International Journal of Computer Science and Technology
Vol 6.1 ver – 1 Jan to Mar, 2015

S.No. Research Topic Paper ID Download

EWFPC: Extraction of Web Forums Using Page Type Classifiesr

Gotivada Swamy, Srinivasa Rao Dalai, A.Phani Sridhar

In this paper, we present EWFPC (EXTRACTION OF WEB FORUMS USING PAGE TYPE CLASSIFIESR), a supervised web-scale forum crawler. The goal of EWFPC is to only trawl relevant forum content from the web with minimal overhead. Forum threads contain information content that is the target of forum crawlers. Although forums have different layouts or styles and are powered by different forum software packages, they always have similar implicit navigation paths connected by specific URL types to lead users from entry pages to thread pages.
Based on this observation, we reduce the web forum crawling problem to a URL type recognition problem and show how to learn accurate and effective regular expression patterns of implicit navigation paths from an automatically created training set using aggregated results from weak page type classifiers. In this paper, we present EWFPC( EXTRACTION OF WEB FORUMS USING PAGE TYPE CLASSIFIESR), a supervised web-scale forum crawler, to address these challenges. The goal of EWFPC is to trawl relevant content, i.e. user posts, from forums with minimal overhead.
By applying Index URL Thread URL Detection,Page-Flipping URL Thread URL Detection,Entry URL Discovery algorithms.
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The Efficiency of Data Sharing in a Cloud by Security Analysis Using Mona

Gunde Priyanka, Sayeed Yasin

The cloud serversmanaged by cloud providers are not completely trusted by userswhile the data files stored in the cloud may be responsive andconfidential such as business plans. To preserve data privacy a basic solution is to encrypt data files and thenupload the encrypted data into the cloud. Regrettablyconnivingacapable and secure data sharing scheme forgroups in the cloud is not an effortless task due to the followingchallenging issues. Sharing data in a multi owner manner while preserving data and characteristics privacy from anuntrusted cloud is often a challenging issue due to the frequent change of the membership. In this paper we propose a secure multiowner data sharing scheme named Mona for dynamic groups in the cloud.We look at the problem in the context of a network augmentedwith storage nodes and target at range query a very general andpowerful type of query.
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Two Novel Approaches on the Node Clone Detection in Wireless Sensor Networks

G.Sunitha, A.Phani Sridhar, G.Kalyan Chakravarthi

Wireless sensor networks are vulnerable to the node clone, and several distributed protocols have been proposed to detect this attack. In this paper, it presents two novel, practical node clone detection protocols with different tradeoffs on network conditions and performance.
The first proposal is based on a Distributed Hash Table (DHT) by which a fully decentralized, key-based caching and checking system is constructed to catch cloned nodes. The protocol’s performance on memory consumption and a critical security metric are theoretically deducted through a probability model, and the resulting equations, with necessary adjustment for real application, are supported by the simulations. In accordance with our analysis, the comprehensive simulation results show that the DHT-based protocol can detect node clone with high security level and holds strong resistance against adversary’s attacks.
The second protocol, named Randomly Directed Exploration, is intended to provide highly efficient communication performance with adequate detection probability for dense sensor networks. In the protocol, initially nodes send claiming messages containing a neighbor-list along with a maximum hop limit to randomly selected neighbors; then, the subsequent message transmission is regulated by a probabilistic directed technique to approximately maintain a line property through the network as well as to incur sufficient randomness for better performance on communication and resilience against adversary. In addition, border determination mechanism is employed to further reduce communication payload.
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Fastening Sore Information in Social Network Data Anonymization

Kanagala Krishna Kanth, T.Sri Lakshmi

Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node re-identification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer one’s private information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by pure structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals A novel anonymization methodology based on adding noise nodes has proposed. New algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties most importantly completed the rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. Extensive experiments used to evaluate the effectiveness of the proposed technique.
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Privacy Enhancing Technologies & Issues in Online Social Networks

S. Prasanna Kumari, Srinivasa Rao Dalai, A.Phani Sridhar

Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In tackling these problems they have also treated them as if they were independent. We argue that the different privacy problems are entangled and that research on privacy in OSNs would benefit from a more holistic approach. In this article, we first provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions, goals and methods. We then juxtapose the differences between these two approaches in order to understand their complementarily, and to identify potential integration challenges as well as research questions that so far have been left unanswered.
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A Perceptual Model Based on Computational Features for Texture Representation and Retrieval

