S. Shenbaga Devi

451 total citations
32 papers, 284 citations indexed

About

S. Shenbaga Devi is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, S. Shenbaga Devi has authored 32 papers receiving a total of 284 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 12 papers in Cognitive Neuroscience and 11 papers in Artificial Intelligence. Recurrent topics in S. Shenbaga Devi's work include EEG and Brain-Computer Interfaces (12 papers), Image and Signal Denoising Methods (9 papers) and ECG Monitoring and Analysis (6 papers). S. Shenbaga Devi is often cited by papers focused on EEG and Brain-Computer Interfaces (12 papers), Image and Signal Denoising Methods (9 papers) and ECG Monitoring and Analysis (6 papers). S. Shenbaga Devi collaborates with scholars based in India, United States and Thailand. S. Shenbaga Devi's co-authors include Vikrant Bhateja, Rengaraj Venkatesh, K. Vidhya, M. Sasikala, Raja J. Selvaraj, Santhosh Satheesh, Meenakshi Sood, Muthu Subash Kavitha, S. Saraswathi and Sanjoy Das and has published in prestigious journals such as Journal of Applied Biomedicine, Journal of Applied Sciences and Annals of Noninvasive Electrocardiology.

In The Last Decade

S. Shenbaga Devi

32 papers receiving 271 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Shenbaga Devi India 12 129 71 66 63 51 32 284
Tessamma Thomas India 10 137 1.1× 49 0.7× 61 0.9× 23 0.4× 35 0.7× 62 343
Wenhui Huang China 10 121 0.9× 118 1.7× 16 0.2× 33 0.5× 70 1.4× 33 334
R. Gopikakumari India 10 174 1.3× 34 0.5× 60 0.9× 28 0.4× 19 0.4× 39 286
Shunbo Hu China 10 156 1.2× 100 1.4× 19 0.3× 25 0.4× 55 1.1× 47 310
Geet Sahu India 9 262 2.0× 28 0.4× 151 2.3× 34 0.5× 22 0.4× 17 359
Rashima Mahajan India 9 84 0.7× 61 0.9× 40 0.6× 86 1.4× 27 0.5× 32 264
Charturong Tantibundhit Thailand 9 82 0.6× 97 1.4× 14 0.2× 52 0.8× 30 0.6× 53 346
Abu Sayeed Bangladesh 9 114 0.9× 63 0.9× 36 0.5× 20 0.3× 41 0.8× 35 258
Jun-Mo Kim South Korea 6 180 1.4× 29 0.4× 29 0.4× 45 0.7× 17 0.3× 16 277
Ritwik Kumar United States 11 196 1.5× 51 0.7× 30 0.5× 11 0.2× 14 0.3× 16 307

Countries citing papers authored by S. Shenbaga Devi

Since Specialization
Citations

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

Fields of papers citing papers by S. Shenbaga Devi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Shenbaga Devi. 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 S. Shenbaga Devi. The network helps show where S. Shenbaga Devi may publish in the future.

Co-authorship network of co-authors of S. Shenbaga Devi

This figure shows the co-authorship network connecting the top 25 collaborators of S. Shenbaga Devi. A scholar is included among the top collaborators of S. Shenbaga Devi 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 S. Shenbaga Devi. S. Shenbaga Devi 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.
Devi, S. Shenbaga, et al.. (2024). The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG). Biomedical Physics & Engineering Express. 10(4). 45007–45007. 3 indexed citations
2.
Devi, S. Shenbaga, et al.. (2024). IDENTIFICATION OF SCHIZOPHRENIA USING ALPHA POWER AND THETA PEAK FREQUENCY DURING COGNITIVE ACTIVITY. Biomedical Engineering Applications Basis and Communications. 36(6). 1 indexed citations
3.
Devi, S. Shenbaga, et al.. (2023). A method for noninvasive beat‐by‐beat visualization of His bundle signals. Annals of Noninvasive Electrocardiology. 28(5). e13076–e13076. 1 indexed citations
4.
Devi, S. Shenbaga, et al.. (2021). An epoch based methodology to denoise magnetocardiogram (MCG) signals and its application to measurements on subjects with implanted devices. Biomedical Physics & Engineering Express. 7(3). 35006–35006. 11 indexed citations
5.
Devi, S. Shenbaga, et al.. (2019). New Similarity Measure between Intuitionistic Fuzzy Multisets based on Tangent Function and its Application in Medical Diagnosis. International Journal of Recent Technology and Engineering (IJRTE). 8(2S3). 161–165. 2 indexed citations
6.
Devi, S. Shenbaga, et al.. (2019). EEG power spectrum analysis for schizophrenia during mental activity. Australasian Physical & Engineering Sciences in Medicine. 42(3). 887–897. 18 indexed citations
7.
Devi, S. Shenbaga, et al.. (2018). Peak frequency analysis for schizophrenia using electroencephalogram power spectrum during mental activity. International Journal of Biomedical Engineering and Technology. 28(1). 18–18. 3 indexed citations
8.
Devi, S. Shenbaga, et al.. (2015). Wavelet analysis of EEG for seizure detection: Coherence and phasesynchrony estimation.. Biomedical Research-tokyo. 26(3). 0. 9 indexed citations
9.
Devi, S. Shenbaga, et al.. (2015). Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection. Biomedical Papers. 159(4). 607–615. 20 indexed citations
10.
Devi, S. Shenbaga, et al.. (2015). Analysis of Spectral Features of EEG signal in Brain Tumor Condition. Measurement Science Review. 15(4). 219–225. 17 indexed citations
11.
Kavitha, Muthu Subash, et al.. (2012). Fuzzy-Based Classification of Breast Lesions Using Ultrasound Echography and Elastography. Ultrasound Quarterly. 28(3). 159–167. 9 indexed citations
12.
Bhateja, Vikrant & S. Shenbaga Devi. (2011). A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter. 38. 419–424. 20 indexed citations
13.
Vidhya, K. & S. Shenbaga Devi. (2011). Medical image compression with edge detection. International Journal of Biomedical Engineering and Technology. 6(4). 413–413. 1 indexed citations
14.
Vidhya, K. & S. Shenbaga Devi. (2011). Medical image compression using SPIHT technique. International Journal of Biomedical Engineering and Technology. 6(3). 295–295. 1 indexed citations
15.
Devi, S. Shenbaga, et al.. (2011). A new Method for Color Image Quality Assessment. International Journal of Computer Applications. 15(2). 10–17. 40 indexed citations
16.
Devi, S. Shenbaga, et al.. (2010). Grayscale image quality measure in spatial domain. 224–229. 1 indexed citations
17.
Bhateja, Vikrant & S. Shenbaga Devi. (2010). Mammographic Image Enhancement using Double Sigmoid Transformation Function. 259–264. 11 indexed citations
18.
Devi, S. Shenbaga, et al.. (2009). An approach to automated classification of epileptic seizures using Artificial Neural Network. International Journal of Biomedical Engineering and Technology. 2(4). 382–382. 3 indexed citations
19.
Devi, S. Shenbaga, et al.. (2007). Medical Image Fusion Transforms-2D Approach. Journal of Medical Sciences(Faisalabad). 7(5). 870–874. 2 indexed citations
20.
Devi, S. Shenbaga, et al.. (2007). Transform-based medical image fusion. International Journal of Biomedical Engineering and Technology. 1(1). 101–101. 15 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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