Nidhi Arora

49 papers receiving 609 citations

Peers

Nidhi Arora
Comparison fields: 5 of 129
  • Molecular Biology 185
  • Artificial Intelligence 117
  • Organic Chemistry 98
  • Computer Vision and Pattern Recognition 97
  • Radiology, Nuclear Medicine and Imaging 80
Replace Mei‐Hua Hsu with:
Mei‐Hua Hsu Taiwan
Chris Williams United States
Mizuki Morita Japan
Ruchi Mittal India
Bernd Wiswedel Germany
Rudolf Mayer Austria
Vijil Chenthamarakshan United States
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Irene Li United States
Nidhi Arora relative to Mei‐Hua Hsu Taiwan Mei‐Hua Hsu's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nidhi Arora

Since Specialization
Citations

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

Fields of papers citing papers by Nidhi Arora

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nidhi Arora

This figure shows the co-authorship network connecting the top 25 collaborators of Nidhi Arora. A scholar is included among the top collaborators of Nidhi Arora 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 Nidhi Arora. Nidhi Arora 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
#WorkIndexed citations
1 0
2 0
3 1
4 0
5 0
6 1
7 56
8 75
9
Evaluation of antioxidant profile in subclinical mastitis in dairy buffaloes
1
10 117
11 2
12
Agriculture production and food security in Himalayan state Uttarakhand of India
1
13
Review on Task Scheduling Algorithms in Cloud Computing Environment
6
14
Analysis of Petrol Pumps Reachability in Anand District of Gujarat
1
15
Information Systems Project Management
4
16
Analyzing Moonlighting as HR Retention Policy: A New Trend
8
17
A Fuzzy Probabilistic Neural Network for StudentâÂÂs Academic Performance Prediction
6
18 7
19
Combining available standards and tools to build a compliance oriented website management system
1
20 14

About Nidhi Arora

Nidhi Arora is a scholar working on Health Informatics, Statistical and Nonlinear Physics and Computer Science Applications, having authored 57 papers that have together received 633 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Opinion Dynamics and Social Influence (5 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Health Informatics (19 citations), Computer Science Applications (38 citations) and Computer Vision and Pattern Recognition (97 citations). Nidhi Arora has collaborated with scholars based in India, United States and South Korea. Frequent co-authors include B. Jayaram, Punniyakoti T. Veeraveedu, Vikramdeep Monga, Hema Banati, Shubham Dodia, B. Annappa, Rahul Kumar Jain, Tariq Ahamed Ahanger, Rajnish Ratna and Anil Audumbar Pise. Their work appears in journals such as The Journal of Physical Chemistry B, Analytical Biochemistry and Expert Systems with Applications.

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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