Amitava Datta

3.2k total citations
164 papers, 1.7k citations indexed

About

Amitava Datta is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Amitava Datta has authored 164 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Networks and Communications, 40 papers in Computer Vision and Pattern Recognition and 36 papers in Artificial Intelligence. Recurrent topics in Amitava Datta's work include Mobile Ad Hoc Networks (19 papers), Computational Geometry and Mesh Generation (15 papers) and Opportunistic and Delay-Tolerant Networks (13 papers). Amitava Datta is often cited by papers focused on Mobile Ad Hoc Networks (19 papers), Computational Geometry and Mesh Generation (15 papers) and Opportunistic and Delay-Tolerant Networks (13 papers). Amitava Datta collaborates with scholars based in Australia, India and Germany. Amitava Datta's co-authors include Asad Amir Pirzada, Chris McDonald, Ghulam Mubashar Hassan, Jana Sperschneider, Kwan Hui Lim, Rachel Cardell‐Oliver, Michael J. Wise, Max Ward, Swapan K. Parui and Abdülhamit Subaşı and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Amitava Datta

150 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amitava Datta Australia 23 552 308 255 235 232 164 1.7k
Xiaoyang Liu China 24 360 0.7× 411 1.3× 455 1.8× 239 1.0× 42 0.2× 131 1.7k
Michael E. Papka United States 26 1.0k 1.9× 209 0.7× 98 0.4× 545 2.3× 59 0.3× 180 2.2k
Larry M. Manevitz Israel 15 200 0.4× 924 3.0× 98 0.4× 215 0.9× 112 0.5× 50 1.5k
R.M. Goodman United States 24 380 0.7× 844 2.7× 372 1.5× 376 1.6× 63 0.3× 90 2.4k
Qing Wu China 21 128 0.2× 325 1.1× 100 0.4× 317 1.3× 140 0.6× 63 1.2k
Денис Бутусов Russia 21 320 0.6× 284 0.9× 290 1.1× 627 2.7× 65 0.3× 154 1.6k
Frederick Ducatelle Switzerland 17 1.1k 2.0× 388 1.3× 284 1.1× 374 1.6× 75 0.3× 28 2.1k
F. Sandoval Spain 24 322 0.6× 456 1.5× 503 2.0× 768 3.3× 56 0.2× 187 2.1k
Ioannis G. Tollis United States 21 410 0.7× 376 1.2× 252 1.0× 1.5k 6.3× 190 0.8× 111 2.8k
Hongxia Wang China 28 355 0.6× 392 1.3× 182 0.7× 1.7k 7.0× 60 0.3× 298 2.7k

Countries citing papers authored by Amitava Datta

Since Specialization
Citations

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

Fields of papers citing papers by Amitava Datta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amitava Datta

This figure shows the co-authorship network connecting the top 25 collaborators of Amitava Datta. A scholar is included among the top collaborators of Amitava Datta 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 Amitava Datta. Amitava Datta 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.
Li, Nan, et al.. (2025). Machine learning reveals novel targets for both glioblastoma and osteosarcoma. Heliyon. 11(5). e42997–e42997. 1 indexed citations
2.
Hassan, Ghulam Mubashar, et al.. (2025). MMPU-Net: A parameter-efficient network for fine-stage of pancreas and pancreas-tumor segmentation on CT scans. Biomedical Signal Processing and Control. 110. 108224–108224.
3.
Farhadinia, Bahram, et al.. (2024). A generalized optimization-based generative adversarial network. Expert Systems with Applications. 248. 123413–123413. 2 indexed citations
4.
Hassan, Ghulam Mubashar, et al.. (2023). MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG. Biomedical Signal Processing and Control. 84. 104780–104780. 20 indexed citations
5.
Hassan, Ghulam Mubashar, et al.. (2023). Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM. Sensors. 23(13). 5960–5960. 17 indexed citations
6.
Keating, Adrian, et al.. (2023). Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation. Applied Soft Computing. 144. 110546–110546. 7 indexed citations
7.
Hassan, Ghulam Mubashar, et al.. (2023). Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review. Applied Sciences. 13(24). 13280–13280. 6 indexed citations
8.
Wise, Michael J., et al.. (2022). Deep learning models for RNA secondary structure prediction (probably) do not generalize across families. Bioinformatics. 38(16). 3892–3899. 40 indexed citations
9.
Cardell‐Oliver, Rachel, et al.. (2020). An Advanced Sensor Placement Strategy for Small Leaks Quantification Using Lean Graphs. Water. 12(12). 3439–3439. 5 indexed citations
10.
Datta, Amitava, et al.. (2019). Comprehensive Review on Deep Learning for Neuronal Disorders. UWA Profiles and Research Repository (University of Western Australia). 9(1). 27–44. 1 indexed citations
11.
Chatterjee, C., L. Wen, Kevin Vinsen, M. Kovalam, & Amitava Datta. (2019). Using deep learning to localize gravitational wave sources. Physical review. D. 100(10). 26 indexed citations
12.
Datta, Amitava, et al.. (2018). Influence Propagation Model for Clique-Based Community Detection in Social Networks. IEEE Transactions on Computational Social Systems. 5(2). 563–575. 37 indexed citations
13.
Datta, Amitava, et al.. (2014). An Interactive Multimedia Development Life Cycle Model Based on a Cognitive Theory of Multimedia Learning. UWA Profiles and Research Repository (University of Western Australia). 2014(1). 746–761. 4 indexed citations
14.
Datta, Amitava, et al.. (2014). The Design and Implementation of an Educational Multimedia Mathematics Software: Using ADDIE to Guide Instructional System Design. UWA Profiles and Research Repository (University of Western Australia). 4(1). 37–49. 25 indexed citations
15.
Datta, Amitava, et al.. (2012). A Computer-Assisted Framework Based on a Cognitivist Learning Theory for Teaching Mathematics in the Early Primary Years.. UWA Profiles and Research Repository (University of Western Australia). 27(2). 1–12. 1 indexed citations
16.
Datta, Amitava, et al.. (2004). Visual Mining of Market Basket Association Rules. Lecture notes in computer science. 3046. 479–488. 1 indexed citations
17.
Datta, Amitava, et al.. (2003). A Fragment Culling Technique for Rendering Arbitrary Portals. Lecture notes in computer science. 2657. 915–924. 1 indexed citations
18.
Datta, Amitava, et al.. (2003). Techniques for Accelerated View-Dependent Mesh Refinement. UWA Profiles and Research Repository (University of Western Australia). 479–488. 2 indexed citations
19.
Datta, Amitava, et al.. (2003). Synthesising Textures Using Variable Neighbourhood Searching. UWA Profiles and Research Repository (University of Western Australia). 643–652. 2 indexed citations
20.
Datta, Amitava & Kamala Krithivasan. (1990). Efficient Algorithms for the Maximum Empty Rectangle Problem in Shared Memory and Other Architectures.. Proceedings of the International Conference on Parallel Processing. 344–345. 3 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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