N. Arunkumar

109 papers receiving 5.4k citations

N. Arunkumar's Hit Papers

Enabling technologies for fog computing in healthcare IoT systems 2018 · 455 citations
4550+2+5Years since publication100200300400

Peers

N. Arunkumar
Comparison fields: 5 of 180
  • Cognitive Neuroscience 1.3k
  • Neurology 508
  • Urban Studies 331
  • Signal Processing 547
  • Computer Vision and Pattern Recognition 1.0k
Replace Jun Wang with:
Jun Wang China
Yuki Hagiwara Singapore
Mohamed Adel Hammad Egypt
Prayag Tiwari China
Shu Lih Oh Singapore
Preetha Phillips United States
Kup‐Sze Choi Hong Kong
Paweł Pławiak Poland
Ateeq Ur Rehman Pakistan
Habib Hamam Canada
N. Arunkumar relative to Jun Wang China Jun Wang's profile →
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Countries citing papers authored by N. Arunkumar

Since Specialization
Citations

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

Fields of papers citing papers by N. Arunkumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 115 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
Hit paper breakdown →
2018488
2
Enabling technologies for fog computing in healthcare IoT systems
Hit paper breakdown →
2018455
3
Optimal deep learning model for classification of lung cancer on CT images
Hit paper breakdown →
2018421
4
Secure Medical Data Transmission Model for IoT-Based Healthcare Systems
Hit paper breakdown →
2018398
5 2018223
6 2018200
7 2018187
8 2019186
9 2018160
10 2017158
11 2018145
12 2018141
13 2018124
14 2018123
15 2021116
16 201893
17 201787
18 201977
19 201774
20 201873

About N. Arunkumar

N. Arunkumar is a scholar working on Cognitive Neuroscience, Cardiology and Cardiovascular Medicine, Signal Processing, Computer Vision and Pattern Recognition and Neurology, having authored 115 papers that have together received 5.7k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (29 papers), Brain Tumor Detection and Classification (8 papers), ECG Monitoring and Analysis (8 papers), Blind Source Separation Techniques (6 papers), Fractal and DNA sequence analysis (6 papers), Non-Invasive Vital Sign Monitoring (5 papers), Drug Solubulity and Delivery Systems (5 papers) and Advanced DC-DC Converters (5 papers). The work is most often cited by research in Cognitive Neuroscience (1.3k citations), Neurology (508 citations), Urban Studies (331 citations), Signal Processing (547 citations) and Computer Vision and Pattern Recognition (1.0k citations). N. Arunkumar has collaborated with scholars based in India, Jordan and United States. Frequent co-authors include Gustavo Ramírez-González, Enas Abdulhay, Mazin Abed Mohammed, U. Rajendra Acharya, Mohd Khanapi Abd Ghani, Shu Lih Oh, Victor Hugo C. de Albuquerque, K. Shankar, Lakshmanaprabu SK and Othman Mohd. Their work appears in journals such as Future Generation Computer Systems, IEEE Access, Computers & Electrical Engineering, International Journal of Heavy Vehicle Systems and Cognitive Systems Research.

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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