N. V. Subba Reddy

141 total papers · 1.5k total citations
76 papers, 984 citations indexed

About

N. V. Subba Reddy is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Organic Chemistry. According to data from OpenAlex, N. V. Subba Reddy has authored 76 papers receiving a total of 984 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 19 papers in Organic Chemistry. Recurrent topics in N. V. Subba Reddy's work include Handwritten Text Recognition Techniques (14 papers), Vehicle License Plate Recognition (8 papers) and Catalytic C–H Functionalization Methods (7 papers). N. V. Subba Reddy is often cited by papers focused on Handwritten Text Recognition Techniques (14 papers), Vehicle License Plate Recognition (8 papers) and Catalytic C–H Functionalization Methods (7 papers). N. V. Subba Reddy collaborates with scholars based in India, China and United States. N. V. Subba Reddy's co-authors include U. Dinesh Acharya, Ähmed Kamal, Jayant R. Haritsa, Veena Nayak, K. Rajender Reddy, Chandrakant Bagul, R. Arun Kumar, Alka Rao, G. Sathish Kumar and Vunnam Srinivasulu and has published in prestigious journals such as Chemical Communications, The Journal of Urology and BMC Bioinformatics.

In The Last Decade

N. V. Subba Reddy

69 papers receiving 914 citations

Author Peers

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

Author						Papers	Cites
N. V. Subba Reddy	394	211	188	146	74	76	984
Wentao Chen	206 0.5×	202 1.0×	106 0.6×	211 1.4×	24 0.3×	72	957
Piotr A. Kowalski	340 0.9×	178 0.8×	64 0.3×	269 1.8×	16 0.2×	80	1.1k
Thorsten Meinl	69 0.2×	484 2.3×	86 0.5×	191 1.3×	14 0.2×	29	1.2k
Ran Liu	64 0.2×	75 0.4×	136 0.7×	462 3.2×	14 0.2×	71	1.1k
Nidhi Goel	94 0.2×	82 0.4×	147 0.8×	207 1.4×	22 0.3×	88	866
Mohammed Gollapalli	311 0.8×	146 0.7×	27 0.1×	168 1.2×	8 0.1×	53	892
Xin Ma	140 0.4×	349 1.7×	22 0.1×	140 1.0×	8 0.1×	58	1.1k
Jiabing Wang	217 0.6×	448 2.1×	103 0.5×	116 0.8×	14 0.2×	49	974
Shalini Ghosh	148 0.4×	293 1.4×	192 1.0×	353 2.4×	17 0.2×	54	996
Yanli Wang	91 0.2×	373 1.8×	38 0.2×	190 1.3×	8 0.1×	36	879

Countries citing papers authored by N. V. Subba Reddy

Since Specialization

Citations

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

Fields of papers citing papers by N. V. Subba Reddy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by N. V. Subba Reddy. 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 N. V. Subba Reddy. The network helps show where N. V. Subba Reddy may publish in the future.

Co-authorship network of co-authors of N. V. Subba Reddy

This figure shows the co-authorship network connecting the top 25 collaborators of N. V. Subba Reddy. A scholar is included among the top collaborators of N. V. Subba Reddy 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 N. V. Subba Reddy. N. V. Subba Reddy 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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