International Journal of Computer Science and Technology Vol 5.3-2

S.No. Research Topic Paper ID Download

Prevention Against Rushing Attack on MZRP in Mobile Ad-Hoc Networks

Shaveta Jain, Kushagra Agrawal

A Mobile ad-hoc network could be a self organizing system of mobile nodes that communicate with one another via wireless link with no fixed infrastructure or central controller like base station or access points. Nodes act as host or router to forward packets to other nodes in a multi-hop fashion. There are various attacks in MANET that affect the routing. For e.g. Rushing attack, flooding attack, wormhole attack etc. This paper illustrates how the rushing attack can affect the performance of MZRP routing protocol in wireless network and also see the impact of rushing attack at the different position of attacker i.e. near sender, near destination node and anywhere in the network. The simulation is done using NS-2 simulator.
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Segmentation of Masses in Digital Mammograms using Optimal Global Thresholding with Otsu’s method

Shankar Thawkar, Dr. Ranjana Ingolikar

Currently there are no methods to prevent breast cancer that is the reason why early detection represents a very important factor in cancer treatment. Mammography is currently the best available radiological technique for early detection of breast cancer. Mass detection using Computer Aided Design and Detection systems serve as a second decision tool to radiologists for discovering masses in the mammograms. This work presents a methodology for segmentation of masses in Mammogram Images. The proposed algorithm uses Median filtering, Optimal Global thresholding using Otsu’s method and morphological processing in order to: (1) enhance quality of mammography image, (2) Segmentation of masses based on threshold resulting Region of Interest (ROI)(3) extracting segmented masses from image. The proposed techniques are implemented and tested under MATLAB environment.
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Document Clustering – Efficient Retrieval of Documents Using Keyword Neighbourhood Analysis

D. Murali, Dr. Kahkashan Tabassum, Dr. A. Damodaram

Retrieval of relevant documents using a set of keywords from a repository for a given context is a challenging task. In this paper, we propose an approach for extending keywords using neighbourhood analysis and architecture for context based document retrieval system. We use the WordNet ontology to identify semantic relationships among various keywords and retrieve the relevant documents from the document repository. We first preproces the documents, which includes two major steps: (i) stop word removal and (ii) stemming process. The outcome of preprocessing provides an indexing for important keywords and their extended set. When a user enters a keyword for document retrival, we present a mechanisim to refine and extend the set of keywords using the ontology. Finally, the extended keywords are matched with index and relevant documents are retrieved. We also present the experimental results and the performance analysis to show the viability of our approach.
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A Comprehensive Survey of Personalized Recommender Systems

Mrs.M. Sridevi, Mr. R.Rajeshwara Rao

The proliferation of information on internet and growth of e-commerce has resulted in information overload. As the size and richness of information grows,so does the users are faced with the complexity of trying to accomplish the most relevant and reliable information effectively and efficiently. Recommender systems gained prominence as it alleviated the information overload problem and presented different methods by inferring individual personalized Recommendation. This paper provides a comprehensive survey on state of the art of Recommender Systems, portrays us with an outline of different methods and challenges in the areas of personalized web information gathering which make Recommender Systems capture the user’s behavior and can be used for personalized web applications.
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A Review of Recommender System: From Past to the Future

Pallab Dutta, Dr. A. Kumaravel

Last decade and half had been witnessed to an unprecedented expansion of Internet, huge amount of information is available in almost all domains and subjects and is ever expanding. This has resulted in data overloading; due to which it has become an increasing problem for retrieving useful information from internet. Users searching for products or content have endless number of Web pages to navigate and require enormous efforts, requires judgmental aptitude and intuitiveness to extract meaningful information from the ever expanding web of data. Recommender systems are meant to be an important solution to the data overload problem that persists today in World Wide Web. The job of the recommender system is to provide the consumer with a selection of products or content which suit his/her needs so that the user are relived from the herculean task of browsing through enormous number of web pages. Recommender system has undergone a lot of improvements in last two decades in terms of accuracy in output and has been an intense area of research in recent times.
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Data Security Using Modified Merkle’s Tree

