Dejan Gjorgjevikj

1.3k total citations · 1 hit paper
19 papers, 847 citations indexed

About

Dejan Gjorgjevikj is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Dejan Gjorgjevikj has authored 19 papers receiving a total of 847 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 5 papers in Molecular Biology. Recurrent topics in Dejan Gjorgjevikj's work include Face and Expression Recognition (6 papers), Image Retrieval and Classification Techniques (6 papers) and Text and Document Classification Technologies (6 papers). Dejan Gjorgjevikj is often cited by papers focused on Face and Expression Recognition (6 papers), Image Retrieval and Classification Techniques (6 papers) and Text and Document Classification Technologies (6 papers). Dejan Gjorgjevikj collaborates with scholars based in North Macedonia, Slovenia and China. Dejan Gjorgjevikj's co-authors include Gjorgji Madjarov, Sašo Džeroski, Dragi Kocev, Ivan Chorbev, Eftim Zdravevski, Andrea Kulakov, Petre Lameski, Chuan Li, Shaohui Zhang and Jianyu Long and has published in prestigious journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Measurement Science and Technology.

In The Last Decade

Dejan Gjorgjevikj

18 papers receiving 806 citations

Hit Papers

An extensive experimental... 2012 2026 2016 2021 2012 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. An extensive experimental comparison of methods for multi-label learning
    2012 497 citations Gjorgji Madjarov, Dragi Kocev et al. Pattern Recognition profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dejan Gjorgjevikj North Macedonia 10 538 264 184 135 63 19 847
Jin-Seon Lee South Korea 4 469 0.9× 227 0.9× 66 0.4× 106 0.8× 50 0.8× 10 760
M. Cevdet İnce Türkiye 9 392 0.7× 270 1.0× 68 0.4× 113 0.8× 138 2.2× 25 808
Hussein Almuallim Saudi Arabia 7 687 1.3× 402 1.5× 254 1.4× 96 0.7× 47 0.7× 19 1.1k
Jaya Sil India 16 327 0.6× 270 1.0× 95 0.5× 50 0.4× 39 0.6× 101 1.1k
Xiaolong Teng China 5 436 0.8× 347 1.3× 152 0.8× 65 0.5× 63 1.0× 5 884
Boonserm Kijsirikul Thailand 16 376 0.7× 258 1.0× 64 0.3× 42 0.3× 32 0.5× 85 764
Abdelkader Benyettou Algeria 14 442 0.8× 165 0.6× 45 0.2× 103 0.8× 49 0.8× 78 929
Domingo Ortíz-Boyer Spain 13 626 1.2× 216 0.8× 66 0.4× 50 0.4× 47 0.7× 23 873
Ritam Guha India 12 404 0.8× 164 0.6× 67 0.4× 47 0.3× 35 0.6× 24 594

Countries citing papers authored by Dejan Gjorgjevikj

Since Specialization
Citations

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

Fields of papers citing papers by Dejan Gjorgjevikj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dejan Gjorgjevikj

This figure shows the co-authorship network connecting the top 25 collaborators of Dejan Gjorgjevikj. A scholar is included among the top collaborators of Dejan Gjorgjevikj 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 Dejan Gjorgjevikj. Dejan Gjorgjevikj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Wei, Yadong, et al.. (2021). Mechanical fault diagnosis by using dynamic transfer adversarial learning. Measurement Science and Technology. 32(10). 104005–104005. 9 indexed citations
2.
Yang, Zhe, Dejan Gjorgjevikj, Jianyu Long, et al.. (2021). Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties. Chinese Journal of Mechanical Engineering. 34(1). 22 indexed citations
3.
Gjorgjevikj, Dejan, et al.. (2020). Precision Apiculture – IoT System for remote monitoring of honeybee colonies. 2 indexed citations
4.
Gjorgjevikj, Dejan, et al.. (2018). Data Collection Module for Human Activity Recognition. 1 indexed citations
5.
Gjorgjevikj, Dejan, et al.. (2017). Static analysis of source code written by novice programmers. 825–830. 15 indexed citations
6.
Madjarov, Gjorgji, Dejan Gjorgjevikj, Ivica Dimitrovski, & Sašo Džeroski. (2016). The use of data-derived label hierarchies in multi-label classification. Journal of Intelligent Information Systems. 47(1). 57–90. 9 indexed citations
7.
Zdravevski, Eftim, et al.. (2015). Robust histogram-based feature engineering of time series data. SHILAP Revista de lepidopterología. 5. 381–388. 25 indexed citations
8.
Madjarov, Gjorgji, et al.. (2014). Evaluation of different feature sets for gait recognition using skeletal data from Kinect. 1304–1308. 38 indexed citations
9.
Zdravevski, Eftim, Petre Lameski, Andrea Kulakov, & Dejan Gjorgjevikj. (2014). Feature selection and allocation to diverse subsets for multi-label learning problems with large datasets. SHILAP Revista de lepidopterología. 2. 387–394. 7 indexed citations
10.
Gjorgjevikj, Dejan, Gjorgji Madjarov, & Sašo Džeroski. (2013). HYBRID DECISION TREE ARCHITECTURE UTILIZING LOCAL SVMs FOR EFFICIENT MULTI-LABEL LEARNING. International Journal of Pattern Recognition and Artificial Intelligence. 27(7). 1351004–1351004. 3 indexed citations
11.
Madjarov, Gjorgji, Dragi Kocev, Dejan Gjorgjevikj, & Sašo Džeroski. (2012). An extensive experimental comparison of methods for multi-label learning. Pattern Recognition. 45(9). 3084–3104. 497 indexed citations breakdown →
12.
Madjarov, Gjorgji, et al.. (2011). Hierarchical classification architectures applied to Magnetic Resonance Images. Information Technology Interfaces. 501–506. 2 indexed citations
13.
Madjarov, Gjorgji, Dejan Gjorgjevikj, & Sašo Džeroski. (2011). Two stage architecture for multi-label learning. Pattern Recognition. 45(3). 1019–1034. 26 indexed citations
14.
Gjorgjevikj, Dejan & Gjorgji Madjarov. (2011). Two Stage Classifier Chain Architecture for efficient pair-wise multi-label learning. 14. 1–6. 3 indexed citations
15.
Gjorgjevikj, Dejan, et al.. (2010). Classification of magnetic resonance images. Information Technology Interfaces. 597–602. 3 indexed citations
16.
Loškovska, Suzana, et al.. (2010). Towards Improving Magnetic Resonance Image Classification. 1 indexed citations
17.
Madjarov, Gjorgji, et al.. (2010). Ensembles of Binary SVM Decision Trees. 3 indexed citations
18.
Gjorgjevikj, Dejan, et al.. (2009). A Multi-class SVM Classifier Utilizing Binary Decision Tree. 33(2). 225–233. 143 indexed citations
19.
Gjorgjevikj, Dejan, et al.. (2009). Multi-class classification using support vector machines in decision tree architecture. 288–295. 38 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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