Darko Aleksovski

619 total citations · 1 hit paper
9 papers, 389 citations indexed

About

Darko Aleksovski is a scholar working on Artificial Intelligence, Control and Systems Engineering and Finance. According to data from OpenAlex, Darko Aleksovski has authored 9 papers receiving a total of 389 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Control and Systems Engineering and 3 papers in Finance. Recurrent topics in Darko Aleksovski's work include Neural Networks and Applications (4 papers), Stock Market Forecasting Methods (3 papers) and Fault Detection and Control Systems (3 papers). Darko Aleksovski is often cited by papers focused on Neural Networks and Applications (4 papers), Stock Market Forecasting Methods (3 papers) and Fault Detection and Control Systems (3 papers). Darko Aleksovski collaborates with scholars based in Slovenia, Italy and Germany. Darko Aleksovski's co-authors include Igor Mozetič, Miha Grćar, Gabriele Ranco, Guido Caldarelli, Dragana Miljković, Daniele Bravi, Angelo Antonini, Juš Kocijan, Sašo Džeroski and Dejan Dovžan and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Fuzzy Systems and Advanced Engineering Informatics.

In The Last Decade

Darko Aleksovski

9 papers receiving 369 citations

Hit Papers

The Effects of Twitter Se... 2015 2026 2018 2022 2015 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Darko Aleksovski Slovenia 5 123 117 97 93 63 9 389
Marco Bee Italy 12 48 0.4× 202 1.7× 136 1.4× 59 0.6× 30 0.5× 53 457
Moritz Sudhof United States 4 77 0.6× 183 1.6× 114 1.2× 52 0.6× 4 323
Jonathan Weinstein United States 12 289 2.3× 264 2.3× 27 0.3× 50 0.5× 5 0.1× 25 512
Simon Behrendt Germany 7 92 0.7× 209 1.8× 147 1.5× 21 0.2× 14 314
Markus Spiwoks Germany 11 69 0.6× 116 1.0× 83 0.9× 31 0.3× 32 260
Olivier Gossner France 12 278 2.3× 256 2.2× 45 0.5× 35 0.4× 40 483
Manuel Ferreira Portugal 7 172 1.4× 122 1.0× 131 1.4× 13 0.1× 1 0.0× 165 521
Hanqing Jin United Kingdom 12 290 2.4× 299 2.6× 348 3.6× 16 0.2× 4 0.1× 29 611
Paolo Barucca United Kingdom 13 29 0.2× 237 2.0× 207 2.1× 38 0.4× 36 475
Andrew C. Pollock United Kingdom 9 181 1.5× 66 0.6× 43 0.4× 78 0.8× 21 396

Countries citing papers authored by Darko Aleksovski

Since Specialization
Citations

This map shows the geographic impact of Darko Aleksovski'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 Darko Aleksovski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Darko Aleksovski more than expected).

Fields of papers citing papers by Darko Aleksovski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Darko Aleksovski. 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 Darko Aleksovski. The network helps show where Darko Aleksovski may publish in the future.

Co-authorship network of co-authors of Darko Aleksovski

This figure shows the co-authorship network connecting the top 25 collaborators of Darko Aleksovski. A scholar is included among the top collaborators of Darko Aleksovski based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Darko Aleksovski. Darko Aleksovski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Aleksovski, Darko, et al.. (2020). Stock Market Reactions to International Climate Negotiations. SSRN Electronic Journal. 5 indexed citations
2.
Aleksovski, Darko, Dragana Miljković, Daniele Bravi, & Angelo Antonini. (2018). Disease progression in Parkinson subtypes: the PPMI dataset. Neurological Sciences. 39(11). 1971–1976. 73 indexed citations
3.
Aleksovski, Darko, et al.. (2017). Twitter sentiment around the Earnings Announcement events. PLoS ONE. 12(2). e0173151–e0173151. 25 indexed citations
4.
Aleksovski, Darko, Dejan Dovžan, Sašo Džeroski, & Juš Kocijan. (2016). A comparison of fuzzy identification methods on benchmark datasets. IFAC-PapersOnLine. 49(5). 31–36. 4 indexed citations
5.
Ranco, Gabriele, Darko Aleksovski, Guido Caldarelli, & Igor Mozetič. (2015). Investigating the Relations between Twitter Sentiment and Stock Prices.. 2 indexed citations
6.
Ranco, Gabriele, Darko Aleksovski, Guido Caldarelli, Miha Grćar, & Igor Mozetič. (2015). The Effects of Twitter Sentiment on Stock Price Returns. PLoS ONE. 10(9). e0138441–e0138441. 268 indexed citations breakdown →
7.
Aleksovski, Darko, Juš Kocijan, & Sašo Džeroski. (2015). Ensembles of Fuzzy Linear Model Trees for the Identification of Multioutput Systems. IEEE Transactions on Fuzzy Systems. 24(4). 916–929. 8 indexed citations
8.
Aleksovski, Darko, Juš Kocijan, & Sašo Džeroski. (2014). Model Tree Ensembles for the Identification of Multiple-Output Systems. 92. 750–755. 1 indexed citations
9.
Aleksovski, Darko, Juš Kocijan, & Sašo Džeroski. (2014). Model-Tree Ensembles for noise-tolerant system identification. Advanced Engineering Informatics. 29(1). 1–15. 3 indexed citations

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.

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