Volume-1 , Issue-5 , Oct 2013, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Applying Rough Set Theory for Medical Informatics Data Analysis

  M.Durairaj and T.Sathyavathi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.1-8, Sep-2013

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  Abstract
  In the medical field each and every data is important, because these data are very essential for human life. Medical data analysis is a very big and complex task. The medical data consists of imprecise, (or) uncertainty, (or) incomplete data. Therefore the medical data analysis process requires excellent techniques for processing, storing and accessing the datasets. Some of the traditional techniques are available to process the incomplete data and these techniques requires additional information to process the imprecise dataset. In this paper, we propose an intelligent technique of rough set theory for analyzing the imprecise medical data, which could be used for extracting knowledge without changing the knowledge of the original. In comparison to traditional techniques, rough set theory gives the optimal result from the analysis process without loss of information. ROSETTA is a toolkit for analyzing tabular data within the framework of rough set theory that could be applied in the original dataset to compute the reduced set without the loss of the knowledge of the original set. In this paper, the medical data set of recorded information from IVF (in-vitro fertilization) tests are used for data analysis, in which the influential parameters (tests) are identified using Rough Set Theory. The identified influential parameters display the determining impact on the result of IVF treatment (Test tube baby treatment). ROSETTA toolkit used to predict the influential parameters in the IVF treatment.

  Key-Words / Index Term
  Rough Sets Theory, Medical Data Analysis, ROSETTA tool kit, in-vitro Fertilization

  References
  [1]. Z. Pawlak. (1991). Rough Sets - Theoretical Aspect of Reasoning about Data, Kluwer Academic Publishers.
  [2]. Z. Pawlak. (1982). “Rough Sets”, International Journal of Computer and Information Sciences, Vol.11.
  [3]. M. Durairaj and K.Meena“Application of Artificial Neural Network for Predicting Fertilization Potential of Frozen Spermatozoa of Cattle and Buffalo” “International Journal of Computer and Information Sciences” (June 2008).
  [4]. M. Durairaj, K.Meena and K.R.Subramanian “IRNNS: A Hybrid Prediction System for Medical Database”, National Journal of System and Information Technology (Dec 2008).
  [5]. M. Durairaj, K.Meena and S.Selvaraju,“Applying a Data Mining Approach of Rough Sets on Spermatological Data Analysis as Predictors of In-Vitro Fertility of Bull Semen”, International Journal of Computer, Mathematical Sciences and Application (September 2008).
  [6]. M. Durairaj and K.Meena ,“A Hybrid Approach of Neural Network and Rough Set Theory for Prediction of Fertility Rate From IVF Outcomes”, The Icfai University Journal of Science & Technology (2009).
  [7]. AleksanderØhrn, AleksanderØhrn ,Department of Computer and Information Science, Norwegian University of Science and Technology,N-7491 Trondheim, Norway “Discernibility and Rough Sets in Medicine: Tools and Applications”
  [8]. Torgeir R. Hvidsten“A tutorial-based guide to the ROSETTA system : A Rough Set Toolkit for Analysis of Data” Edition 1: May, 2006 Edition 2: April, 2010
  [9]. A. Øhrn, J. Komorowski, A. Skowron, P. Synak (1998), The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets: The ROSETTA System, In Rough Sets in Knowledge Discovery 1: Methodology and Applications, L. Polkowski and A. Skowron (eds.), Studies in Fuzziness and Soft Computing.
  [10]. M. Durairaj and K.Meena“Intelligent Classification using Rough Sets and Neural Networks” “The IcfaiJournal of Information Technology” (Dec 2007).
  [11]. M. Durairaj, K.Meena and K.R.Subramanian“Machine Learning Techniques to Predict Fertility Rate of Sperm from the Outcome of IVF Functional Tests” “The IcfaiJournal of Information Technology” (March 2009).
  [12]. A. Øhrn, J. Komorowski, A. Skowron, P. Synak (1998), The ROSETTA Software System, In Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, L. Polkowski and A. Skowron (eds.), Studies in Fuzziness and Soft Computing.
  [13]. A.Øhrn(2000), ROSETTA Technical Reference Manual, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  

