A. Suruliandi

615 total citations
70 papers, 396 citations indexed

About

A. Suruliandi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, A. Suruliandi has authored 70 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 17 papers in Media Technology. Recurrent topics in A. Suruliandi's work include Image Retrieval and Classification Techniques (26 papers), Face and Expression Recognition (17 papers) and Remote-Sensing Image Classification (13 papers). A. Suruliandi is often cited by papers focused on Image Retrieval and Classification Techniques (26 papers), Face and Expression Recognition (17 papers) and Remote-Sensing Image Classification (13 papers). A. Suruliandi collaborates with scholars based in India, Spain and Canada. A. Suruliandi's co-authors include K. Meena, S. P. Raja, K. Ramar, E. Srinivasan and P. Arockia Jansi Rani and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Medical Systems and Brazilian Archives of Biology and Technology.

In The Last Decade

A. Suruliandi

63 papers receiving 356 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
A. Suruliandi India	10	258	101	66	49	32	70	396
Silvio Jamil F. Guimarães Brazil	13	342 1.3×	85 0.8×	63 1.0×	74 1.5×	19 0.6×	75	443
Dora M. Ballesteros Colombia	12	322 1.2×	100 1.0×	45 0.7×	112 2.3×	17 0.5×	46	447
Diego Renza Colombia	13	296 1.1×	102 1.0×	101 1.5×	77 1.6×	34 1.1×	44	458
Qi Cui China	12	166 0.6×	70 0.7×	46 0.7×	28 0.6×	12 0.4×	36	335
Taisong Jin China	12	234 0.9×	118 1.2×	56 0.8×	34 0.7×	10 0.3×	30	391
Samuel G. Fadel Brazil	6	194 0.8×	158 1.6×	29 0.4×	52 1.1×	19 0.6×	9	332
Ivica Dimitrovski North Macedonia	12	244 0.9×	263 2.6×	78 1.2×	21 0.4×	19 0.6×	36	527
Karamjeet Singh India	11	212 0.8×	144 1.4×	93 1.4×	14 0.3×	15 0.5×	27	466
Samy Ait-Aoudia Algeria	11	154 0.6×	40 0.4×	60 0.9×	61 1.2×	29 0.9×	28	268

Countries citing papers authored by A. Suruliandi

Since Specialization

Citations

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

Fields of papers citing papers by A. Suruliandi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Suruliandi

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

All Works

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20 of 20 papers shown

Suruliandi, A., et al.. (2025). Prediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches.. International Journal of Interactive Multimedia and Artificial Intelligence. 9(4). 82–98.

Suruliandi, A., et al.. (2024). COVID-19 Severity Prediction Using Combined Machine Learning and Transfer Learning Approaches. Brazilian Archives of Biology and Technology. 67.

Suruliandi, A., et al.. (2022). A Hybrid Feature Ranking Algorithm for Assisted Reproductive Technology Outcome Prediction. Brazilian Archives of Biology and Technology. 65. 1 indexed citations

Suruliandi, A., et al.. (2021). Dynamic Model for Assisted Reproductive Technology Outcome Prediction. Brazilian Archives of Biology and Technology. 64. 3 indexed citations

Suruliandi, A., et al.. (2021). Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation.. International Journal of Interactive Multimedia and Artificial Intelligence. 7(2). 66–77. 6 indexed citations

Suruliandi, A., et al.. (2020). Balancing Assisted Reproductive Technology Dataset for Improving the Efficiency of Incremental Classifiers and Feature Selection Techniques. Journal of Circuits Systems and Computers. 30(6). 2130007–2130007. 5 indexed citations

Suruliandi, A., et al.. (2020). Person re-identification using kNN classifier-based fusion approach. International Journal of Advanced Intelligence Paradigms. 16(2). 113–113. 3 indexed citations

Suruliandi, A., et al.. (2019). Machine Learning Techniques for Assisted Reproductive Technology: A Review. Journal of Circuits Systems and Computers. 29(11). 2030010–2030010. 7 indexed citations

Suruliandi, A., et al.. (2018). Feature Extraction Using Dominant Local Texture-Color Patterns (DLTCP) and Classification of Color Images. Journal of Medical Systems. 42(11). 220–220. 6 indexed citations

10.

Suruliandi, A. & S. P. Raja. (2018). Secured image compression using AES in bandelet domain. International Journal of Computational Science and Engineering. 17(2). 208–208. 1 indexed citations

11.

Suruliandi, A., et al.. (2017). A survey on face annotation techniques. 1–9. 1 indexed citations

12.

Suruliandi, A., et al.. (2016). Semi-Supervised Personal Name Disambiguation Technique for the Web. International Journal of Modern Education and Computer Science. 8(3). 28–36. 2 indexed citations

13.

Meena, K., et al.. (2016). An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition. Brazilian Archives of Biology and Technology. 59(spe2). 5 indexed citations

14.

Suruliandi, A., et al.. (2016). String Variant Alias Extraction Method using Ensemble Learner. International Journal of Intelligent Systems and Applications. 8(2). 59–65. 1 indexed citations

15.

Suruliandi, A., et al.. (2015). Comparison of Soft Computing Approaches for Texture Based Land Cover Classification of Remotely Sensed Image. Research Journal of Applied Sciences Engineering and Technology. 10(10). 1216–1226. 1 indexed citations

16.

Suruliandi, A., et al.. (2015). VALIDATING THE PERFORMANCE OF PERSONALIZATION TECHNIQUES IN SEARCH ENGINE. ICTACT Journal on Soft Computing. 5(3). 965–970.

17.

Suruliandi, A., et al.. (2014). LOCAL TEXTURE DESCRIPTION FRAMEWORK FOR TEXTURE BASED FACE RECOGNITION. SHILAP Revista de lepidopterología. 4(3). 773–784. 1 indexed citations

18.

Meena, K., et al.. (2014). Enhancing the performance of texture-based face recognition through multi-resolution techniques. International Journal of Biometrics. 6(4). 363–363. 2 indexed citations

19.

Srinivasan, E., K. Ramar, & A. Suruliandi. (2012). Color Image Segmentation using Fuzzy Local Texture Patterns. International Journal of Computer Applications. 41(18). 16–23.

20.

Suruliandi, A. & K. Ramar. (2008). Local Texture Patterns ¿ A Univariate Texture Model for Classification of Images. 5. 32–39. 23 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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