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.
A Survey on Deep Learning
2018915 citationsSamira Pouyanfar, Saad Sadiq et al.ACM Computing Surveysprofile →
Grid coverage for surveillance and target location in distributed sensor networks
2002653 citationsKrishnendu Chakrabarty, S. S. Iyengar et al.IEEE Transactions on Computersprofile →
A Survey on Malware Detection Using Data Mining Techniques
2017413 citationsTao Li, S. S. Iyengar et al.ACM Computing Surveysprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of S. S. Iyengar'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 S. S. Iyengar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. S. Iyengar more than expected).
This network shows the impact of papers produced by S. S. Iyengar. 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 S. S. Iyengar. The network helps show where S. S. Iyengar may publish in the future.
Co-authorship network of co-authors of S. S. Iyengar
This figure shows the co-authorship network connecting the top 25 collaborators of S. S. Iyengar.
A scholar is included among the top collaborators of S. S. Iyengar 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 S. S. Iyengar. S. S. Iyengar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pouyanfar, Samira, Saad Sadiq, Yilin Yan, et al.. (2018). A Survey on Deep Learning. ACM Computing Surveys. 51(5). 1–36.915 indexed citations breakdown →
4.
Raghavendra, S, et al.. (2018). Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions. ePrints@Bangalore University (Bangalore University). 28(1). 8–57.15 indexed citations
5.
Pouyanfar, Samira, Yimin Yang, Shu‐Ching Chen, Mei‐Ling Shyu, & S. S. Iyengar. (2018). Multimedia Big Data Analytics. ACM Computing Surveys. 51(1). 1–34.145 indexed citations
6.
Venugopal, K. R., et al.. (2016). Query Recommendation based on Query Relevance Graph.. ePrints@Bangalore University (Bangalore University). 9(1). 3–26.1 indexed citations
Ye, Zhengmao, Habib Mohamadian, Su‐Seng Pang, & S. S. Iyengar. (2008). Contrast enhancement and clustering segmentation of gray level images with quantitative information evaluation. WSEAS Transactions on Information Science and Applications archive. 5(2). 181–188.7 indexed citations
13.
Iyengar, S. S., et al.. (2006). Augmentation of a term/document matrix with part-of-speech tags to improve accuracy of latent semantic analysis. 573–578.5 indexed citations
14.
Iyengar, S. S., et al.. (2005). Improving Prediction Accuracies Using Data Imputation.. Software Engineering Research and Practice. 741–747.2 indexed citations
Soloway, Elliot & S. S. Iyengar. (1986). Empirical studies of programmers : papers presented at the First Workshop on Empirical Studies of Programmers, June 5-6, 1986, Washington, D.C..1 indexed citations
19.
Oommen, B. John, S. S. Iyengar, Nageswara S. V. Rao, & R.L. Kashyap. (1986). Robot navigation in unknown terrains of convex polygonal obstacles using learned visibility graphs. National Conference on Artificial Intelligence. 1101–1105.11 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.