IJEA - Volume 3 Issue 11 (November 2014)


Sr/No/ Title Pdf
1 Enhancing Data Security by Using Hybrid Cryptographic Algorithm
Sonali R. Raut

Abstract-Internet is a public-interacted system, the amount of information exchanged over the internet is completely not safe. Protecting the information transmitted over the network is a difficult task and the data security issues become increasingly important. The algorithm is designed using combination of two symmetric cryptographic techniques. These two primitives can be achieved with the help of Advanced Encryption Standard (AES) and Data Encryption Standard (DES). This new hybrid cryptographic algorithm has been designed for better security with integrity.

2 Effective Classification of Background Subtracted Videos Using Learning Classifier
C.K.Bharathi, D.Suresh

Abstract:-In this project we present the concept of effective background subtraction for videos which is present in both spatial-temporal models to conquer the open problem in the context of the complex scenarios, e.g., dynamic backgrounds, illumination variation and imprecise foreground objects. In this context of the effective background subtraction, first is to apply frame extraction at any location of the scene of the video which is in the AVI format. Next is to find the threshold value for frames by using pixel intensity of it. This threshold value separates the foreground and background of the frame by applying background subtraction. It will get rid of the background of each and every frame and run as a video. Finally we proposed the classification for the output video that has underwent background subtraction & reveal the type of the object more precisely then existing system by using learning classifier-neural network.

3 Privacy - preserving search in Personilized web search
Aruchana.P, Anitha.B, Agalya.D

Abstract— As the size of the Internet continues to grow the users of search providers continually demand search results that are accurate to their needs. Personalized Search is one of the options available to users in order to sculpt search results returned to them based on their personal data provided to the search provider. This raises concerns of privacy issues however as users are typically uncomfortable revealing personal information to an often faceless service provider on the Internet. Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users' reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user- specified privacy requirements. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework.

4 SVM Based Classification with Enhanced K-Means Clustering Algorithm for CT Brain Image Diagnosis
S.Sarmilee, K.Dhanalakshmi

Abstract-Brain tumor is one of the most common cause of cancer related deaths among the human beings which occur due to the abnormal cell growth in brain. In order to slow down the progression of the disease, detecting brain tumors in its earliest stage is important. CT scan is one of the screening methods of brain tumor. The phases involved in this work are pre-processing, segmentation, feature extraction, classification and clustering of brain CT images. The segmentation of brain CT images is based on Mumford-Shah functional method. The features are extracted from the segmented images using Tensor Decomposition. Spatial and spectral characteristics are simultaneously captured using this approach. Classification is the important step in image analysis. The classification is performed using Support Vector Machine (SVM) classifiers to discriminate benign and malignant subjects. In case of malignant subjects clustering is done using enhanced K-means algorithm to identify the stages of malignancy.

5 Human Activity Recognition Using NTB Algorithm
Mrs.K.Gayathri, Mrs.K.Dhanalakshmi

Abstract- A new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an errorfree network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages, while human activities can be modeled as the process of package transmission in the network. By analyzing these specific package transmission processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in this paper. Experimental results demonstrate the effectiveness of our proposed algorithm.

6 Performance analysis of blind Estimators for OFDM/OQAM Systems
Mr.K.Ujwal, Mr. E V Narayana

Abstract- In this paper, we used an algorithm for joint blind symbol timing and carrier frequency offset (CFO) estimation for orthogonal frequency-division multiplexing (OFDM) systems based on offset quadrature amplitude modulation (OQAM). Specifically, by using the approximate Conjugate- Symmetry Property (CSP) of the starting of a group of OFDM/OQAM symbols, due to presence of timing offset, a method for symbol timing and CFO synchronization is used for multipath channels with AWGN, RICIAN and ETU channel. The performance of the derived blind ST and CFO estimators are analysed by computer simulations; the results show that these methods may provide suitable performance for reasonable values of the signal-to-noise ratio.

7 Performance analysis of papr reduction Using nonlinear companding transform in OFDM systems
Mr. P.Bhanu Kiran, Mr. E V Narayana

Abstract—In general, the high peak-to-average power ratio (PAPR) of transmitted signals for OFDM systems reduces the system efficiency and hence increases the cost of the radiofrequency (RF) power amplifier. Nonlinearity of the high power amplifier causes high out-of-band radiation, inter carrier interference, and bit error rate performance degradation. Here we use a nonlinear companding algorithm which transforms the OFDM signals into the desirable signal form defined by a linear piecewise function, there should be an effective tradeoff between PAPR and BER more flexibility in the companding form and an effective trade-off between the PAPR and bit error rate. For this algorithm a theoretical performance study is presented and closedform expressions regarding the achievable transform gain and signal attenuation factor are provided. Here the BER performance is analysed using AWGN channel, Rician channel and also using ETU channel. The theoretical analyses of the algorithm are well verified via computer simulations.
8 Multi Level Hierarchy Stable Election Protocol for Wireless Sensor Networks
Sake Pothalaiah, D. Sreenivasa Rao

Abstract—Recently wireless sensor networks are having many applications such as medical systems, environment monitoring, military applications and disaster management. Usually sensors are placed in the desired locations to gather information frequently and then transfer it to the users. WSN consists of a collection of application specific sensors, in wireless sensor network major challenging issue is energy because sensors have limited energy source which are not replaceable in most of the cases, the effective use of energy requires efficient routing protocol. The cluster based routing protocol most suitable in terms of energy efficiency. In this paper we propose MLHSEP (Multi Level Hierarchy Stable Election Protocol). This proposed protocol is improved version of SEP protocol to increase the lifetime of the WSN. The MLHSEP takes an account of different energy levels. Cluster size and selection of cluster heads depends upon the different energy levels, starting from the higher energy nodes form cluster with cluster heads. This similar process is continuing up to last energy level. The performance of the MLHSEP protocol is compared with SEP protocol in terms of energy consumption and dead nodes.
9 Improving the efficiency of each B.PAC influencers on AfB
Tejaswini N, Kanthimathi

Abstract—Every point in the agenda for Bangalore, Item requires series of interventions including opinion formation and dissemination, thought leadership, raising funds and required resources and managing the success. Opinion formation and dissemination can use Emails and social media to a large extent. Emails are used in order to get sponsorship and resources like venue for B.PAC events , permissions, donations, etc. The main aim of this project is to automate the Email sending process and categorize the Emails based on subject, priority etc and to track the Emails and improve the efficiency of each B.PAC influencers