Volume 2 Number 1 June 2012


Performance Analysis for Shared Service Systems
Vikas Shinde, Kamal Wadhwa, Mukta Kalra

Abstract: This paper present an alternative technique for performance analysis of shared services and demonstrates the usefulness of these techniques in implementing shared service. This technique is based on multi-class product form queueing network models and Summation method. Shared service is the standardization and consolidation of common functions across the multiple organizations to reduce operational cost and to increase the information and knowledge sharing.


Comparative Performance Analysis of AODV,DSR,DYMO,OLSR and ZRP Routing Protocols in MANET using Random Waypoint Mobility Model
Jogendra Kumar

Abstract: In this article, we compare performance of some routing protocols for Mobile Ad-Hoc Networks (MANET’s). A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes that communicates with each other without using any existing infrastructure, access point or centralized administration. In MANET, due to mobility of nodes network topology changes frequently and thus, routing becomes a challenging task. A variety of routing protocols with varying network conditions are analyzed to find an optimized route from a source to some destination. This article presents performance comparison of five popular mobile ad-hoc network routing protocols i.e. Ad hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), Dynamic MANET on- Demand (DYMO), Optimization Link State Routing (OLSR) and Zone Routing Protocol (ZRP) in variable pause time. We used well known network simulator QualNet 5.0.2 from scalable networks to evaluate the performance of these protocols. The performance analysis is based on different network metrics such as throughput, TTL Based Average Hop Count, Energy Consumed in Transmit Mode, Energy Consumed in Received Mode, Residual Battery Capacity (in mAhr) and Peak Queue Size (byte).


Model Driven Development for Software Architect in Reengineering with Cloud Computing
E. Kirubakaran, S. Manimekalai

Abstract: Skill Development varies from person to person due to their way of thinking. Presently, most of the organizations are grappling to maintain their information with the older development tools, they trade – off into more modern and leverage to search for an efficient programming languages to develop a new one with a minimal cost. In the Software Development Architecture, there exist several phases to develop an application. To computerize the system first step is to design the application based on the requirements. For example, the software architect who is known in markup language (i.e., HTML) can create the design and further convert into implementation phase. Here, the design time will be reduced by a special technique called as Re-Engineering. After converting the application, the organization has to maintain the architecture of their work process. At that situation this Cloud Computing is used to integrate into our components. The cloud computer has many factors that contribute to the success and survival of the company during transition one of them are to assess learning curves of many different individuals. This paper aims to shed light on the realities of screen scraping and discuss some of the possibilities and limitations of automated language converters.


Biclustering Analysis of Coregulated Biclusters from Gene Expression Data
C. P. Chandran, K. IswaryaLakshmi

Abstract: In this paper, the Biclustering analysis of coregulated biclusters from gene expression data is carried out. Gene expression is the process, which produces functional product from the gene information. Data mining is used to find relevant and useful information from databases. Clustering groups the genes according to the given conditions. Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix. In this paper a new algorithm, Enhanced Bimax algorithm is proposed. The normalization technique is included which is used to display a coregulated biclusters from gene expression data and grouping the genes in the particular order [1]. In this work, Synthetic Gene Expression dataset is used to display the coregulated genes, developed by Prelic et.al., It contains constant values and coherent values over the conditions and non-overlapping and overlapping clusters. The data matrix contains 10 overlapping cluster and each cluster extends over 5 genes and 15 conditions.


Design and analysis of Genetic Clustering Bee Colony Optimizaiton for Flexible Protein-Ligand Docking
C. P. Chandran, E. Kiruba Nesamalar

Abstract: In this paper, the design and analysis of Genetic Clustering Bee Colony Optimization for Flexible Protein-Ligand docking is carried out. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. Genetic Algorithm (GA) is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. A Genetic Clustering algorithm combine a GA with the K-medians clustering algorithm. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, a new algorithm called Genetic Clustering Bee Colony Optimization (GCBCO) is proposed. The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.


