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.
Performance of deep learning vs machine learning in plant leaf disease detection
2020396 citationsR. Sujatha, Jyotir Moy Chatterjee et al.profile →
COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
2020317 citationsR. Sujatha, Jyotir Moy Chatterjee et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of R. Sujatha'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 R. Sujatha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Sujatha more than expected).
This network shows the impact of papers produced by R. Sujatha. 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 R. Sujatha. The network helps show where R. Sujatha may publish in the future.
Co-authorship network of co-authors of R. Sujatha
This figure shows the co-authorship network connecting the top 25 collaborators of R. Sujatha.
A scholar is included among the top collaborators of R. Sujatha 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 R. Sujatha. R. Sujatha is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sujatha, R. & D. Kavitha. (2018). Learner retention in MOOC environment: Analyzing the role of motivation, self-efficacy and perceived effectiveness. The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies). 14(2). 62–74.21 indexed citations
14.
Balasubramanian, A., et al.. (2018). Influence of Host on the Early Growth Performance of Sandal Tree (Santalum album) Grown in Farm Settings. International Journal of Ecology and Environmental Sciences. 44(4). 369–372.1 indexed citations
Sujatha, R., et al.. (2017). Healthcare Prediction Analysis in Big Data using Random Forest Classifier. International journal of advance research, ideas and innovations in technology. 3(2).3 indexed citations
17.
Krishnaveni, R. & R. Sujatha. (2012). Communities of Practice: An Influencing Factor for Effective Knowledge Transfer in Organizations. SSRN Electronic Journal. 26–40.19 indexed citations
18.
Seenivasan, C., Periyakali Saravana Bhavan, R. Sujatha, & Thirunavukkarasu Muralisankar. (2012). Effects of Probiotics on Survival, Growth and Biochemical Characteristics of Freshwater prawn Macrobrachium rosenbergii Post Larvae. Turkish Journal of Fisheries and Aquatic Sciences. 12(2). 331–338.15 indexed citations
19.
Seenivasan, C., Periyakali Saravana Bhavan, R. Sujatha, & R. Shanthi. (2012). Enrichment of Artemia nauplii with Lactobacillus sporogenes for Enhancing the Survival, Growth and Levels of Biochemical Constituents in the Post- Larvae of the Freshwater Prawn Macrobrachium rosenbergii. Turkish Journal of Fisheries and Aquatic Sciences. 12(1). 23–31.27 indexed citations
20.
Sujatha, R., et al.. (2010). Metazoan parasites of smooth-backed blow fish Lagocephalus inermis from Kerala, south-west coast of India. Indian Journal of Fisheries. 57(4). 71–76.3 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.