Marcin Paprzycki

4.4k total citations · 1 hit paper
215 papers, 1.8k citations indexed

About

Marcin Paprzycki is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Marcin Paprzycki has authored 215 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Artificial Intelligence, 76 papers in Computer Networks and Communications and 46 papers in Information Systems. Recurrent topics in Marcin Paprzycki's work include Multi-Agent Systems and Negotiation (46 papers), Distributed and Parallel Computing Systems (30 papers) and Mobile Agent-Based Network Management (23 papers). Marcin Paprzycki is often cited by papers focused on Multi-Agent Systems and Negotiation (46 papers), Distributed and Parallel Computing Systems (30 papers) and Mobile Agent-Based Network Management (23 papers). Marcin Paprzycki collaborates with scholars based in Poland, United States and Bulgaria. Marcin Paprzycki's co-authors include Maria Ganzha, Ajith Abraham, Katarzyna Wasielewska, Paweł Szmeja, Wiesław Pawłowski, Costin Bădică, Maciej Gawinecki, Stefka Fidanova, Olympia Roeva and Mirjana Ivanović and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Information Sciences.

In The Last Decade

Marcin Paprzycki

194 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities

2023 123 citations Marcin Paprzycki, Maria Ganzha et al. profile →

Peers

Marcin Paprzycki

CNC

AI

IS

CVPR

CTM

Nadia Nedjah Brazil

Pierfrancesco Bellini Italy

Kalyan Veeramachaneni United States

Zhiqiu Huang China

Fei Hao China

Jia Wang China

Imtiaz Ahmad Kuwait

Manju Khari India

Aiiad Albeshri Saudi Arabia

Yang Cao China

Nadia Nedjah Brazil

Marcin Paprzycki

586 ×0.8

CNC

979 ×1.8

AI

424 ×1.1

IS

409 ×1.7

CVPR

261 ×1.8

CTM

Citations per year, relative to Marcin Paprzycki Marcin Paprzycki (= 1×) peers Nadia Nedjah

Countries citing papers authored by Marcin Paprzycki

Since Specialization

Citations

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

Fields of papers citing papers by Marcin Paprzycki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcin Paprzycki

This figure shows the co-authorship network connecting the top 25 collaborators of Marcin Paprzycki. A scholar is included among the top collaborators of Marcin Paprzycki 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 Marcin Paprzycki. Marcin Paprzycki 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

1.

Ganzha, Maria, et al.. (2025). Leveraging Network Analysis and NLP for Intelligent Data Mining of Taxonomies and Folksonomies of PornHub. Applied Sciences. 15(17). 9250–9250.

2.

Sowiński, Piotr, et al.. (2024). Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum. Electronics. 13(10). 1888–1888. 1 indexed citations

3.

Wróblewska, Anna, et al.. (2024). Under the Influence: A Survey of Large Language Models in Fake News Detection. IEEE Transactions on Artificial Intelligence. 6(2). 458–476. 7 indexed citations

4.

Wróblewska, Anna, et al.. (2024). Comparative Analysis of Graph Neural Networks and Transformers for Robust Fake News Detection: A Verification and Reimplementation Study. Electronics. 13(23). 4784–4784. 5 indexed citations

5.

Paszkiewicz, Andrzej, et al.. (2023). Use of virtual reality to facilitate engineer training in the aerospace industry. 32(2). 19–44. 1 indexed citations

6.

Sowiński, Piotr, et al.. (2023). Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites. Sensors. 23(14). 6464–6464. 6 indexed citations

7.

Paprzycki, Marcin, et al.. (2023). Advanced Communication and Intelligent Systems. Communications in computer and information science. 1 indexed citations

8.

Sowiński, Piotr, et al.. (2023). Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety. Electronics. 12(20). 4257–4257. 7 indexed citations

9.

Singh, Pradeep Kumar, et al.. (2022). Sustainable Smart Cities. Studies in computational intelligence. 5 indexed citations

10.

Paprzycki, Marcin, Sabu M. Thampi, Sushmita Mitra, Ljiljana Trajković, & El-Sayed M. El-Alfy. (2021). Intelligent Systems, Technologies and Applications. Advances in intelligent systems and computing. 8 indexed citations

11.

Moreira, João, Laura Daniele, Luís Ferreira Pires, et al.. (2017). Towards IoT platforms' integration: Semantic Translations between W3C SSN and ETSI SAREF. University of Twente Research Information. 10 indexed citations

12.

Crossland, Martin D., et al.. (2015). Developing an empirical study of how qualified subjects might be selected for IT system security penetration testing. Studia Iuridica Lublinensia (Uniwersytet Marii Curie-Skłodowskiej w Lublinie). 1 indexed citations

13.

Bădică, Costin, et al.. (2014). DISTRIBUTED AGENT-BASED ONLINE AUCTION SYSTEM. Computing and Informatics / Computers and Artificial Intelligence. 33(3). 518–552. 9 indexed citations

14.

Brennan, John, et al.. (2013). Scaling Campus Grids: Implementing a modified ontology based EMI-WMS on Campus Grids. University of Huddersfield Repository (University of Huddersfield). 1 indexed citations

15.

Paprzycki, Marcin, et al.. (2011). Parallel alternating directions algorithm for 3D Stokes equation. Bulgarian Portal for Open Science.

16.

Filippone, Salvatore, et al.. (2010). Utilizing recursive storage in sparse matrix-vector multiplication - preliminary considerations. Cineca Institutional Research Information System (Tor Vergata University). 300–305. 19 indexed citations

17.

Paprzycki, Marcin. (2001). Distributed Computing: Fundamentals, Simulations and Advanced Topics. Scalable Computing Practice and Experience. 4(2). 114 indexed citations

18.

Margenov, Svetozar, et al.. (1999). Parallel Conjugate Gradient Method with Circulant Block-Factorization Preconditioners for 3D Elliptic Problems.. PPSC. 21(1). 31–6. 1 indexed citations

19.

Paprzycki, Marcin. (1995). Parallel Gaussian Elimination Algorithms on a Cray Y-MP.. Informatica (slovenia). 19. 2 indexed citations

20.

Paprzycki, Marcin & Draga Vidakovic. (1994). Prospective Teacher's Attitudes Toward Computers. Society for Information Technology & Teacher Education International Conference. 1994(1). 74–76. 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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