Sunil Kumar Prabhakar

1.4k total citations
88 papers, 775 citations indexed

About

Sunil Kumar Prabhakar is a scholar working on Cognitive Neuroscience, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Sunil Kumar Prabhakar has authored 88 papers receiving a total of 775 indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Cognitive Neuroscience, 47 papers in Signal Processing and 39 papers in Artificial Intelligence. Recurrent topics in Sunil Kumar Prabhakar's work include EEG and Brain-Computer Interfaces (61 papers), Blind Source Separation Techniques (41 papers) and Neural Networks and Applications (24 papers). Sunil Kumar Prabhakar is often cited by papers focused on EEG and Brain-Computer Interfaces (61 papers), Blind Source Separation Techniques (41 papers) and Neural Networks and Applications (24 papers). Sunil Kumar Prabhakar collaborates with scholars based in South Korea, India and Nigeria. Sunil Kumar Prabhakar's co-authors include Harikumar Rajaguru, Seong‐Whan Lee, Dong-Ok Won, Sun‐Hee Kim, Deepa Kumari, Chulho Kim, Semin Ryu, In Cheol Jeong, Jae Jun Lee and Jong Ho Kim and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Sunil Kumar Prabhakar

80 papers receiving 593 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sunil Kumar Prabhakar South Korea 15 403 281 251 141 105 88 775
Mahmut Hekim Türkiye 7 400 1.0× 138 0.5× 274 1.1× 95 0.7× 59 0.6× 23 663
Mohammad-Parsa Hosseini United States 11 457 1.1× 110 0.4× 146 0.6× 108 0.8× 28 0.3× 14 766
Péter Kovács Hungary 9 440 1.1× 79 0.3× 318 1.3× 182 1.3× 32 0.3× 49 678
Sugondo Hadiyoso Indonesia 14 284 0.7× 109 0.4× 122 0.5× 170 1.2× 30 0.3× 150 729
Jinpeng Li China 16 972 2.4× 362 1.3× 150 0.6× 112 0.8× 25 0.2× 45 1.5k
Deon Garrett United States 7 453 1.1× 177 0.6× 200 0.8× 51 0.4× 31 0.3× 15 695
Juan Guerrero Spain 19 256 0.6× 120 0.4× 129 0.5× 442 3.1× 69 0.7× 63 905
Inung Wijayanto Indonesia 12 238 0.6× 82 0.3× 129 0.5× 65 0.5× 43 0.4× 90 438
Rekh Ram Janghel India 16 301 0.7× 298 1.1× 43 0.2× 358 2.5× 50 0.5× 62 977
Mahmoud Shoman Egypt 7 232 0.6× 142 0.5× 74 0.3× 46 0.3× 52 0.5× 14 519

Countries citing papers authored by Sunil Kumar Prabhakar

Since Specialization
Citations

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

Fields of papers citing papers by Sunil Kumar Prabhakar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunil Kumar Prabhakar. 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 Sunil Kumar Prabhakar. The network helps show where Sunil Kumar Prabhakar may publish in the future.

Co-authorship network of co-authors of Sunil Kumar Prabhakar

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

All Works

20 of 20 papers shown
1.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2025). Pragmatic Models for Detection of Hypertension Using Ballistocardiograph Signals and Machine Learning. Bioengineering. 13(1). 43–43.
2.
Prabhakar, Sunil Kumar, Chulho Kim, Jae Jun Lee, et al.. (2025). Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review. Biomedical Engineering Letters. 15(4). 717–734. 2 indexed citations
3.
Prabhakar, Sunil Kumar, et al.. (2024). HM–GDM: Hybrid Measures and Graph-Dependent Modeling for Environmental Sound Classification. International Journal of Computational Intelligence Systems. 17(1).
4.
Prabhakar, Sunil Kumar, Harikumar Rajaguru, & Dong-Ok Won. (2024). Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification. Diagnostics. 14(17). 1857–1857.
5.
Prabhakar, Sunil Kumar, Jae Jun Lee, & Dong-Ok Won. (2024). Ensemble Fusion Models Using Various Strategies and Machine Learning for EEG Classification. Bioengineering. 11(10). 986–986. 3 indexed citations
6.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2023). Phonocardiogram signal classification for the detection of heart valve diseases using robust conglomerated models. Expert Systems with Applications. 221. 119720–119720. 7 indexed citations
7.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2023). Efficient strategies for finger movement classification using surface electromyogram signals. Frontiers in Neuroscience. 17. 1168112–1168112. 2 indexed citations
8.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2023). Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals. Frontiers in Artificial Intelligence. 6. 1156269–1156269. 3 indexed citations
9.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2023). HISET: Hybrid interpretable strategies with ensemble techniques for respiratory sound classification. Heliyon. 9(8). e18466–e18466. 6 indexed citations
10.
Prabhakar, Sunil Kumar, Harikumar Rajaguru, Chulho Kim, & Dong-Ok Won. (2022). A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification. Frontiers in Human Neuroscience. 16. 895761–895761. 6 indexed citations
11.
Prabhakar, Sunil Kumar, et al.. (2022). Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification. Frontiers in Computational Neuroscience. 16. 1016516–1016516. 1 indexed citations
12.
Prabhakar, Sunil Kumar, et al.. (2022). A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network. Frontiers in Computational Neuroscience. 16. 900885–900885. 5 indexed citations
13.
Prabhakar, Sunil Kumar, Semin Ryu, In Cheol Jeong, & Dong-Ok Won. (2022). A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia. BioMed Research International. 2022(1). 2052061–2052061. 4 indexed citations
14.
Prabhakar, Sunil Kumar & Dong-Ok Won. (2021). Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention. Computational Intelligence and Neuroscience. 2021(1). 9425655–9425655. 39 indexed citations
15.
Prabhakar, Sunil Kumar & Harikumar Rajaguru. (2020). Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification. Heliyon. 6(12). e05689–e05689. 19 indexed citations
16.
Prabhakar, Sunil Kumar & Seong‐Whan Lee. (2020). An Integrated Approach for Ovarian Cancer Classification With the Application of Stochastic Optimization. IEEE Access. 8. 127866–127882. 24 indexed citations
17.
Prabhakar, Sunil Kumar & Harikumar Rajaguru. (2017). Conceptual analysis of epilepsy classification using probabilistic mixture models. 37. 81–84. 7 indexed citations
18.
Rajaguru, Harikumar & Sunil Kumar Prabhakar. (2017). Epilepsy classification using fuzzy optimization and Kernel Fisher discriminant analysis. 8. 183–186. 3 indexed citations
19.
Rajaguru, Harikumar & Sunil Kumar Prabhakar. (2016). A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks. Circuits and Systems. 7(8). 1455–1464. 3 indexed citations
20.
Prabhakar, Sunil Kumar & Harikumar Rajaguru. (2016). ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES. Asian Journal of Pharmaceutical and Clinical Research. 9(5). 91–91. 5 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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