K. Premalatha

1.4k total citations
77 papers, 927 citations indexed

About

K. Premalatha is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, K. Premalatha has authored 77 papers receiving a total of 927 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 24 papers in Molecular Biology and 20 papers in Information Systems. Recurrent topics in K. Premalatha's work include Gene expression and cancer classification (21 papers), Data Mining Algorithms and Applications (13 papers) and Evolutionary Algorithms and Applications (11 papers). K. Premalatha is often cited by papers focused on Gene expression and cancer classification (21 papers), Data Mining Algorithms and Applications (13 papers) and Evolutionary Algorithms and Applications (11 papers). K. Premalatha collaborates with scholars based in India and China. K. Premalatha's co-authors include S. Kannimuthu, A.M. Natarajan, C. Gunavathi, G. Sathish Kumar, D Mathai, Anand Manoharan, P. Rajesh Kanna, M. Gunasekaran, Mahabalesh Shetty and R. Balamurugan and has published in prestigious journals such as Expert Systems with Applications, Applied Soft Computing and Chaos Solitons & Fractals.

In The Last Decade

K. Premalatha

65 papers receiving 805 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
K. Premalatha India 17 422 185 152 117 107 77 927
Rajat Kumar Pal India 16 240 0.6× 70 0.4× 87 0.6× 94 0.8× 112 1.0× 141 923
Patrick Gallinari France 18 726 1.7× 202 1.1× 160 1.1× 46 0.4× 97 0.9× 88 1.3k
Wenbo Mao China 13 425 1.0× 256 1.4× 111 0.7× 52 0.4× 317 3.0× 37 889
Kolin Paul India 17 110 0.3× 83 0.4× 71 0.5× 151 1.3× 347 3.2× 123 1.0k
Meng Han China 13 438 1.0× 124 0.7× 163 1.1× 95 0.8× 53 0.5× 50 1.1k
Xiao Huang Hong Kong 23 1.4k 3.4× 444 2.4× 219 1.4× 57 0.5× 238 2.2× 94 2.0k
Jamie Twycross United Kingdom 18 291 0.7× 45 0.2× 271 1.8× 22 0.2× 258 2.4× 37 1.1k
Jonathan Vincent United Kingdom 13 528 1.3× 105 0.6× 176 1.2× 229 2.0× 146 1.4× 26 1.3k

Countries citing papers authored by K. Premalatha

Since Specialization
Citations

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

Fields of papers citing papers by K. Premalatha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Premalatha

This figure shows the co-authorship network connecting the top 25 collaborators of K. Premalatha. A scholar is included among the top collaborators of K. Premalatha 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 K. Premalatha. K. Premalatha 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.
2.
Premalatha, K., et al.. (2024). Termite Diversity and its Extent of Crop Damage in Vayalogam Soil Series of Tamil Nadu, India. Journal of Advances in Biology & Biotechnology. 27(5). 178–184.
3.
Premalatha, K., et al.. (2024). HERBIVORE-INDUCED PLANT VOLATILES IN RICE: A NATURAL DEFENSE MECHANISM SHAPING ARTHROPOD COMMUNITY. Applied Ecology and Environmental Research. 22(4). 3047–3058. 1 indexed citations
4.
Kumar, G. Sathish, et al.. (2023). Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation. Engineering Applications of Artificial Intelligence. 128. 107399–107399. 37 indexed citations
5.
Kumar, G. Sathish & K. Premalatha. (2023). STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation. Distributed and Parallel Databases. 41(3). 233–266. 8 indexed citations
6.
Premalatha, K., et al.. (2021). Haematological parameters of Cyprinus carpio with reference to probiotic feed: A machine learning approach. Israeli Journal of Aquaculture - Bamidgeh. 73. 4 indexed citations
7.
Premalatha, K., et al.. (2019). Particle Swarm Optimization for Triclustering High Dimensional Microarray Gene Expression Data. Research Journal of Pharmacy and Technology. 12(5). 2222–2222. 1 indexed citations
8.
Premalatha, K., et al.. (2018). TrioCuckoo: A Multi Objective Cuckoo Search Algorithm for Triclustering Microarray Gene Expression Data. Journal of information science and engineering. 34(6). 1617–1631. 7 indexed citations
9.
Premalatha, K., et al.. (2017). Integrated Cox model for survival analysis and biomarker discovery with a feature ranking technique based on z-score transformation in non-small cell lung cancer patients. Biomedical Research-tokyo. 28(5). 1975–1983. 2 indexed citations
10.
Premalatha, K., et al.. (2017). Cuckoo search with mutation for biclustering of microarray gene expression data.. The International Arab Journal of Information Technology. 14. 300–306. 3 indexed citations
11.
Premalatha, K., et al.. (2017). An effective Feature Selection for Heart Disease Prediction with Aid of Hybrid Kernel SVM. International Journal of Business Intelligence and Data Mining. 1(1). 1–1. 2 indexed citations
12.
Gunavathi, C., et al.. (2017). Biomarker Selection from Gene Expression Data for Tumour Categorization Using Bat Algorithm. International journal of intelligent engineering and systems. 10(3). 401–408. 2 indexed citations
13.
Premalatha, K., et al.. (2017). An application of fuzzy normalization in miRNA data for novel feature selection in cancer classification. Biomedical Research-tokyo. 28(9). 4187–4195. 2 indexed citations
14.
Premalatha, K., et al.. (2017). Privacy Preserving Data Clustering using hybrid Particle Swarm Optimization Algorithm. Asian Journal of Research in Social Sciences and Humanities. 7(3). 601–601. 1 indexed citations
15.
Premalatha, K., et al.. (2016). A Cuckoo Search With Differential Evolution For Clustering Microarray Gene Expression Data. Zenodo (CERN European Organization for Nuclear Research). 10(2). 379–384.
16.
Kannimuthu, S., K. Premalatha, & Shankar Subramanian. (2013). A Novel Approach to Extract High Utility Itemsets from Distributed Databases. Computing and Informatics / Computers and Artificial Intelligence. 31. 1597–1615. 9 indexed citations
17.
18.
Manoharan, Anand, et al.. (2011). Correlation of TEM, SHV and CTX-M extended-spectrum beta lactamases among Enterobacteriaceae with their in vitro antimicrobial susceptibility. Indian Journal of Medical Microbiology. 29(2). 161–164. 69 indexed citations
19.
Premalatha, K. & A.M. Natarajan. (2009). Genetic Algorithm for Document Clustering with Simultaneous and Ranked Mutation. Modern Applied Science. 3(2). 7 indexed citations
20.
Premalatha, K.. (2009). Hybrid PSO and GA for Global Maximization. 125 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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