Arunava Banerjee

1.5k total citations
54 papers, 986 citations indexed

About

Arunava Banerjee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Arunava Banerjee has authored 54 papers receiving a total of 986 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 13 papers in Cognitive Neuroscience. Recurrent topics in Arunava Banerjee's work include Neural dynamics and brain function (12 papers), Advanced Memory and Neural Computing (6 papers) and Face recognition and analysis (6 papers). Arunava Banerjee is often cited by papers focused on Neural dynamics and brain function (12 papers), Advanced Memory and Neural Computing (6 papers) and Face recognition and analysis (6 papers). Arunava Banerjee collaborates with scholars based in United States, India and Australia. Arunava Banerjee's co-authors include Anand Rangarajan, Ajit Rajwade, Debasis Kundu, Baba C. Vemuri, Anand Paul, Ritwik Kumar, Haym Hirsh, Hanspeter Pfister, Sofus A. Macskassy and Aydın Alptekinoğlu and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and European Journal of Operational Research.

In The Last Decade

Arunava Banerjee

49 papers receiving 945 citations

Peers

Arunava Banerjee
Comparison fields: 5 of 122
  • Computer Vision and Pattern Recognition 386
  • Artificial Intelligence 153
  • Computational Mechanics 123
  • Media Technology 120
  • Cognitive Neuroscience 95
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Citations per field, relative to Arunava Banerjee
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Citations per year, relative to Arunava Banerjee
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Countries citing papers authored by Arunava Banerjee

Since Specialization
Citations

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

Fields of papers citing papers by Arunava Banerjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arunava Banerjee

This figure shows the co-authorship network connecting the top 25 collaborators of Arunava Banerjee. A scholar is included among the top collaborators of Arunava Banerjee 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 Arunava Banerjee. Arunava Banerjee 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
# Work Indexed citations
1 39
2 2
3 1
4 2
5 278
6
Trait prediction using hierarchical probabilistic matrix factorization.
0
7 33
8 34
9
A Novel Kernel for Learning a Neuron Model from Spike Train Data
5
10 13
11 27
12 5
13
Evaluation of the technical details of bronchoscopic endobronchial sealing : Review of 67 patients
1
14 11
15 3
16 4
17
Dynamical Constraints on Computing with Spike Timing in the Cortex
1
18 15
19
Using text classifiers for numerical classification
7
20
Human performance on clustering web pages: a preliminary study
31

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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