Ι. Εργασίες σε ερευνητικά περιοδικά με σύστημα κριτών

• I.E. Livieris and P. Pintelas. An improved spectral conjugate gradient neural network training algorithm. International Journal on Artificial Intelligence Tools, Volume 21, Number 1, 2012.
• I.E. Livieris and P. Pintelas. An advanced conjugate gradient training algorithm based on a modified secant equation. ISRN Artificial Intelligence, 2012.
• 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, 2012.
• I.E. Livieris and P. Pintelas. Globally convergent modified Perry conjugate gradient method. Applied Mathematics and Computation, Volume 218, Number 18, pp. 9197–9207, 2012.
• 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, 2012.
• 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.
• I.E. Livieris and P. Pintelas. A modified Perry conjugate gradient method and its global convergence. Optimization Letters, Volume 218, Number 18, pp. 9197-9207, 2014.
• I.E. Livieris and P. Pintelas. A new class of nonmonotone conjugate gradient training algorithms. Applied Mathematics and Computation, Volume 266, pp. 404-413, 2015.
• I.E. Livieris and P. Pintelas. A limited memory descent Perry conjugate gradient method. Optimization Letters, Volume 10, Number 8, pp. 1725–1742, 2016.
• I.E. Livieris, T.A. Mikropoulos and P. Pintelas. A decision support system for predicting student’s performance. Themes in Science & Technology Education, Volume 9, Number 1, pp. 43-57, 2016.
• G. Kostopoulos, I.E. Livieris, S. Kotsiantis and V. Tampakas. CST-Voting A semi-supervised ensemble method for classification problems. Journal of Intelligent & Fuzzy Systems, Volume 35, Issue 1, 2017.
• I.E. Livieris, K. Drakopoulou, V. Tampakas, T.A. Mikropoulos and P. Pintelas. Predicting secondary school students’ performance utilizing a semi-supervised approach. Journal of Educational Computing Research, Volume 52, Number 2, pp. 448-470, 2018.
• I.E. Livieris, V. Tampakas and P. Pintelas. A descent hybrid conjugate gradient method based on the memoryless BFGS update. Numerical Algorithms, Volume 79, Issue 4, pp. 1169-1185, 2018.
• I.E. Livieris, A. Kanavos, V. Tampakas and P. Pintelas. An ensemble SSL algorithm for efficient chest X-rays image classification. Journal of Imaging, Volume 4, Number 7, 2018.
• I.E. Livieris, A. Kanavos, V. Tampakas and P. Pintelas. An auto adjustable semi-supervised self-training algorithm Algorithms, Volume 11, Number 9, 2018.
• I.E. Livieris, E. Pintelas, A. Kanavos and P. Pintelas. Identification of blood cells subtypes from images using an improved SSL algorithm. Biomedical Journal of Scientific & Technical Research, Volume 9, Issue 1, 2018.
• I.E. Livieris, T. Kotsilieris, V. Tampakas and P. Pintelas. Improving the evaluation process of students’ performance utilizing a decision support software. Neural Computing and Applications, pp. 1- 12, 2019.
• I.E. Livieris, N. Kiriakidou, A. Kanavos, V. Tampakas and P. Pintelas. On ensemble SSL algorithms for credit scoring problem. Informatics, Volume 5, Number 4, 2018.
• I.E. Livieris, T. Kotsilieris, I. Dimopoulos and P. Pintelas. A decision support software for forecasting patient’s length of stay. Algorithms, Volume 11, Number 12, 2018.S.D. Dafnis, A.N. Philippou and I.E. Livieris. An identity relating Fibonacci and Lucas numbers of order k. Electronic Notes in Discrete Mathematics, Volume 70, pp. 37-42, 2018.
• I.E. Livieris. Improving the classification efficiency of an ANN utilizing a new training methodology. Informatics, Volume 6, Number 1, 2018.
• I.E. Livieris. A new ensemble semi-supervised self-labeled algorithm. Informatica, Volume 43, Number 2, pp. 221-234, 2019.
