Marko V. Jankovic

641 total citations
27 papers, 400 citations indexed

About

Marko V. Jankovic is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Signal Processing. According to data from OpenAlex, Marko V. Jankovic has authored 27 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Electrical and Electronic Engineering and 8 papers in Signal Processing. Recurrent topics in Marko V. Jankovic's work include Neural Networks and Applications (11 papers), Blind Source Separation Techniques (8 papers) and Multilevel Inverters and Converters (5 papers). Marko V. Jankovic is often cited by papers focused on Neural Networks and Applications (11 papers), Blind Source Separation Techniques (8 papers) and Multilevel Inverters and Converters (5 papers). Marko V. Jankovic collaborates with scholars based in Serbia, Japan and Switzerland. Marko V. Jankovic's co-authors include Stavroula Mougiakakou, Srdjan Srdic, Zoran Radaković, Lia Bally, Christoph Stettler, Haruo Ogawa, Peter Diem, Masashi Sugiyama, Branimir Reljin and Hidemitsu Ogawa and has published in prestigious journals such as IEEE Transactions on Power Electronics, IEEE Transactions on Energy Conversion and IEEE Signal Processing Letters.

In The Last Decade

Marko V. Jankovic

26 papers receiving 388 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marko V. Jankovic Serbia	9	162	125	81	78	49	27	400
Konstanze Kölle Norway	10	59 0.4×	83 0.7×	15 0.2×	7 0.1×	10 0.2×	20	233
Marcelo Espinoza Belgium	10	135 0.8×	74 0.6×	92 1.1×	4 0.1×	26 0.5×	14	345
Yazhou Tu United States	6	37 0.2×	60 0.5×	44 0.5×	1 0.0×	19 0.4×	14	188
Marco Forgione Switzerland	10	10 0.1×	74 0.6×	69 0.9×	21 0.3×	8 0.2×	35	346
Chengyuan Liu United Kingdom	7	19 0.1×	299 2.4×	94 1.2×	1 0.0×	11 0.2×	8	478
Yi-Chen Lee Taiwan	7	20 0.1×	92 0.7×	66 0.8×	1 0.0×	20 0.4×	26	335
Dayu Lv United States	7	9 0.1×	750 6.0×	54 0.7×	4 0.1×	15 0.3×	14	891
Xuan Quynh Nguyen Vietnam	8	71 0.4×	24 0.2×	92 1.1×	3 0.0×	3 0.1×	11	349
Yangang Wang China	11	40 0.2×	66 0.5×	13 0.2×	8 0.1×	7 0.1×	45	473
Павло Іванович Ткаченко Ukraine	8	4 0.0×	33 0.3×	42 0.5×	4 0.1×	14 0.3×	68	206

Countries citing papers authored by Marko V. Jankovic

Since Specialization

Citations

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

Fields of papers citing papers by Marko V. Jankovic

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marko V. Jankovic. 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 Marko V. Jankovic. The network helps show where Marko V. Jankovic may publish in the future.

Co-authorship network of co-authors of Marko V. Jankovic

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Jankovic, Marko V., et al.. (2019). Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy. PubMed. 2019. 3609–3612. 10 indexed citations

Jankovic, Marko V., et al.. (2018). Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network. 1–5. 105 indexed citations

Jankovic, Marko V., et al.. (2018). A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics. 23(6). 2633–2641. 51 indexed citations

Jankovic, Marko V., et al.. (2016). Deep prediction model: The case of online adaptive prediction of subcutaneous glucose. 1–5. 8 indexed citations

Jankovic, Marko V., et al.. (2014). Applications of probabilistic model based on joystick probability selector. 1028–1035. 1 indexed citations

Jankovic, Marko V., et al.. (2012). Probabilistic model for minor component analysis based on born rule. 17. 85–88. 1 indexed citations

Jankovic, Marko V., et al.. (2012). Hybrid three-phase rectifier with switched current injection device. LS3e.2–1. 5 indexed citations

Jankovic, Marko V. & Masashi Sugiyama. (2009). Probabilistic principal component analysis based on JoyStick Probability Selector. 3. 1414–1421. 6 indexed citations

Jankovic, Marko V. & Neil Rubens. (2009). A new probabilistic approach to on-line learning in artificial neural networks. 226–231. 2 indexed citations

10.

Jankovic, Marko V. & Masashi Sugiyama. (2008). A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule. IEEE Signal Processing Letters. 15. 677–680. 3 indexed citations

11.

Jankovic, Marko V., et al.. (2008). Tensor based image segmentation. 14. 145–148. 2 indexed citations

12.

Jankovic, Marko V. & Haruo Ogawa. (2006). Modulated Hebb–Oja Learning Rule—A Method for Principal Subspace Analysis. IEEE Transactions on Neural Networks. 17(2). 345–356. 15 indexed citations

13.

Jankovic, Marko V. & Branimir Reljin. (2005). A new minor component analysis method based on Douglas-Kung-Amari minor subspace analysis method. IEEE Signal Processing Letters. 12(12). 859–862. 5 indexed citations

14.

Jankovic, Marko V. & Branimir Reljin. (2005). Neural Learning on Grassman/Stiefel Principal/Minor Submanifold. e75 d. 249–252. 2 indexed citations

15.

Jankovic, Marko V. & Hidemitsu Ogawa. (2004). TIME-ORIENTED HIERARCHICAL METHOD FOR COMPUTATION OF PRINCIPAL COMPONENTS USING SUBSPACE LEARNING ALGORITHM. International Journal of Neural Systems. 14(5). 313–323. 4 indexed citations

16.

Jankovic, Marko V.. (2003). A new simple ∞OH neuron model as a biologically plausible principal component analyzer. IEEE Transactions on Neural Networks. 14(4). 853–859. 11 indexed citations

17.

Jankovic, Marko V. & Hidemitsu Ogawa. (2003). A New Modulated Hebbian Learning Rule — Biologically Plausible Method for Local Computation of a Principal Subspace. International Journal of Neural Systems. 13(4). 215–223. 4 indexed citations

18.

Jankovic, Marko V., et al.. (2002). Analysis of the tuned-average current-mode control of the constant frequency DC/DC converters. 2. 885–890. 1 indexed citations

19.

Jankovic, Marko V., et al.. (2002). The realization of a novel speed-sensorless induction motor drive. 3. 1621–1626. 1 indexed citations

20.

Jankovic, Marko V., et al.. (1999). An improved method of damping of generator oscillations. IEEE Transactions on Energy Conversion. 14(4). 1624–1629. 9 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.

Explore authors with similar magnitude of impact