4. Ioannis Livieris · Emmanuel Pintelas · Andreas Kanavos · Panagiotis Pintelas, An Improved self-labeled algorithm for cancer prediction, October 2018, Chapter In book: Advances in Experimental Medicine and Biology, Publisher: Springer, pp.1-10 (short listed for best paper award).
5. Ioannis Livieris, Andreas Kanavos and Panagiotis Pintelas, Detecting Lung Abnormalities from X-Rays using an Improved SSL Algorithm, Nov 2018, European Conference on Behavioral Change and Ambient Intelligence for Sustainability (BRAINS18).
6. Ioannis Livieris, Andreas Kanavos, Vassilis Tampakas, Panagiotis Pintelas, An Ensemble SSL Algorithm for Efficient Chest X-rays Image Classification, Journal of Imaging, 2018, 4, 95; doi:10.3390/jimaging4070095 [PDF] An Ensemble SSL Algorithm for Efficient Chest X-Ray Image Classification
7. I.E. Livieris, A. Kanavos, V. Tampakas, and P. Pintelas, An auto-adjustable semi-supervised self-training algorithm, Journal Algorithms 2018, 11(9), 139; PDF Full-text (325 KB) | HTML Full-text | XML Full-text
8. Livieris, I.; Drakopoulou, K.; Tampakas, V.; Mikropoulos, T.; Pintelas, P. An ensemble-based semi-supervised approach for predicting students’ performance. In Research on e-Learning and ICT in education; Elsevier, 2018.
9. Ioannis E. Livieris, Vassilis Tampakas and Panagiotis Pintelas, A descent hybrid conjugate gradient method based on the memoryless BFGS update, Numerical Algorithms (NUMA), pp 1-17, 2018.
10. I.E. Livieris, T. Kotsilieris, V. Tampakas, P. Pintelas, Improving the evaluation process of students’ performance utilizing a decision support software, Neural Computing and Applications (NCAA), Neural Computing and Applications https://doi.org/10.1007/s00521-018-3756-y(0123456789().,-volV)(0123456789().,-volV)
11. Ioannis E. Livieris, Konstantina Drakopoulou, Vassilis Tampakas, Tassos Mikropoulos, Panagiotis Pintelas, Predicting secondary school students’ performance utilizing a semi-supervised learning approach, Journal of Educational Computing Research (JECR), Jan. 2018.  http://journals.sagepub.com/eprint/zpKz9UZcgEhfbjM8scDZ/full
12. Livieris I.E., Drakopoulou K., Kotsilieris T., Tampakas V., Pintelas P. (2017) DSS-PSP - A Decision Support Software for Evaluating Students’ Performance. In: Boracchi G., Iliadis L., Jayne C., Likas A. (eds) Engineering Applications of Neural Networks. EANN 2017 pp 63-74. Communications in Computer and Information Science, vol 744. Springer, Cham
13. Ioannis E. Livieris, Tassos A. Mikropoulos, Panagiotis Pintelas. A decision support system for predicting students’ performance, Themes in Science & Technology Education, 9(1), 43-57, 2016.
14. I.E. Livieris and P. Pintelas. A limited memory descent Perry conjugate gradient method. Optimization Letters, (accepted for publication), 2015.
15. I.E. Livieris and P. Pintelas. "A new class of nonmonotone conjugate gradient training algorithms", Journal of Applied Mathematics and Computation (AMC), 266 (2015), pp 404-413.
16. I.E. Livieris and P. Pintelas. A modified Perry conjugate gradient method and its global convergence. Optimization Letters, Volume 9, Issue 5, p.p. 999-1015, June 2015.
17. Tassos A. Mikropoulos, Demetrios G. Sampson, Alexandros Nikopoulos, Panayotis Pintelas, “The evolution of Educational Technology based on a bibliometric study”, in Volume “Research on e-Learning and ICT in Education” by Springer, pp 15-24, 25 June 2014 .
18. I.E. Livieris and P. Pintelas, A new conjugate gradient algorithm for training neural networks based on a modified secant equation. Applied Mathematics and Computation, Volume 221, pp. 491-502, 2013.
