Sunil Vadera

2.9k total citations · 1 hit paper
58 papers, 1.6k citations indexed

About

Sunil Vadera is a scholar working on Artificial Intelligence, Information Systems and Control and Systems Engineering. According to data from OpenAlex, Sunil Vadera has authored 58 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 15 papers in Information Systems and 6 papers in Control and Systems Engineering. Recurrent topics in Sunil Vadera's work include Imbalanced Data Classification Techniques (10 papers), AI-based Problem Solving and Planning (8 papers) and Bayesian Modeling and Causal Inference (8 papers). Sunil Vadera is often cited by papers focused on Imbalanced Data Classification Techniques (10 papers), AI-based Problem Solving and Planning (8 papers) and Bayesian Modeling and Causal Inference (8 papers). Sunil Vadera collaborates with scholars based in United Kingdom, Mexico and Egypt. Sunil Vadera's co-authors include Farid Meziane, Said A. Salloum, Tarek Gaber, Khaled Shaalan, K. A. H. Kobbacy, Luis Enrique Sucar, Nathan Proudlove, Taha Mansouri, Vijayan Sugumaran and Mohamad Saraee and has published in prestigious journals such as IEEE Transactions on Power Systems, Expert Systems with Applications and IEEE Access.

In The Last Decade

Sunil Vadera

53 papers receiving 1.5k citations

Hit Papers

Systematic software development using VDM 1986 2026 1999 2012 1986 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Systematic software development using VDM
    1986 624 citations Sunil Vadera profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sunil Vadera United Kingdom 16 847 449 334 320 206 58 1.6k
Luca Console Italy 22 1.1k 1.3× 427 1.0× 310 0.9× 256 0.8× 428 2.1× 73 1.8k
Patrizio Pelliccione Italy 24 1.1k 1.3× 851 1.9× 244 0.7× 677 2.1× 609 3.0× 165 2.0k
Howard Shrobe United States 21 1.2k 1.4× 520 1.2× 98 0.3× 238 0.7× 651 3.2× 86 2.1k
Radu Călinescu United Kingdom 22 1.0k 1.2× 688 1.5× 268 0.8× 528 1.6× 706 3.4× 146 1.9k
Pierre America Netherlands 15 1.3k 1.5× 892 2.0× 420 1.3× 370 1.2× 695 3.4× 52 2.1k
Alberto Sillitti Italy 26 496 0.6× 1.1k 2.3× 83 0.2× 307 1.0× 459 2.2× 116 1.8k
Dayang N. A. Jawawi Malaysia 18 500 0.6× 989 2.2× 130 0.4× 631 2.0× 426 2.1× 154 1.7k
Holger Giese Germany 21 965 1.1× 700 1.6× 365 1.1× 806 2.5× 488 2.4× 188 1.7k
Andrea Bondavalli Italy 23 558 0.7× 396 0.9× 243 0.7× 557 1.7× 1.0k 5.1× 236 2.1k
Rafael H. Bordini Brazil 20 1.8k 2.2× 348 0.8× 267 0.8× 97 0.3× 467 2.3× 124 2.3k

Countries citing papers authored by Sunil Vadera

Since Specialization
Citations

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

Fields of papers citing papers by Sunil Vadera

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunil Vadera

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Vadera. A scholar is included among the top collaborators of Sunil Vadera 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 Vadera. Sunil Vadera 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.
Gornale, Shivanand S., et al.. (2025). DPMMN: A dual performer-multi-modal network for emotion recognition. Signal Processing Image Communication. 142. 117464–117464.
2.
Vadera, Sunil, et al.. (2025). Vision transformers for automated detection of pig interactions in groups. Smart Agricultural Technology. 10. 100774–100774. 3 indexed citations
3.
Mansouri, Taha & Sunil Vadera. (2023). Explainable fault prediction using learning fuzzy cognitive maps. Expert Systems. 40(8). 7 indexed citations
4.
Salloum, Said A., Tarek Gaber, Sunil Vadera, & Khaled Shaalan. (2023). A New English/Arabic Parallel Corpus for Phishing Emails. ACM Transactions on Asian and Low-Resource Language Information Processing. 22(7). 1–17. 9 indexed citations
5.
Shi, Zhongzhi, Sunil Vadera, & Elizabeth Chang. (2020). Intelligent Information Processing X. IFIP advances in information and communication technology. 3 indexed citations
6.
Vadera, Sunil, et al.. (2019). Pruning Neural Networks Using Multi-Armed Bandits. The Computer Journal. 63(7). 1099–1108. 2 indexed citations
7.
Shi, Zhongzhi, Sunil Vadera, & Gang Li. (2016). Intelligent information processing VIII : 9th IFIP TC 12 International Conference, IIP 2016, Melbourne, VIC, Australia, November 18-21, 2016, Proceedings. CERN Document Server (European Organization for Nuclear Research). 4 indexed citations
8.
Vadera, Sunil, et al.. (2016). Learning cost-sensitive Bayesian networks via direct and indirect methods. Integrated Computer-Aided Engineering. 24(1). 17–26. 7 indexed citations
9.
10.
Kobbacy, K. A. H. & Sunil Vadera. (2011). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management. 22(6). 706–733. 31 indexed citations
11.
Wu, Jia, et al.. (2010). A comparison of data mining methods in microfinance. 97. 499–502. 3 indexed citations
12.
Meziane, Farid & Sunil Vadera. (2009). Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects. University of Salford Institutional Repository (University of Salford). 20 indexed citations
13.
Vadera, Sunil, et al.. (2007). Using Wittgenstein’s Family Resemblance Principle to Learn Exemplars. Foundations of Science. 13(1). 67–74. 3 indexed citations
14.
Vadera, Sunil, et al.. (2006). AI and OR in management of operations: history and trends. Journal of the Operational Research Society. 58(1). 10–28. 41 indexed citations
15.
Kobbacy, K. A. H. & Sunil Vadera. (2004). European Conference on Intelligent Management Systems in Operations. Journal of the Operational Research Society. 55(2). 101–146. 3 indexed citations
16.
Vadera, Sunil, et al.. (1999). PEBM: A Probabilistic Exemplar Based Model. International Joint Conference on Artificial Intelligence. 242–247. 2 indexed citations
17.
Vadera, Sunil, et al.. (1997). Machine Learning in LacePro Multi-Functional Framework.
18.
Vadera, Sunil & Farid Meziane. (1997). Tools for producing formal specifications: a view of current architectures and future directions. University of Salford Institutional Repository (University of Salford). 3(1). 273–290. 2 indexed citations
19.
Sucar, Luis Enrique, et al.. (1996). A probabilistic model for sensor validation. Uncertainty in Artificial Intelligence. 332–339. 16 indexed citations
20.
Vadera, Sunil. (1994). From English to Formal Specifications. The Computer Journal. 37(9). 753–763. 33 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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