Zoran Bosnić

1.9k total citations
54 papers, 1.2k citations indexed

About

Zoran Bosnić is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Zoran Bosnić has authored 54 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 11 papers in Information Systems and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Zoran Bosnić's work include Machine Learning and Data Classification (9 papers), Data Stream Mining Techniques (7 papers) and Topic Modeling (6 papers). Zoran Bosnić is often cited by papers focused on Machine Learning and Data Classification (9 papers), Data Stream Mining Techniques (7 papers) and Topic Modeling (6 papers). Zoran Bosnić collaborates with scholars based in Slovenia, Serbia and United Kingdom. Zoran Bosnić's co-authors include Igor Kononenko, Matjaž Gams, Matjaž Kukar, Petar Vračar, Rok Rupnik, Jože Rugelj, Luka Čehovin Zajc, Mirjana Ivanović, Vladimir Kurbalija and Tomaž Curk and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Computers & Education.

In The Last Decade

Zoran Bosnić

50 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zoran Bosnić Slovenia 19 454 201 99 93 81 54 1.2k
Manik Sharma India 21 562 1.2× 163 0.8× 150 1.5× 85 0.9× 143 1.8× 74 1.7k
S. Vimal India 19 376 0.8× 107 0.5× 157 1.6× 93 1.0× 136 1.7× 68 1.2k
Babita Pandey India 21 384 0.8× 203 1.0× 144 1.5× 77 0.8× 68 0.8× 87 1.2k
Shuhong Chen China 22 375 0.8× 132 0.7× 145 1.5× 40 0.4× 60 0.7× 96 1.3k
Dharmendra Singh Rajput India 17 776 1.7× 246 1.2× 290 2.9× 98 1.1× 149 1.8× 95 2.1k
Noor Akhmad Setiawan Indonesia 14 386 0.9× 224 1.1× 158 1.6× 35 0.4× 42 0.5× 160 1.1k
Jin Young Kim South Korea 19 321 0.7× 111 0.6× 287 2.9× 69 0.7× 100 1.2× 179 1.4k
Md. Saiful Islam Australia 20 514 1.1× 155 0.8× 167 1.7× 209 2.2× 39 0.5× 112 1.8k
Harleen Kaur India 22 585 1.3× 416 2.1× 132 1.3× 42 0.5× 19 0.2× 119 1.9k
Saroj Kr. Biswas India 19 756 1.7× 155 0.8× 267 2.7× 87 0.9× 143 1.8× 108 1.6k

Countries citing papers authored by Zoran Bosnić

Since Specialization
Citations

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

Fields of papers citing papers by Zoran Bosnić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zoran Bosnić

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

All Works

20 of 20 papers shown
1.
Vračar, Petar, et al.. (2025). Explainable Heart Failure Voice Prediction using Machine Learning Ensembles. Pure (Coventry University).
2.
Bosnić, Zoran, Leonard A. Kaminsky, Jonathan Myers, et al.. (2023). A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults. Journal of Cardiopulmonary Rehabilitation and Prevention. 43(5). 377–383.
3.
Milošević, Miljan, Vladimir Simić, Lazar Velicki, et al.. (2023). Machine learning and physical based modeling for cardiac hypertrophy. Heliyon. 9(6). e16724–e16724. 3 indexed citations
4.
Kurbalija, Vladimir, Zoltan Geler, Igor Petrušić, et al.. (2023). Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment. International Journal of Medical Informatics. 178. 105195–105195. 2 indexed citations
5.
Kononenko, Igor, et al.. (2023). Conditional generative positive and unlabeled learning. Expert Systems with Applications. 224. 120046–120046. 2 indexed citations
6.
Žunkovič, Bojan, Marko Robnik‐Šikonja, Matjaž Kukar, et al.. (2022). Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning. JMIR Medical Informatics. 10(2). e30483–e30483. 14 indexed citations
7.
Hribar-Lee, Barbara, et al.. (2022). CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network. Pharmaceutics. 15(1). 119–119. 7 indexed citations
8.
Šoštarič, Maja, et al.. (2020). Extracorporeal Hemadsorption versus Glucocorticoids during Cardiopulmonary Bypass: A Prospective, Randomized, Controlled Trial. Cardiovascular Therapeutics. 2020. 1–15. 22 indexed citations
9.
Bosnić, Zoran, et al.. (2020). Extracorporeal hemadsorption versus glucocorticoids during cardiopulmonary bypass: a prospective, randomized, controlled trial. Journal of Cardiothoracic and Vascular Anesthesia. 34. S25–S25. 9 indexed citations
10.
Novak–Jankovič, Vesna, et al.. (2018). Influence of dexmedetomidine and lidocaine on perioperative opioid consumption in laparoscopic intestine resection: a randomized controlled clinical trial. Journal of International Medical Research. 46(12). 5143–5154. 21 indexed citations
11.
Rugelj, Jože, et al.. (2015). Improving matrix factorization recommendations for examples in cold start. Expert Systems with Applications. 42(19). 6784–6794. 27 indexed citations
12.
Bosnić, Zoran, et al.. (2013). ROC analysis of classifiers in machine learning: A survey. Intelligent Data Analysis. 17(3). 531–558. 91 indexed citations
13.
Bosnić, Zoran, Petar Vračar, Miloš Radović, et al.. (2011). Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation Models. IEEE Transactions on Information Technology in Biomedicine. 16(2). 248–254. 11 indexed citations
14.
Bosnić, Zoran & Igor Kononenko. (2010). Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs. Computing and Informatics / Computers and Artificial Intelligence. 29(6). 929–946. 3 indexed citations
15.
Bosnić, Zoran, et al.. (2010). Extending applications using an advanced approach to DLL injection and API hooking. Software Practice and Experience. 40(7). 567–584. 14 indexed citations
16.
Bosnić, Zoran & Igor Kononenko. (2010). Automatic selection of reliability estimates for individual regression predictions. The Knowledge Engineering Review. 25(1). 27–47. 5 indexed citations
17.
Bosnić, Zoran & Igor Kononenko. (2009). An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis. 13(2). 385–401. 46 indexed citations
18.
Štrumbelj, Erik, Zoran Bosnić, Igor Kononenko, Branko Zakotnik, & Cvetka Grašič Kuhar. (2009). Explanation and reliability of prediction models: the case of breast cancer recurrence. Knowledge and Information Systems. 24(2). 305–324. 34 indexed citations
19.
Bosnić, Zoran & Igor Kononenko. (2008). Estimation of Regressor Reliability. Journal of Intelligent Systems. 17(1-3). 297–311. 3 indexed citations
20.
Bosnić, Zoran & Igor Kononenko. (2008). Comparison of approaches for estimating reliability of individual regression predictions. Data & Knowledge Engineering. 67(3). 504–516. 44 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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