R Elakkiya

1.4k total citations
68 papers, 795 citations indexed

About

R Elakkiya is a scholar working on Artificial Intelligence, Human-Computer Interaction and Computer Vision and Pattern Recognition. According to data from OpenAlex, R Elakkiya has authored 68 papers receiving a total of 795 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 22 papers in Human-Computer Interaction and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in R Elakkiya's work include Hand Gesture Recognition Systems (22 papers), Natural Language Processing Techniques (10 papers) and Human Pose and Action Recognition (10 papers). R Elakkiya is often cited by papers focused on Hand Gesture Recognition Systems (22 papers), Natural Language Processing Techniques (10 papers) and Human Pose and Action Recognition (10 papers). R Elakkiya collaborates with scholars based in India, United Arab Emirates and Russia. R Elakkiya's co-authors include B Natarajan, K. Selvamani, V. Subramaniyaswamy, Ketan Kotecha, Pandi Vijayakumar, Lubna A. Gabralla, Ajith Abraham, Srete Nikolovski, Belqasem Aljafari and V. Indragandhi and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Power Electronics and Expert Systems with Applications.

In The Last Decade

R Elakkiya

56 papers receiving 744 citations

Peers

R Elakkiya
Vassilis Charissis United Kingdom
Sehat Ullah Pakistan
Andre L. C. Barczak New Zealand
Jiayang Liu China
Mauro Dragone Ireland
Venu Vasudevan United States
Xavier Baró Spain
Jehan Wickramasuriya United States
Yang Jiang China
Vassilis Charissis United Kingdom
R Elakkiya
Citations per year, relative to R Elakkiya R Elakkiya (= 1×) peers Vassilis Charissis

Countries citing papers authored by R Elakkiya

Since Specialization
Citations

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

Fields of papers citing papers by R Elakkiya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R Elakkiya

This figure shows the co-authorship network connecting the top 25 collaborators of R Elakkiya. A scholar is included among the top collaborators of R Elakkiya 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 Elakkiya. R Elakkiya 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.
Elakkiya, R, et al.. (2025). Building explainable artificial intelligence for reinforcement learning based debt collection recommender system using large language models. Engineering Applications of Artificial Intelligence. 159. 111622–111622. 1 indexed citations
2.
Anand, S., et al.. (2025). CHASHNIt for enhancing skin disease classification using GAN augmented hybrid model with LIME and SHAP based XAI heatmaps. Scientific Reports. 15(1). 31138–31138. 1 indexed citations
3.
Elakkiya, R, et al.. (2024). Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning. Biomedical Signal Processing and Control. 102. 107268–107268. 14 indexed citations
4.
Kumar, R Prasanna, et al.. (2024). Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach. Multimedia Tools and Applications. 84(25). 29949–29965. 3 indexed citations
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Elakkiya, R, et al.. (2024). Flexible recommendation for optimizing the debt collection process based on customer risk using deep reinforcement learning. Expert Systems with Applications. 256. 124951–124951. 7 indexed citations
7.
Elakkiya, R, et al.. (2024). Data-driven approach for identifying the factors related to debt collector performance. Journal of Open Innovation Technology Market and Complexity. 10(4). 100385–100385.
8.
Elakkiya, R, et al.. (2024). Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction. International Journal of Computational Intelligence Systems. 17(1). 7 indexed citations
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Elakkiya, R, et al.. (2024). Enhancing Road Semantic Segmentation Using Generative Adversarial Networks. 1–6. 1 indexed citations
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Elakkiya, R, et al.. (2023). A Systematic Study on Reinforcement Learning Based Applications. Energies. 16(3). 1512–1512. 71 indexed citations
13.
Kumar, R Prasanna, et al.. (2023). Image Captioning for Chest X-Rays Using GRU - Based Attention Mechanism. 519. 518–525.
14.
Kumar, R Prasanna, et al.. (2023). Transformer-based Models for Language Identification: A Comparative Study. 1–6. 2 indexed citations
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Elakkiya, R, et al.. (2023). Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers. ACS Omega. 9(1). 215–226. 5 indexed citations
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Elakkiya, R, et al.. (2021). Recognition of Russian and Indian Sign Languages Based on Machine Learning. 53–74. 4 indexed citations
19.
Elakkiya, R, et al.. (2020). Recognition of Russian and Indian sign languages used by the deaf people. Science Bulletin of the Novosibirsk State Technical University. 57–76. 3 indexed citations
20.
Elakkiya, R & K. Selvamani. (2019). Subunit sign modeling framework for continuous sign language recognition. Computers & Electrical Engineering. 74. 379–390. 28 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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