Sunil Devidas Bobade, Dr. Vijay Mankar

For securing embedded system, deprived of vital resources, the foremost task is to ensure data integrity and security of the data stored in untrusted memory. As on chip storage is limited in secure processor, designer is forced to push data and program code on an untrusted external memory which is always fully susceptible and open to adversarial tampering. This calls for efficient process that verifies and ensures integrity of data stored in untrusted zone. Available memory integrity technique like Merkle trees suffers from a disadvantage of a very high communication overhead as processor has to quiz memory too often to perform integrity check. This slows down processor capability. Proposed technique suggests two algorithms and performs integrity check with low communication overhead and less silicon area occupancy.
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Focused Web Crawling Using Neural Network, Decision Tree Induction and Naïve Bayes Classifier

Prabhjit Singh Sekhon, Shruti Aggarwal

In this modern world technology enhancement is growing due to that internet usage is continuously increasing. The main aim of focused web crawler is to visit unvisited URL to check whether it is relevant to search topic or not and avoid the irrelevant web documents and reduce network traffic. Even large search engines cover only a portion of the publically available data on web. The effective relevance prediction can help to avoid downloading and visiting many irrelevant pages. In this paper links related to the topic are taken and different attributes relevancy is calculated which is URL words relevancy, anchor text relevancy, parent pages relevancy, and surrounding text relevancy and analyzed by different algorithms like Neural Network, Decision Tree Induction and Naïve Bayes Classifier.The comparative analysis of these algorithms based on accuracy, complexity and time is also discussed.
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Fast Textual Dimension Reduction Using K-means Clustering

Mehak Chawla, Shabnam Parveen

Main problem with high dimensional datasets is that not all the measured entities are “significant” for gaining knowledge about the data during data mining process. High dimensional datasets have many mathematical challenges but there are many opportunities for the same. While many computationally expensive methods can construct predictive models with high accuracy for such as data but it is still of interest for many applications to reduce the dimension of the original data prior to modeling of the data. This work is about Dimensionality Reduction, which in turn is about converting data of very high dimensionality into data of much lower dimensionality such that each of the lower dimensions conveys much more information.
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Association Process Over Clustering Based on Link Based Cluster

V N K Veni Kanikcharla, D. Thakur, Saritha Seelam

Social network like Face book, Twitter, Flicker, and You Tube present opportunities and challenges to study collective behavior on a large scale. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. For mining different associations of mining behavioral features like user activities and temporal spatial information collected from different social media, and integrates them with social networking information to improve prediction performance. To integrate these sources of information, it is necessary to identify individuals across social media sites. It consists of three key components: The first component identifies users’ unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. In this we also include cluster ensemble approach with consistent results of various social networks using a link based clustering ensemble approach. These experimental results show efficient process generation of collecting behaviors of various users with different dimensionality exclusion.
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Web Usage Mining for Predicting Behaviour of User Based on Time Clustering

Neeraj Raheja, Arvind Grewal

In this research work a new clustering technique is introduced to predict the behaviour of user based on time clustering. Web Usage Mining is described as the finding along with study of user access patterns, through the web log files and related data from the Web site. Web usage mining is basically concerned with finding out what users are looking for on the internet (With the help of various data sources). A large number of websites are coming into existence daily and number of internet users are growing daily at a very fast rate. There are millions of internet users from different geographical areas. The users are of different types, they are of different age groups, educational level and living in different geographical locations. Furthermore the way and motive of accessing the web is different. So to take care of all these different kind of users, the Websites companies are interested in enquiring about the users who are accessing the websites. So there should be a way to differentiate between the different users, so that their requirements can be taken care of. The distinguishing of users can be done by refining the data gathered from the various sources (Weblog etc.), and the clustering the data by applying different clustering technique.So that we can get more accurate result and then these results can be applied to Websites for their popularity and improvement in user friendliness.
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A Novel Approach for Secure Intrusion Detection System in Wireless MANETs

