S. Osowski

4.9k total citations
191 papers, 3.6k citations indexed

About

S. Osowski is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, S. Osowski has authored 191 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Artificial Intelligence, 48 papers in Electrical and Electronic Engineering and 47 papers in Computer Vision and Pattern Recognition. Recurrent topics in S. Osowski's work include Neural Networks and Applications (43 papers), AI in cancer detection (20 papers) and Advanced Chemical Sensor Technologies (18 papers). S. Osowski is often cited by papers focused on Neural Networks and Applications (43 papers), AI in cancer detection (20 papers) and Advanced Chemical Sensor Technologies (18 papers). S. Osowski collaborates with scholars based in Poland, Japan and Vietnam. S. Osowski's co-authors include Trần Hoài Linh, Tomasz Markiewicz, Krzysztof Siwek, K. Brudzewski, Robert Sałat, Jarosław Kurek, Bartosz Świderski, Michał Kruk, Andrzej Cichocki and Andrzej Rysz and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Power Systems and Expert Systems with Applications.

In The Last Decade

S. Osowski

170 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Osowski Poland 29 930 838 793 784 782 191 3.6k
Radek Martínek Czechia 31 1.0k 1.1× 624 0.7× 261 0.3× 1.2k 1.5× 1.0k 1.3× 322 4.1k
Muhammad Arif Pakistan 26 414 0.4× 297 0.4× 571 0.7× 333 0.4× 388 0.5× 150 2.5k
Xi Zhang China 32 309 0.3× 267 0.3× 480 0.6× 778 1.0× 576 0.7× 382 4.1k
Seán McLoone United Kingdom 33 227 0.2× 475 0.6× 710 0.9× 1.3k 1.7× 469 0.6× 269 4.7k
Lars Karlsson Sweden 25 445 0.5× 268 0.3× 858 1.1× 234 0.3× 247 0.3× 116 2.9k
Elif Derya Übeylï Türkiye 38 1.6k 1.8× 2.7k 3.3× 1.6k 2.0× 260 0.3× 743 1.0× 132 5.3k
Jiuwen Cao China 38 199 0.2× 883 1.1× 2.2k 2.8× 735 0.9× 235 0.3× 220 4.9k
Weihai Chen China 38 258 0.3× 507 0.6× 409 0.5× 871 1.1× 2.0k 2.5× 434 7.1k
Hui Yang United States 29 546 0.6× 303 0.4× 409 0.5× 187 0.2× 397 0.5× 206 3.4k
Ravi Sankar United States 22 156 0.2× 156 0.2× 757 1.0× 672 0.9× 352 0.5× 171 2.9k

Countries citing papers authored by S. Osowski

Since Specialization
Citations

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

Fields of papers citing papers by S. Osowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Osowski

This figure shows the co-authorship network connecting the top 25 collaborators of S. Osowski. A scholar is included among the top collaborators of S. Osowski 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 S. Osowski. S. Osowski 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.
Świderski, Bartosz, et al.. (2022). Random CNN structure: tool to increase generalization ability in deep learning. EURASIP Journal on Image and Video Processing. 2022(1). 15 indexed citations
2.
Osowski, S.. (2018). Głębokie sieci neuronowe i ich zastosowania w eksploracji danych. Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne. 1 indexed citations
3.
Osowski, S., et al.. (2014). Badanie jakości predykcji obciążeń elektroenergetycznych za pomocą sieci neuronowych SVM, RBF i MLP. PRZEGLĄD ELEKTROTECHNICZNY. 148–151. 3 indexed citations
4.
Siwek, Krzysztof & S. Osowski. (2014). Comparison of methods of feature generation for face recognition. PRZEGLĄD ELEKTROTECHNICZNY. 4 indexed citations
5.
Kruk, Michał, S. Osowski, Wojciech Kozłowski, et al.. (2013). Computer-assisted Fuhrman grading system for the analysis of clear-cell renal carcinoma: a pilot study. PRZEGLĄD ELEKTROTECHNICZNY. 1 indexed citations
6.
Kruk, Michał, S. Osowski, & Robert Koktysz. (2011). Numerical characterization of the images of prostate cancer for recognition of Gleason scale. PRZEGLĄD ELEKTROTECHNICZNY. 81–83. 1 indexed citations
7.
Siwek, Krzysztof, S. Osowski, & Bartosz Świderski. (2011). Trend elimination of time series of 24-hour load demand in the power system and its application in power forecasting power system and its application in power forecasting. PRZEGLĄD ELEKTROTECHNICZNY. 249–253.
8.
Papierz, W, et al.. (2010). New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas.. SHILAP Revista de lepidopterología. 3 indexed citations
9.
Osowski, S., Jarosław Kurek, & Krzysztof Siwek. (2010). Computerised system for fault diagnosis of the rotor bars of squirrel-cage induction motor. Problemy Eksploatacji. 135–151. 1 indexed citations
10.
Kruk, Michał & S. Osowski. (2010). Segmentacja i parametryzacja struktur histologicznych w obrazach mikroskopowych prostaty dla oceny skali Gleasona. PRZEGLĄD ELEKTROTECHNICZNY. 5–9. 1 indexed citations
11.
Kurek, Jarosław & S. Osowski. (2010). Diagnostic feature selection for efficient recognition of different faults of rotor bars in the induction machine. PRZEGLĄD ELEKTROTECHNICZNY. 121–123. 7 indexed citations
12.
Osowski, S.. (2009). Sztuczne sieci neuronowe - podstawowe struktury sieciowe i algorytmy uczące. PRZEGLĄD ELEKTROTECHNICZNY. 1–8. 3 indexed citations
13.
Siwek, Krzysztof, et al.. (2009). Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System. International Journal of Applied Mathematics and Computer Science. 19(2). 303–315. 65 indexed citations
14.
Markiewicz, Tomasz, et al.. (2009). Application of SVM for cell recognition in BCC skin pathology.. The European Symposium on Artificial Neural Networks.
15.
Markiewicz, Tomasz & S. Osowski. (2008). Morphological operations for blood cells extraction from the image of the bone marrow smear. PRZEGLĄD ELEKTROTECHNICZNY. 24–26. 1 indexed citations
16.
Markiewicz, Tomasz & S. Osowski. (2006). Data mining techniques for feature selection in blood cell recognition. The European Symposium on Artificial Neural Networks. 407–412. 12 indexed citations
17.
Osowski, S., Krzysztof Siwek, & Tomasz Markiewicz. (2004). MLP and SVM networks - a comparative study. 37–40. 51 indexed citations
18.
Sałat, Robert, S. Osowski, & Krzysztof Siwek. (2003). Principal component analysis (PCA) for feature selection at the diagnosis of electrical circuits. PRZEGLĄD ELEKTROTECHNICZNY. 79(10). 667–670. 4 indexed citations
19.
Linh, Trần Hoài & S. Osowski. (2002). Neuro-fuzzy TSK network for approximation of static and dynamic functions. Control and Cybernetics. 31(2). 309–326. 5 indexed citations
20.
Osowski, S.. (2002). Sieci neuronowe typu SVM w zastosowaniu do klasyfikacji wzorców. PRZEGLĄD ELEKTROTECHNICZNY. 29–36. 4 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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