S. Thomas George

1.6k total citations
69 papers, 907 citations indexed

About

S. Thomas George is a scholar working on Cognitive Neuroscience, Signal Processing and Artificial Intelligence. According to data from OpenAlex, S. Thomas George has authored 69 papers receiving a total of 907 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Cognitive Neuroscience, 23 papers in Signal Processing and 13 papers in Artificial Intelligence. Recurrent topics in S. Thomas George's work include EEG and Brain-Computer Interfaces (36 papers), Blind Source Separation Techniques (20 papers) and ECG Monitoring and Analysis (10 papers). S. Thomas George is often cited by papers focused on EEG and Brain-Computer Interfaces (36 papers), Blind Source Separation Techniques (20 papers) and ECG Monitoring and Analysis (10 papers). S. Thomas George collaborates with scholars based in India, United States and Iraq. S. Thomas George's co-authors include M. S. P. Subathra, N. J. Sairamya, Mazin Abed Mohammed, Easter S. Suviseshamuthu, J. Prasanna, D. Narain Ponraj, M. Premkumar, Balakrishnan Ramasamy, Nallapaneni Manoj Kumar and Robertas Damaševičius and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

S. Thomas George

59 papers receiving 874 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. Thomas George India 17 452 192 161 143 120 69 907
Yunyuan Gao China 17 636 1.4× 153 0.8× 123 0.8× 91 0.6× 62 0.5× 59 898
Sugondo Hadiyoso Indonesia 14 284 0.6× 122 0.6× 109 0.7× 170 1.2× 77 0.6× 150 729
Ömer Faruk Alçin Türkiye 16 365 0.8× 105 0.5× 138 0.9× 134 0.9× 121 1.0× 39 685
Ali Hassan Pakistan 19 313 0.7× 314 1.6× 279 1.7× 123 0.9× 93 0.8× 91 1.1k
M. L. Dewal India 16 482 1.1× 345 1.8× 156 1.0× 139 1.0× 83 0.7× 67 1.1k
S. Raghu India 14 812 1.8× 468 2.4× 166 1.0× 201 1.4× 62 0.5× 21 1.1k
Mahmut Hekim Türkiye 7 400 0.9× 274 1.4× 138 0.9× 95 0.7× 44 0.4× 23 663
Shahab Abdulla Australia 21 418 0.9× 203 1.1× 182 1.1× 113 0.8× 155 1.3× 65 1.1k
A. Sharmila India 13 454 1.0× 250 1.3× 75 0.5× 115 0.8× 91 0.8× 51 689
Md. Kafiul Islam Bangladesh 14 565 1.3× 203 1.1× 60 0.4× 129 0.9× 94 0.8× 62 945

Countries citing papers authored by S. Thomas George

Since Specialization
Citations

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

Fields of papers citing papers by S. Thomas George

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Thomas George

This figure shows the co-authorship network connecting the top 25 collaborators of S. Thomas George. A scholar is included among the top collaborators of S. Thomas George 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. Thomas George. S. Thomas George 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.
George, S. Thomas, Geno Peter, Albert Alexander Stonier, et al.. (2025). Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN‐LSTM Model. IET Signal Processing. 2025(1).
2.
3.
Prasanna, J., et al.. (2024). Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern. Neural Computing and Applications. 36(31). 19417–19433.
4.
George, S. Thomas, et al.. (2024). A Bird’s Eye View Approach on the Usage of Deep Learning Methods in Lung Cancer Detection and Future Directions Using X-Ray and CT Images. Archives of Computational Methods in Engineering. 31(5). 2589–2609. 3 indexed citations
5.
Kanaga, E. Grace Mary, et al.. (2024). An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 238(7). 837–847.
6.
Manimegalai, P., S. Thomas George, Mazin Abed Mohammed, et al.. (2024). A systematic review of techniques and clinical evidence to adopt virtual reality in post-stroke upper limb rehabilitation. Virtual Reality. 28(4). 9 indexed citations
7.
George, S. Thomas, et al.. (2023). Nine novel ensemble models for solar radiation forecasting in Indian cities based on VMD and DWT integration with the machine and deep learning algorithms. Computers & Electrical Engineering. 108. 108691–108691. 25 indexed citations
8.
Kanaga, E. Grace Mary, et al.. (2022). Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 237(1). 134–143. 3 indexed citations
9.
George, S. Thomas, et al.. (2022). IntramuscularEMGclassifier for detecting myopathy and neuropathy. International Journal of Imaging Systems and Technology. 33(2). 659–669.
10.
George, S. Thomas, et al.. (2021). Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction. International Journal of Imaging Systems and Technology. 31(4). 2144–2157. 8 indexed citations
11.
George, S. Thomas, et al.. (2021). Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN. Clinical EEG and Neuroscience. 53(1). 12–23. 41 indexed citations
12.
George, S. Thomas, et al.. (2021). Lung cancer detection and classification with DGMM-RBCNN technique. Neural Computing and Applications. 33(22). 15601–15617. 38 indexed citations
13.
14.
George, S. Thomas, et al.. (2020). Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders. IEEE Open Journal of Engineering in Medicine and Biology. 1. 235–242. 13 indexed citations
15.
Subathra, M. S. P., Nallapaneni Manoj Kumar, Maria Malvoni, et al.. (2020). A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network. Energies. 13(16). 4238–4238. 30 indexed citations
16.
George, S. Thomas, et al.. (2020). Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families. International Journal of Circuit Theory and Applications. 48(12). 2219–2241. 6 indexed citations
17.
George, S. Thomas, et al.. (2020). Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection. International Journal of Imaging Systems and Technology. 30(4). 1324–1336. 15 indexed citations
18.
Prasanna, J., M. S. P. Subathra, Mazin Abed Mohammed, et al.. (2020). Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. Sensors. 20(17). 4952–4952. 48 indexed citations
19.
George, S. Thomas, et al.. (2019). DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications. Signal Image and Video Processing. 14(3). 601–608. 10 indexed citations
20.
George, S. Thomas, et al.. (2019). Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms. Clinical EEG and Neuroscience. 51(1). 19–33. 18 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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