N. Yuvaraj

953 citations
36 papers · 554 indexed · 1 hit paper · h-index 11
Topics
Artificial Intelligence in Healthcare (4 papers)Digital Imaging for Blood Diseases (3 papers)Sentiment Analysis and Opinion Mining (3 papers)

In The Last Decade

N. Yuvaraj

30 papers receiving 518 citations

Hit Papers

Surface crack detection using deep learning with shallow ...202120262022202420214080120

Peers

N. Yuvaraj
Comparison fields: 5 of 94
  • Civil and Structural Engineering 184
  • Artificial Intelligence 128
  • Health Information Management 109
  • Mechanical Engineering 50
  • Environmental Engineering 47
Replace K. R. Sri Preethaa with:
K. R. Sri Preethaa India
Junhong Zhao China
Dhirendra Prasad Yadav India
Jianning Wang China
Wenbo Wang China
Peishun Liu China
Nihat Yılmaz Türkiye
Sudha Radhika India
Ruichun Tang China
Maulana Azad Iran
N. Yuvaraj relative to K. R. Sri Preethaa India K. R. Sri Preethaa's profile →
Citations per field
00.5×5.2×
K. R. Sri Preethaa · 1×
Citations per year

Countries citing papers authored by N. Yuvaraj

Since Specialization
Citations

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

Fields of papers citing papers by N. Yuvaraj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of N. Yuvaraj

This figure shows the co-authorship network connecting the top 25 collaborators of N. Yuvaraj. A scholar is included among the top collaborators of N. Yuvaraj based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with N. Yuvaraj. N. Yuvaraj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 1
4 1
5 3
6 10
7 36
8 8
9 52
10 19
11 1
12
Predicting housing prices using advanced regression techniques
1
13
Enhancing the accuracy of digit recognition using machine learning algorithms
2
14 1
15 11
16 105
17
A Survey on Crop Yield Prediction Models
4
18
A Survey on Leaf Disease Prediction Algorithms using Digital Image Processing
5
19 13
20 3

About N. Yuvaraj

N. Yuvaraj is a scholar working on Health Information Management, Media Technology and Computer Vision and Pattern Recognition, having authored 36 papers that have together received 554 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (4 papers), Digital Imaging for Blood Diseases (3 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Health Information Management (109 citations), Civil and Structural Engineering (184 citations) and Artificial Intelligence (128 citations). N. Yuvaraj has collaborated with scholars based in India, South Korea and United States. Frequent co-authors include Bubryur Kim, K. R. Sri Preethaa, Dong‐Eun Lee, Gang Hu, K.T. Tse, M. Ramkumar, R.G. Vidhya, M. Balaji, J. Surendiran and M. Saravanan. Their work appears in journals such as Sensors, Automation in Construction 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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