Tomasz Ząbkowski

894 total citations
52 papers, 597 citations indexed

About

Tomasz Ząbkowski is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Signal Processing. According to data from OpenAlex, Tomasz Ząbkowski has authored 52 papers receiving a total of 597 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 18 papers in Electrical and Electronic Engineering and 9 papers in Signal Processing. Recurrent topics in Tomasz Ząbkowski's work include Energy Load and Power Forecasting (16 papers), Smart Grid Energy Management (15 papers) and Imbalanced Data Classification Techniques (7 papers). Tomasz Ząbkowski is often cited by papers focused on Energy Load and Power Forecasting (16 papers), Smart Grid Energy Management (15 papers) and Imbalanced Data Classification Techniques (7 papers). Tomasz Ząbkowski collaborates with scholars based in Poland, Malaysia and United States. Tomasz Ząbkowski's co-authors include Krzysztof Gajowniczek, Arkadiusz Orłowski, Mariya Sodenkamp, Chandrajit Bajaj, K. Karpio, Yitao Liang, Tal Friedman, Guy Van den Broeck, Katarzyna Zawada and Arkadiusz Szterk and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

In The Last Decade

Tomasz Ząbkowski

45 papers receiving 573 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomasz Ząbkowski Poland 14 342 140 94 80 68 52 597
Krzysztof Gajowniczek Poland 14 336 1.0× 135 1.0× 89 0.9× 72 0.9× 68 1.0× 42 562
Yeming Dai China 13 449 1.3× 146 1.0× 57 0.6× 105 1.3× 91 1.3× 30 606
Minh Thanh Vo Vietnam 11 237 0.7× 203 1.4× 97 1.0× 56 0.7× 36 0.5× 20 675
Shailendra Singh Canada 7 293 0.9× 134 1.0× 97 1.0× 31 0.4× 46 0.7× 13 637
Sheng-Xiang Lv China 12 410 1.2× 248 1.8× 39 0.4× 204 2.5× 38 0.6× 14 697
Silja Meyer-Nieberg Germany 9 411 1.2× 180 1.3× 75 0.8× 182 2.3× 40 0.6× 31 645
Dabeeruddin Syed United States 11 343 1.0× 102 0.7× 98 1.0× 35 0.4× 31 0.5× 26 501
Vijay Arya India 16 482 1.4× 124 0.9× 75 0.8× 36 0.5× 80 1.2× 58 728
Bilin Shao China 15 142 0.4× 278 2.0× 23 0.2× 68 0.8× 26 0.4× 56 657
Hyoseop Lee South Korea 10 297 0.9× 63 0.5× 118 1.3× 14 0.2× 76 1.1× 19 466

Countries citing papers authored by Tomasz Ząbkowski

Since Specialization
Citations

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

Fields of papers citing papers by Tomasz Ząbkowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomasz Ząbkowski

This figure shows the co-authorship network connecting the top 25 collaborators of Tomasz Ząbkowski. A scholar is included among the top collaborators of Tomasz Ząbkowski 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 Tomasz Ząbkowski. Tomasz Ząbkowski 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.
Ząbkowski, Tomasz, et al.. (2023). Cluster-Based Approach to Estimate Demand in the Polish Power System Using Commercial Customers’ Data. Energies. 16(24). 8070–8070.
2.
Ząbkowski, Tomasz, et al.. (2018). ZASTOSOWANIE DRZEW KLASYFIKACYJNYCH DO ANALIZY POKERA ONLINE. 19(2). 192–201.
3.
Gajowniczek, Krzysztof, Arkadiusz Orłowski, & Tomasz Ząbkowski. (2018). Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks. Entropy. 20(4). 249–249. 10 indexed citations
4.
Gajowniczek, Krzysztof & Tomasz Ząbkowski. (2017). Electricity forecasting on the individual household level enhanced based on activity patterns. PLoS ONE. 12(4). e0174098–e0174098. 76 indexed citations
5.
Gajowniczek, Krzysztof, et al.. (2017). Electricity peak demand classification with artificial neural networks. Annals of Computer Science and Information Systems. 11. 307–315. 20 indexed citations
6.
Gajowniczek, Krzysztof & Tomasz Ząbkowski. (2017). Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms. Energies. 10(10). 1547–1547. 35 indexed citations
7.
Gajowniczek, Krzysztof & Tomasz Ząbkowski. (2015). Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data. Energies. 8(7). 7407–7427. 53 indexed citations
8.
Gajowniczek, Krzysztof & Tomasz Ząbkowski. (2015). Short term electricity forecasting based on user behavior from individual smart meter data. Journal of Intelligent & Fuzzy Systems. 30(1). 223–234. 14 indexed citations
9.
Ząbkowski, Tomasz, et al.. (2015). Independent Component Analysis for Ensemble Predictors with Small Number of Models. Acta Physica Polonica A. 127(3a). A–139.
10.
Ząbkowski, Tomasz, et al.. (2014). Some Remarks on Logistics Investments Among Polish Food Processing and Agribusiness Companies. Information Systems Management. 3(2). 122–133. 2 indexed citations
11.
Gajowniczek, Krzysztof, et al.. (2014). Estimating the ROC Curve and Its Significance for Classification Models' Assessment. Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego. 15(2). 382–391. 11 indexed citations
12.
Ząbkowski, Tomasz & Krzysztof Gajowniczek. (2013). FORECASTING OF INDIVIDUAL ELECTRICITY USAGE USING SMART METER DATA. Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego. 14(2). 289–297. 3 indexed citations
13.
Ząbkowski, Tomasz & Krzysztof Gajowniczek. (2013). SMART METERING AND DATA PRIVACY ISSUES. Information Systems Management. 2(3). 238–249. 10 indexed citations
14.
Ząbkowski, Tomasz, et al.. (2012). Badanie atrakcyjności oferty dostępu do internetu za pomocą analizy gradacyjnej. Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego. 13(3). 276–287.
15.
Ząbkowski, Tomasz, et al.. (2012). SMART METERING – A BRIEF OVERVIEW OF PROJECTS, BENEFITS AND APPLICATIONS. Information Systems Management. 1(1). 72–83. 3 indexed citations
16.
Gajowniczek, Krzysztof & Tomasz Ząbkowski. (2012). Problemy modelowania rezygnacji klientów w telefonii komórkowej. Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego. 13(3). 65–78.
17.
Kozłowska, Mariola, Arkadiusz Szterk, Katarzyna Zawada, & Tomasz Ząbkowski. (2012). New Opportunities of the Application of Natural Herb and Spice Extracts in Plant Oils: Application of Electron Paramagnetic Resonance in Examining the Oxidative Stability. Journal of Food Science. 77(9). C994–9. 9 indexed citations
18.
Ząbkowski, Tomasz, et al.. (2011). Rozwiązania informatyczne w logistyce małych i średnich przedsiębiorstw sektora rolno-spożywczego. Logistyka. 62–65. 2 indexed citations
19.
Ząbkowski, Tomasz. (2008). Zastosowanie sztucznych sieci neuronowych do oceny ryzyka kredytowego klienta w telekomunikacji. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu. 15. 502–510. 1 indexed citations
20.
Ząbkowski, Tomasz, et al.. (2006). Blind Signal Separation Methods for Integration of Neural Networks Results. 14. 1–6. 1 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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