function Derivative Estimation. IEEE International Symposium on Intelligent Control 4-6 October.
  • Moustakidis, S.P., Rovithakis, G.A. and Theocharis, J.B. (2006). An adaptive neuro-fuzzy control approach for nonlinear systems via lyapunov function derivative estimation.
  • Nikolakis, G., Tzovaras, D., Moustakidis, S. and Strintzis, M.G. (2004). CyberGrasp and PHANTOM Integration: Enhanced Haptic Access for Visually Impaired Users. SPECOM'2004: 9th Conference Speech and Computer 20-22 September, Saint-Petersburg, Russia.

Journal articles (34)

  • Moustakidis, S., Kokkotis, C., Tsaopoulos, D., Sfikakis, P., Tsiodras, S., Sypsa, V. … Paraskevis, D. (2022). Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning. Viruses, 14(3), pp. 625–625. doi:10.3390/v14030625.
  • Anagnostis, A., Moustakidis, S., Papageorgiou, E. and Bochtis, D. (2022). A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling. Energies, 15(6), pp. 1959–1959. doi:10.3390/en15061959.
  • Kokkotis, C., Ntakolia, C., Moustakidis, S., Giakas, G. and Tsaopoulos, D. (2022). Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Physical and Engineering Sciences in Medicine, 45(1), pp. 219–229. doi:10.1007/s13246-022-01106-6.
  • Moustakidis, S., Siouras, A., Vassis, K., Misiris, I., Papageorgiou, E. and Tsaopoulos, D. (2022). Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. Algorithms, 15(3), pp. 77–77. doi:10.3390/a15030077.
  • Siouras, A., Moustakidis, S., Giannakidis, A., Chalatsis, G., Liampas, I., Vlychou, M. … Tsaopoulos, D. (2022). Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review. Diagnostics, 12(2), pp. 537–537. doi:10.3390/diagnostics12020537.
  • Ntakolia, C., Anagnostis, A., Moustakidis, S. and Karcanias, N. (2022). Machine learning applied on the district heating and cooling sector: a review. Energy Systems, 13(1), pp. 1–30. doi:10.1007/s12667-020-00405-9.
  • Ntakolia, C., Kokkotis, C., Moustakidis, S. and Tsaopoulos, D. (2021). Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients. International Journal of Medical Informatics, 156, pp. 104614–104614. doi:10.1016/j.ijmedinf.2021.104614.
  • Ntakolia, C., Kokkotis, C., Karlsson, P. and Moustakidis, S. (2021). An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management. Sensors, 21(23), pp. 7926–7926. doi:10.3390/s21237926.
  • Kampaktsis, P.N., Tzani, A., Doulamis, I.P., Moustakidis, S., Drosou, A., Diakos, N. … Briasoulis, A. (2021). State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database. Clinical Transplantation, 35(8). doi:10.1111/ctr.14388.
  • Kampaktsis, P.N., Moustakidis, S., Tzani, A., Doulamis, I.P., Drosou, A., Tzoumas, A. … Briasoulis, A. (2021). State‐of‐the‐art machine learning improves predictive accuracy of 1‐year survival after heart transplantation. ESC Heart Failure, 8(4), pp. 3433–3436. doi:10.1002/ehf2.13425.
  • Doulamis, I.P., Tzani, A., Moustakidis, S., Kampaktsis, P.N. and Briasoulis, A. (2021). Effect of Hepatitis C donor status on heart transplantation outcomes in the United States. Clinical Transplantation, 35(4). doi:10.1111/ctr.14220.
  • Kokkotis, C., Moustakidis, S., Baltzopoulos, V., Giakas, G. and Tsaopoulos, D. (2021). Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare, 9(3), pp. 260–260. doi:10.3390/healthcare9030260.
  • Ntakolia, C., Kokkotis, C., Moustakidis, S. and Tsaopoulos, D. (2021). Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics, 11(2), pp. 285–285. doi:10.3390/diagnostics11020285.
  • Moustakidis, S. and Karlsson, P. (2020). A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection. Cybersecurity, 3(1). doi:10.1186/s42400-020-00056-4.
  • Ntakolia, C., Diamantis, D.E., Papandrianos, N., Moustakidis, S. and Papageorgiou, E.I. (2020). A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients. Healthcare, 8(4), pp. 493–493. doi:10.3390/healthcare8040493.
  • Moustakidis, S., Papandrianos, N.I., Christodolou, E., Papageorgiou, E. and Tsaopoulos, D. (2020). Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Computing and Applications. doi:10.1007/s00521-020-05459-5.
