Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

A Cloud Model to Implement SaaS

Abstract: Cloud computing is a paradigm where tasks are assigned to a combination of connections, software and services accessed over a network. Clouds provide processing power, which is made possible through distributed computing. Cloud computing can be seen as a traditional desktop computing model, where the resources of a single desktop or computer used to complete tasks, and an expansion of the client/server model. The paper describes a model for cloud computing to implement software as a service (SaaS).

Lattice Based Tools for Cryptanalysis in Various Applications

Abstract: Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Lattice reduction has been successfully utilizing in Number Theory, Linear algebra and Cryptology. Not only the existence of lattice based cryptosystems of hard in nature, but also has vulnerabilities by lattice reduction techniques. In this survey paper, we are focusing on point lattices and then describing an introduction to the theoretical and practical aspects of lattice reduction. Finally, we describe the applications of lattice reduction in cryptanalysis like subset sum problem of low density, modular equations, Attacking RSA with small e by knowing parts of the message and Diophantine Approximation using LLL algorithm.

Detection of solitary ocean internal waves from SAR images by using U-Net and KDV solver technique

Abstract: The internal waves are different phenomenon in oceanography and these are also waves but occur in the inside of the ocean. Many techniques exist for the detection of internal waves but every method has its own advantages and drawbacks. Earth observations systems keep an eye in monitoring ocean internal waves at regular intervals of time. But automatic detection of internal waves is still a challenging problem. This paper proposes a method which gives complete methodology for internal wave detection. The method is divided into three main stages that are input data pre-processing, parameter extraction and modeling. The proposed system will initially take Synthetic Aperture Radar (SAR) images, pre-process for noise. Augmentation techniques such as flip and rotation resolve occlusion caused by clouds. U-Net is used for segmentation and feature extraction of wave parameters such as frequency, amplitude, longitude, latitude. Finally, for ocean internal wave modeling, KdV (Korteweg–de Vries) solver is used. KdV solver takes the internal wave parameters as input and it gives the velocity, density plots of internal waves. The deep learning model is tested on SAR images and proved to give accurate results for the internal wave detection.

ECMCRR-MPDNL for Cellular Network Traffic Prediction With Big Data

Abstract: Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily basis. Processing such volume of data is a complex task as well as the challenging one. This big data is applied in the cellular network for traffic prediction. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In order to improve the traffic prediction accuracy with minimum time, Expected Conditional Maximization Clustering and Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL) technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of data over the spatial and temporal aspects of cellular networks. Then the collected data are trained with multiple layers such as one input layer, two hidden layers, and one output layer. The activation function is used at the output layer to predict the network traffic based on the similarity value with higher accuracy. These predictors are evaluated using real network traces. Finally, the error rate is calculated for minimizing the prediction error. Experimental evaluation is carried out using a big dataset with different metrics such as prediction accuracy, false-positive and prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique improves on an average the 98% of performance of network traffic prediction with higher accuracy and 20 % minimum time as well as the false-positive rate as compared to the state-of-the-art methods.