M. Srinivas

757 citations
48 papers · 438 indexed · h-index 12
Topics
Anomaly Detection Techniques and Applications (5 papers)Digital Imaging for Blood Diseases (4 papers)Glaucoma and retinal disorders (4 papers)

In The Last Decade

M. Srinivas

42 papers receiving 403 citations

Peers

M. Srinivas
Comparison fields: 5 of 100
  • Artificial Intelligence 156
  • Radiology, Nuclear Medicine and Imaging 114
  • Electrical and Electronic Engineering 69
  • Health Information Management 46
  • Information Systems 45
Replace Warda M. Shaban with:
Warda M. Shaban Egypt
Mahmoud M. Saafan Egypt
Amir Mosavi Hungary
Mohammed Alkhathami Saudi Arabia
Prabhdeep Singh India
Bilal Alhayani Türkiye
Sunday Adeola Ajagbe Nigeria
Liza Abdul Latiff Malaysia
Dhiaa Musleh Saudi Arabia
Sungchang Lee South Korea
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Citations per field
00.5×2.6×
Warda M. Shaban · 1×
Citations per year

Countries citing papers authored by M. Srinivas

Since Specialization
Citations

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

Fields of papers citing papers by M. Srinivas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Srinivas

This figure shows the co-authorship network connecting the top 25 collaborators of M. Srinivas. A scholar is included among the top collaborators of M. Srinivas 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 M. Srinivas. M. Srinivas 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 1
2 0
3 6
4 3
5 4
6 4
7 1
8 2
9 0
10 1
11 2
12 104
13 4
14
Importance of Circular Data in Sports Science – a Review
0
15 24
16 28
17 3
18 20
19 1
20
Binomially Distributed Populations for Modelling GAs
10

About M. Srinivas

M. Srinivas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ophthalmology, having authored 48 papers that have together received 438 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (5 papers), Digital Imaging for Blood Diseases (4 papers) and Glaucoma and retinal disorders (4 papers). The work is most often cited by research in Health Information Management (46 citations), Modeling and Simulation (44 citations) and Health Informatics (10 citations). M. Srinivas has collaborated with scholars based in India, United States and United Arab Emirates. Frequent co-authors include T.S. Ramu, C. Krishna Mohan, Amit Jain, P. Mahalakshmi, Vps Naidu, P. Rajalakshmi, R Bharath, P. Vivekh, G. Narsimha and L.M. Patnaik. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Industry 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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