K.N.Sindhuri, N.Leelavathy, B.Srinivas

A new perception model based on image representation and retrieval for a set of computational measures is proposed in this paper. We consider a set of textural features that individuals use to identify and categorize textures having a perceptual meaning and their application to content-based image retrieval. Such features include coarseness, directionality, contrast, and busyness. This paper proposed a new method to calculate a set of perceptual texture features. The perceptual model presented is judged using a psychometric method (based on rank-correlation) and found to represent very well to human judgements. For these measures large database is required. Therefore the Brodatz database and benchmarking based on exploratory results gives exciting performance. This paper proposes to use two representations for better retrieval efficiency: the original image representation and the autocorrelation function representation. In this paper with the help of autocorrelation function related images are presented to the given input image (based on texture and colour). The related images are displayed either the user satisfies or until no change. The compatibility of the preferred computational measures is shown by human judgement. Firstly, based on the spearman rank-correlation coefficient. Second, the proposed computational measures in texture retrieval shows exciting results and their application mostly when using results returned by each of two representations.
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Bayes Classifier for Different Data Clustering-Based Extra Selection Methods

Abhinav. Kunja, Ch.Heyma Raju

Feature choice involves distinctive a set of the foremost helpful options that produces compatible results because the original entire set of options. A feature choice formula could also be evaluated from each the potency and effectiveness points of read. Whereas the potency considerations the time needed to seek out a set of options, the effectiveness is expounded to the standard of the set of options. Supported these criteria, a quick clustering-based feature choice formula (FAST) is planned and by experimentation evaluated during this paper. The quick formula works in 2 steps. Within the opening, options are divided into clusters by mistreatment graph-theoretic agglomeration strategies. Within the second step, the foremost representative feature that’s powerfully associated with target categories is chosen from every cluster to create a set of options. Options in numerous clusters are comparatively freelance; the clustering-based strategy of quick contains a high likelihood of manufacturing a set of helpful and independent options. To confirm the potency of quick, we tend to adopt the economical minimumspanning tree (MST) agglomeration methodology. The potency associate degreed effectiveness of the quick formula is evaluated through an empirical study. in depth experiments are disbursed to check quick and several other representative feature choice algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with relation to four varieties of well-known classifiers, namely, the probability based Naive Bayes, the tree-based C4.5, the instancebased IB1, and also the rule-based manslayer before and once feature choice. The results, on thirty five publically accessible realworld high-dimensional image, microarray, and text information, demonstrate that the quick not solely produces smaller subsets of options however conjointly improves the performances of the four varieties of classifiers.
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Implementation of LAR Protocol Using MANETS

Priyanka Yadav, Monalisa Lenka, Jyoti Kumari, Ravi Kumar Balleda

Mobile Ad-Hoc networks consist of wireless mobile hosts that communicate with each other, in the absence of a fixed infrastructure. Routes between two hosts in a Mobile Ad hoc network (MANET) may consist of hops through other hosts in the network. Host mobility can cause frequent unpredictable topology changes. Therefore, the task of finding and maintaining routes in MANET is nontrivial. Many protocols have been proposed for mobile ad hoc networks, with the goal of achieving efficient routing .These algorithms differ in the approach used for searching a new route and/or modifying a known route.
In this paper, we suggest an approach to decrease overhead of route discovery by utilizing location information for mobile hosts. Such location information may be obtained using the Global Positioning System (GPS) .We demonstrate how location information may be used by means of two Location-Aided Routing (LAR) protocols for route discovery. The LAR protocols use location information (which may be out of date, by the time it is used) to reduce the search space for a desired route. Limiting the search space results in fewer route discovery messages.
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A Novel Architecture For Peer-To-Peer System to Provide Security

T.B.Priyadarshini, A.Phani Sridhar, G.Kalyanchakravarthi

This paper propose A NOVEL ARCHITECTURE FOR PEER-TOPEER SYSTEM TO PROVIDE SECURITY that aims to decrease malicious activity in a P2P system by establishing trust relations among peers in their proximity. In THIS ARCHITECTURE, peers are assumed to be strangers to each other at the beginning. A peer becomes an acquaintance of another peer after providing a service, e.g., uploading a file. If a peer has no acquaintance, it chooses to trust strangers.
THIS ARCHITECTURE defines three trust metrics. Reputation metric is calculated based on recommendations. It is important when deciding about strangers and new acquaintances. Reputation loses its importance as experience with an acquaintance increases. Service trust and recommendation trust are primary metrics to measure trustworthiness in the service and recommendation contexts, respectively.
The service trust metric is used when selecting service providers. The recommendation trust metric is important when requesting recommendations. When calculating the reputation metric, recommendations are evaluated based on the recommendation trust metric.
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Segmentation of Structured Objects in Image

