Dimitar Kazakov

803 citations
64 papers · 380 · h-index 10

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Advanced Text Analysis Techniques
    • Text and Document Classification Technologies
    • Semantic Web and Ontologies

Papers in

Dimitar Kazakov

56 papers receiving 339 citations

Peers

Dimitar Kazakov
Comparison fields: 5 of 82
  • Artificial Intelligence 218
  • Signal Processing 40
  • Information Systems 78
  • Management Science and Operations Research 38
  • Cultural Studies 23
Replace Gábor Bella with:
Gábor Bella Italy
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Weihua Peng China
Juan‐Manuel Torres‐Moreno France
Weitao Ma China
Teven Le Scao United States
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Citations per field
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Citations per year

Countries citing papers authored by Dimitar Kazakov

Since Specialization
Citations

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

Fields of papers citing papers by Dimitar Kazakov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dimitar Kazakov, 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 Dimitar Kazakov Line = papers co-authored together Dimitar Kazakov links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200492
2 200125
3 201424
4 200322
5 201414
6
Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments
200513
7 200510
8 201110
9 20129
10 20209
11 20099
12 20218
13 20048
14 20128
15
A Hybrid Approach t Word Segmentation
19987
16
A Clustering Approach to Generalized Pattern Identification Based on Multi-instanced Objects with DARA
20077
17
Learning from Ordinal Data with Inductive Logic Programming in Description Logic.
20176
18 20105
19 20205
20 20205

About Dimitar Kazakov

Dimitar Kazakov is a scholar working on Artificial Intelligence, Information Systems, Economics and Econometrics, Signal Processing and Computational Theory and Mathematics, having authored 64 papers that have together received 380 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Complex Systems and Time Series Analysis (8 papers), Topic Modeling (8 papers), Stock Market Forecasting Methods (7 papers), Semantic Web and Ontologies (7 papers), Data Mining Algorithms and Applications (7 papers), Language and cultural evolution (6 papers) and Syntax, Semantics, Linguistic Variation (6 papers). The work is most often cited by research in Artificial Intelligence (218 citations), Signal Processing (40 citations), Information Systems (78 citations), Management Science and Operations Research (38 citations) and Cultural Studies (23 citations). Dimitar Kazakov has collaborated with scholars based in United Kingdom, Indonesia and United States. Frequent co-authors include Suresh Manandhar, Mark Bartlett, Daniel Kudenko⋆, Eduardo Alonso, Rayner Alfred, Iain Bate, Ahmad Nurul Fajar, Ahmad Raza Shahid, David C. Osmon and Octavio A. Santos. Their work appears in journals such as Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems, Cognitive Computation, Applied Artificial Intelligence, Machine Learning and Connection 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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