Author
Abhimanyu Singh Garhwal
Bio: Abhimanyu Singh Garhwal is an academic researcher. The author has contributed to research in topics: Phylogenetic tree & Pattern matching. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.
Papers
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01 Jan 2018
TL;DR: This dissertation aims to provide a chronology of the events leading up to and including the invention of the determinants of infectious disease.
Abstract: .................................................................................................................. III List of Figures ......................................................................................................... VI List of Tables ....................................................................................................... VIII Attestation of Authorship ........................................................................................ X Acknowledgement .................................................................................................. XI
6 citations
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01 Jan 2016
TL;DR: This guide to medical image analysis methods and algorithms helps people to enjoy a good book with a cup of tea in the afternoon instead of having to cope with some harmful virus inside their computer.
Abstract: Thank you very much for downloading guide to medical image analysis methods and algorithms. As you may know, people have look numerous times for their chosen readings like this guide to medical image analysis methods and algorithms, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their computer.
38 citations
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8 citations
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01 Jan 2018
TL;DR: It is suggested that the number of children under the age of five should be counted as one in a family rather than two in the case of a family of five.
Abstract: ............................................................................................................................ i Table of
6 citations
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23 Oct 2018TL;DR: Deep Autoencoders, a form of deep neural networks for classification and identification of watermarked and non-watermarked images is proposed and the experiment results show that,Deep neural networks performed better that traditional feed forward neural networks.
Abstract: Digital watermarking is the process of embedding an unique mark into digital data to prevent counterfeit. With the exponential increase in the data, the process of segregating a watermarked and non-watermarked images is very time consuming. It is necessary to automate the process of differentiating a watermarked and a non-watermarked images as well as identifying whether the given image is watermarked or not for identifying the authenticity. In this paper, we propose to use Deep Autoencoders, a form of deep neural networks for classification and identification of watermarked and non-watermarked images. The experiments are carried out using NWND dataset originally with 444 images. These images are watermarked using image, shape and text watermarking techniques to make the entire dataset to 1776 images. The experiment results show that, deep neural networks performed better that traditional feed forward neural networks. The classification accuracies with Original - IW for DAEN and ANN are 77.9% and 25.9 % respectively. Whereas for Original - SW and Original - TW, it is 82.1% and 32.7%, 64.2% and 20.06% respectively. The DAEN was able to identify 86 images correctly out of 100 images supplied which is 86% of accuracy with an average training rmse of 0.06423 and testing rmse of 0.0784.
4 citations
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01 Aug 2019TL;DR: In this scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark and extracts a signature from the label (which is represented in amino acid form) using biological tools.
Abstract: Fake food label is one of the leading ways to distribute a low quality food item as a high quality branded product. For example, under fake labels, significantly higher amount of fake Manuka honey is sold than what is actually being produced. In this paper, we propose a scheme to combat the spread of such low quality food items by identifying fake food labels. In our scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark. The proposed biosequence analysis is such that it can detect duplicate labels, for example a photocopy of the genuine label. The proposed method works by converting a label image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the label (which is represented in amino acid form) using biological tools. These signatures are then matched against a query label image to find out its originality. Experiment with honey food labels (honey watermarked dataset created by us) shows that the proposed method has true positive rate of 91:67%.
1 citations