Pratima Kumari

1.5k total citations · 1 hit paper
11 papers, 1.1k citations indexed

About

Pratima Kumari is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, Pratima Kumari has authored 11 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 6 papers in Electrical and Electronic Engineering and 4 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in Pratima Kumari's work include Photovoltaic System Optimization Techniques (4 papers), Solar Radiation and Photovoltaics (4 papers) and Energy Load and Power Forecasting (4 papers). Pratima Kumari is often cited by papers focused on Photovoltaic System Optimization Techniques (4 papers), Solar Radiation and Photovoltaics (4 papers) and Energy Load and Power Forecasting (4 papers). Pratima Kumari collaborates with scholars based in India and United States. Pratima Kumari's co-authors include Durga Toshniwal, Rajesh Wadhvani, Sachin Kadian and Prashanthi Vemuri and has published in prestigious journals such as Journal of Cleaner Production, Applied Energy and Electrochimica Acta.

In The Last Decade

Pratima Kumari

11 papers receiving 1.0k citations

Hit Papers

Deep learning models for ... 2021 2026 2022 2024 2021 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pratima Kumari India 10 548 486 353 267 219 11 1.1k
I. Santiago Spain 14 142 0.3× 523 1.1× 296 0.8× 116 0.4× 26 0.1× 38 875
Liqun Liu China 15 181 0.3× 217 0.4× 248 0.7× 90 0.3× 332 1.5× 65 979
Galen Maclaurin United States 10 406 0.7× 452 0.9× 320 0.9× 182 0.7× 18 0.1× 21 1.2k
Caleb Robinson United States 12 120 0.2× 188 0.4× 101 0.3× 99 0.4× 24 0.1× 34 882
Dan Assouline Switzerland 10 364 0.7× 184 0.4× 162 0.5× 199 0.7× 31 0.1× 21 808
Nurulkamal Masseran Malaysia 19 88 0.2× 269 0.6× 16 0.0× 110 0.4× 245 1.1× 78 929
Alessandra Di Gangi Italy 17 539 1.0× 655 1.3× 882 2.5× 25 0.1× 31 0.1× 24 1.4k
S. Karatasou Greece 13 109 0.2× 219 0.5× 221 0.6× 44 0.2× 65 0.3× 20 909
Ahmet Duran Şahin Türkiye 19 468 0.9× 697 1.4× 469 1.3× 110 0.4× 10 0.0× 45 1.5k
Qingping Zhou China 11 85 0.2× 289 0.6× 47 0.1× 59 0.2× 184 0.8× 35 722

Countries citing papers authored by Pratima Kumari

Since Specialization
Citations

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

Fields of papers citing papers by Pratima Kumari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pratima Kumari

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

All Works

11 of 11 papers shown
1.
Kumari, Pratima, et al.. (2025). Recent Advancements in Topic Modeling Techniques for Healthcare, Bioinformatics, and Other Potential Applications. Advanced Intelligent Systems. 8(1). 1 indexed citations
2.
Kadian, Sachin, et al.. (2023). Machine learning enabled onsite electrochemical detection of lidocaine using a microneedle array integrated screen printed electrode. Electrochimica Acta. 475. 143664–143664. 20 indexed citations
3.
Kumari, Pratima & Durga Toshniwal. (2021). Analysis of ANN-based daily global horizontal irradiance prediction models with different meteorological parameters: a case study of mountainous region of India. International Journal of Green Energy. 18(10). 1007–1026. 20 indexed citations
4.
Kumari, Pratima & Durga Toshniwal. (2021). Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting. Applied Energy. 295. 117061–117061. 207 indexed citations
5.
Kumari, Pratima & Durga Toshniwal. (2021). Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production. 318. 128566–128566. 262 indexed citations breakdown →
6.
Kumari, Pratima & Durga Toshniwal. (2020). Impact of lockdown measures during COVID-19 on air quality– A case study of India. International Journal of Environmental Health Research. 32(3). 503–510. 165 indexed citations
7.
Kumari, Pratima & Durga Toshniwal. (2020). Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance. Journal of Cleaner Production. 279. 123285–123285. 189 indexed citations
8.
Kumari, Pratima & Durga Toshniwal. (2020). Impact of lockdown on air quality over major cities across the globe during COVID-19 pandemic. Urban Climate. 34. 100719–100719. 161 indexed citations
10.
Kumari, Pratima & Rajesh Wadhvani. (2018). Wind Power Prediction Using KLMS Algorithm. 154–161. 12 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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