Ioannis E. Livieris
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Artificial Intelligence top 2%
- Neural Networks and Applications
Papers in
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- Neural Networks and Applications 12
- Machine Learning and ELM 6
- Machine Learning and Data Classification 6
- Co-authors
- Panagiotis Pintelas (56 shared papers)Emmanuel Pintelas (24 shared papers)Stavros Stavroyiannis (8 shared papers)Theodore Kotsilieris (10 shared papers)Andreas Kanavos (7 shared papers)Sotiris Kotsiantis (5 shared papers)Tassos A. Mikropoulos (2 shared papers)Lazaros Iliadis (2 shared papers)
In The Last Decade
Ioannis E. Livieris
73 papers receiving 1.9k citations
Ioannis E. Livieris's Hit Papers
Peers
Comparison fields: 5 of 151
- Management Science and Operations Research 500
- Artificial Intelligence 680
- Numerical Analysis 106
- Signal Processing 200
- Computer Science Applications 95
Countries citing papers authored by Ioannis E. Livieris
This map shows the geographic impact of Ioannis E. Livieris's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ioannis E. Livieris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ioannis E. Livieris more than expected).
Fields of papers citing papers by Ioannis E. Livieris
This network shows the impact of papers produced by Ioannis E. Livieris. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ioannis E. Livieris. The network helps show where Ioannis E. Livieris may publish in the future.
Co-authors
The 25 scholars most cited alongside Ioannis E. Livieris, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A CNN–LSTM model for gold price time-series forecasting Hit paper breakdown → | 2020 | 547 |
| 2 | 2021 | 112 | |
| 3 | 2020 | 96 | |
| 4 | 2020 | 95 | |
| 5 | 2020 | 63 | |
| 6 | 2018 | 61 | |
| 7 | 2020 | 52 | |
| 8 | 2020 | 51 | |
| 9 | 2020 | 39 | |
| 10 | 2018 | 38 | |
| 11 | 2019 | 36 | |
| 12 | 2019 | 35 | |
| 13 | 2018 | 33 | |
| 14 | 2018 | 29 | |
| 15 | 2021 | 27 | |
| 16 | 2021 | 26 | |
| 17 | 2013 | 26 | |
| 18 | 2021 | 25 | |
| 19 | 2021 | 24 | |
| 20 | 2012 | 24 |
About Ioannis E. Livieris
Ioannis E. Livieris is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Numerical Analysis, Management Science and Operations Research and Computational Mechanics, having authored 76 papers that have together received 2.0k indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (12 papers), Neural Networks and Applications (12 papers), Advanced Optimization Algorithms Research (12 papers), Iterative Methods for Nonlinear Equations (8 papers), Machine Learning and ELM (6 papers), Time Series Analysis and Forecasting (6 papers), Machine Learning and Data Classification (6 papers) and COVID-19 diagnosis using AI (5 papers). The work is most often cited by research in Management Science and Operations Research (500 citations), Artificial Intelligence (680 citations), Numerical Analysis (106 citations), Signal Processing (200 citations) and Computer Science Applications (95 citations). Ioannis E. Livieris has collaborated with scholars based in Greece, Germany and Bulgaria. Frequent co-authors include Panagiotis Pintelas, Emmanuel Pintelas, Stavros Stavroyiannis, Theodore Kotsilieris, Andreas Kanavos, Sotiris Kotsiantis, Tassos A. Mikropoulos, Lazaros Iliadis, V. Tampakas and Ioannis Dimopoulos. Their work appears in journals such as Neural Computing and Applications, Electronics, Evolving Systems, Applied Mathematics and Computation and Optimization Letters.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.