Research publications

Find below selected recent publications of our team demonstrating the application of AI in an variety of domains including industrial sectors, healthcare, energy and security

2022

  • Siouras, A.; Moustakidis, S.; Giannakidis, A.; Chalatsis, G.; Liampas, I.; Vlychou, M.; Hantes, M.; Tasoulis, S.; Tsaopoulos, D. Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review. Diagnostics 2022, 12, 537. https://doi.org/10.3390/diagnostics12020537

  • Moustakidis, S.; Siouras, A.; Vassis, K.; Misiris, I.; Papageorgiou, E.; Tsaopoulos, D. Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. Algorithms 2022, 15, 77. https:// doi.org/10.3390/a15030077

  • Kokkotis, C., Ntakolia, C., Moustakidis, S. et al. Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Phys Eng Sci Med (2022). https://doi.org/10.1007/s13246-022-01106-6

2021

  • S. Moustakidis, P. Kampaktsis, A. Tzani, I. Doulamis, A. Tzoumas, A. Drosou, G. Filippatos, A. Briasoulis, Machine Learning Based Prediction of 1-year Survival after Isolated Heart Transplant, The Journal of Heart and Lung Transplantation, 40, 4, Supplement, 2021,https://doi.org/10.1016/j.healun.2021.01.1849.

  • Ntakolia, C.; Kokkotis, C.; Moustakidis, S.; Tsaopoulos, D. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics 2021, 11, 285. https://doi.org/10.3390/diagnostics11020285

  • Kokkotis, C.; Moustakidis, S.; Baltzopoulos, V.; Giakas, G.; Tsaopoulos, D. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare 2021, 9, 260. https://doi.org/10.3390/healthcare9030260

  • Ntakolia, C., Anagnostis, A., Moustakidis, S. et al. Machine learning applied on the district heating and cooling sector: a review. Energy Systems 2021. https://doi.org/10.1007/s12667-020-00405-9

  • Ntakolia, C., Kokkotis, C., Moustakidis, S., & 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, 104614.

  • Dikopoulou, Z., Moustakidis, S., & Karlsson, P. (2021). GLIME: A new graphical methodology for interpretable model-agnostic explanations. arXiv preprint arXiv:2107.09927.

  • 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), e14388.

  • Ntakolia, C., Kokkotis, C., Karlsson, P., & Moustakidis, S. (2021). An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management. Sensors, 21(23), 7926.

  • Doulamis, I. P., Tzani, A., Moustakidis, S., Kampaktsis, P. N., & Briasoulis, A. (2021). Effect of Hepatitis C donor status on heart transplantation outcomes in the United States. Clinical Transplantation, 35(4), e14220.

2020

  • Moustakidis, S., Papandrianos, N.I., Christodolou, E. et al. Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Computing & Applications (2020). https://doi.org/10.1007/s00521-020-05459-5

  • K. Liakos, G. Georgakilas, S. Moustakidis, N. Sklavos & F. Plessas, Conventional and machine learning approaches as countermeasures against hardware trojan attacks. Microprocessors And Microsystems 2020, 79, 103295. doi: 10.1016/j.micpro.2020.103295

  • C. Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, & D. Tsaopoulos, Machine learning in knee osteoarthritis: A review. Osteoarthritis And Cartilage Open 2020, 2(3), 100069. doi: 10.1016/j.ocarto.2020.100069

  • C. Kokkotis, S. Moustakidis, G. Giakas, D. Tsaopoulos, Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. Applied Sciences 2020, 10(19), 6797https://doi.org/10.3390/app10196797.

  • S. Moustakidis, P. Karlsson, A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection. Cybersecurity 3(16), 2020. https://doi.org/10.1186/s42400-020-00056-4

  • Ntakolia, C., Diamantis, D. E., Papandrianos, N., Moustakidis, S., & Papageorgiou, E. I. (2020, December). A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine: A case study on prostate cancer patients. In Healthcare (Vol. 8, No. 4, p. 493). Multidisciplinary Digital Publishing Institute.

2019

  • S. Moustakidis, E. Christodoulou, E. Papageorgiou, C. Kokkotis, N. Papandrianos and D. Tsaopoulos, Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective, Quantum Machine Intelligence, 2019. Available: 10.1007/s42484-019-00008-3 

  • S. Moustakidis, M. Omar, J. Aguirre, P. Mohajerani, and V. Ntziachristos, V., Fully automated identification of skin morphology in raster‐scan optoacoustic mesoscopy using artificial intelligence. Medical Physics, 46(9), 4046-4056, 2019. doi: 10.1002/mp.13725 

  • S. Moustakidis, I. Meintanis, G. Halikias, and N. Karcanias, An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results, Resources, vol. 8, no. 1, p. 27, 2019. Available: 10.3390/resources8010027 

till 2018

  • S. Moustakidis et al., Excitation-invariant pre-processing of thermographic data, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 232, no. 4, pp. 435-446, 2018. Available: 10.1177/1748006x18770888

  • S. Moustakidis et al., Non-destructive inspection of aircraft composite materials using triple IR imaging. IFAC-PapersOnLine 2016, 49, 291-296.

  • S. Moustakidis et al., An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves, Journal of Nondestructive Evaluation, vol. 33, no. 4, pp. 694–710, Oct. 2014. 

  • S. Moustakidis, G. Mallinis, N. Koutsias, J. B. Theocharis and V. Petridis, SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 50 (1) , art. no. 5976442 , pp. 149-169, 2012. 

  • S. Moustakidis, J. B. Theocharis, and G. Giakas, A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements, Medical Engineering and Physics 32(10): 1145-1160, 2010. 

  • S. Moustakidis and J. B. Theocharis, SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion, Pattern Recognition, vol. 43, pp. 3712-3729, 2010

  • S. Moustakidis, J. B. Theocharis, and G. Giakas, Subject Recognition Based on Ground Reaction Force Measurements of Gait Signals, IEEE Transactions on Systems, Man, and Cybernetics—Part B, vol.38, no.6, pp.1476-1485, Dec. 2008

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