Rajeshwari Rasal, Prof. N.M.Shahane

Detection of foreground structured objects in the images is an essential task in many image processing applications. This paper presents a region merging and region growing approach for automatic detection of the foreground objects in the image. The proposed approach identifies objects in the given image based on general properties of the objects without depending on the prior knowledge about specific objects. The region contrast information is used to separate the regions of the structured objects from the background regions. The perceptual organization laws are used in the region merging process to group the various regions i.e. parts of the object. The system is adaptive to the image content. The results of the experiments show that the proposed scheme can efficiently extract object boundary from the background.
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Diversifying Subset Feature With Ranking For High Dimensional Data

Monalisa Lenka, Priyanka Yadav, Jyoti Kumari, S.Venkata Lakshmi

Feature selection involves identifying a subset of the most representative features.Feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view.Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is proposed.The Feature Subset Selection generally works in two steps:Features are divided into clusters by using graphtheoretic clustering methods. The most representative feature that is strongly related to target class is selected.To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.There are several algorithms applied to find the efficiency and effectiveness. Here we consider the efficiency as the time taken to retrieve the data’s from the database and effectiveness is from the most datasets (or) subsets which are relevant to the users search. By using FAST algorithm we can retrieve the data’s without the irrelevant features. Here the irrelevant features are carried out by means of various levels of the query input and the output the relevant information can be carried out in case of the subset selection and clustering methods.
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Performance Study of Proactive, Reactive and Hybrid Routing Protocols for MANET in Multiple CBR Scenario

Jyothi V, Suhas K R, Mohankumar N M, Devaraju J T

Mobile Ad-Hoc Network (MANET) comprises of numerous ubiquitous mobile computing devices called nodes which form distributed network and support dynamic topology without any centralized infrastructure like base station. In such distributed network, the communication between the nodes relies on multihop technique. Since, the nodes in a MANET do not have a priori knowledge of the network topology, it discovers the route through broadcasting and listening to announcement from the neighbours. As the process continues, each node finds one or more routes to all other nodes. Hence, the end-to-end communication in a MANET does not rely on any underlying static network infrastructure but implicates routing via several intermediate nodes. The routing of data in the network depends on the protocol which determines the most appropriate path to forward packets to the intended destination. The routing protocols are classified into proactive, reactive and hybrid.
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Comparison Between Various Types of Adder Topologies

Jasbir Kaur, Lalit Sood

Adders are one of the most widely digital components in the digital integrated circuit design and are the necessary part of Digital Signal Processing (DSP) applications. With the advances in technology, researchers have tried and are trying to design adders which offer either high speed, low power consumption, less area or the combination of them. In this paper, the design of various adders such as Ripple Carry Adder, Carry Skip Adder, Carry Increment Adder, Carry Look Ahead Adder, Carry Save Adder, Carry Select Adder, Carry Bypass Adder are discussed and are compared on the basis of their performance parameters such as area, delay and power distribution.
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Technological Innovation and Its Role in Promoting and Facilitating the Process of Organizational Learning in Small and Medium Enterprises

Satoutah Samira, Rouabhia Meriem

This study aims to identify the role of technological innovation in promoting organizational learning in small and medium enterprises , from the premise that organizational learning is a key step in the learning organization as an appropriate climate for the adoption of organizational learning for sustainability success within the organization , and keep up with the pace of competition in an environment of rapid and continuous change in terms of mutations and technological achievements unprecedented .
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A New Advance Role Based Access Control Model to Enhance the Transactions at Cloud Server

Meena Rani, Cherry

In Cloud computing, RBAC enables users to carry out a wide range of authorized tasks by dynamically regulating their actions according to flexible functions, relationships and constraints. This is in contrast to conventional methods of access control, which grant or revoke user access on a rigid, object-by-object basis. In RBAC, roles can be easily created, changed or discontinued as the needs of the enterprise evolve without having to individually update the privileges for every user. In this paper, we have added the concept of GIFT, SWAP and GAIN terms in order to pass the transactions between the users so that each user can do work effectively. This design is implemented in the ASP.NET environment.
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Semantic Analysis of Context Attributes for Recommender System using Ontology

