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Sivaramakrishnan Rajaraman
PeerJ Editor, Author & Reviewer
1,465 Points

Contributions by role

Author 405
Reviewer 60
Editor 1,000
Answers 15

Contributions by subject area

Hematology
Infectious Diseases
Computational Science
Data Mining and Machine Learning
Data Science
Bioengineering
Radiology and Medical Imaging
Bioinformatics
Computational Biology
Artificial Intelligence
Computer Networks and Communications
Computer Vision
Visual Analytics
Neural Networks
Blockchain
Algorithms and Analysis of Algorithms
Optimization Theory and Computation
Social Computing

By Q&A topic

Hematology
Infectious-diseases
Computational-science
Data-mining-and-machine-learning
Data-science

Sivaramakrishnan Rajaraman

PeerJ Editor, Author & Reviewer

Summary

Dr. Sivarama Krishnan Rajaraman is working as a research scientist at the National Library of Medicine (NLM), National Institutes of Health (NIH). Dr. Rajaraman received his Ph.D. in Information and Communication Engineering from Anna University, India. He is involved in projects that aim to apply computational sciences and engineering techniques toward advancing life science applications. These projects involve the use of medical images for aiding healthcare professionals in low-cost decision-making at the POC screening/diagnostics. He is a versatile researcher with expertise in machine learning, data science, biomedical image analysis, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise. Dr. Rajaraman is an Editorial Board member of the PLOS ONE, PeerJ Computer Science, MDPI Knowledge, and MDPI Electronics journals. He is reviewing manuscripts for more than 75 journals including those published by Nature, LANCET, IEEE, MDPI, Elsevier, and other conferences including CVPR, EMBS, CBMS, and MICCAI. Dr. Rajaraman is a Life Member of the Society of Photo-optical Instrumentation Engineers (SPIE), a regular member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine & Biology Society (EMBS), and the Biomedical Engineering Society (BMES).

Algorithms & Analysis of Algorithms Artificial Intelligence Bioengineering Bioinformatics Computer Aided Design Data Mining & Machine Learning

Editorial Board Member

PeerJ Computer Science

Work details

Research Scientist

National Library of Medicine
Computational Health Research Branch
Work on projects that apply computational sciences and engineering techniques toward advancing life science applications. These projects involve the use of medical images for aiding healthcare professionals in low-cost decision-making at the POC screening/diagnostics.

Identities

@raaju_shiv

Websites

  • Google Scholar
  • National Library of Medicine
  • LinkedIn
  • GitHub
  • ResearcherID

PeerJ Contributions

  • Articles 3
  • Edited 4
  • Answers 1
March 17, 2020
Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles
Sivaramakrishnan Rajaraman, Incheol Kim, Sameer K. Antani
https://doi.org/10.7717/peerj.8693 PubMed 32211231
May 28, 2019
Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images
Sivaramakrishnan Rajaraman, Stefan Jaeger, Sameer K. Antani
https://doi.org/10.7717/peerj.6977 PubMed 31179181
April 16, 2018
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Sivaramakrishnan Rajaraman, Sameer K. Antani, Mahdieh Poostchi, Kamolrat Silamut, Md. A. Hossain, Richard J. Maude, Stefan Jaeger, George R. Thoma
https://doi.org/10.7717/peerj.4568 PubMed 29682411

Academic Editor on

February 28, 2025
Blockchain and explainable-AI integrated system for Polycystic Ovary Syndrome (PCOS) detection
Gowthami Jaganathan, Shanthi Natesan
https://doi.org/10.7717/peerj-cs.2702
November 28, 2024
Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer’s disease classification
Krishnakumar Vaithianathan, Julian Benadit Pernabas, Latha Parthiban, Mamoon Rashid, Sultan S. Alshamrani
https://doi.org/10.7717/peerj-cs.2502
August 29, 2023
MSCDNet-based multi-class classification of skin cancer using dermoscopy images
Vankayalapati Radhika, B. Sai Chandana
https://doi.org/10.7717/peerj-cs.1520
April 25, 2022
A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?
KC Santosh, Nicholas Rasmussen, Muntasir Mamun, Sunil Aryal
https://doi.org/10.7717/peerj-cs.958

1 Answer

0
accepted Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images.