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Dissertation

Bioinformatics-inspired analysis for watermarked images with multiple print and scan

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
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

Dissertation
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


Cites methods from "Bioinformatics-inspired analysis fo..."

  • ...55 The analytical feature of any research method, given the primary research question (as stated above) and lack of preliminary work in the space covered by the research question, it was deemed a requirement to construct a hypothesis, design the experiment, collect the results, and analyse the results, report the outcomes and then if necessary reconstruct the hypothesis, and so on (Garhwal, 2018)....

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Book ChapterDOI
23 Oct 2018
TL;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

Proceedings ArticleDOI
01 Aug 2019
TL;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


Cites background from "Bioinformatics-inspired analysis fo..."

  • ...virus signature database and a database of potential malicious files as inputs [35]....

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References
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Journal ArticleDOI
TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.

88,255 citations

Journal ArticleDOI
TL;DR: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved and modifications are incorporated into a new program, CLUSTAL W, which is freely available.
Abstract: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

63,427 citations

Journal ArticleDOI
TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

44,587 citations

Journal ArticleDOI
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

43,540 citations

Journal ArticleDOI
TL;DR: The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models, inferring ancestral states and sequences, and estimating evolutionary rates site-by-site.
Abstract: Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.

39,110 citations