Mallika De

683 citations
32 papers · 259 · h-index 9

Impact in

Papers in

Mallika De

32 papers receiving 234 citations

Peers

Mallika De
Comparison fields: 5 of 56
  • Hardware and Architecture 29
  • Computer Vision and Pattern Recognition 62
  • Ophthalmology 25
  • Computational Theory and Mathematics 43
  • Computer Networks and Communications 52
Replace Ambedkar Dukkipati with:
Ambedkar Dukkipati India
Soumya Dutta United States
H.D. Cheng United States
Linnan Wang United States
Kunal Banerjee India
Anup Kumar Bhattacharjee India
Peter Schneider–Kamp Denmark
Keshou Wu China
Mallika De relative to Ambedkar Dukkipati India Ambedkar Dukkipati's profile →
Citations per field
00.5×10×14.5×
Ambedkar Dukkipati · 1×
Citations per year

Countries citing papers authored by Mallika De

Since Specialization
Citations

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

Fields of papers citing papers by Mallika De

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Mallika De, 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 Mallika De Line = papers co-authored together Mallika De links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201334
2 201231
3 199926
4 201322
5 200017
6 199316
7 201214
8 199714
9 20128
10 20128
11 20126
12 20166
13 19945
14 20135
15 20125
16 20154
17 20134
18 20204
19 20174
20 20183

About Mallika De

Mallika De is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 32 papers that have together received 259 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (6 papers), Interconnection Networks and Systems (6 papers), Quantum-Dot Cellular Automata (4 papers), Data Mining Algorithms and Applications (4 papers), Advanced Database Systems and Queries (3 papers), Advanced Memory and Neural Computing (3 papers), Data Management and Algorithms (2 papers) and Advancements in Semiconductor Devices and Circuit Design (2 papers). The work is most often cited by research in Hardware and Architecture (29 citations), Computer Vision and Pattern Recognition (62 citations), Ophthalmology (25 citations), Computational Theory and Mathematics (43 citations) and Computer Networks and Communications (52 citations). Mallika De has collaborated with scholars based in India, Australia and Bhutan. Frequent co-authors include Bhabani P. Sinha, Himadri Sekhar Dutta, Debashis De, Kunal Das, Swagatam Das, N. K. Goel, Debraj Das, Rajdeep Chowdhury, Krishna Gopal Dhal and Ruhul Sarker. Their work appears in journals such as IEEE Transactions on Computers, Stochastic Environmental Research and Risk Assessment, Parallel Processing Letters, Micro & Nano Letters and International Journal of Data Analysis Techniques and Strategies.

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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