Parul Arora

11 papers receiving 425 citations

Peers

Parul Arora
Comparison fields: 5 of 76
  • Modeling and Simulation 135
  • Health Informatics 9
  • Radiology, Nuclear Medicine and Imaging 127
  • Artificial Intelligence 146
  • Health Information Management 21
Replace Reza Paki with:
Reza Paki Iran
Mohammad Abid Bazaz India
Neeraj Gupta India
Amal I. Saba Egypt
H. Swapnarekha India
Misbah Ahmad Pakistan
Gitanjali R. Shinde India
Toshitaka Hayashi Japan
Sourabh Shastri India
Warda M. Shaban Egypt
Parul Arora relative to Reza Paki Iran Reza Paki's profile →
Citations per field
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Citations per year

Countries citing papers authored by Parul Arora

Since Specialization
Citations

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

Fields of papers citing papers by Parul Arora

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Parul Arora, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Parul Arora Line = papers co-authored together Parul Arora links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2020227
2 202088
3 202261
4 202119
5 201015
6 202210
7
Study of Artificial Neural Networks and Neural Implants
201310
8 20183
9 20223
10 20182
11 20212
12 20200

About Parul Arora

Parul Arora is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Environmental Engineering and Information Systems, having authored 12 papers that have together received 440 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (7 papers), Electric Power System Optimization (4 papers), Solar Radiation and Photovoltaics (3 papers), Image and Signal Denoising Methods (2 papers), COVID-19 diagnosis using AI (1 paper), Advanced Physical and Chemical Molecular Interactions (1 paper), Power Transformer Diagnostics and Insulation (1 paper) and Wind and Air Flow Studies (1 paper). The work is most often cited by research in Modeling and Simulation (135 citations), Health Informatics (9 citations), Radiology, Nuclear Medicine and Imaging (127 citations), Artificial Intelligence (146 citations) and Health Information Management (21 citations). Parul Arora has collaborated with scholars based in India, Singapore and Australia. Frequent co-authors include Bijaya Ketan Panigrahi, Himanshu Kumar, Ponnuthurai Nagaratnam Suganthan, Rammohan Mallipeddi, Partha Pratim Biswas, Abbas Khosravi, Seyed Mohammad Jafar Jalali, Sajad Ahmadian, Alok Kumar Gupta and Ashish Sharma. Their work appears in journals such as Chaos Solitons & Fractals, IEEE Transactions on Industrial Informatics, IEEE Transactions on Emerging Topics in Computational Intelligence, Neural Computing and Applications and Electric Power Systems Research.

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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