Alexander I. Luik

798 citations
3 papers · 630 indexed · 1 hit paper · h-index 3
Topics
Neural Networks and Applications (2 papers)Fault Detection and Control Systems (1 paper)Machine Learning and Data Classification (1 paper)
Journals
Journal of Medicinal ChemistryJournal of Chemical Information and Computer Sciences
Partner nations
UkraineBelarus

In The Last Decade

Alexander I. Luik

3 papers receiving 603 citations

Hit Papers

Neural network studies. 1. Comparison of overfitting and ...19952026200520151995100200300400500

Peers

Alexander I. Luik
Comparison fields: 5 of 136
  • Computational Theory and Mathematics 168
  • Artificial Intelligence 119
  • Molecular Biology 96
  • Analytical Chemistry 72
  • Spectroscopy 70
Replace S. Feyo de Azevedo with:
S. Feyo de Azevedo Portugal
Evgeny Byvatov Germany
Mark J. Willis United Kingdom
Nathan O. Hodas United States
Joe H. Mize United States
Horia F. Pop Romania
Robert Burbidge United Kingdom
Bing Fan China
Barry J. Wythoff United States
Jesús Álvarez Mexico
Alexander I. Luik relative to S. Feyo de Azevedo Portugal S. Feyo de Azevedo's profile →
Citations per field
00.5×1.5×2.1×
S. Feyo de Azevedo · 1×
Citations per year

Countries citing papers authored by Alexander I. Luik

Since Specialization
Citations

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

Fields of papers citing papers by Alexander I. Luik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander I. Luik. 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 Alexander I. Luik. The network helps show where Alexander I. Luik may publish in the future.

Co-authorship network of co-authors of Alexander I. Luik

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1 59
2
Neural network studies. 1. Comparison of overfitting and overtrainingbreakdown →
531
3 40

About Alexander I. Luik

Alexander I. Luik is a scholar working on Artificial Intelligence, Control and Systems Engineering and Infectious Diseases, having authored 3 papers that have together received 630 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Fault Detection and Control Systems (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Computational Theory and Mathematics (168 citations), Analytical Chemistry (72 citations) and Spectroscopy (70 citations). Alexander I. Luik has collaborated with scholars based in Ukraine and Belarus. Frequent co-authors include Igor V. Tetko, David J. Livingstone, Vladyslav Kholodovych, Alessandro E. P. Villa, Vasyl Kovalishyn, Valery P. Kukhar and Gennady Poda. Their work appears in journals such as Journal of Medicinal Chemistry and Journal of Chemical Information and Computer Sciences.

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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