Vimal Shanmuganathan

1.2k total citations
32 papers, 665 citations indexed

About

Vimal Shanmuganathan is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vimal Shanmuganathan has authored 32 papers receiving a total of 665 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Networks and Communications, 8 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vimal Shanmuganathan's work include Network Security and Intrusion Detection (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Internet of Things and AI (3 papers). Vimal Shanmuganathan is often cited by papers focused on Network Security and Intrusion Detection (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Internet of Things and AI (3 papers). Vimal Shanmuganathan collaborates with scholars based in India, South Korea and Lebanon. Vimal Shanmuganathan's co-authors include Harold Robinson, Janmenjoy Nayak, Suyel Namasudra, Pratima Sharma, Bighnaraj Naik, Manju Khari, Seungmin Rho, Manohar Mishra, K. Lakshmi Narayanan and Rubén González Crespo and has published in prestigious journals such as Scientific Reports, Environmental Research and Applied Soft Computing.

In The Last Decade

Vimal Shanmuganathan

32 papers receiving 624 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vimal Shanmuganathan India 18 175 152 141 84 84 32 665
Ali Najm Jasim Iraq 16 128 0.7× 169 1.1× 125 0.9× 102 1.2× 62 0.7× 23 731
Senthil Kumar Jagatheesaperumal United States 15 213 1.2× 191 1.3× 152 1.1× 93 1.1× 72 0.9× 68 889
Avinash Sharma India 18 206 1.2× 195 1.3× 172 1.2× 151 1.8× 108 1.3× 113 964
M. J. Baqer Malaysia 10 108 0.6× 121 0.8× 105 0.7× 64 0.8× 50 0.6× 12 544
Revathi Sundarasekar India 8 245 1.4× 217 1.4× 206 1.5× 138 1.6× 103 1.2× 9 808
Pravin R. Kshirsagar India 19 200 1.1× 185 1.2× 143 1.0× 57 0.7× 90 1.1× 61 807
Nancy Victor India 16 170 1.0× 251 1.7× 192 1.4× 106 1.3× 77 0.9× 35 800
Abul Bashar Saudi Arabia 17 243 1.4× 156 1.0× 146 1.0× 143 1.7× 126 1.5× 67 683
Hamid Mcheick Canada 14 179 1.0× 159 1.0× 172 1.2× 98 1.2× 81 1.0× 106 680
Tariq Shahzad Pakistan 17 197 1.1× 218 1.4× 168 1.2× 50 0.6× 104 1.2× 74 799

Countries citing papers authored by Vimal Shanmuganathan

Since Specialization
Citations

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

Fields of papers citing papers by Vimal Shanmuganathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vimal Shanmuganathan

This figure shows the co-authorship network connecting the top 25 collaborators of Vimal Shanmuganathan. A scholar is included among the top collaborators of Vimal Shanmuganathan 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 Vimal Shanmuganathan. Vimal Shanmuganathan 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
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Pradeepa, S., K. S. Ravichandran, Vimal Shanmuganathan, et al.. (2024). Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique. Scientific Reports. 14(1). 28984–28984. 3 indexed citations
3.
Nayak, Janmenjoy, et al.. (2024). A Systematic Literature Review on Swarm Intelligence Based Intrusion Detection System: Past, Present and Future. Archives of Computational Methods in Engineering. 31(5). 2717–2784. 17 indexed citations
4.
Nauman, Ali, Muhammad Ali Jamshed, Malik Muhammad Saad, et al.. (2023). Injecting cognitive intelligence into beyond-5G networks: A MAC layer perspective. Computers & Electrical Engineering. 108. 108717–108717. 5 indexed citations
5.
Pradeepa, S., A. Revathi, Vimal Shanmuganathan, et al.. (2023). Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images. Scientific Reports. 13(1). 22803–22803. 13 indexed citations
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Biswas, Suparna, et al.. (2022). HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT based healthcare. Microprocessors and Microsystems. 104622–104622. 65 indexed citations
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Gopikumar, S., J. Rajesh Banu, Harold Robinson, et al.. (2021). Novel framework of GIS based automated monitoring process on environmental biodegradability and risk analysis using Internet of Things. Environmental Research. 194. 110621–110621. 17 indexed citations
11.
Jacob, I. Jeena, et al.. (2021). Image Retrieval Using Intensity Gradients and Texture Chromatic Pattern. International Journal of Data Warehousing and Mining. 17(1). 57–73. 1 indexed citations
12.
Subbulakshmi, P., et al.. (2021). Trend analysis using agglomerative hierarchical clustering approach for time series big data. The Journal of Supercomputing. 77(7). 6505–6524. 23 indexed citations
13.
Shanmuganathan, Vimal, et al.. (2021). OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO. EURASIP Journal on Image and Video Processing. 2021(1). 27 indexed citations
14.
Shanmuganathan, Vimal, L. Kalaivani, Seifedine Kadry, et al.. (2021). EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN. Information Technology And Control. 50(1). 171–187. 4 indexed citations
15.
Narayanan, K. Lakshmi, N. Muthukumaran, Harold Robinson, et al.. (2021). Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier. Computers, materials & continua/Computers, materials & continua (Print). 67(3). 3045–3055. 40 indexed citations
16.
Mishra, Manohar, et al.. (2021). A power quality detection and classification algorithm based on FDST and hyper-parameter tuned light-GBM using memetic firefly algorithm. Measurement. 187. 110260–110260. 22 indexed citations
17.
Nayak, Janmenjoy, Bighnaraj Naik, Pandit Byomakesha Dash, Alireza Souri, & Vimal Shanmuganathan. (2021). Hyper-parameter tuned light gradient boosting machine using memetic firefly algorithm for hand gesture recognition. Applied Soft Computing. 107. 107478–107478. 36 indexed citations
18.
Gopikumar, S., S. Raja, Harold Robinson, et al.. (2020). A method of landfill leachate management using internet of things for sustainable smart city development. Sustainable Cities and Society. 66. 102521–102521. 48 indexed citations
19.
Shanmuganathan, Vimal, Harold Robinson, Mohammad S. Khan, Manju Khari, & Amir H. Gandomi. (2020). R-CNN and wavelet feature extraction for hand gesture recognition with EMG signals. Neural Computing and Applications. 32(21). 16723–16736. 42 indexed citations
20.
Pradeepa, S., et al.. (2020). IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique. Electronics. 9(9). 1469–1469. 17 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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