Indira Gandhi Delhi Technical University for Women

17.7k citations
2.2k papers ·

Impact in

Papers in

Indira Gandhi Delhi Technical University for Women

1.8k papers receiving 16.9k citations

Peers

Indira Gandhi Delhi Technical University for Women
Comparison fields: 5 of 224
  • Signal Processing 1.3k
  • Software 451
  • Information Systems 2.3k
  • Health, Toxicology and Mutagenesis 1.2k
  • Computer Networks and Communications 2.2k
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Indira Gandhi Delhi Technical University for Women relative to Punjab Engineering College India Punjab Engineering College's profile →
Citations per field
00.5×1.5×2.2×
Punjab Engineering College · 1×
Citations per year

Countries citing scholars working at Indira Gandhi Delhi Technical University for Women

Since Specialization
Citations

This map shows the geographic impact of research produced by authors working at Indira Gandhi Delhi Technical University for Women. 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 papers produced at Indira Gandhi Delhi Technical University for Women with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Indira Gandhi Delhi Technical University for Women more than expected).

Fields of papers published by authors at Indira Gandhi Delhi Technical University for Women

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers affiliated with Indira Gandhi Delhi Technical University for Women at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Indira Gandhi Delhi Technical University for Women at the time of their publication.

About Indira Gandhi Delhi Technical University for Women

In recent decades, authors affiliated with Indira Gandhi Delhi Technical University for Women have published 2.2k papers, which have received a total of 17.7k indexed citations . Scholars at this organization have produced 199 papers in Signal Processing, 68 papers in Software, 316 papers in Computer Vision and Pattern Recognition, 19 papers in Health Informatics and 490 papers in Artificial Intelligence on the topics of Analog and Mixed-Signal Circuit Design (91 papers), Advanced Malware Detection Techniques (88 papers), Advancements in Semiconductor Devices and Circuit Design (80 papers), Sentiment Analysis and Opinion Mining (80 papers), Network Security and Intrusion Detection (76 papers), Low-power high-performance VLSI design (67 papers), Topic Modeling (62 papers) and Spam and Phishing Detection (61 papers). Their work is cited by papers focused on Signal Processing (1.3k citations), Software (451 citations), Information Systems (2.3k citations), Health, Toxicology and Mutagenesis (1.2k citations) and Computer Networks and Communications (2.2k citations). Authors at Indira Gandhi Delhi Technical University for Women collaborate with scholars in India, United States and United Kingdom and have published in prestigious journals including Multimedia Tools and Applications, Wireless Personal Communications, Sadhana, Journal of Ambient Intelligence and Humanized Computing and Computers & Electrical Engineering. Some of Indira Gandhi Delhi Technical University for Women's most productive authors include Ranu Gadi, Omendra Kumar Singh, K. R. Seeja, Akash Tayal, Arun Sharma, T. K. Mandal, Devendra K. Tayal, Amar Kumar Mohapatra, Chhaya Ravi Kant and Pankaj Gupta.

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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