Subarna Shakya

2.2k citations
130 papers · 1.3k indexed · h-index 19

Subarna Shakya

118 papers receiving 1.2k citations

Peers

Subarna Shakya
Comparison fields: 5 of 134
  • Computer Networks and Communications 308
  • Information Systems 289
  • Management Science and Operations Research 130
  • Computer Vision and Pattern Recognition 207
  • Signal Processing 106
Replace John Soldatos with:
John Soldatos Greece
Paul Davidsson Sweden
B. Prabadevi India
Utku Köse Türkiye
Ali Alzahrani Saudi Arabia
Asad Masood Khattak United Arab Emirates
Natalia Kryvinska Slovakia
Ravi Sharma India
Lihong Jiang China
Abdelaziz Bouras Qatar
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Citations per year

Countries citing papers authored by Subarna Shakya

Since Specialization
Citations

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

Fields of papers citing papers by Subarna Shakya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Subarna Shakya, 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 Subarna Shakya Line = papers co-authored together Subarna Shakya links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20243
2 20230
3 20234
4 20230
5 20233
6 20221
7 20213
8 20216
9
Nepali POS Tagging Using Deep Learning Approaches
20205
10 201845
11 201810
12 20172
13 201740
14 20151
15 20151
16 20153
17
Constraints and farmer's perception on off season green onion production in Chitwan - A Survey
20151
18 20153
19 201413
20 20025

About Subarna Shakya

Subarna Shakya is a scholar working on Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 130 papers that have together received 1.3k indexed citations. Recurring topics across this work include E-Government and Public Services (12 papers), Topic Modeling (11 papers), IoT and Edge/Fog Computing (11 papers), Stock Market Forecasting Methods (11 papers), Natural Language Processing Techniques (10 papers), Network Security and Intrusion Detection (7 papers), Energy Load and Power Forecasting (7 papers) and Blockchain Technology Applications and Security (7 papers). The work is most often cited by research in Computer Networks and Communications (308 citations), Information Systems (289 citations) and Management Science and Operations Research (130 citations). Subarna Shakya has collaborated with scholars based in Nepal, India and United States. Frequent co-authors include S. Smys, Ashwin Dhakal, Smys S., Sujan Shrestha, Gajendra Sharma, Santosh Pandey, Robert Bešťák, Keshav Raj Adhikari, Anil Kumar Sah and Valentina Emilia Bălaş. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Open Innovation Technology Market and Complexity and Journal of Applied Sciences.

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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