Mateusz Lango

460 citations
16 papers · 240 · h-index 8

Impact in

Papers in

    • Imbalanced Data Classification Techniques 6
    • Topic Modeling 5
    • Text and Document Classification Technologies 4
    • Natural Language Processing Techniques 3
    • Sentiment Analysis and Opinion Mining 3
    • Advanced Text Analysis Techniques 2
    • Electricity Theft Detection Techniques 4

Mateusz Lango

15 papers receiving 228 citations

Peers

Mateusz Lango
Comparison fields: 5 of 64
  • Health Information Management 28
  • Orthopedics and Sports Medicine 43
  • Artificial Intelligence 157
  • Accounting 19
  • Complementary and alternative medicine 10
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Mohammad Tarek Aziz Bangladesh
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Tomáš Kupka Norway
Daniel Dinu France
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Ravi Kumar Sachdeva India
Yufan Wang China
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Citations per field
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Citations per year

Countries citing papers authored by Mateusz Lango

Since Specialization
Citations

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

Fields of papers citing papers by Mateusz Lango

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 5 scholars most cited alongside Mateusz Lango, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mateusz Lango Line = papers co-authored together Mateusz Lango links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201757
2 202242
3 201936
4 202032
5 201922
6 202113
7 202410
8 20169
9 20236
10
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)
20184
11
ImWeights: Classifying Imbalanced Data Using Local and Neighborhood Information
20182
12
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping
20202
13 20202
14 20231
15 20241
16 20231

About Mateusz Lango

Mateusz Lango is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Information Systems, Orthopedics and Sports Medicine and Developmental and Educational Psychology, having authored 16 papers that have together received 240 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (6 papers), Topic Modeling (5 papers), Electricity Theft Detection Techniques (4 papers), Text and Document Classification Technologies (4 papers), Natural Language Processing Techniques (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Advanced Text Analysis Techniques (2 papers) and Sport Psychology and Performance (2 papers). The work is most often cited by research in Health Information Management (28 citations), Orthopedics and Sports Medicine (43 citations), Artificial Intelligence (157 citations), Accounting (19 citations) and Complementary and alternative medicine (10 citations). Mateusz Lango has collaborated with scholars based in Poland and Czechia. Frequent co-authors include Jerzy Stefanowski, Marcin Andrzejewski, Dariusz Brzeziński, Ondřej Dušek and Zdeněk Žabokrtský. Their work appears in journals such as Language Resources and Evaluation, Biology of Sport, The Journal of Strength and Conditioning Research, Machine Learning and International Journal of Applied Mathematics and Computer Science.

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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