Dr. Jyothisha J. Nair currently serves as Associate Professor and Vice Chairperson at the Department of Computer Science and Engineering at Amrita School of Engineering, Amritapuri.

She has completed her Ph. D. from National Institute of Technology (NIT), Calicut.


Publication Type: Journal Article
Year of Publication Publication Type Title
2015 Journal Article R. G. Gayathri and J.J. Nair, “Towards efficient analysis of massive networks”, International Journal of Applied Engineering Research, vol. 10, pp. 222-227, 2015.[Abstract]

Graph algorithms are very useful in solving many problems in all major domains like social networking, stock market analysis etc. Increasing demands of such kind of problems grow in scale and reveal the need of parallel computing resources to meet the computational and memory needs. Graph processing systems have to deal with the three Vs of big data - variety, velocity and volume. Loading the entire graph into the memory of a single machine seems to be impossible. In such cases, parallel processing is a solution to tackle the resource limitations posed by single processors. In this paper, we present the requirement for graph partitioning and the issues in designing partitioning technique for real world graphs. The paper also talks about the temporal metrics that provide a more effective analysis of real-world networks compared to their static counterparts. Finally, we aim at the reachability queries which are indispensable in networks and their usage in the dynamic graphs which evolve over time. We focus on the current challenges in this area and feature some future research recommendations. © Research India Publications.

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Publication Type: Conference Paper
Year of Publication Publication Type Title
2015 Conference Paper B. Bhadran and J.J. Nair, “Classification of patterns on high resolution SAR images”, in 2015 International Conference on Computing and Network Communications, CoCoNet 2015, 2015, pp. 784-792.[Abstract]

Synthetic Aperture Radar being an all weather adaptive and deeply penetrating, forms an inevitable part of all processes of investigation. Classifying different patterns like rivers, buildings, land areas, farm land etc has got prominent role in remote sensing applications, military applications etc and hence has been actively researched in recent years. This paper presents a novel approach for classifying high resolution SAR images. Image denoising is the first step in certain applications like classification problem, pattern matching etc. Here a modified Non Local Means filter method is used for denoising and also explores the possibility of using Artificial Neural Networks (ANN) for classifying different patterns on high resolution SAR images based on a fusion method. The proposed method uses the features of Local Binary Patterns (LBP), features in RGB color space and features in HSV color space. The experiments on high resolution SAR images obtained from Quickbird and Ikonos satellites shows that the proposed method outperforms the other widely used feature extracting methods in SAR image classification. © 2015 IEEE.

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