Neha Gaud

16 total papers · 606 total citations
8 papers, 349 citations indexed

About

Neha Gaud is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Neha Gaud has authored 8 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Biomedical Engineering and 2 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Neha Gaud's work include Context-Aware Activity Recognition Systems (5 papers), Gait Recognition and Analysis (4 papers) and Diabetic Foot Ulcer Assessment and Management (2 papers). Neha Gaud is often cited by papers focused on Context-Aware Activity Recognition Systems (5 papers), Gait Recognition and Analysis (4 papers) and Diabetic Foot Ulcer Assessment and Management (2 papers). Neha Gaud collaborates with scholars based in India and South Korea. Neha Gaud's co-authors include Vijay Bhaskar Semwal, Vishwanath Bijalwan, Abhay Kumar Alok, Praveen Lalwani, Ugrasen Suman, Ghanapriya Singh, Abdul Manan Khan and Youngshik Kim and has published in prestigious journals such as IEEE Sensors Journal, Artificial Intelligence Review and Sensor Review.

In The Last Decade

Neha Gaud

7 papers receiving 340 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Neha Gaud	180	142	55	39	30	8	349
T Togawa	186 1.0×	136 1.0×	37 0.7×	25 0.6×	17 0.6×	15	347
Phuc Huu Truong	131 0.7×	126 0.9×	35 0.6×	13 0.3×	43 1.4×	18	330
Edmond Mitchell	175 1.0×	175 1.2×	53 1.0×	25 0.6×	6 0.2×	12	371
James Calusdian	144 0.8×	80 0.6×	69 1.3×	17 0.4×	23 0.8×	14	390
Ondřej Ťupa	191 1.1×	103 0.7×	37 0.7×	37 0.9×	43 1.4×	18	355
Jonathan Feng-Shun Lin	134 0.7×	161 1.1×	77 1.4×	37 0.9×	12 0.4×	23	390
Ghanapriya Singh	75 0.4×	102 0.7×	40 0.7×	24 0.6×	19 0.6×	28	301
Nicole A. Capela	129 0.7×	179 1.3×	31 0.6×	15 0.4×	20 0.7×	5	329
Kabalan Chaccour	139 0.8×	169 1.2×	45 0.8×	48 1.2×	9 0.3×	18	297
Diliang Chen	212 1.2×	76 0.5×	18 0.3×	41 1.1×	36 1.2×	24	337

Countries citing papers authored by Neha Gaud

Since Specialization

Citations

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

Fields of papers citing papers by Neha Gaud

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neha Gaud

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

All Works

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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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