  Citation
  M.Durairaj and T.Sathyavathi, "Applying Rough Set Theory for Medical Informatics Data Analysis," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.1-8, 2013

• Open Access   Article

  A Comparative Study of Security Issues& Challenges of Cloud Computing

  Mithilesh Mittal, Pradeep Sharma and Pankaj Kumar Gehlot

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.9-15, Sep-2013

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  Abstract
  Cloud Computing is rapidly changing the whole scenario of distributed system and it has given a fresh meaning to distributed system. Although Cloud Computing is delivering essential services and benefits such as low price services, on demand self-service, broad network access, large scale computation and highly availability of resources, but there are still some drawback behind services due to security issues and concerns. Cloud security Issues and challenges of Cloud Computing are identified in this paper through comparative study of three standard working organizations NIST(National Institute of Standards and Technology), CSA (Cloud Security Alliance) and ENISA (European Network and Information Security Agency).We briefly described all security issues and challenges of cloud computing identified by these working organization. After comparative study we observed a comparative based table which presents comparison of security issues and challenges of cloud computing.At the end of study we observed that all security issues are covered by all working organizations in term of security domain which are- ‘strategic and policy issues’ and ‘technical issues’ and ‘legal issues’.

  Key-Words / Index Term
  Cloud Computing; Cloud Service Models, Security Issues and challenges

  References
  [1] P.Mell and T. Grance, “The NIST Definition of Cloud Computing” US National Institute of Science and Technology (NIST), 2011 http://csrc.nist.gov/publication/nistpubs/800-145/SP 800-145.pdf.
  [2] Rajkumar Buyya, Chee Shin Yeo and Srikumar Venugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”.
  [3] Rajesh Piplode, Pradeep Sharma and Umesh Kumar Singh, “Study of Threats, Risk and Challenges in Cloud Computing”, ISROSET-IJSRCSE Volume-01 , Issue-01, Page No : 26-30, Jan-Feb 2013
  [4] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, " Security Enhancement in Cloud Computing (CC)”, ISROSET- IJSRCSE, Vol-1, Issue-1, pp(31-37), Jan-Feb 2013.
  [5] Wikipedia- “Cloud Computing Security”
  http://en.wikipedia.org/wiki/cloud_computing_security.
  [6] Devki Gaurav Pal, Ravi Krishna, Prashant Srivastava, Sushil Kumar, Monark Bag, Vijendra Singh, “A Noval Open Security Framework For Cloud Computing” IJ-CLOSER Vol 1, No.2, june 2012, pp.45-52.
  [7] Gurudatt Kulkarni , Jayant Gambhir, Tejswini Patil and Amruta Dongare “A Security Aspects in Cloud Computing” , 2012 IEEE.
  [8] Cloud Security Alliance “Security Guidance for Critical Areas of Focus in Cloud Computing” April. 2009; http://cloudsecurityalliance.org/guidance/csaguide.v1.0.pdf.
  [9] Cloud Security Alliance “Security Guidance for Critical Areas of Focus in Cloud Computing v2.1” Dec. 2009;
  [10] D.Catteddn and G.Hogen, “Benefits, risk and recommendations for information security” ENISA (European Network and Information Security Agency), 2009.
  

