Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

In recent days, due to the advancement in technology there are increasing numbers of threats to multimedia data which are floating around in the Internet especially in the form of image data. Many methods exist to provide security for digital images but transform domain based digital watermarking could be considered as a promising method. Many transformation techniques are used to insert watermark in cover data, but this chapter deals with watermarking approaches in Hadamard transform domain. In traditional watermarking approaches the scaling parameter is empirically considered for inserting watermark but to maintain the quality of underlying cover images it needs to be calculated based on the content of the cover images. In order to make the watermarking algorithm completely automated the embedding and scaling parameters are calculated using the content of cover images. Many methods are existing for calculating scaling parameter adaptively but this chapter discusses various approaches using computational intelligence to arrive at optimum value of scaling and embedding parameters.

Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain

Advances in technologies facilitate the end users to carry out unauthorized manipulation and duplication of multimedia data with less effort. Because of these advancements, the two most commonly encountered problems are (1) copyright protection and (2) unauthorized manipulation of multimedia data. Thus a scheme is required to protect multimedia data from those two above said problems. Digital Watermarking is considered as one of the security mechanisms to protect copyrights of multimedia data. The literature review reveals that the calculation of scaling and embedding parameters are not completely automated. In order to automate the procedure of calculating scaling and embedding parameters the computational intelligence need to be incorporated in the watermarking algorithm. Moreover the quality of the watermarked images could also be preserved by combining computational intelligence concepts. Thus watermarking schemes utilizing computational intelligence concepts could be called as intelligence based watermarking schemes and it is presented in this chapter in detail.

A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset

BACKGROUND

In the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential.

METHOD

In this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches.

RESULTS

To test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy.

CONCLUSIONS

The obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.