Paawan Sharma

1.0k total citations
50 papers, 558 citations indexed

About

Paawan Sharma is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Paawan Sharma has authored 50 papers receiving a total of 558 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Electrical and Electronic Engineering, 12 papers in Artificial Intelligence and 10 papers in Control and Systems Engineering. Recurrent topics in Paawan Sharma's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and COVID-19 diagnosis using AI (4 papers). Paawan Sharma is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and COVID-19 diagnosis using AI (4 papers). Paawan Sharma collaborates with scholars based in India, United Kingdom and Saudi Arabia. Paawan Sharma's co-authors include Samir Patel, Parita Oza, Adesh Kumar, Alessandro Bruno, Festus Fatai Adedoyin, Mukul Kumar Gupta, Amit V. Sant, Roushan Kumar, Amit Kumar Mondal and Kamal Kant Hiran and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Neural Computing and Applications.

In The Last Decade

Paawan Sharma

46 papers receiving 527 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paawan Sharma India 13 223 137 136 98 88 50 558
Javad Rahebi Türkiye 16 197 0.9× 188 1.4× 188 1.4× 105 1.1× 186 2.1× 75 759
R. Joshua Samuel Raj India 8 132 0.6× 100 0.7× 56 0.4× 34 0.3× 100 1.1× 28 513
Biswaranjan Acharya India 12 165 0.7× 55 0.4× 104 0.8× 33 0.3× 120 1.4× 77 590
V. Muthukumaran India 15 201 0.9× 79 0.6× 88 0.6× 32 0.3× 96 1.1× 63 729
Mohamed Youssfi Morocco 13 186 0.8× 58 0.4× 104 0.8× 31 0.3× 133 1.5× 107 652
Pi-Yun Chen Taiwan 12 112 0.5× 107 0.8× 93 0.7× 58 0.6× 80 0.9× 46 415
Ahmad O. Aseeri Saudi Arabia 17 250 1.1× 242 1.8× 67 0.5× 41 0.4× 91 1.0× 50 741
Nanqing Dong China 10 286 1.3× 174 1.3× 70 0.5× 75 0.8× 268 3.0× 25 763
Abul Bashar Saudi Arabia 17 156 0.7× 126 0.9× 76 0.6× 38 0.4× 143 1.6× 67 683
Sibo Qiao China 14 145 0.7× 60 0.4× 61 0.4× 60 0.6× 117 1.3× 85 603

Countries citing papers authored by Paawan Sharma

Since Specialization
Citations

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

Fields of papers citing papers by Paawan Sharma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paawan Sharma

This figure shows the co-authorship network connecting the top 25 collaborators of Paawan Sharma. A scholar is included among the top collaborators of Paawan Sharma 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 Paawan Sharma. Paawan Sharma 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.
2.
Shukla, Vipin, et al.. (2024). An explainable artificial intelligence based approach for the prediction of key performance indicators for 1 megawatt solar plant under local steppe climate conditions. Engineering Applications of Artificial Intelligence. 131. 107809–107809. 6 indexed citations
3.
Sharma, Paawan, et al.. (2024). An energy efficient system for unmanned aerial vehicle-based surveillance. IET conference proceedings.. 2023(39). 480–486. 1 indexed citations
4.
Das, Anirban, et al.. (2024). Predictive modeling of water quality index (WQI) classes in Indian rivers: Insights from the application of multiple Machine Learning (ML) models on a decennial dataset. Stochastic Environmental Research and Risk Assessment. 38(8). 3221–3238. 5 indexed citations
5.
6.
Oza, Parita, Paawan Sharma, & Samir Patel. (2023). Breast lesion classification from mammograms using deep neural network and test-time augmentation. Neural Computing and Applications. 36(4). 2101–2117. 12 indexed citations
7.
Oza, Parita, et al.. (2023). Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis. Biomedical Engineering Letters. 14(2). 317–330. 9 indexed citations
8.
Sharma, Abhinav, et al.. (2023). Recent Trends and Best Practices in Industry 4.0. River Publishers eBooks. 2 indexed citations
9.
Kuribayashi, Minoru, et al.. (2023). Designing Practical End-to-End System for Soft Biometric-Based Person Retrieval From Surveillance Videos. IEEE Access. 11. 133640–133657. 1 indexed citations
10.
Oza, Parita, Paawan Sharma, Samir Patel, Festus Fatai Adedoyin, & Alessandro Bruno. (2022). Image Augmentation Techniques for Mammogram Analysis. Journal of Imaging. 8(5). 141–141. 66 indexed citations
11.
Oza, Parita, Paawan Sharma, & Samir Patel. (2022). Deep ensemble transfer learning-based framework for mammographic image classification. The Journal of Supercomputing. 79(7). 8048–8069. 18 indexed citations
12.
Sharma, Paawan, et al.. (2022). Transfer Learning Assisted Classification of Artefacts Removed and Contrast Improved Digital Mammograms. Scalable Computing Practice and Experience. 23(3). 115–127. 4 indexed citations
13.
Sant, Amit V., et al.. (2021). Selection of three RC Branches in Equivalent Circuit Model of Lithium-ion Batteries for Improved Accuracy. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12(14). 1478–1489. 5 indexed citations
14.
Mahrishi, Mehul, et al.. (2020). Machine Learning and Deep Learning in Real-Time Applications. VBN Forskningsportal (Aalborg Universitet). 33 indexed citations
15.
Raval, Mehul S., et al.. (2020). Experiments with Multinational Cross-Course Project. 570–575. 1 indexed citations
16.
Sharma, Paawan, Sushabhan Choudhury, & Vinay Chowdary. (2019). Mobile Sensors Deployment Methods: A Review. Advances in intelligent systems and computing. 883–893.
17.
Sharma, Paawan, et al.. (2017). Applications of Building Information Modeling in Electrical Systems Design. Journal of Engineering Science and Technology Review. 10(6). 119–128. 13 indexed citations
18.
Sharma, Paawan, et al.. (2017). Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location. International Journal of Electronics and Telecommunications. 63(4). 389–397. 5 indexed citations
19.
Mondal, Amit Kumar, et al.. (2015). Tracing of shading effect on underachieving SPV cell of an SPV grid using wireless sensor network. Engineering Science and Technology an International Journal. 18(3). 475–484. 16 indexed citations
20.
Sharma, Paawan, N. Murali, & T. Jayakumar. (2014). Effect of thermocouple time constant on sensing of temperature fluctuations in a fast reactor subassembly. Journal of sensors and sensor systems. 3(1). 55–60. 1 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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