• I.E. Livieris, A. Kanavos and P.Pintelas. Detecting lung abnormalities from X-rays using an improved SSL algorithm. Electronic Notes on Theoretical Computer Science, Volume 343, pp. 19-33, 2019.
• I.E. Livieris, V. Tampakas, N. Karacapilidis and P. Pintelas. A semi-supervised self trained two-level algorithm for forecasting students’ graduation time. Intelligent Decision Technologies, 2019.
• T. Kotsilieris, E. Pintelas, I.E. Livieris and P. Pintelas. Predicting anxiety disorders and suicide tendency using machine learning a review. International Journal of Medical Engineering and Informatics, 2019.
• I.E. Livieris, E. Pintelas and P. Pintelas. Gender recognition by voice using an improved self-labeled algorithm. Machine Learning and Knowledge Extraction, Volume 1, Number 1, pp. 492-503, 2019.
• I.E. Livieris, A. Kanavos, V. Tampakas and P. Pintelas. A weighted voting ensemble SSL algorithm for the detection of lung abnormalities from X-rays. Algorithms, Volume 12, Number 3, 2019.
• I.E. Livieris. Forecasting economy related data utilizing weight constrained recurrent neural networks. Volume 12, Number 85, Algorithms, Volume 12, Number 4, 2019.
• I.E. Livieris and P. Pintelas. An adaptive nonmonotone active-set weight constrained neural network training algorithm. Neurocomputing, pp. 294-303, 2019.
• I.E. Livieris and P. Pintelas. An improved weight constrained neural network training algorithm. Neural Computing and Applications, 2019.
• I.E. Livieris, E. Pintelas, T. Kotsilieris, S. Stavroyiannis and P. Pintelas. Weight constrained neural networks in forecasting tourist volumes: a case study. Electronics, Volume 8, 2020.
• I.E. Livieris, T. Kotsilieris, S. Stavroyiannis and P. Pintelas. Forecasting stock price index movement using a constrained deep neural network training algorithm. Intelligent Decision Technologies, 2019.
• A. Kanavos and I.E. Livieris. Fuzzy information diffusion in Twitter by considering user’s influence. International Journal on Artificial Intelligence Tools, 2019.
• I.E. Livieris. An advanced active set L-BFGS algorithm for training constrained neural networks. Neural Computing and Applications, 2019.
• I.E. Livieris, L. Iliadis and P. Pintelas. On ensemble techniques of weight constrained neural networks. Evolving Systems.
• E. Pintelas, I.E. Livieris and P. Pintelas. A Grey Box ensemble model exploiting Black Box accuracy and White Box intrinsic interpretability. Algorithms, 2020.
• I.E. Livieris, E. Pintelas and P. Pintelas. A CNN-LSTM model for gold price time series forecasting. Neural Computing and Applications, 2020.
• I.E. Livieris, E. Pintelas, S. Stavroyiannis and P. Pintelas. Ensemble deep learning models for forecasting cryptocurrency time series. Algorithms, 2020.
• E. Pintelas, M. Liaskos, I.E. Livieris, S. Kotsiantis and P. Pintelas. Explainable machine learning fra mework for image classification problems: case study on Glioma cancer prediction. Journal of Imaging, 2020.
• I.E. Livieris, E. Pintelas, S. Stavroyiannis and P. Pintelas. A novel validation framework to enhance deep learning models in time series forecasting. Neural Computing and Applications, 2020.
• S.D. Dafnis, A.N. Philippou and I.E. Livieris. An alternating sum of Fibonacci and Lucas numbers of order k. Mathematics, 2020.
• I.E. Livieris, S.D. Dafnis, G.K. Papadopoulos and D. Kalivas. A multiple input neural network model for predicting cotton production quantity: A case study. Algorithms, Volume 13, 2020.
• S.G. Gocheva Ilieva, A.V. Ivanov and I.E. Livieris. High performance machine learning models of large-scale air pollution data in urban area. Cybernetics and Information Technologies, 2020.
• I.E. Livieris, S. Stavroyiannis, E. Pintelas, T. Kotsilieris and P. Pintelas. A dropout weight constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index. Evolving Systems. 2020.