19. I.E. Livieris and P. Pintelas. A new class of spectral conjugate gradient methods based on a modified secant equation for unconstrained optimization. Journal of Computational and Applied Mathematics, Volume 239, pp. 396-405, 2013.
20. I.E. Livieris and P. Pintelas, Globally convergent modified Perry conjugate gradient method, Applied Mathematics and Computation, Volume 218, Issue 18, May 2012, pp. 9197-9207.  http://dx.doi.org/10.1016/j.amc.2012.02.076
21. I. E. Livieris and P. Pintelas, An Advanced Conjugate Gradient Training Algorithm Based on a Modified Secant Equation, ISRN Artificial Intelligence, Volume 2012 (2012), pp.1-9.
22. I.E. Livieris and P. Pintelas. A descent Dai-Liao conjugate gradient method based on a modified secant equation and its global convergence. ISRN Computational Mathematics, Volume 2012 (2012), Article ID 435495, 8 pages
23. I.E. Livieris and P. Pintelas, An improved spectral conjugate gradient neural network training algorithm. International Journal on Artificial Intelligence and Tools, IJAIT's Volume No.21, Issue No. 1. 2012.
24. Marianna S. Apostolopoulou; Dimitris G. Sotiropoulos;  C.A. Botsaris; P. Pintelas, "A practical method for solving large-scale TRS", Optimization Letters, Vol. 5, No. 2, pp. 207-227, May 2011. (Fortran code)
25. C. T. Rodosthenous, A. D. Kameas, P. E. Pintelas, Diplek: An open LMS that supports fast composition of educational services,   book chapter in  E-Infrastructures and Technologies for Lifelong Learning: Next Generation Environments/ George Magoulas, editor,  pp 59-89, Copyright © 2011 by IGI Global.
26. S.B. Kotsiantis and P.E. Pintelas, Selective costing ensemble for handling imbalanced data sets, International Journal of Hybrid Intelligent Systems 6(3) (2009) pp.123-133.
27. D.N. Kanellopoulos, S.B. Kotsiantis, P.E. Pintelas, “An Ontology-Based Framework for Supporting Localization of e-Learning Content”, Annals of Mathematics, Computing & Teleinformatics, vol 1 no 6-7, pp 1-10, 2009
28. Ioannis Messinis, Ioannis Vrellis and Panagiotis Pintelas, Presence in stressful virtual environments, Int. J. Teaching and Case Studies, Vol. 2, No. 2, 2009  pp 136-145.
29. M.S. Apostolopoulou, D.G. Sotiropoulos and P. Pintelas. Σφάλμα! Η παραπομπή υπερ-σύνδεσης δεν είναι έγκυρη., Journal of Optimization Methods and Software, Volume 23, Issue 5, pp. 651-674, October 2008.
30. S. B. Kotsiantis, P. E. Pintelas, Local Random Subspace of Decision Stumps for Regression Problems, International Conference on Knowledge Discovery (ICKD 2009), June 6-8, 2009 • Manila, Philippines, Accepted (http://www.ickd.org/ ).
31. Sotiris Kotsiantis, Panayotis Pintelas “Predictive Data mining: A Survey of Regression Methods,” the Encyclopedia of Information Science and Technology, 2nd edition, (M. Khosrow-Pour Ed.), Vol VI, Mu-Q, pp 3105-3110.
32. Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayotis Pintelas, “Increasing the Accuracy of Predictive Data Mining Algorithms: A Review of Ensembles of Classifiers,” the Encyclopedia of Information Science and Technology, 2nd edition, (M. Khosrow-Pour Ed.), Vol VI, Mu-Q, pp 2176-2182.
33. Dimitris Kanellopoulos, Sotiris Kotsiantis, Panayotis Pintelas, ”Internet and Multimedia Communications”, the Encyclopedia of Information Science and Technology, 2nd edition, (M. Khosrow-Pour Ed.), Vol VI, Mu-Q, pp 1906-1910.
