H-Index & Metrics Top Publications
Venkatesan Rajinikanth

Venkatesan Rajinikanth

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 30 Citations 3,060 111 World Ranking 8725 National Ranking 77

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Venkatesan Rajinikanth focuses on Artificial intelligence, Pattern recognition, Thresholding, Segmentation and Image processing. His research integrates issues of Magnetic resonance imaging and Computer vision in his study of Artificial intelligence. In general Pattern recognition study, his work on Feature extraction, Feature selection, Support vector machine and Principal component analysis often relates to the realm of Abnormality, thereby connecting several areas of interest.

His study on Thresholding is mostly dedicated to connecting different topics, such as Grayscale. His Segmentation study integrates concerns from other disciplines, such as Tsallis entropy, Entropy and RGB color model. Venkatesan Rajinikanth focuses mostly in the field of Image segmentation, narrowing it down to topics relating to Firefly algorithm and, in certain cases, Bat algorithm and Otsu's method.

His most cited work include:

  • Multi-level image thresholding using Otsu and chaotic bat algorithm (132 citations)
  • Entropy based segmentation of tumor from brain MR images a study with teaching learning based optimization (131 citations)
  • Otsu based optimal multilevel image thresholding using firefly algorithm (92 citations)

What are the main themes of his work throughout his whole career to date?

Venkatesan Rajinikanth mainly investigates Artificial intelligence, Pattern recognition, Thresholding, Segmentation and Image segmentation. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer vision. His Support vector machine study in the realm of Pattern recognition connects with subjects such as Abnormality.

His Thresholding research is multidisciplinary, incorporating perspectives in Image quality, Pixel, Grayscale, Tsallis entropy and Algorithm. His research integrates issues of Fluid-attenuated inversion recovery, Magnetic resonance imaging, Firefly algorithm and RGB color model in his study of Segmentation. His studies deal with areas such as Binary image, Entropy and Medical imaging as well as Image segmentation.

He most often published in these fields:

  • Artificial intelligence (61.29%)
  • Pattern recognition (46.77%)
  • Thresholding (34.68%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (61.29%)
  • Pattern recognition (46.77%)
  • Thresholding (34.68%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Pattern recognition, Thresholding, Segmentation and Abnormality. His Artificial intelligence study combines topics in areas such as Machine learning, Bat algorithm and Computer vision. His work carried out in the field of Pattern recognition brings together such families of science as Artificial neural network, Entropy and Transfer of learning.

His studies in Thresholding integrate themes in fields like Pixel, Grayscale, Cluster analysis, RGB color model and Algorithm. His Segmentation study integrates concerns from other disciplines, such as Particle swarm optimization, Firefly algorithm, Retinal and Brain mri. He combines subjects such as Markov random field, Active contour model, Optic disc and Optic nerve with his study of Firefly algorithm.

Between 2019 and 2021, his most popular works were:

  • Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images (66 citations)
  • Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images. (43 citations)
  • Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (28 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary scientific interests are in Artificial intelligence, Pattern recognition, Abnormality, Entropy and Disease. The Deep learning and Transfer of learning research Venkatesan Rajinikanth does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Concatenation, Brain tumor and Stomach cancer, therefore creating a link between diverse domains of science. Venkatesan Rajinikanth has included themes like Artificial neural network and Thresholding in his Pattern recognition study.

His work deals with themes such as Classifier and Particle swarm optimization, which intersect with Artificial neural network. The study incorporates disciplines such as Image processing, Plant phenotyping, Image database and Condition monitoring in addition to Entropy. His Segmentation research includes themes of RGB color model, Ground truth and Firefly algorithm.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Entropy based segmentation of tumor from brain MR images a study with teaching learning based optimization

V. Rajinikanth;Suresh Chandra Satapathy;Steven Lawrence Fernandes;S. Nachiappan.
Pattern Recognition Letters (2017)

185 Citations

Multi-level image thresholding using Otsu and chaotic bat algorithm

Suresh Chandra Satapathy;N. Sri Madhava Raja;V. Rajinikanth;Amira S. Ashour.
Neural Computing and Applications (2018)

167 Citations

Otsu based optimal multilevel image thresholding using firefly algorithm

N. Sri Madhava Raja;V. Rajinikanth;K. Latha.
Modelling and Simulation in Engineering (2014)

139 Citations

RGB Histogram Based Color Image Segmentation Using Firefly Algorithm

V. Rajinikanth;M.S. Couceiro.
Procedia Computer Science (2015)

129 Citations

Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya;Steven Lawrence Fernandes;Joel En WeiKoh;Edward J. Ciaccio.
Journal of Medical Systems (2019)

128 Citations

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Nilanjan Dey;Venkatesan Rajinikanth;Amira S. Ashour;João Manuel R. S. Tavares.
Symmetry (2018)

101 Citations

Segmentation of Ischemic Stroke Lesion in Brain MRI Based on Social Group Optimization and Fuzzy-Tsallis Entropy

V. Rajinikanth;Suresh Chandra Satapathy.
Arabian Journal for Science and Engineering (2018)

99 Citations

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

Sakshi Ahuja;Bijaya Ketan Panigrahi;Nilanjan Dey;Venkatesan Rajinikanth.
Applied Intelligence (2021)

91 Citations

Automated detection of schizophrenia using nonlinear signal processing methods.

V. Jahmunah;Shu Lih Oh;V. Rajinikanth;Edward J. Ciaccio.
Artificial Intelligence in Medicine (2019)

83 Citations

Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation

N. Sri Madhava Raja;S. L. Fernandes;Nilanjan Dey;Suresh Chandra Satapathy.
Journal of Ambient Intelligence and Humanized Computing (2018)

82 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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