  Citation
  Mithilesh Mittal, Pradeep Sharma and Pankaj Kumar Gehlot, "A Comparative Study of Security Issues& Challenges of Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.9-15, 2013

• Open Access   Article

  A Systematic Approach for Ensuring Security and Efficiency in Cloud Computing

  Vineet Chaturvedi, Ashok Verma and Neha Agarwal

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.16-20, Sep-2013

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  Cloud Computing is the latest trend of today’s IT industries. It is the solution for the problem that occurs due to the resources availability and its utilization over the network. The popularity of cloud computing has increased due to its great policy of pay-as-you-use or resource-on-demand but still it lacks with certain issues of security, trust and efficiency when implemented or deployed on large enterprise such as in geological surveys, scientific applications, astrological applications, oceanography etc. There exist lots of solution in security issues and lots of research for its efficiency but none of them focus on the both issues together. In this paper we have proposed a model that will provide solution for both of these issues. The work mainly focus on the fast scheduling of events in cloud computing by using private key cryptosystem instead of public key cryptosystem and instead of using traditional IKE i.e. internet key exchange mechanism we used the concept of random key reusability which is an approach for providing authenticate and efficient key exchange as that of IKE. Also the various section are made for storing users data on basis of the confidentiality, authentication and integrity that will enhance the overall security of the data stored and retrieved in the cloud computing.

  Key-Words / Index Term
  CLC, IKE, Certification Authority, Sensitivity Rating, Random Key Reusability

  References
  [1]. Dimitrios Zissis, Dimitrios Lekkas “Addressing cloud computing security issues” Future Generation Computer Systems, Elsevier, 2012.
  [2]. Rabai, L.B.A. et al., A cyber security model in cloud computing environments. Journal of King Saud University – Computer and Information Sciences (2012).
  [3]. Rong C et al. Beyond lightning: A survey on security challenges in cloud computing. Comput Electr Eng (2012)
  [4]. Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, " Security Enhancement in Cloud Computing (CC)”, ISROSET- IJSRCSE, Vol-1, Issue-1, pp(31-37), Jan-Feb 2013
  [5]. Shivlal Mewada, Umesh Kumar Singh, Pradeep Sharma,“Security Based Model for Cloud Computing”, Int. Journal of Computer Networks and Wireless Communications (IJCNWC), Vol. 1, No. 1, pp (13-19), December 2011.
  [6]. R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems 25 (2009) 599–616.
  [7]. Cong Wang, Qian Wang, and Kui Ren, Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing, IEEE INFOCOM 2010.
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  [9]. S. Marston et al. / Decision Support Systems 51 (2011) 176–189
  [10]. Shuai Zhang and Shufen Zhang “Cloud Computing Research and Development Trend”, IEEE, Second International Conference on Future Networks, 2010
  [11]. K. Salah ,J. M. Alcaraz-Calero,S. Zeadally ,S. Almulla and M. Alzaabi “Using Cloud Computing to Implement a Security Overlay Network”, IEEE, Security and Privacy, 2011
  [12]. S. Marston et al.,Decision Support Systems 51 (2011)
  [13]. National Institute of Standards and Technology. The NIST definition of cloud computing; 2011.http://www.nist.gov/itl/cloud/upload/cloud-defv15. pdf
  [14]. J. Yao, S. Chen, S. Nepal, D. Levy, J. Zic, Truststore: making Amazon S3 trustworthy with services composition, in: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’08, Melbourne, Australia, 2010, pp. 600–605
  [15]. N. Cao, Z. Yang, C. Wang, K. Ren, W. Lou, Privacy-preserving query over encrypted graph-structured data in cloud computing, in: IEEE International Conference on Distributed ComputingSystems, ICDCS’11, 2011, pp. 393–402.
  [16]. Pradeep Bhosale, Priyanka Deshmukh, Girish Dimbar, Ashwin iDeshpande: Enhancing Data Security in Cloud Computing Using 3D Framework & Digital Signature with Encryption-International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 8, October – 2012
  [17]. F. Hu, M. Qiu, J. Li, T. Grant, D. Tylor, S. Mccaleb, L. Butler, R. Hamner, A Review on Cloud Computing : Design Challenges in Architecture and Security, Journal of Computing and Information Technology, 19 (1) (2011)
  [18]. A. Groce, J. Katz, A new framework for efficient password-based authenticated key exchange, in: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS’10, Chicago, USA, 2010, pp. 516–525
  