34. S. Kotsiantis, I. Zaharakis and P. Pintelas, "Machine learning: a review of classification and combining techniques," Journal of Artif. Intell. Rev. vol. 26, pp. 159-190, 11. 2006.
35. D. N. Kanellopoulos, P. E. Pintelas, S. Giannoulis, QoS in Wireless Multimedia Networks, Annals of Mathematics, Computing and Teleinformatics (AMCT), 1(4):66-75.
36. S. Kotsiantis, E. Athanasopoulou, and P. Pintelas, Logitboost of Multinomial Bayesian Classifier for Text Classification, IRECOS (International Review on Computers and Software), Vol 1(3), pp. 243-250, November 2006.
37. S. Kotsiantis, D. Kanellopoulos, P. Pintelas, Local Boosting of Decision Stumps for Regression and Classification Problems, Journal of Computers (JCP), Vol.4 (1), 2006, pp. 30-37.
38. S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, Data Preprocessing for Supervised Learning, International Journal of Computer Science, 2006, Vol 1 N. 2, pp 111-117 [pdf].
39. S. Kotsiantis, D. Kanellopoulos, P. Pintelas, Handling imbalanced datasets: A review, GESTS International Transactions on Computer Science and Engineering, Vol.30 (1), 2006, pp. 25-36
40. D. Kanellopoulos, S. Kotsiantis, P. Pintelas, Intelligent Knowledge Management for the Travel Domain, GESTS International Transactions on Computer Science and Engineering, Vol.30 (1), 2006, pp. 95-106
41. S. Kotsiantis, P. Pintelas, Selective Averaging of Regression Models, Annals of Mathematics, Computing & TeleInformatics, Vol 1, No 3, 2005, pp. 66-75.
42. S. Kotsiantis, D. Kanellopoulos,P. Pintelas, Local Additive Regression of Decision Stumps, Lecture Notes in Computer Science (LNCS), Springer-Verlag,Vol 3955, pp. 148-157, 2006.  http://dx.doi.org/10.1007/11752912_17
43. A. Triantis and P. Pintelas, An Architecture for Developing Multi-agent Educational Applications for the Web, in (Ed. Eshaa M. Alkhalifa) Cognitively Informed Systems: Utilizing Practical Approaches to Enrich Information Presentation and Transfer, Idea Group Inc., 2006, pp. 235-259.
44. D. Kanellopoulos, S. Kotsiantis, P. Pintelas, Considering the Educational Semantic Web, Themes in Education Journal, Vol 7, Issue2, pp.145-164, Jan. 2007.
45. George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis,  Panagiotis E. Pintelas, A Fuzzy Logic-based Approach for Detecting Shifting Patterns in Cross-Cultural Data, Lecture Notes in Artificial Intelligence (LNAI), Springer-Verlag Vol 3533, pp. 705-708.
46. S. Kotsiantis, P. Pintelas, Combining Bagging and Boosting, International Journal of Computational Intelligence, Vol. 1, No. 4 (324-333), 2004.
47. George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis,  Panagiotis E. Pintelas, Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data, , International Journal of Computational Intelligence, Vol. 1, No. 2 (147-151), 2004.
48. S. B. Kotsiantis, P. E. Pintelas, Local Voting of Weak Classifiers, International Journal of Knowledge-Based & Intelligent Engineering Systems (KES), Vol. 9, No 3, 2005, pp. 239-248. http://iospress.metapress.com/openurl.asp?genre=article&issn=1327-2314&volume=9&issue=3&spage=239
49. S. Kotsiantis, G. Tsekouras, P. Pintelas, Local Bagging of Decision Stumps, Lecture Notes in Computer Science (LNCS), Springer-Verlag Vol 3533, pp. 406-411, 2005  http://dx.doi.org/10.1007/11504894_57
50. A. Triantis, A. Kameas, I. Zaharakis and P. Pintelas, (2005) "4Ds: An Architecture that Dynamically Synthesizes Distributed Content with Distributed Expertise into Educational Applications that Support Sustainable Sessions for Distributed Learners". THEMES in Education Journal vol. 6 (2) pp 169-187.