Parvase Syed, Prof Mr. G.Rajesh

In recent years, the use of mobile adhoc networks (MANETs) has been well-known in various applications, including some mission acute applications, and as such security has become one of the most important concerns in MANETs. MANETs have some unique characteristics, Due to that prevention methods alone are not enough to make them secure; therefore, detection should be added as an additional defense before an attacker can breach the device. In general, the intrusion detection techniques for usual wireless networks are not well-matched for MANETs. In this paper, A novel intrusion detection system named Better Adaptive Acknowledgement (BAACK) especially designed for MANETs. By the adoption of MRA scheme, BAACK is proficient of detecting nodes in spite of the existence of false misbehavior report and it compared with other popular mechanisms in different scenarios. This scenarios gives an outline for enhancing security level of IDS architecture in MANETs based on secure attributes and then various algorithms, namely RSA and DSA.
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Organ Donor Identification Through Improved K- Medoids Clustering

Bondu Venkateswarlu, G.S.V.Prasada Raju

The level of increase in the severity of diseases and the level of insecurity at travel are alarming to the greater need of organ transplantations. It is therefore very essential to have complete details of organ donors, aiming to donate their organs; after death, by making their organs immortal and thus giving life to the needy. Clustering is the one of the best methodologies in data mining for classifying and categorising data. Maintaining details and tracking complete medical backgrounds of pre-enrolled organ donors is no way a simple task, noise level in such types especially at outlier’s matters a lot. Thus K-medoids is generally used, which minimizes the sum of dissimilarities between each object and its corresponding reference point. This paper refers the process of aiming at a improvising the most traditional algorithm, k-means as it is very likely to fail at identifying outliers which in turn effect the clusters formed; by k-medoid which chooses data points as centres termed as exemplars or medoid and generally works with an arbitrary matrix of distances between data points to extract correct information about the donors.
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Emotion Recognition System

Vipin Sharma

This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions like happy, sad, angry, fear, surprised, neutral etc. Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations witexisting system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 99% accurateusing brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.
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Security Remote Storage For Dynamic Groups in the Cloud

V.N.V.Swathi, Dr. P. Radhika

Several security schemes for data sharing on un trusted servers have been proposed. In these approaches, data owners store the encrypted data files in un trusted storage and distribute the corresponding decryption keys only to authorized users. Thus, unauthorized users as well as storage servers cannot learn the content of the data files because they have no knowledge of the decryption keys. However, the complexities of user participation and revocation in these schemes are linearly increasing with the number of data owners and the number of revoked users, respectively. By setting a group with a single attribute, Lu et al. Proposed a secure provenance scheme based on the cipher text policy attribute-based encryption technique, which allows any member in a group to share data with others. In this paper, we propose a novel Mona protocol for secure data sharing in cloud computing. Our proposed scheme is able to support dynamic groups efficiently. Specifically, new granted users can directly decrypt data files uploaded before their participation without contacting with data owners.
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Sensitivity Analysis of Cloud Computing Under Non Homogeneous Conditions

A.Satyanarayana, P. Suresh Varma

Cloud computing refers to act of providing various resources to the job requests over internet. With the tremendous increase in Cloud services utilization, the allocation of resources to the job requests that enter the Cloud has become a challenging task that arrive in non-homogeneous fashion. In this paper we analyzed and performed sensitivity analysis of a Cloud computing model for allocation of resources to the jobs that enter into the cloud by using queuing models.We derived the sensitivities of various performance measures of the Cloud model under Non- Homogeneous conditions and made a comparative study of the sensitivities in the Cloud model between Homogeneous and Non-Homogeneous conditions by using various performance measures such as Mean number of job requests in the Cloud, Utilization, Throughput and Mean Delay of jobs in the cloud.It is observed that by using queuing theory, varying parameter values of jobs that arrive under non-homogeneous Poisson process has tremendous influence on performance measures as Mean number of job requests, Throughput, Utilization and Mean Delay of the jobs in the Cloud.
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A Survey on Web Usage Mining Techniques for Clustering and Online Navigation Prediction

