Subbaram Naidu

981 total citations
6 papers, 100 citations indexed

About

Subbaram Naidu is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Artificial Intelligence. According to data from OpenAlex, Subbaram Naidu has authored 6 papers receiving a total of 100 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Radiology, Nuclear Medicine and Imaging, 2 papers in Health Informatics and 2 papers in Artificial Intelligence. Recurrent topics in Subbaram Naidu's work include COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Subbaram Naidu is often cited by papers focused on COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Subbaram Naidu collaborates with scholars based in United States, India and Italy. Subbaram Naidu's co-authors include Suneet Kumar Gupta, Ayman El‐Baz, Zeno Falaschi, Luca Saba, Alessio Paschè, Alessandro Carriero, Luca Saba, Pietro Danna, Jasjit S. Suri and Mohit Agarwal and has published in prestigious journals such as IEEE Transactions on Instrumentation and Measurement, Multimedia Tools and Applications and Soft Computing.

In The Last Decade

Subbaram Naidu

5 papers receiving 98 citations

Peers

Subbaram Naidu

RNMI

AI

PRM

HI

CCM

Pietro Danna Italy

Ittai Dayan United States

Valeria Chernina Russia

Ivan A. Blokhin Russia

Anudeep Puvvula India

Tzu-Ming Harry Hsu United States

Maliazurina B. Saad United States

Benson A. Babu United States

Elvira Stellato Italy

M. Benkebil France

Pietro Danna Italy

Subbaram Naidu

80 ×1.9

RNMI

25 ×0.8

AI

23 ×1.0

PRM

10 ×0.5

HI

10 ×0.6

CCM

Citations per year, relative to Subbaram Naidu Subbaram Naidu (= 1×) peers Pietro Danna

Countries citing papers authored by Subbaram Naidu

Since Specialization

Citations

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

Fields of papers citing papers by Subbaram Naidu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Subbaram Naidu

This figure shows the co-authorship network connecting the top 25 collaborators of Subbaram Naidu. A scholar is included among the top collaborators of Subbaram Naidu 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 Subbaram Naidu. Subbaram Naidu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Tiwari, Ekta, Mustafa Al-Maini, Vijay Rathore, et al.. (2025). StockAI 3.0: Ensemble fusion paradigms using novel gating mechanism in long short-term memory architectures for forecasting sentiment-based stock trends. Soft Computing. 29(21-22). 5803–5829.

2.

Agarwal, Sushant, Alessandro Carriero, R. Gobinath, et al.. (2024). COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography. Frontiers in Artificial Intelligence. 7. 1304483–1304483. 2 indexed citations

3.

Kumar, Ashish, Suneet Kumar Gupta, Mrinalini Bhagawati, et al.. (2023). Artificial intelligence bias in medical system designs: a systematic review. Multimedia Tools and Applications. 83(6). 18005–18057. 18 indexed citations

4.

Suri, Jasjit S., Sushant Agarwal, Biswajit Jena, et al.. (2022). Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review. IEEE Transactions on Instrumentation and Measurement. 1–1. 15 indexed citations

5.

Agarwal, Mohit, Luca Saba, Suneet Kumar Gupta, et al.. (2021). A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort. Journal of Medical Systems. 45(3). 28–28. 41 indexed citations

6.

Viswanathan, Vijay, Anudeep Puvvula, Ankush D. Jamthikar, et al.. (2021). Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review. World Journal of Diabetes. 12(3). 215–237. 24 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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