Effective Feature Selection via Featuristic Genetic on Heart Data
A. Pethalakshmi, A.Anushya

Abstract: This work proposes a new algorithm namely compound featuristic genetic algorithm. We evaluate the problems while using genetic based feature selection, and propose more efficient technique for improving heart disease prediction. Genetic algorithm have been proposed and applied successfully to solve a wide variety of problems. Feature selection is the process of removing irrelevant features. It brings into play in reducing execution time and improving predictive accuracy of the classifier. In this paper, we examine the performance of four fuzzy classifiers using the proposed algorithm on heart data. The fusion of Fuzzy Logic with the classifiers Decision trees, K-means, Naive bayes and Neural network are used to evaluate the accuracy of occurrence of heart disease. The experiments are carried out on heart data set of UCI machine learning repository and it is implemented in MATLAB.


Quick and Efficient Approach for Semantic Service Discovery using Ontology based Indexing
Chellammal Surianarayanan, Gopinath Ganapathy

Abstract: A two-step indexing based method which indexes services by their ontologies is proposed for efficient and quick semantic service discovery. In first step, for a given service request, a set of candidate services are chosen from the index by matching the ontologies of the request with the keys of index. In the second step, the request is semantically matched with candidate services to find a list of matched services ranked by their similarity score. The indexing of services helps in eliminating the irrelevant services of a request. Semantic matching will be performed only to candidate services rather than all available services. From experimentation, it is found that the proposed method quickens service discovery by an average elimination of irrelevancy of 92%. The time characteristics are analyzed using measures such as ‘service loading time’ and ‘service matching time’. The average service loading time and service matching time of the proposed method are significantly reduced to 18.91 seconds 38.7 milli seconds when compared to sequential method which has average service loading time and service matching time of 577.7 seconds and 353.4 milli seconds respectively. When compared to the method which indexes services by outputs, the proposed work exhibits excellent recall and precision.


Smart Way for Secured Communication in Mobile Ad-hoc Networks
P. Infant Kingsly, C. Jayakumar, Mahendran Sadhasivam, S. Deepan Chakravarthy

Abstract: The application of multi-modal biometric methods in securing mobile ad-hoc network has been addressed in this paper. A MANET is an infra structure less network for mobile devices connected by wireless link. The mobile network is often vulnerable to security attacks even though there are many traditional approaches, due to its features of open medium and dynamic changing topology. Multi-modal biometrics is deployed to work with intrusion detection systems (IDSs) to overcome the shortcomings of uni-modal biometric systems. The cluster head is elected in which Dempster-Shafer theory is evaluated in order to increase the observation accuracy to maintain high security and trusted MANET. Since each device in the network has measurement and estimated limitations, more than one device needs to be chosen, and observations can be fused to increase observation accuracy using Dempster–Shafer theory for data fusion.


Liver Cancer Tumor Segmentation on ultrasound Images
V. Ulagamuthalvi, D. Sridharan

Abstract: In image analysis the segmentation is the first step. Segmentation is a process of subdivides the image into its constituent parts and objects. The objective of the segmentation is to simplify the representation of an image into something that is more meaningful and easier to analyze. In this paper, we propose a full automatic region growing algorithm with texture parameters. The seed point has been automatically selected based on textural features from co-occurrence matrix and run length method. High Pass Filter, Histogram Equalization, Otsu Thresholding and the spatial information of pixels used for segmentation of ultrasound liver cancer tumor images. In this method can reduce the time for manual post processing due to over-segmentation result from ordinary region growing with intensity criteria


A Network-based Remote Controlling and Monitoring System with More Security and Platform-free Features (NRCM-SP)
Chiranji Lal Chowdhary, Chandra Mouli P.V.S.S.R.

Abstract- With increasing use of internet technologies in general life, it’s becoming more challenging to solve problems like, remote monitoring and controlling and other related operations. It is not easy to access huge data from many network systems to a single network system. If it is widely distributed then it demands a more strong system. To overcome such problems, a more reliable and secured platform-free remote controller is required which is having the ability to monitor the system. Many researchers are working for this to apply internet technology for developing general and expendable system architecture. In this paper a novel Network-based Remote Controlling and Monitoring System with More Security and Platform-free Features (NRCM-SP) is proposed. The major contribution in this work is to use the network base for the purpose of real-time remote monitoring and controlling of processing equipment.