Prajoy Podder

2.4k total citations
52 papers, 1.2k citations indexed

About

Prajoy Podder is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Prajoy Podder has authored 52 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 19 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Prajoy Podder's work include COVID-19 diagnosis using AI (17 papers), AI in cancer detection (9 papers) and Digital Imaging for Blood Diseases (4 papers). Prajoy Podder is often cited by papers focused on COVID-19 diagnosis using AI (17 papers), AI in cancer detection (9 papers) and Digital Imaging for Blood Diseases (4 papers). Prajoy Podder collaborates with scholars based in Bangladesh, United States and India. Prajoy Podder's co-authors include Subrato Bharati, M. Rubaiyat Hossain Mondal, V. B. Surya Prasath, Tanvir Zaman Khan, Mohammad Atikur Rahman, Joarder Kamruzzaman, Md Junayed Hasan, Utku Köse, Ali Rohan and Rafi Ahmed and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Access.

In The Last Decade

Prajoy Podder

51 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Prajoy Podder Bangladesh 19 463 298 143 117 117 52 1.2k
M. Rubaiyat Hossain Mondal Bangladesh 18 518 1.1× 304 1.0× 123 0.9× 109 0.9× 121 1.0× 75 1.2k
Subrato Bharati Bangladesh 17 411 0.9× 290 1.0× 73 0.5× 99 0.8× 122 1.0× 43 887
Gaurav Gupta India 19 376 0.8× 224 0.8× 252 1.8× 148 1.3× 42 0.4× 88 1.4k
Mahmoud Ragab Saudi Arabia 17 465 1.0× 250 0.8× 124 0.9× 167 1.4× 30 0.3× 139 1.1k
Hazrat Ali Pakistan 19 519 1.1× 332 1.1× 430 3.0× 145 1.2× 91 0.8× 119 1.5k
Mohammad Khubeb Siddiqui Australia 13 722 1.6× 760 2.6× 252 1.8× 109 0.9× 164 1.4× 43 1.8k
Melissa Berthelot United Kingdom 6 511 1.1× 346 1.2× 203 1.4× 44 0.4× 99 0.8× 11 1.3k
Fatma Taher United Arab Emirates 21 464 1.0× 483 1.6× 297 2.1× 165 1.4× 29 0.2× 110 1.3k
Soumya Ranjan Nayak India 24 619 1.3× 439 1.5× 429 3.0× 261 2.2× 56 0.5× 123 1.9k
Amany Sarhan Egypt 15 415 0.9× 291 1.0× 479 3.3× 184 1.6× 38 0.3× 96 1.4k

Countries citing papers authored by Prajoy Podder

Since Specialization
Citations

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

Fields of papers citing papers by Prajoy Podder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prajoy Podder

This figure shows the co-authorship network connecting the top 25 collaborators of Prajoy Podder. A scholar is included among the top collaborators of Prajoy Podder 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 Prajoy Podder. Prajoy Podder 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.
Podder, Prajoy, Maciej Zawodniok, & Sanjay Madria. (2024). Deep Learning for UAV Detection and Classification via Radio Frequency Signal Analysis. 165–174. 3 indexed citations
2.
Mondal, M. Rubaiyat Hossain, et al.. (2024). Weighted Rank Difference Ensemble: A New Form of Ensemble Feature Selection Method for Medical Datasets. SHILAP Revista de lepidopterología. 4(1). 477–488. 6 indexed citations
3.
Podder, Prajoy, M. Rubaiyat Hossain Mondal, Somasundar Kannan, et al.. (2023). Enhancing Brain Tumor Classification with Transfer Learning across Multiple Classes: An In-Depth Analysis. SHILAP Revista de lepidopterología. 3(4). 1124–1144. 42 indexed citations
4.
Podder, Prajoy, et al.. (2023). Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases. Computers. 12(5). 95–95. 14 indexed citations
5.
Podder, Prajoy, et al.. (2023). LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases. Sensors. 23(1). 480–480. 36 indexed citations
6.
Podder, Prajoy, et al.. (2023). RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases. PLoS ONE. 18(12). e0293125–e0293125. 10 indexed citations
7.
Khamparia, Aditya, Babita Pandey, Fadi Al‐Turjman, & Prajoy Podder. (2021). An intelligent IoMT enabled feature extraction method for early detection of knee arthritis. Expert Systems. 40(4). 5 indexed citations
8.
Bharati, Subrato, Prajoy Podder, M. Rubaiyat Hossain Mondal, & V. B. Surya Prasath. (2021). Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review. arXiv (Cornell University). 13. 91–112. 1 indexed citations
9.
Mondal, M. Rubaiyat Hossain, Subrato Bharati, & Prajoy Podder. (2021). CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images. PLoS ONE. 16(10). e0259179–e0259179. 43 indexed citations
10.
Bharati, Subrato, Prajoy Podder, M. Rubaiyat Hossain Mondal, & V. B. Surya Prasath. (2021). CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images. International Journal of Hybrid Intelligent Systems. 17(1-2). 71–85. 59 indexed citations
11.
Bharati, Subrato, Prajoy Podder, M. Rubaiyat Hossain Mondal, & V. B. Surya Prasath. (2021). Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review. arXiv (Cornell University). 27 indexed citations
12.
Bharati, Subrato & Prajoy Podder. (2020). Disease Detection from Lung X-ray Images based on Hybrid Deep Learning. arXiv (Cornell University). 2 indexed citations
13.
Podder, Prajoy, et al.. (2020). Review on the Security Threats of Internet of Things. International Journal of Computer Applications. 176(41). 37–45. 20 indexed citations
14.
Mondal, M. Rubaiyat Hossain, et al.. (2020). Data analytics for novel coronavirus disease. Informatics in Medicine Unlocked. 20. 100374–100374. 65 indexed citations
15.
Podder, Prajoy, et al.. (2020). Progression of Internet Banking System in Bangladesh and its Challenges. International Journal of Computer Applications. 177(29). 11–15. 10 indexed citations
16.
Hasan, Md. Mehedi, et al.. (2019). Capacity Enhancement and Voltage Stability Improvement of Power Transmission Line by Series Compensation. SHILAP Revista de lepidopterología. 6(23). e3–e3. 2 indexed citations
17.
Bharati, Subrato, et al.. (2019). Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data. International Journal of Hybrid Intelligent Systems. 15(2). 91–100. 11 indexed citations
18.
Bharati, Subrato, et al.. (2018). EEG Eye State Prediction and Classification in order to Investigate Human Cognitive State. 1–4. 3 indexed citations
19.
Bharati, Subrato, Mohammad Atikur Rahman, & Prajoy Podder. (2018). Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA. 581–584. 35 indexed citations
20.
Khan, Tanvir Zaman, Prajoy Podder, & Md. Foisal Hossain. (2014). Fast and efficient iris segmentation approach based on morphology and geometry operation. 37. 1–8. 3 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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