• I.E. Livieris, N. Kiriakidou, S. Stavroyiannis and P. Pintelas. An advanced CNN-LSTM model for crypto currency forecasting. Electronics. 2021.
• I.E. Livieris, S. Stavroyiannis, L. Iliadis and P. Pintelas. Smoothing and stationarity enforcement framework for deep learning time series forecasting. Neural Computing and Applications, 2021.
• I.D. Apostolopoulos, E.G. Pintelas, I.E. Livieris, D.J. Apostolopoulos, N.D. Papathanasiou, P. Pintelas and G.S. Panagiotakis. Automatic Classification of Solitary Pulmonary Nodules in PET/CT imaging employing Transfer Learning. Medical & Biological Engineering & Computing, 2021.
• E. Pintelas, M. Liaskos, I.E. Livieris, S. Kotsiantis, P. Pintelas. A novel explainable image classification framework: case study on skin cancer and plant disease prediction. Neural Computing and Applications, 2021.
• E. Pintelas, I.E. Livieris, P. Pintelas. A convolutional autoencoder topology for classification in high dimensional noisy image datasets. Sensors, Volume 21, 2021.
• I.E. Livieris, and P. Pintelas. A novel multi step forecasting strategy for enhancing deep learning models’ performance. Neural Computing and Applications, Volume 34, 19453 19470, 2022.
• I.E. Livieris. A novel forecasting strategy for improving the performance of deep learning models. Expert Systems with Applications, 2023.
• E. Pintelas, I.E. Livieris, and P. Pintelas. Explainable Feature Extraction and Prediction Framework for 3D Image Recognition Applied to Pneumonia Detection. Electronics, Volume 12, 2023.
• E. Pintelas, I.E. Livieris, S. Kotsiantis, and P. Pintelas. A multi view CNN framework for deep representa tion learning in image classification. Computer Vision and Image Understanding, 2023.
• E. Pintelas, and I.E. Livieris. XSC – An eXplainable Image Segmentation and Classification Framework: A Case Study on Skin Cancer. Electronics, Volume 12, 2023.
• I.E. Livieris, E. Pintelas, N. Kiriakidou, and P. Pintelas. Explainable Image Similarity: Integrating Siamese Networks and Grad CAM. Journal of Imaging, Volume 9, 224, 2023.
• Kiriakidou, I.E. Livieris and P. Pintelas. Mutual information-based neighbor selection method for causal effect estimation. Neural Computing and Applications, 2024.
• E. Pintelas, I.E. Livieris and P. Pintelas. Adaptive augmentation framework for domain independent few-shot learning. Knowledge Based Systems, 2024.

ΙΙ. Ερευνητικές εργασίες σε κεφάλαια βιβλίων με σύστημα κριτών

• I.E. Livieris and P. Pintelas. Performance evaluation of descent CG methods for neural network training. In Hermis An International Journal of Computer Mathematics and its Applications, E.A. Lipitakis editor, Volume 11, pp. 40-46, 2009.
• I.E. Livieris, K. Drakopoulou, Th. Kotsilieris, V. Tampakas and P. Pintelas. DSS-PSP – A decision support software for evaluating students’ performance. In Communications in Computer and Information. Springer publishing series, Volume 744, pp. 63-74, 2017.
• I.E. Livieris, K. Drakopoulou, V. Tampakas, T.A. Mikropoulos and P. Pintelas. An ensemble based semi-supervised approach for predicting students’ performance. In Research on e-Learning and ICT in Education, Springer Verlag, 2018.
• I.E. Livieris, E. Pintelas, A. Kanavos and P. Pintelas. An improved self-labeled algorithm for cancer prediction. In Advances in Experimental Medicine and Biology, Springer Verlag, 2019.
• I.E. Livieris, T. Kotsilieris, I. Anagnostopoulos and V. Tampakas, DTCo: An ensemble SSL algorithm for X-rays classification. In Advances in Experimental Medicine and Biology, Springer Verlag, 2019.
• A. Kanavos, I.E. Livieris, F. Mylonas, S. Sioutas and G. Vonitsanos. Apache Spark implementations for string patterns in DNA sequences. In Advances in Experimental Medicine and Biology, Springer Verlag, 2019.