Pallab Dutta, Dr. A. Kumaravel

There has been anunprecedented expansion of Internet in last couple of decades; huge amount of information and content isavailable in almost all domains and subjects and is ever expanding in both breadth and depth. On the flip side, this colossal expansion has resulted in data overloading problem; due to which it has become an increasingly difficult task toretrieve useful information from internet and separate out the unwanted ones. Recommender systems have evolved as a solution to the data overload problem that persists today in World Wide Web. Context aware recommender system has been an active research hotspot in current times. It has been found that when contexts parameters are induced appropriately in recommender system, the prediction accuracy increases but if contexts are not properly assimilated, the accuracy of recommender system suffers.The contexts always do not match exactly, but when contexts are meaningfully similar or nearer within a givenknowledge domain, these can be considered and exploited for further processing. This paper discusses the semantic analysis of context attributes of recommender system towards increasing the prediction accuracy and overcome data sparseness.
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A Review on Camera Based Attacks on Android Smart Phones

Anushree Pore, Mahip Bartere

Nowadays, almost all the smart phones have features like camera and touch screen. These features may lead attacks on our smart phones. Modern smart phone platforms let users customize their device via third-party applications found on “app stores” or traditional websites. Application provenance is a problem so users are constantly at risk of installing malicious apps that steal personal data or gain root access to their device. For example, while using such malicious application, the response from application provider may contain the hidden request to have control on different devices connected to our mobile such as camera, front or main no issues phone is been attacked, recognizing our current location through main camera as it will show our surroundings and trying to recognize PIN’s through front camera. This paper reviews new security threats are emerged for mobile devices and survey on arious techniques for detection of mobile malware.
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Data Linkage for BigData using Hadoop MapReduce

Bargavi C, Blessa Binolin Pepsi M

One-to-many data linkage is important in datamining. In earlier works data linkage is performed among entities of the same type. To link between matching entities of different types in larger datasets a new one-to-many data linkage method with mapreduce is proposed that links between entities of different natures. The proposed method is based on a one-class clustering tree (OCCT) that characterizes the entities that should be linked together. The tree is built using hadoop mapreduce framework for linkage. Using mapreduce for linkage results in a reduced execution time due to its distributed environment. It also results in an efficient processing of larger datasets.
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A Survey of Filtering System for OSN (Online Social Networks)

Shareen Anthony, Nitin Shelke

In recent years, Online Social Networks (OSNs) have become an important part of daily life. Users build explicit networks to represent their social relationships. Users can upload and share information related to their personal lives. The potential privacy risks of such behavior are often ignored. And the fundamental issue in today On-line Social Networks is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Today OSNs provide very little support to prevent unwanted messages on user walls. For that purpose, we proposed a new system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system that allows usersto customize the filtering criteria to be applied to their walls, and a Machine Learning (ML) based soft classifier automatically labeling messages in support of content-based filtering. The system exploits a ML soft classifier to enforce customizable contentdependent Filtering Rules. And the flexibility of the system in terms of filtering options is enhanced through the management of Blacklists. The proposed system gives security to the On-line Social Networks.
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Geometrical Concepts and Graph Theory For Linear Curve Approximation

K.Venkatasubramanian, Dr. S.K.Srivatsa, Dr. C.Parthasarathy

In this paper, a brand new methodology for curve approximation is bestowed. The tactic is appropriate for each self-intersected and non self-intersected curves, it combines elements from graph theory and from parabolic geometry and it is absolutely machinecontrolled. Additional specifically, graph theory tools square measure utilized in order: (1) to get rid of the small print that square measure irrelevant to the general form of the curve below study and (2) to decompose the curve into non self-intersecting smaller curves. Then, each such smaller curve is processed via geometrical tools so as to approximate it with efficiency with linear segments. Experimental results show that the planned technique compares well with several alternative ways of constant purpose.
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Solving Maximum Flow and Minimum Cut Network Problems by Labeling Method

Radhe Shyam Soni, S.P. Varma

There are several methods available for the solution of maximum flow network problems. Labeling method is an alternative method for maximum flow network problems. The basic thing in the labeling procedure is to systematically attach labels to the nodes of a network until optimum solution is obtained. Labeling techniques can be used to solve different types of network problems. Such as shortest-path problems, maximal-flow problems, general minimalcost flow problems etc.
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In the Multi-Hop Wireless N/W: Avoiding Packet Drop For Improved Throughput

Manish Kumar Rajak, Prof. Sanjay Gupta

In this paper, a new approach of controlling congestion is being proposed on the basis of a few more information maintained by nodes locally to slow down the network traffic inflow so that information shall not be gathered on a node and hence no need of dropping access packets in the network. The mechanism proposed is having various threshold values of the queue and flow rates decided in advance locally on each node and on occurrence of RTS (request to send) signal, CTS (clear to send) signal is sent back if and only if queue is not full or on the ratio of flow rates already decided.
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