Institution
DAV College, Chandigarh
About: DAV College, Chandigarh is a based out in . It is known for research contribution in the topics: Radon & Adsorption. The organization has 349 authors who have published 676 publications receiving 6233 citations.
Topics: Radon, Adsorption, Aqueous solution, Phosphor, Thermoluminescence
Papers published on a yearly basis
Papers
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TL;DR: In this article, a series of white light emitting Dy3+ doped lead borate (DY) and DYA glasses have been prepared by melt quench technique and are explored by XRD, FTIR, optical absorptions, fluorescence and density measurements.
35 citations
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TL;DR: In this article, the propagation of Rayleigh surface waves in homogeneous isotropic, thermodiffusive elastic half-space was studied and the results regarding phase velocity, attenuation coefficient, specific loss and coupling factors of thermo-mechanical diffusive waves were obtained and presented graphically.
35 citations
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TL;DR: In this article, the Judd-Ofelt theory was applied on the optical absorption spectra of the glasses to evaluate the three phenomenological intensity parameters Ω2, Ω4 and Ω6.
35 citations
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TL;DR: The authors have proposed to use deep learning model as a feature extractor as well as a classifier for the recognition of 33 classes of basic characters of Devanagari ancient manuscripts and the accuracy achieved is better than other state-of-the-art techniques.
Abstract: Devanagari script is the most widely used script in India and other Asian countries. There is a rich collection of ancient Devanagari manuscripts, which is a wealth of knowledge. To make these manuscripts available to people, efforts are being done to digitize these documents. Optical Character Recognition (OCR) plays an important role in recognizing these documents. Convolutional Neural Network (CNN) is a powerful model that is giving very promising results in the field of character recognition, pattern recognition etc. CNN has never been used for the recognition of the Devanagari ancient manuscripts. Our aim in the proposed work is to use the power of CNN for extracting the wealth of knowledge from Devanagari handwritten ancient manuscripts. In addition, we aim is to experiment with various design options like number of layes, stride size, number of filters, kenel size and different functions in various layers and to select the best of these. In this paper, the authors have proposed to use deep learning model as a feature extractor as well as a classifier for the recognition of 33 classes of basic characters of Devanagari ancient manuscripts. A dataset containing 5484 characters has been used for the experimental work. Various experiments show that the accuracy achieved using CNN as a feature extractor is better than other state-of-the-art techniques. The recognition accuracy of 93.73% has been achieved by using the model proposed in this paper for Devanagari ancient character recognition.
35 citations
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TL;DR: The present research work reveals semi-interpenetrating network (semi-IPN) synthesis using response surface methodology-central composite design (RSM-CCD) based optimization and reveals recyclability of the adsorbent is superior as tested by desorption-adsorption tests.
35 citations
Authors
Showing all 349 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rajesh Kumar | 149 | 4439 | 140830 |
Pradeep Mathur | 36 | 288 | 5223 |
Manpreet Kaur | 18 | 97 | 1280 |
Neha Sharma | 16 | 45 | 604 |
Vikram Dhuna | 15 | 23 | 656 |
Vijay Mago | 15 | 92 | 913 |
Raghu Raj | 15 | 34 | 809 |
Kashma Sharma | 15 | 37 | 661 |
Ajay Kumar | 15 | 58 | 500 |
Kulwinder Singh Mann | 14 | 26 | 571 |
Aditi Shreeya Bali | 14 | 29 | 1241 |
Sumit Sharma | 14 | 96 | 578 |
Vikas Chawla | 14 | 81 | 1044 |
Poonam Khullar | 14 | 37 | 704 |
Kanika Khanna | 13 | 45 | 683 |