• I.E. Livieris, V. Tampakas, N. Kiriakidou, T. Mikropoulos and P. Pintelas. Forecasting students’ performance using an ensemble SSL algorithm. In Technology and Innovation in Learning, Teaching and Education, Technology and Innovation in Learning, Teaching and Education, Volume 993, Springer 2019.
• V. Tampakas, I.E. Livieris, E. Pintelas, N. Karacapilidis and P. Pintelas. Prediction of students’ graduation time using a two-level classification algorithm. In Technology and Innovation in Learning, Teaching and Education, Technology and Innovation in Learning, Teaching and Education, Volume 993, Springer 2019.
• E. Dritsas, G. Vonitsanos, I.E. Livieris, A. Kanavos, A. Ilias, C. Makris and A. Tsakalidis. Pre-processing framework for Twitter sentiment classification. In Advances in Information and Communication Techno logy, Springer, pp. 138-149, 2019.
• F. Kounelis, A. Kanavos, I.E. Livieris, G. Vonitsanos and P. Pintelas. Predicting secondary structure for human proteins based on Chou Fasman method. In Advances in Information and Communication Technology, Springer, pp. 232-241, 2019.
• I.E. Livieris, N. Kiriakidou, A. Kanavos, G. Vonitsanos and V. Tampakas. Employing constrained neural networks for forecasting new product’s sales increase. In Advances in Information and Communication Technology, Springer, pp. 161-172, 2019.
• E. Pintelas, I.E. Livieris, S. Stavroyiannis, T. Kotsilieris and P. Pintelas. Investigating the problem of cryptocurrency price prediction A deep learning approach. In IFIP Advances in Information and Communication Technology, 2020.
• I.E. Livieris, E. Pintelas, N. Kiriakidou and S. Stavroyiannis. An advanced deep learning model for short term forecasting U.S. natural gas price and movement. In IFIP Advances in Information and Com munication Technology, 2020.
• E. Pintelas, I.E. Livieris, N. Barotsis, G. Panayiotakis and P. Pintelas. An autoencoder convolutional neural network framework for Sarcopenia detection based on multi frame ultrasound image slices. In IFIP Advances in Information and Communication Technology, 2021.
• E. Pintelas, I.E. Livieris, and P. Pintelas. A Deep Learning Based Methodology for Detecting and Visuali zing Continuous Gravitational Waves. In IFIP Advances in Information and Communication Technology, pp. 3-14, 2023.

III. Ερευνητικές εργασίες σε πρακτικά συνεδρίων με σύστημα κριτών

• D.G. Sotiropoulos and I.E. Livieris. A greedy approach to transversal selection for nonlinear systems of equations. In Proceedings of Conference in Numerical Analysis (NumAn07), pp. 133-136, Kalamata, 2007.
• M.S. Apostolopoulou, D.G. Sotiropoulos, I.E. Livieris and P. Pintelas. A memoryless BFGS neural network training algorithm. In Proceedings of 7th IEEE International Conference on Industrial Informatics (INDIN’09), Cardiff, U.K., pp. 216-221, 2009.
• I.E. Livieris, D.G. Sotiropoulos, M.S. Apostolopoulou, S.A. Sioutas and P. Pintelas. Classification of large biomedical data using ANNs based on BFGS method. In Proceedings of 13th Panhellenic Conference on Informatics (PCI’09), Corfu, pp. 87-91, 2009.
• I.E. Livieris, D.G. Sotiropoulos and P. Pintelas. On descent spectral CG algorithms for training recurrent neural networks. In Proceedings of 13th Panhellenic Conference on Informatics (PCI’09), Corfu, pp. 65-69, 2009.
• I.E. Livieris, K. Drakopoulou and P. Pintelas. Predicting students’ performance using artificial neural networks. In Proceedings of Information and Communication Technologies in Education, 2012.
• G. Kostopoulos, I.E. Livieris, S. Kotsiantis and V. Tampakas. Enhancing high school students’ performance based on semi-supervised methods. In IEEE 8th International Conference on Information, Intelligence, Systems and Applications, 2017.
