M. Subramoniam

840 total citations · 1 hit paper
35 papers, 549 citations indexed

About

M. Subramoniam is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, M. Subramoniam has authored 35 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Biomedical Engineering, 15 papers in Electrical and Electronic Engineering and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in M. Subramoniam's work include Advanced Chemical Sensor Technologies (13 papers), Gas Sensing Nanomaterials and Sensors (12 papers) and Insect Pheromone Research and Control (7 papers). M. Subramoniam is often cited by papers focused on Advanced Chemical Sensor Technologies (13 papers), Gas Sensing Nanomaterials and Sensors (12 papers) and Insect Pheromone Research and Control (7 papers). M. Subramoniam collaborates with scholars based in India and United Arab Emirates. M. Subramoniam's co-authors include V A Binson, Luke Mathew, S. Madhu, J. Arun, S. Naveen, V. Rajini, Ajay Kumar and Immanuel Rajkumar and has published in prestigious journals such as Clinica Chimica Acta, IEEE Sensors Journal and Annals of Biomedical Engineering.

In The Last Decade

M. Subramoniam

31 papers receiving 534 citations

Hit Papers

A Review of Machine Learning Algorithms for Biomedical Ap... 2024 2026 2025 2024 10 20 30 40
What are hit papers?

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.

  1. A Review of Machine Learning Algorithms for Biomedical Applications
    2024 46 citations V A Binson, M. Subramoniam et al. Annals of Biomedical Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Subramoniam India 11 287 175 83 71 65 35 549
Selda Güney Türkiye 12 255 0.9× 120 0.7× 61 0.7× 8 0.1× 81 1.2× 38 414
Thomas Roddelkopf Germany 11 137 0.5× 94 0.5× 12 0.1× 6 0.1× 18 0.3× 50 317
Md. Ahasan Kabir Bangladesh 11 59 0.2× 110 0.6× 2 0.0× 9 0.1× 69 1.1× 35 330
Junlin Wang China 13 170 0.6× 26 0.1× 3 0.0× 7 0.1× 14 0.2× 60 544
Xue Qin China 12 50 0.2× 38 0.2× 7 0.1× 59 0.8× 69 1.1× 43 564
G. M. Naik India 10 74 0.3× 67 0.4× 3 0.0× 18 0.3× 23 0.4× 43 273
Marie-Louise O’Connell Ireland 7 86 0.3× 10 0.1× 11 0.1× 3 0.0× 29 0.4× 9 331
Peng Wan China 9 60 0.2× 64 0.4× 2 0.0× 11 0.2× 46 0.7× 42 489
G. Geetharamani India 9 68 0.2× 113 0.6× 5 0.1× 14 0.2× 27 0.4× 18 832

Countries citing papers authored by M. Subramoniam

Since Specialization
Citations

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

Fields of papers citing papers by M. Subramoniam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Subramoniam

This figure shows the co-authorship network connecting the top 25 collaborators of M. Subramoniam. A scholar is included among the top collaborators of M. Subramoniam 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 M. Subramoniam. M. Subramoniam 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.
Binson, V A, et al.. (2025). VOC pattern recognition for lung cancer detection using a compact E-nose system. Microchemical Journal. 218. 115581–115581.
2.
Subramoniam, M., et al.. (2025). IoT sensor-influenced intelligent waste management system using deep learning and multi-objective shortest pathrouting. Intelligent Buildings International. 17(2). 64–90. 1 indexed citations
3.
4.
Binson, V A, et al.. (2024). A Review of Machine Learning Algorithms for Biomedical Applications. Annals of Biomedical Engineering. 52(5). 1159–1183. 46 indexed citations breakdown →
5.
6.
Subramoniam, M., et al.. (2023). Transfer learning convolutional neural network with modified Lion optimization for multimodal biometric system. Computers & Electrical Engineering. 108. 108664–108664. 10 indexed citations
7.
Subramoniam, M., et al.. (2023). Precision Diagnostic Algorithm for Multisubtype Arrhythmia Classification. 25. 1–4. 1 indexed citations
8.
Subramoniam, M., et al.. (2022). Multimodal Biometric Reorganization System using Deep Learning Convolutional Neural Network. 2022 International Conference on Edge Computing and Applications (ICECAA). 1282–1286. 4 indexed citations
9.
Binson, V A, M. Subramoniam, & Luke Mathew. (2021). MOS Based Sensor Array System for the Detection of Human Breath Volatile Organic Compounds. Annals of the Romanian Society for Cell Biology. 9 indexed citations
10.
Binson, V A, M. Subramoniam, & Luke Mathew. (2021). Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods. Clinica Chimica Acta. 523. 231–238. 70 indexed citations
11.
Binson, V A, M. Subramoniam, & Luke Mathew. (2021). Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose. Journal of Breath Research. 15(4). 46003–46003. 58 indexed citations
12.
Binson, V A & M. Subramoniam. (2021). Design and development of an e-nose system for the diagnosis of pulmonary diseases. Acta of Bioengineering and Biomechanics. 23(1). 35–44. 39 indexed citations
13.
Subramoniam, M., et al.. (2020). Optimal object detection and tracking in occluded video using DNN and gravitational search algorithm. Soft Computing. 24(24). 18301–18320. 10 indexed citations
14.
Subramoniam, M., et al.. (2020). Machine Learning Based Robust Access for Multimodal Biometric Recognition. International Journal of Recent Technology and Engineering (IJRTE). 8(5). 1325–1329. 1 indexed citations
15.
Subramoniam, M., et al.. (2019). A hybrid gray wolf and genetic whale optimization algorithm for efficient moving object analysis. Multimedia Tools and Applications. 78(18). 26633–26659. 7 indexed citations
16.
Binson, V A & M. Subramoniam. (2018). Advances in Early Lung Cancer Detection: A Systematic Review. 1–5. 18 indexed citations
17.
Subramoniam, M., et al.. (2018). A robust single and multiple moving object detection, tracking and classification. Applied Computing and Informatics. 17(1). 2–18. 42 indexed citations
18.
Subramoniam, M., et al.. (2015). A non-invasive computer aided diagnosis of osteoarthritis from digitalx-ray images. Biomedical Research-tokyo. 26(4). 0. 7 indexed citations
19.
20.
Subramoniam, M. & V. Rajini. (2013). Statistical feature based classification of arthritis in knee X-ray images using local binary pattern. 42. 873–875. 4 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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