  Citation
  Vineet Chaturvedi, Ashok Verma and Neha Agarwal, "A Systematic Approach for Ensuring Security and Efficiency in Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.16-20, 2013

• Open Access   Article

  Secure Images for Copyright by Watermarking in Transform Sphere using Bidirectional Neural Network

  Dharmendra Gwala, Vivek Shrivastava and Pankaj Singh Parihar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.21-25, Sep-2013

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  Abstract
  Person beings knowledge a non preventable circumstances of distribution and redistribution their pictures, documents, videos etc on the internet about every week. Intruders are frequently the ones who do not desire to work hard and they maintain on somebody’s original work. watermarking is the process of embedding data called a watermark into a multimedia object such that watermark can be detected or extracted later to make an declaration about the object. Watermarking is adding an “ownership” information in multimedia inside to show the dependability. There are generally two most popular ways of embedding the watermark in the digital content ie spatial sphere and transform sphere. In spatial sphere, Least-Significant Bit (LSB), SSM Modulation-Based Technique has been developed. For DCT sphere, block based approach and for wavelet sphere, multi-level wavelet transformation technique and CDMA based approaches has been developed. presented techniques based on spatial and occurrence domain suffer from the problems of low Peak Signal to Noise Ratio (PSNR) of watermark and image quality poverty in unreliable quantity. This paper offers a technique based on Back propagation Neural Network to instruct a given cover image to construct a preferred watermark image.

  Key-Words / Index Term
  Digital Watermarking , Transform sphere, Spatial sphere, Neural Network, PSNR, Discrete Cosine Transform

  References
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  [5]. Chitralekha Dwivedi and Ashish Bansal – “Preventing copyrights infringement of images by Watermarking in transform domain using full counter propagation Neural Network” UACEE International Journal of Computer Science and its Applications - Volume 2: Issue 3 No.6, November 2012
  [6]. Chen Chien Chang and Kao De-Sheng ― “DCT Based Reversible Image Watermarking Approach”, Third IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2007, pp 1-5
  [7]. Bamatraf A, Rosziati Ibrahim, Mohd Salleh, Mohd Najib, “A new digital watermarking algorithm using combination of least significant bit and Inverse Bit”, Journal of Computing, Vol 3, Issue 4, April, 2011,pp 1-8.
  [8]. Manjunatha Prasad. R, Dr. Shivaprakash Koliwad ― “A Comprehensive Survey of Contemporary Researches in Watermarking fo Copyright Protection of Digital Images”, International Journal of Computer Science and Network Security (IJCSNS), Vol.9 No.4, April 2009, pp.91-102.
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  [16]. Yang W, Zheng Jin – “An Algorithm for Color Image Watermarking Based On the Combination of DWT and DCT”, Journal of Central South Univers ty of Forestry & Technology, December,2008, pp 139 143.
  [17]. Surekha B and Swamy G.N, ― “A spatial domain public image watermarking‘‘, international journal of Security and its applications ,Jan 2011,Vol 5.
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  Citation
  Dharmendra Gwala, Vivek Shrivastava and Pankaj Singh Parihar, "Secure Images for Copyright by Watermarking in Transform Sphere using Bidirectional Neural Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.21-25, 2013

• Open Access   Article

  Online Intrusion Alert Aggregation with Generative Data Stream Modeling

  Ramchandar Durgam and R.V.Krishnaiah

  Technical Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.23-23, Sep-2013

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  Abstract
  Security plays an important role in IT systems. Intrusion detection systems can be used to ensure security in a network. The existing IDSs (Intrusion Detection Systems) such as Firewall, Snort provide huge number of alerts as they monitor the network flows. Since the number of alerts is plenty, the network administrator might be confused to know exact problem. This will delay indecision making in the presence of any security threats. As it takes more time to understand the alerts when they are more number, the network administrator needs to spend some time to make effective decisions. In this paper, we proposed a framework which aggregates alerts and generates few Meta alerts. These Meta alerts can be understood by the network personnel quickly and take decisions immediately. A data stream version of maximum likelihood approach is used in the framework. The experimental results revealed that the framework is very useful and can be used in the real world networks.

