Vikas Deep

674 citations
35 papers · 373 · h-index 10

Impact in

Papers in

Vikas Deep

32 papers receiving 346 citations

Peers

Vikas Deep
Comparison fields: 5 of 98
  • Health Information Management 42
  • Artificial Intelligence 106
  • Oncology 80
  • Medical Laboratory Technology 4
  • Computer Vision and Pattern Recognition 57
Replace Mahamudul Hasan with:
Mahamudul Hasan Bangladesh
Muhammad Ahsan Raza Pakistan
R. Suganya India
Muhammad Golam Kibria South Korea
Muhammad Rukunuddin Ghalib India
Hamed Tabrizchi Iran
Jinn‐Yi Yeh Taiwan
Varun Malik India
Shuangyuan Yang China
Fatma Helmy Ismail Egypt
Vikas Deep relative to Mahamudul Hasan Bangladesh Mahamudul Hasan's profile →
Citations per field
00.5×
Mahamudul Hasan · 1×
Citations per year

Countries citing papers authored by Vikas Deep

Since Specialization
Citations

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

Fields of papers citing papers by Vikas Deep

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Vikas Deep, 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 Vikas Deep Line = papers co-authored together Vikas Deep links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020110
2 201645
3 201840
4 202029
5 202020
6 200319
7 201618
8 201812
9 202111
10 20199
11 20166
12 20166
13 20185
14 20215
15
Expert Systems In Agriculture: An Overview
20145
16 20165
17 20195
18 20184
19 20143
20
Expert system for the management of insect-pests in pulse crops
20152

About Vikas Deep

Vikas Deep is a scholar working on Artificial Intelligence, Information Systems, Electrical and Electronic Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 373 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (3 papers), IoT and GPS-based Vehicle Safety Systems (3 papers), IoT-based Smart Home Systems (3 papers), Software Engineering Research (3 papers), Network Security and Intrusion Detection (3 papers), Vehicle License Plate Recognition (2 papers), Face and Expression Recognition (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Health Information Management (42 citations), Artificial Intelligence (106 citations), Oncology (80 citations), Medical Laboratory Technology (4 citations) and Computer Vision and Pattern Recognition (57 citations). Vikas Deep has collaborated with scholars based in India, United Arab Emirates and Saudi Arabia. Frequent co-authors include Purushottam Sharma, Manoj Kumar, Mohammed Alshehri, Rayed AlGhamdi, Vinod Kumar Shukla, S. K. Gupta, Osama Alfarraj, Deepti Mehrotra, Naveen Garg and Rahul Garg. Their work appears in journals such as Mobile Networks and Applications, Plant Foods for Human Nutrition, Concurrency and Computation Practice and Experience, Recent Advances in Computer Science and Communications and International Journal of Engineering and Advanced Technology.

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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