Nth Order Binary Encoding with Split-Protocol

Nth Order Binary Encoding with Split-Protocol

Bharat S. Rawal (Penn State Abington, Abington, Pennsylvania, United States), Songjie Liang (SoroTek Consulting Inc, North Potomac, Maryland, United States), Shiva Gautam (Harvard Medical School, Boston, Massachusetts, United States), Harsha Kumara Kalutarage (Queen's University of Belfast, Belfast, United Kingdom) and P Vijayakumar (University College of Engineering Tindivanam, Ayyanthoppu, India)
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJRSDA.2018040105

Abstract

To cope up with the Big Data explosion, the Nth Order Binary Encoding (NOBE) algorithm with the Split-protocol has been proposed. In the earlier papers, the application Split-protocol for security, reliability, availability, HPC have been demonstrated and implemented encoding. This technology will significantly reduce the network traffic, improve the transmission rate and augment the capacity for data storage. In addition to data compression, improving the privacy and security is an inherent benefit of the proposed method. It is possible to encode the data recursively up to N times and use a unique combination of NOBE's parameters to generate encryption keys for additional security and privacy for data on the flight or at a station. This paper describes the design and a preliminary demonstration of (NOBE) algorithm, serving as a foundation for application implementers. It also reports the outcomes of computable studies concerning the performance of the underlying implementation.
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Kecman provides a detailed insight of learning from experimental data and soft computing. Soft computing mimics the intelligent mathematical structures, or models, that underlie learning found in the nature (Kecman, 2001). According to Wang and Fu, the data dimensionality reduction (DDR) can reduce the dimensionality of hypothesis search space, reduce data collection and storage costs, enhance data mining performance and simplify the data mining result. They describe the use of the Genetic algorithm for feature selections as well (Wang & Fu, 2005). Zhang, Wang & Lin propose a unique subspace learning structure, Conjunctive Patches Subspace Learning (CPSL) process by employing the user historical feedback log data for a collaborative image retrieval (CIR) task. The CPSL can assimilate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information (Zhang, Wang, & Lin, 2012). In addition, they have proposed a CIR for the reduction of labeling works of the user by resorting to the ancillary data. The support vector machine (SVM) vibrant learning can decide vague samples as the most instructive and thus, lessens the labeling works of conventional RF (Zhang, Wang, & Lin, 2014). Chen, Su, Gimson, Liu & Shine propose an object segmentation method to extract the image features, and showed that new feature types can be incorporated into the algorithm to further improve the image retrieval performance. They suggest that content-based image retrieval (CBIR) can be carried out to evaluate the object segmentation capability in dealing with the large-scale database images (Chen, Su, Gimson, Liu & Shine, 2012).

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