  Key-Words / Index Term
  IDS, Online Intrusion Detection, Probabilistic Model, Online Intrusion Detection, Alert Aggregation

  References
  [1] S. Axelsson, ―Intrusion Detection Systems: A Survey and Taxonomy,‖ Technical Report 99-15, Dept. of Computer Eng., Chalmers Univ. Of Technology, 2000.
  [2] C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
  [3] M.R. Henzinger, P. Raghavan, and S. Rajagopalan, Computing on Data Streams. Am. Math. Soc., 1999.
  [4] A. Allen, ―Intrusion Detection Systems: Perspective,‖ Technical Report DPRO-95367, Gartner, Inc., 2003.
  [5] F. Valeur, G. Vigna, C. Krugel, and R.A. Kemmerer, ―A Comprehensive Approach to Intrusion Detection Alert Correla- tion,‖ IEEE Trans. Dependable and Secure Computing, vol. 1, no. 3, pp. 146-169, July-Sept. 2004.
  [6] H. Debar and A. Wespi, ―Aggregation and Correlation of Intrusion-Detection Alerts,‖ Recent Advances in Intrusion Detection, W. Lee, L. Me, and A. Wespi, eds., pp. 85-103, Springer, 2001.
  [7] D. Li, Z. Li, and J. Ma, ―Processing Intrusion Detection Alerts in Large-Scale Network,‖ Proc. Int’l Symp. Electronic Commerce and Security, pp. 545-548, 2008.
  [8] F. Cuppens, ―Managing Alerts in a Multi-Intrusion Detection Environment,‖ Proc. 17th Ann. Computer Security Applications Conf. (ACSAC ’01), pp. 22-31, 2001.
  [9] A. Valdes and K. Skinner, ―Probabilistic Alert Correlation,‖ Recent Advances in Intrusion Detection, W. Lee, L. Me, and A. Wespi, eds. pp. 54-68, Springer, 2001.
  [10] K. Julisch, ―Using Root Cause Analysis to Handle IntrusionDetection Alarms,‖ PhD dissertation, Universitat
  Dortmund, 2003. 294
  [12] T. Pietraszek, ―Alert Classification to Reduce False Positives in ̈Intrusion Detection ,‖ PhD dissertation, Universitat Freiburg, 2006.
  [13] F. Autrel and F. Cuppens, ―Using an Intrusion Detection Alert Similarity Operator to Aggregate and Fuse Alerts,‖
  
