Mark van Heeswijk

969 total citations
14 papers, 649 citations indexed

About

Mark van Heeswijk is a scholar working on Artificial Intelligence, Cancer Research and Control and Systems Engineering. According to data from OpenAlex, Mark van Heeswijk has authored 14 papers receiving a total of 649 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Cancer Research and 2 papers in Control and Systems Engineering. Recurrent topics in Mark van Heeswijk's work include Machine Learning and ELM (11 papers), Neural Networks and Applications (5 papers) and MicroRNA in disease regulation (3 papers). Mark van Heeswijk is often cited by papers focused on Machine Learning and ELM (11 papers), Neural Networks and Applications (5 papers) and MicroRNA in disease regulation (3 papers). Mark van Heeswijk collaborates with scholars based in Finland, Spain and China. Mark van Heeswijk's co-authors include Yoan Miché, Amaury Lendasse, Patrick Bas, Olli Simula, Erkki Oja, Qi Yu, Éric Séverin, Rui Nian, Emil Eirola and Michel Verleysen and has published in prestigious journals such as Neurocomputing, Entropy and Cognitive Computation.

In The Last Decade

Mark van Heeswijk

14 papers receiving 632 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark van Heeswijk Finland 11 543 180 156 59 56 14 649
Zuo Bai Singapore 5 425 0.8× 158 0.9× 131 0.8× 44 0.7× 33 0.6× 6 522
Qingping Lin Singapore 5 437 0.8× 171 0.9× 101 0.6× 54 0.9× 33 0.6× 13 586
Chamara Kasun Liyanaarachchi Lekamalage Singapore 9 431 0.8× 121 0.7× 184 1.2× 41 0.7× 31 0.6× 11 605
Khamron Sunat Thailand 15 466 0.9× 147 0.8× 203 1.3× 24 0.4× 13 0.2× 73 764
Gwang-Hoon Park South Korea 3 603 1.1× 173 1.0× 254 1.6× 25 0.4× 16 0.3× 4 841
Anton Akusok Finland 8 322 0.6× 90 0.5× 79 0.5× 26 0.4× 14 0.3× 24 467
Sirapat Chiewchanwattana Thailand 13 393 0.7× 109 0.6× 138 0.9× 19 0.3× 10 0.2× 42 578
Guoqiang Li China 14 438 0.8× 163 0.9× 84 0.5× 25 0.4× 12 0.2× 27 702
Rakesh Katuwal Singapore 9 368 0.7× 99 0.6× 162 1.0× 24 0.4× 7 0.1× 10 590

Countries citing papers authored by Mark van Heeswijk

Since Specialization
Citations

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

Fields of papers citing papers by Mark van Heeswijk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark van Heeswijk. 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 Mark van Heeswijk. The network helps show where Mark van Heeswijk may publish in the future.

Co-authorship network of co-authors of Mark van Heeswijk

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

All Works

14 of 14 papers shown
1.
Heeswijk, Mark van, et al.. (2019). METHOD FOR DETECTING AGING RELATED FAILURES OF PROCESS SENSORS VIA NOISE SIGNAL MEASUREMENT. International Journal of Computing. 135–146. 4 indexed citations
2.
Heeswijk, Mark van, et al.. (2017). Detecting aging of process sensors with noise signal measurement. 35–40. 1 indexed citations
3.
Zhao, Chen, Mark van Heeswijk, & Juha Karhunen. (2016). Air quality forecasting using neural networks. Aaltodoc (Aalto University). 1–7. 10 indexed citations
4.
Heeswijk, Mark van. (2015). Advances in Extreme Learning Machines. Aaltodoc (Aalto University). 7 indexed citations
5.
Heeswijk, Mark van & Yoan Miché. (2014). Binary/ternary extreme learning machines. Neurocomputing. 149. 187–197. 31 indexed citations
6.
Guillén, Alberto, M. G. Arenas, Mark van Heeswijk, et al.. (2014). Fast Feature Selection in a GPU Cluster Using the Delta Test. Entropy. 16(2). 854–869. 10 indexed citations
7.
He, Bo, Rui Nian, Mark van Heeswijk, et al.. (2013). Fast Face Recognition Via Sparse Coding and Extreme Learning Machine. Cognitive Computation. 46 indexed citations
8.
Nian, Rui, Bo He, Bing Zheng, et al.. (2013). Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing. 128. 273–284. 42 indexed citations
9.
Yu, Qi, Mark van Heeswijk, Yoan Miché, et al.. (2013). Ensemble delta test-extreme learning machine (DT-ELM) for regression. Neurocomputing. 129. 153–158. 32 indexed citations
10.
Frénay‬, Benoît, Mark van Heeswijk, Yoan Miché, Michel Verleysen, & Amaury Lendasse. (2012). Feature selection for nonlinear models with extreme learning machines. Neurocomputing. 102. 111–124. 60 indexed citations
11.
Yu, Qi, Yoan Miché, Emil Eirola, et al.. (2012). Regularized extreme learning machine for regression with missing data. Neurocomputing. 102. 45–51. 90 indexed citations
12.
Miché, Yoan, Mark van Heeswijk, Patrick Bas, Olli Simula, & Amaury Lendasse. (2011). TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing. 74(16). 2413–2421. 178 indexed citations
13.
Heeswijk, Mark van, Yoan Miché, Erkki Oja, & Amaury Lendasse. (2011). GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing. 74(16). 2430–2437. 127 indexed citations
14.
Heeswijk, Mark van, Yoan Miché, Erkki Oja, & Amaury Lendasse. (2010). Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs. The European Symposium on Artificial Neural Networks. 309–314. 11 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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