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Ioannis E. Livieris

Ioannis E. Livieris received his B.Sc., M.Sc. and Ph.D. degrees in Mathematics from the University of Patras, Greece in 2006, 2008 and 2012 respectively. His research interests include numerical optimization, neural networks and its application in bioinformatics. He is a member of the ESDLab since 2008. See his personal web page.

 

 

Degrees

  • 2012: Ph.D. from Department of Mathematics, University of Patras.
  • 2008: M.Sc. in "Computational Mathematics & Informatics", Department of Mathematics, University of Patras, Greece.
  • 2006: Bachelor Degree in Mathematics (speciality in Computational Mathematics & Informatics), Department of Mathematics, University of Patras, Greece.

 

Dissertations

  • Ph.D. Thesis: Nonlinear Conjugate Gradient Methods for Optimization and Neural Network Training. Supervisor: Professor P. Pintelas.
  • M.Sc. Thesis: Performance Evaluation of Algorithms for Neural Network Training and Applications. Supevisor: Professor P. Pintelas.
  • B.Sc. Thesis: Constraint Propagation Problems. Bachelor Thesis Supevisor: Associate Professor T.N. Grapsa.

   

Courses

E-mail :      This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Phone :      2610 997833

Fax :          2610 997313




An alternating sum of Fibonacci and Lucas numbers of order k PDF Print E-mail

S.D. Dafnis, A.N. Philippou and I.E. Livieris. An alternating sum of Fibonacci and Lucas numbers of order k. Mathematics, 2020

 
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A novel validation framework to enhance deep learning models in time-series forecasting PDF Print E-mail

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.

 
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An advanced deep learning model for short-term forecasting U.S. natural gas price and movement PDF Print E-mail

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 Communication Technology, 2020.

 
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Investigating the problem of cryptocurrency price prediction - A deep learning approach PDF Print E-mail

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.

 
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A CNN-LSTM model for gold price time series forecastings PDF Print E-mail

I.E. Livieris, E. Pintelas, P. Pintelas. A CNN-LSTM model for gold price time series forecastings. Neural Computing and Applications, 2020.

 
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On ensemble techniques of weight-constrained neural networks PDF Print E-mail

I.E. Livieris, L. Iliadis, P. Pintelas. On ensemble techniques of weight-constrained neural networks. Evolving Systems, 2020.

 
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An advanced active set L-BFGS algorithm for training constrained neural networks PDF Print E-mail

I.E. Livieris. An advanced active set L-BFGS algorithm for training constrained neural networks. Neural Computing and its Applications, 2019.

 
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Fuzzy Information Diffusion in Twitter by Considering User's Influence PDF Print E-mail

A. Kanavos and I.E. Livieris. Fuzzy information diffusion in Twitter by considering user's influence. International Journal on Artificial Intelligence Tools, 2019.

 
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Weight-constrained neural networks in forecasting tourist volumes: a case study PDF Print E-mail

I.E. Livieris, E. Pintelas, T. Kotsilieris, S. Stavroyiannis, P. Pintelas. Weight-constrained neural networks in forecasting tourist volumes: a case study. Electronics, 2019.

 
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Forecasting stock price index movement using a constrained deep neural network training algorithm PDF Print E-mail

I.E. Livieris, T. Kotsilieris, S. Stavroyiannis, P. Pintelas. Forecasting stock price index movement using a constrained deep neural network training algorithm. Intelligent Decision Technologies, 2019

 
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An improved weight-constrained neural network training algorithm PDF Print E-mail

I.E. Livieris and P. Pintelas. An improved weight-constrained neural network training algorithm. Neural Computing and Applications, 2019.

 
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An adaptive nonmonotone active set -weight constrained- neural network training algorithm PDF Print E-mail
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Forecasting economy-related data utilizing constrained recurrent neural networks PDF Print E-mail
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Employing constrained neural networks for forecasting new product's sales increase PDF Print E-mail

I.E. Livieris, N.Kiriakidou, A. Kanavos, G. Vonitsanos and V. Tampakas. Employing Constrained Neural Networks for Forecasting new Products Sales Increase. In IFIP Advances in Information and Communication Technology, Springer, (accepted), 2019.

 
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An efficient preprocessing tool for supervised sentiment analysis on Twitter data PDF Print E-mail

E. Dritsas, G. Vonitsanos, I.E. Livieris, A. Kanavos, A. Ilias, C. Makris and A. Tsakalidis. An efficient preprocessing tool for supervised sentiment analysis on Twitter data. In Advances in Information and Communication Technology, Springer, 2019 (accepted).

 
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Predicting secondary structure for human proteins based on Chou-Fasman method PDF Print E-mail

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, 2019 (accepted).

 
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A weighted voting ensemble SSL algorithm for the detection of lung abnormalities from X-rays PDF Print E-mail

I.E. Livieris, A. Kanavos, V. Tampakas, P. Pintelas. A weighted voting ensemble SSL algorithm for the detection of lung abnormalities from X-rays. Algorithms, 2019.

 
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Gender recognition by voice using an improved self-labeled algorithm PDF Print E-mail

I.E. Livieris, E. Pintelas and P. Pintelas. Gender recognition by voice using an improved self-labeled algorithm. Machine Learning and Knowledge Extraction. 2019

 
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A semi-supervised self-trained two-level algorithm for forecasting students' graduation time PDF Print E-mail

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

 
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Predicting anxiety disorders and suicide tendency using machine learning: a review PDF Print E-mail

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

 
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Improving the classification efficiency of an ANN utilizing a new training methodology PDF Print E-mail
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