  Proc. Fourth Conf. Security and Network Architectures, pp. 312-322, 2005.
  [14] G. Giacinto, R. Perdisci, and F. Roli, ―Alarm Clustering for Intrusion Detection Systems in Computer Networks,‖ Machine Learning and Data Mining in Pattern Recognition, P. Perner and A. Imiya, eds. pp. 184-193, Springer, 2005.
  [15] O. Dain and R. Cunningham, ―Fusing a Heterogeneous Alert Stream into Scenarios,‖ Proc. 2001 ACM Workshop
  Data Mining for Security Applications, pp. 1-13, 2001.
  [16] P. Ning, Y. Cui, D.S. Reeves, and D. Xu, ―Techniques and Tools for Analyzing Intrusion Alerts,‖ ACM Trans. Information Systems Security, vol. 7, no. 2, pp. 274-318, 2004.
  [17] F. Cuppens and R. Ortalo, ―LAMBDA: A Language to Model a Database for Detection of Attacks,‖ Recent Advances in Intrusion Detection, H. Debar, L. Me, and S.F. Wu, eds. pp. 197-216, Springer, 2000.
  [18] S.T. Eckmann, G. Vigna, and R.A. Kemmerer, ―STATL: An Attack Language for State-Based Intrusion Detection,‖ J.
  Computer Security, vol. 10, nos. 1/2, pp. 71-103, 2002.
  [19] A. Hofmann, ―Alarmaggregation und Interessantheitsbewertung in einem dezentralisierten Angriffserkennungsystem,‖ PhD dis- ̈sertation, Universitat Passau, under review.
  [20] M.S. Shin, H. Moon, K.H. Ryu, K. Kim, and J. Kim, ―Applying Data Mining Techniques to Analyze Alert Data,‖ Web Technologies and Applications, X. Zhou, Y. Zhang, and M.E. Orlowska, eds. pp. 193-200, Springer, 2003.
  [21] J. Song, H. Ohba, H. Takakura, Y. Okabe, K. Ohira, and Y. Kwon, ―A Comprehensive Approach to Detect Unknown Attacks via Intrusion Detection Alerts,‖ Advances in Computer Science—ASIAN 2007, Computer and Network Security, I. Cervesato, ed., pp. 247-253, Springer, 2008.
  [22] R. Smith, N. Japkowicz, M. Dondo, and P. Mason, ―Using Unsupervised Learning for Network Alert Correlation,‖ Advances in Artificial Intelligence, R. Goebel, J. Siekmann, and W. Wahlster, eds. pp. 308-319, Springer, 2008.
  

  Citation
  Ramchandar Durgam and R.V.Krishnaiah, "Online Intrusion Alert Aggregation with Generative Data Stream Modeling," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.23-23, 2013

• Open Access   Article

  Design and Implementation of a Cost Effective Ranking Adaptation Algorithm

  K.Chiranjeevi , K.Archana and J.Pradeep Kumar

  Technical Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.5 , pp.24-24, Sep-2013

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  Abstract
  Ranking plays an important role in vertical search domains as it helps users to view the best results quickly. This is required as search engines return huge number of records. Generally a ranking model is required for every domain as the data in each domain is different. However, it is tedious task to develop separate ranking model for each and every domain. A ranking model which can adapt to different domains can solve this problem. This paper proposes a new algorithm which adapts to various domains thus eliminating repetition of writing separate algorithm for each domain. An algorithm adapting to new domain reduces training cost thus making it cost-effective. We also proposed a ranking adaptability measurement for estimating the adaptability of ranking model. A prototype application is built to test the effectiveness of the application. The empirical results revealed that the proposed ranking model adaptation algorithm is capable of adapting to new domains.

  Key-Words / Index Term
  Ranking, Ranking Adaptation, Domain Specific Search, Information Retrieval

  References
  [1] C.J.C. Burges, R. Ragno, and Q.V. Le, “Learning to Rank with Nonsmooth Cost Functions,” Proc. Advances in Neural Information Processing Systems (NIPS ’06), pp. 193-200, 2006.
  
  [2] Z. Cao and T. Yan Liu, “Learning to Rank: From Pairwise Approach to Listwise Approach,” Proc. 24th Int’l Conf. Machine Learning (ICML ’07), pp. 129-136, 2007.
  
  [3] C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to Rank Using
  
  Gradient Descent,” Proc. 22th Int’l Conf. Machine Learning (ICML ’05), 2005.
  
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  Preferences,” J. Machine Learning Research, vol. 4, pp. 933-969, 2003.
  
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  Empirical Methods in Natural Language Processing (EMNLP ’06), pp. 120-128, July 2006.
  
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  [11] B. Zadrozny, “Learning and Evaluating Classifiers Under Sample Selection Bias,” Proc. 21st Int’l Conf. Machine Learning (ICML ’04), p. 114, 2004.
  
  [12] R. Klinkenberg and T. Joachims, “Detecting Concept Drift with Support Vector Machines,” Proc. 17th Int’l Conf.
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  Citation
  K.Chiranjeevi , K.Archana and J.Pradeep Kumar, "Design and Implementation of a Cost Effective Ranking Adaptation Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.5, pp.24-24, 2013