• I.E. Livieris, S. Karlos, V. Tampakas and P. Pintelas. A hybrid conjugate gradient method based on the self-scaled memoryless BFGS update. In Proceedings of ACM 20th Panhellenic Conference on Informatics (PCI’17), 2017.
• E. Pintelas, T. Kotsilieris, I.E. Livieris, and P. Pintelas. A review of machine learning prediction methods for anxiety disorders. In ACM 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info exclusion, 2018.
• I.E. Livieris, I.F. Dimopoulos, T. Kotsilieris and P. Pintelas. Predicting length of stay in hospitalized patients using SSL algorithms. In ACM 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info exclusion, 2018.
• I.E. Livieris, A. Kanavos, G. Vonitsanos, N. Kiriakidou, A. Vikatos, K. Giotopoulos and V. Tampakas. Performance evaluation of a SSL algorithm for forecasting the Dow Jones index stocks. In 9th International Conference on Information, Intelligence, Systems and Applications (IISA 2018), 2018.
• E. Dritsas, I.E. Livieris, K. Giotopoulos and K. Theodorakopoulos. An Apache Spark implementation for graph-based hashtag sentiment classification on Twitter. In Proceedings of ACM 21th Panhellenic Conference on Informatics (PCI’18), 2018.
• I. Meintanis, N. Monios, I.E. Livieris, M. Kampourakis, C. Fourakis, N. Kyriakoulis, S. Kokkorikos, A. Chondronasios. An integrated framework for building’s energy management based on deep learning. Technologie und Klimawandel: Energie Gebaude Umwel, 2020.
• M. Kampourakis, G. Fiotakis, I.E. Livieris, S. Fourakis, N. Kyriakoulis, S. Kokkorikos and A. Chondronasios. A decision support system for online predictive maintenance of distribution transformers. In 2022 International Conference on Electrical, Computer and Energy Technologies, IEEE, 2022.
• M. Kampourakis,I.E. Livieris, G. Fiotakis, S. Fourakis, N. Kyriakoulis, S. Kokkorikos and A. Chondronasios. A semi-supervised approach for electricity theft monitoring: A use case in Cyprus. In 2022 International Conference on Electrical, Computer and Energy Technologies, IEEE, 2022.
• I.E. Livieris, N. Karacapilidis, G. Domalis and D. Tsakalidis. An advanced explainable and interpretable ML based framework for educational data mining. In International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning, pp. 87-96, 2023.
• I.E. Livieris, G. Domalis, N. Karacapilidis, C. Karachristos, V. Komis, D. Tsakalidis and A. Filippidi. Buil ding a reference architecture for integrating emerging technologies in instructional design. In International Conference on Smart Education and e Learning, 2024.
• G. Domalis, N. Giarelis, A. Gioutlakis, N. Karacapilidis, I.E. Livieris, and D. Tsakalidis. Earth Observation Data Management: A Knowledge Graph based Approach. In 17th International Conference on Human Centred Intelligent Systems, 2024.
• N. Kiriakidou, I.E. Livieris, and C. Diou. C XGBoost: A tree boosting model for causal effect estimation. In IFIP International Conference on Artificial Intelligence Applications and Innovations, 2024.
• I.E. Livieris, N. Alimpertis, G. Domalis, and D. Tsakalidis. An evaluation framework for synthetic data generation models. In IFIP International Conference on Artificial Intelligence Applications and Innovations, 2024.

IV. Editorial

• P. Pintelas and I.E. Livieris. Special Issue on ‘‘Ensemble Learning and Applications’’. Algorithms, Volume 13, 2020.
• T. Kotsilieris, I. Anagnostopoulos, I.E. Livieris. Special Issue on ‘‘Regularization techniques for machine learning and their applications’’. Electronics, 11(4), 2022.
• P. Pintelas, and I.E. Livieris. Special Issue on ‘‘Ensemble Learning and/or Explainability’’. Algorithms, 16(1), 2023.
• P. Pintelas, S. Kotsiantis and I.E. Livieris. Special Issue on ‘‘Machine Learning and AI for Sensors’’. Sensors, 23(5), 2023.