Bindu Garg

402 citations
56 papers · 305 · h-index 11

Impact in

Papers in

Bindu Garg

51 papers receiving 286 citations

Peers

Bindu Garg
Comparison fields: 5 of 83
  • Management Science and Operations Research 100
  • Signal Processing 61
  • Computer Vision and Pattern Recognition 51
  • Artificial Intelligence 72
  • Analytical Chemistry 18
Replace Chien-Pang Lee with:
Chien-Pang Lee Taiwan
Vishan Kumar Gupta India
Obed Appiah Ghana
Yifeng Luo China
Devesh Kumar Srivastava India
Adriana Birlutiu Romania
Yangyang Wu China
E. Naresh India
Xiaohai Sun Germany
Bindu Garg relative to Chien-Pang Lee Taiwan Chien-Pang Lee's profile →
Citations per field
00.5×2.6×
Chien-Pang Lee · 1×
Citations per year

Countries citing papers authored by Bindu Garg

Since Specialization
Citations

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

Fields of papers citing papers by Bindu Garg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Bindu Garg, 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 Bindu Garg Line = papers co-authored together Bindu Garg links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201740
2 201638
3
Stock market forecast using sentiment analysis
201516
4 201215
5 201414
6 201713
7 201313
8 201111
9 200911
10 202210
11 201110
12 20168
13 20117
14 19737
15 20116
16 20175
17 20245
18 20215
19
Steric aspects of adrenergic drugs. XVII. Influence of tropolone on the magnitude and duration of action of catecholamine isomers.
19715
20 20234

About Bindu Garg

Bindu Garg is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Management Science and Operations Research, Signal Processing and Information Systems, having authored 56 papers that have together received 305 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (16 papers), Advanced Steganography and Watermarking Techniques (8 papers), Neural Networks and Applications (8 papers), Chaos-based Image/Signal Encryption (8 papers), Forecasting Techniques and Applications (6 papers), Fuzzy Logic and Control Systems (5 papers), Digital Media Forensic Detection (5 papers) and Smart Agriculture and AI (5 papers). The work is most often cited by research in Management Science and Operations Research (100 citations), Signal Processing (61 citations), Computer Vision and Pattern Recognition (51 citations), Artificial Intelligence (72 citations) and Analytical Chemistry (18 citations). Bindu Garg has collaborated with scholars based in India, Canada and Saudi Arabia. Frequent co-authors include Abdul Quaiyum Ansari, M. M. Sufyan Beg, Vijay Kumar, Rachna Jain, Suraj Menon, K Kovács, G. Lázár, Carl K. Buckner, Sándor Szabó and Béatriz Tuchweber. Their work appears in journals such as Hormone and Metabolic Research, Computers & Electrical Engineering, Multimedia Tools and Applications, Data in Brief and Neural Computing and 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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