Pronab Ghosh

1.6k total citations · 2 hit papers
25 papers, 967 citations indexed

About

Pronab Ghosh is a scholar working on Artificial Intelligence, Health Information Management and Epidemiology. According to data from OpenAlex, Pronab Ghosh has authored 25 papers receiving a total of 967 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 14 papers in Health Information Management and 4 papers in Epidemiology. Recurrent topics in Pronab Ghosh's work include Artificial Intelligence in Healthcare (14 papers), Machine Learning in Healthcare (6 papers) and AI in cancer detection (6 papers). Pronab Ghosh is often cited by papers focused on Artificial Intelligence in Healthcare (14 papers), Machine Learning in Healthcare (6 papers) and AI in cancer detection (6 papers). Pronab Ghosh collaborates with scholars based in Bangladesh, Australia and Canada. Pronab Ghosh's co-authors include Sami Azam, Asif Karim, F. M. Javed Mehedi Shamrat, Mirjam Jonkman, Friso De Boer, Shahana Shultana, Abhijith Reddy Beeravolu, Eva Ignatious, Zarrin Tasnim and Khan Md. Hasib and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Pronab Ghosh

23 papers receiving 921 citations

Hit Papers

align trajectories

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.

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

2021 322 citations Pronab Ghosh, Sami Azam et al. IEEE Access profile →
AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images

2023 101 citations F. M. Javed Mehedi Shamrat, Shamima Akter et al. IEEE Access profile →

Peers

Pronab Ghosh

HIM

RNMI

NEURO

CVPR

Soufiane Hamida Morocco

Taye Girma Debelee Ethiopia

Muhammad Hammad Memon China

Md. Rezwanul Haque Bangladesh

Manal Alghamdi Saudi Arabia

Ashir Javeed Sweden

Tahira Nazir Pakistan

Md. Nahiduzzaman Bangladesh

Javad Hassannataj Joloudari Iran

Shamik Tiwari India

Soufiane Hamida Morocco

Pronab Ghosh

450 ×1.0

133 ×0.3

HIM

236 ×0.9

RNMI

108 ×0.8

NEURO

171 ×1.5

CVPR

Citations per year, relative to Pronab Ghosh Pronab Ghosh (= 1×) peers Soufiane Hamida

Countries citing papers authored by Pronab Ghosh

Since Specialization

Citations

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

Fields of papers citing papers by Pronab Ghosh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pronab Ghosh

This figure shows the co-authorship network connecting the top 25 collaborators of Pronab Ghosh. A scholar is included among the top collaborators of Pronab Ghosh 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 Pronab Ghosh. Pronab Ghosh 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:

20 of 20 papers shown

Shamrat, F. M. Javed Mehedi, Pronab Ghosh, Xujuan Zhou, et al.. (2024). Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques. Heliyon. 10(17). e36556–e36556. 6 indexed citations

Ghosh, Pronab, et al.. (2024). Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases. IEEE Transactions on Computational Social Systems. 12(1). 210–237. 7 indexed citations

Shamrat, F. M. Javed Mehedi, Pronab Ghosh, Subrata Das, et al.. (2023). BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Scientific Reports. 13(1). 22874–22874. 7 indexed citations

Ghosh, Pronab, F. M. Javed Mehedi Shamrat, Md Moniruzzaman, et al.. (2023). An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms With Medical Supported Attributes. IEEE Transactions on Artificial Intelligence. 5(6). 2840–2855. 10 indexed citations

Shamrat, F. M. Javed Mehedi, Shamima Akter, Sami Azam, et al.. (2023). AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images. IEEE Access. 11. 16376–16395. 101 indexed citations breakdown →

Montaha, Sidratul, et al.. (2022). A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity. PLoS ONE. 17(8). e0269826–e0269826. 25 indexed citations

Ghosh, Pronab, Sami Azam, Asif Karim, et al.. (2022). SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Frontiers in Oncology. 12. 931141–931141. 37 indexed citations

Karim, Asif, et al.. (2022). A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease. 46–53. 3 indexed citations

Mahmud, Abdullah Al, et al.. (2022). A Robust Deep Learning based Framework for High-Precision Detection of Liver Disease. 7. 9–18. 1 indexed citations

10.

Shamrat, F. M. Javed Mehedi, Sami Azam, Asif Karim, et al.. (2022). LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images. Journal of Personalized Medicine. 12(5). 680–680. 74 indexed citations

11.

Ghosh, Pronab, Sami Azam, Asif Karim, Mirjam Jonkman, & Md. Zahid Hasan. (2021). Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases. 14–20. 27 indexed citations

12.

Karim, Asif, et al.. (2021). A Comparative Study of Different Deep Learning Model for Recognition of Handwriting Digits. SSRN Electronic Journal. 17 indexed citations

13.

Ghosh, Pronab, Sami Azam, Mirjam Jonkman, et al.. (2021). Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques. IEEE Access. 9. 19304–19326. 322 indexed citations breakdown →

14.

Montaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, et al.. (2021). BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images. Biology. 10(12). 1347–1347. 76 indexed citations

15.

Beeravolu, Abhijith Reddy, et al.. (2021). A Robust Deep Learning based Prediction System of Heart Disease using a Combination of Five Datasets. 223–228. 9 indexed citations

16.

Ghosh, Pronab, et al.. (2021). A Comparative Study of Different Machine Learning Tools in Detecting Diabetes. Procedia Computer Science. 192. 467–477. 35 indexed citations

17.

Ghosh, Pronab, et al.. (2021). Expert cancer model using supervised algorithms with a LASSO selection approach. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering. 11(3). 2631–2631. 30 indexed citations

18.

Shamrat, F. M. Javed Mehedi, et al.. (2020). Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease. 2020 IEEE International Conference for Innovation in Technology (INOCON). 1–7. 37 indexed citations

19.

Ghosh, Pronab, et al.. (2020). Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm. 1–6. 55 indexed citations

20.

Ghosh, Pronab, et al.. (2019). A Variable Length Key Based Cryptographic Approach on Cloud Data. 285–290. 6 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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