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Ashutosh Aggarwal
Researcher at Thapar University
Publications - 25
Citations - 397
Ashutosh Aggarwal is an academic researcher from Thapar University. The author has contributed to research in topics: Zernike polynomials & Feature extraction. The author has an hindex of 9, co-authored 25 publications receiving 235 citations. Previous affiliations of Ashutosh Aggarwal include Punjabi University & Khalsa College, Amritsar.
Papers
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Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine.
Vivek Lahoura,Harpreet Singh,Ashutosh Aggarwal,Bhisham Sharma,Mazin Abed Mohammed,Robertas Damaševičius,Robertas Damaševičius,Seifedine Kadry,Korhan Cengiz +8 more
TL;DR: In this article, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed, which combines three research domains: Firstly, ELM was applied for the diagnosis, and to eliminate insignificant features, the gain ratio feature selection method was employed.
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An improved block based joint reversible data hiding in encrypted images by symmetric cryptosystem
TL;DR: Improved block based joint EIRDH algorithm is employed in this paper which embedded n secret bits per block by dividing blocks of same size into n sub-blocks and optimizing visual quality and enhanced embedding rate are acquired.
Handwritten Devanagari Character Recognition Using Gradient Features
Ashutosh Aggarwal,Rajneesh Rani +1 more
TL;DR: Novel methods of feature extraction for recognition of single isolated Devanagari character images with high performance with cross validation accuracy of 94% are described.
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An efficient and robust approach for biomedical image retrieval using Zernike moments
TL;DR: A biomedical image retrieval system which uses Zernike moments (ZMs) for extracting features from CT and MRI medical images and has shown a significant improvement in comparison to the state-of-the-art techniques on the respective databases.
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Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals
TL;DR: Two ways of extracting features using gradient information are explained in this paper, which operate by accumulating gradient information from an image by dividing it into sub-images (blocks) and concatenating the obtained gradient features obtained from each block to form a vector of feature values with dimensionality 200.