Kapil Kundra, Usvir Kaur, Dr. Dheerendra Singh

Due to the growing factor of World Wide Web, today most of companies and private sectors adopt the online business propagandas to spreading their businesses worldwide. In this era of time, we are dealing with huge amount of databases at all around the world, which every milliseconds saving the data on servers like online transactions and many of another online activities that users frequently accessed via the websites, software’s etc. We need some type of advanced techniques that retrieve and capture the information very accurately from web log data and one of the most famous technique for this work is Web Usage Mining, which analyze the web data very accurately to retrieve the valuable information, which stored on servers. In this area of research we are using many techniques,like clustering, online navigation prediction to analyze the web server log data and there are lots of algorithms are available to increase the efficiency of these techniques. So in this paper we review all those algorithms which are using for web data analysis and at the end we describes the best algorithms for Web Usage Mining Process, which are better than the another available algorithms.
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Comparitively Analysis of AES,DES And SDES Algorithms

Amit Singh

In recent years network security has become an important issue. Cryptography has been used to secure data and control access by sharing a private crypto- graphic key over different devices. Cryptography renders the message unintelligible to outside by various transformations Data Cryptography is the scrambling of the content of data like text,image,audio and video to make it unreadable or unintelligible during transmission.Its main goal is to keep the data secure from unauthorized access.
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Data Centric and Energy Efficient Routing Protocol for Wireless Sensor Networks

Deepak Sharma, Navneet Verma, K.K. Paliwal

Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not possible. So, the energy consciousness issue is the primary concern within the domain of Wireless Sensor Networks (WSNs). Most power dissipation occurs during communication and path selection, thus routing protocols in WSNs mainly aim at energy conservation. Moreover, a routing protocol should be flexible, so that its effectiveness does not degrade as the network size increases. In response to these issues, this work describes the development of a data centric and efficient routing protocol, named DCEERP.
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An Integrated and Intelligent Secure Architecture for Mobile Web Services

J Ronald Martin, S Albert Rabara

The emergence of wireless communication networks and the new generation of mobile devices, to access the web services have become possible anywhere at any time. Accessing web services from mobile devices is very common these days. However, the accessing of web services from mobile devices is still having challenges due to limited resources and the lack of bandwidth in its communication network but these challenges are more or less solved by the modern phones because of the modern mobile phones like smart phones provide much of the same functionality that is supported by the desktop computer, which makes them a potentially reliable access of mobile services. But, security has become a major concern to access web services from mobile devices. Web services security is another emerging trend in Web services technology. Since there has been no concrete proposal available for the development of accessing web services from mobile devices in secure manner. Therefore, we propose novel architecture for mobile web services using Mobile agents and Content Filter.
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Performance Optimization in Dissemination of Information in WMNs: A Review

Gurtej Singh, Dr. Paramjeet Singh, Dr. Shaveta Rani

Wireless mesh network is a communication network made up of radio nodes organized in a mesh topology. Wireless mesh-networks is a new technology in wireless communication. WMN’s an emerging technology, may bring the dream of a seamlessly connected world into reality. WMN’s easily, effectively and wirelessly connect entire cities using inexpensive, existing technology. AODV routing protocol also used in WMN’s. AODV use simple request-reply mechanism for the discovery of routes. In this paper study on wireless mesh network and AODV routing protocol . It cover introduction & working of WMN’s, advantages, types, applications and characteristics.
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Content Based Retrieval of Malarial Positive Images

