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Malappuram, India

Goyal A.,C DAC | Bharti,UIET
International Conference on Computing, Communication and Automation, ICCCA 2015 | Year: 2015

MapReduce is a programming model specifically developed for the management and processing of 'Big Data' - extremely large amounts of data that expects high level of analyzing capabilities. With every passing day volumes of data is generated and collected from multiple data resources across the planet. This data must be analyzed in the sense of volume or speed of data moving to and from the data management systems. MapReduce efficiently execute programs on large clusters by utilizing the concept of parallelism. Till now Google's MapReduce framework has been considered as the most successful implementation for Big Data. A number of implementations of MapReduce programming model have been proposed. This paper discusses various emerging implementations of MapReduce model. An emphasis is also given on the leading and lacking strength of these implementations. © 2015 IEEE. Source

Deep G.,IET Bhaddal | Kaur L.,Punjabi University | Gupta S.,UIET
Procedia Computer Science | Year: 2016

This paper focuses on the comparison of two new proposed pattern descriptors i.e., local mesh ternary pattern (LMeTerP) and directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the former descriptor encodes the gray scale relationship among the neighbors for a given center pixel with three selected directions of mess patterns which is generated from 2D image and later descriptor encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0°, 45°, 90° and 135°) for a given center pixel in an image. The novelty of the proposed descriptors is that they use ternary patterns from images to encode more spatial structure information which lead to better retrieval. The experimental results demonstrate the superiority of the new techniques in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques (like LBP, LTP, LQEP, LMeP etc.) on three different types of benchmark biomedical databases. © 2016 The Authors. Published by Elsevier B.V. Source

Asrani K.,B. B.D. N. I. T. M. | Jain R.,UIET
Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013 | Year: 2013

Plants have an important role in our life, but due to environmental changes, many species of plants are facing extinction. It is very important to treasure this great wealth by maintaining complete details of all types of plant which will help in understanding the aspect of their survival. A leaf plays an important role in the identification of plant. Edge details of a leaf are detected using edge detection algorithms and can be stored in the form of feature vectors. Hence to improve the effectiveness of identification of leaf, we are hereby proposing to generate Clustered Database. This paper introduces an approach of Clustering based on Eccentricity. For clustering database, K-means algorithm is used. Experimental results show the effectiveness of forming clusters by calculating entropy and purity. From experimental results, it was found that the proposed approach of clustering is quite effective and would enhance the retrieval efficiency. © 2013 IEEE. Source

Arora A.,JUIT Solan | Dogra A.,UIET
International Journal of Pharmacy and Technology | Year: 2016

This paper work is preferentially concerned with the synthesis of quantum dots (QDs) by solution growth method at room temperature and their characterization. Excellent structural and optical and surface properties have been attained like high luminescence and stable structure. Prepared quantum dots structures and core/shell quantum dots structure are cadmium selenide (CdSe), Zinc sulfide (ZnS) and Cadmium selenide/ Zinc sulfide (CdSe/ZnS). © 2016, International Journal of Pharmacy and Technology. All rights reserved. Source

Singh J.,DAV Institute of Engineering and Technology | Singh S.,UIET | Singh D.,Control Engg. | Uddin M.,Dr. B.R. Ambedkar N.I.T
Signal Processing: Image Communication | Year: 2011

Image compression plays a pivotal role in minimizing the data size and reduction in transmission costs. Many coding techniques have been developed, but the most effective is the JPEG compression. However, the reconstructed images from JPEG compression produce noticeable image degradations near block boundaries called blocking artifacts, particularly in highly compressed images. A method to detect and reduce these artifacts without smoothing images and without removing perceptual features has been presented in this paper. In this work, a low computational deblocking filter with four modes is proposed, including three frequency-related modes (smooth, non-smooth, and intermediate) and a corner mode for the corner of four blocks. Extensive experiments and comparison with other deblocking methods have been conducted on the basis of PSNR, MSSIM, SF, and MOS to justify the effectiveness of the proposed method. The proposed algorithm keeps the computation lower and achieves better detail preservation and artifact removal performance. © 2011 Elsevier B.V. Source

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