  • Liakos, K.G., Georgakilas, G.K., Moustakidis, S., Sklavos, N. and Plessas, F.C. (2020). Conventional and machine learning approaches as countermeasures against hardware trojan attacks. Microprocessors and Microsystems, 79, pp. 103295–103295. doi:10.1016/j.micpro.2020.103295.
  • Kokkotis, C., Moustakidis, S., Giakas, G. and Tsaopoulos, D. (2020). Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. Applied Sciences, 10(19), pp. 6797–6797. doi:10.3390/app10196797.
  • Moustakidis, S., Christodoulou, E., Papageorgiou, E., Kokkotis, C., Papandrianos, N. and Tsaopoulos, D. (2019). Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective. Quantum Machine Intelligence, 1(3-4), pp. 73–86. doi:10.1007/s42484-019-00008-3.
  • Moustakidis, S., Omar, M., Aguirre, J., Mohajerani, P. and Ntziachristos, V. (2019). Fully automated identification of skin morphology in raster‐scan optoacoustic mesoscopy using artificial intelligence. Medical Physics, 46(9), pp. 4046–4056. doi:10.1002/mp.13725.
  • Moustakidis, S., Meintanis, I., Karkanias, N., Halikias, G., Saoutieff, E., Gasnier, P. … Eleftheriou, A. (2019). Innovative Technologies for District Heating and Cooling: InDeal Project. Proceedings. doi:10.3390/proceedings2019005001.
  • Moustakidis, S., Meintanis, I., Halikias, G. and Karcanias, N. (2019). An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results. Resources, 8(1), pp. 27–27. doi:10.3390/resources8010027.
  • Loureiro, T., Rämä, M., Sterling, R., Cozzini, M., Vinyals, M., Descamps, M. … Geyer, P. (2018). District Energy Systems: A Collaborative Exchange of Results on Planning, Operation and Modelling for Energy Efficiency. SP 2018. doi:10.3390/proceedings2151127.
  • Moustakidis, S., Anagnostis, A., Chondronasios, A., Karlsson, P. and Hrissagis, K. (2018). Excitation-invariant pre-processing of thermographic data. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(4), pp. 435–446. doi:10.1177/1748006x18770888.
  • Costa, A., Loureiro, T., Passerini, F., Lopez, S., Pietrushka, D., Klepal, M. … Arias, I. (2017). Development of Future EU District Heating and Cooling Network Solutions, Sharing Experiences and Fostering Collaborations. Proceedings, 1(7), pp. 1105–1105. doi:10.3390/proceedings1071105.
  • Moustakidis, S., Anagnostis, A., Karlsson, P. and Hrissagis, K. (2016). Non-destructive inspection of aircraft composite materials using triple IR imaging. IFAC-PapersOnLine, 49(28), pp. 291–296. doi:10.1016/j.ifacol.2016.11.050.
  • Moustakidis, S., Kappatos, V., Karlsson, P., Selcuk, C., Gan, T.-.H. and Hrissagis, K. (2014). An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves. Journal of Nondestructive Evaluation, 33(4), pp. 694–710. doi:10.1007/s10921-014-0264-6.
  • Moustakidis, S.P. and Theocharis, J.B. (2012). A fast SVM-based wrapper feature selection method driven by a fuzzy complementary criterion. Pattern Analysis and Applications, 15(4), pp. 379–397. doi:10.1007/s10044-012-0293-7.
  • Moustakidis, S.P., Theocharis, J.B. and Giakas, G. (2012). Feature selection based on a fuzzy complementary criterion: application to gait recognition using ground reaction forces. Computer Methods in Biomechanics and Biomedical Engineering, 15(6), pp. 627–644. doi:10.1080/10255842.2011.554408.
  • Moustakidis, S., Mallinis, G., Koutsias, N., Theocharis, J.B. and Petridis, V. (2012). SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 50(1), pp. 149–169. doi:10.1109/tgrs.2011.2159726.
  • Moustakidis, S.P., Theocharis, J.B. and Giakas, G. (2010). A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements. Medical Engineering & Physics, 32(10), pp. 1145–1160. doi:10.1016/j.medengphy.2010.08.006.
  • Moustakidis, S.P. and Theocharis, J.B. (2010). SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion. Pattern Recognition, 43(11), pp. 3712–3729. doi:10.1016/j.patcog.2010.05.007.
  • Moustakidis, S.P., Theocharis, J.B. and Giakas, G. (2008). Subject Recognition Based on Ground Reaction Force Measurements of Gait Signals. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(6), pp. 1476–1485. doi:10.1109/tsmcb.2008.927722.
  • Moustakidis, S.P., Rovithakis, G.A. and Theocharis, J.B. (2008). An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems. Automatica, 44(5), pp. 1418–1425. doi:10.1016/j.automatica.2007.10.019.