Jasdeep Kaur, Kamaljit Kaur

The basic unit of a CBIR system is a bundle of pixels which have some meaning in the semantics of human life, further more the number of objects in an image, the greater is the need for annotation. The interpretation of the objects depicted in an image depends upon the perception and possible mathematical measurements like size/shape of parasites which can be calculated, so that it may be helpful in the diagnosis of malaria and it becomes an aid to doctors.
So developing an annotation based upon a particular hypothesis may lead to a reduction in the semantic gap as it may require exact information like which parasite. The overall performance of CBIR system and the validation of true value facts, distributions based on which the annotation scheme is being developed for particular goal of CBIR becomes questionable. Thus, in this research work we have done the ground truth. Using new site features with multiple model framework is performing well in terms of recall and precision.
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Multimedia Live Drop Application For Android Smart Phones

Chidvilash Vakada, P. Kiran Kumar, K. Sandeep

The number of smart phone users to use micro security Digital card (Micro SD card) for the propose of storing multimedia files are growing rapidly, but the smart phone users facing challenge of limited energy capacity/ performance (e.g., battery life, storage), environment (e.g., scalability, and availability), and security (e.g., reliability and privacy) of these devices has not been solved satisfactorily in smart phone users. The area of MCC (Multimedia Cloud Computing) the limitation on energy capacity, environment and security capacity can be erased off in an efficient way by offloading heavy tasks to the cloud. That means we evaluate this problems (Performance, environment and security) in multimedia applications on smart phones that are connected to Multimedia Cloud Computing (MCC).I have conducted an extensive set of experiments to measure the energy costs to investigate whether or not smart phones save energy by using MCC services. In other words, I investigate the feasibility of MCC to provide the offloading technique and OSGi (Open Service Gateway Initiative)- based mobile cloud service model. Specifically, I compared the energy costs for uploading and downloading a video file to and from MCC with the energy costs of encoding the same video file on a smart phone. The aforementioned comparison was performed by using HTTP and FTP Internet protocols with 3G and Wi-Fi network interfaces.
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A Novel Technique for Extracting Hidden Vital Messages from Multimedia

Yasam Venkatasaikiran, Seethiraju. L.V. V.D.SARMA, A. Siva Shankar

In this paper the problem of extracting blindly data embedded over a wide band in a spectrum domain of a digital medium like image, audio, video. We develop a novel multicarrier/ signature iterative generalized least-squares core procedure to seek unidentified data hidden in hosts via multicarrier spread-spectrum embedding. The original host nor the embedding carriers are assumed available.
Experimental study on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.
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Employing Cloud Computing in Business

Kamta Giri

Cloud computing is a computing platform that inhabits in a service provider’s large data centre and is able to energetically offer servers the ability to address a wide range of needs of clients. The cloud is a symbol for the internet. Some people call it the World Wide Computer. Technically, it is a computing archetype in which tasks are assigned to a combination of connections, software and services accessed over a network.
This paper provides brief details about the cloud computing with an overview of key features to give a sight about the novel focused technology.
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Assigning Tags to the Data Units Within the SRR Reverted From WDB’S

Vemu.Vijaya Prakash, Seethiraju.L.V.V.D.Sarma

An amassed number of databases have become web manageable through HTML form-based search interfaces. We represent aprogrammed annotation approach that first supports the data units on a result page into different sets such that the data in the same set has the equivalent semantic. Then, for each set we annotate it from different aspects and cumulative the different annotations to calculate a final annotation tag for it. An annotation wrapper for the search site is automatically built and can be used to annotate new result pages from the alike web database. Our experiments specify that the proposed methodology is highly effective.
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SMC Model in Privacy Preserving Data Analysis

Mantri.Rajani Kumari, Yenumala.Sankara Rao

The contending revelries who hold secretive data may collaboratively conduct privacy preserving distributed data analysis (PPDA) tasks to studyadvantageous data models or analysis results. The competing parties have different spurs.
Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. Unless accuratespurs (incentives) are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs. The ability to communicate and share data has many benefits, and the idea of an omniscient data source carries great value to research and building accurate data analysis models.
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