iosr-JCE   Volume-7 ~ Issue-6

Abstract:In the current era of modern health problems should be addressed with utmost importance and data should be secured in the competitive market in order to avoid misuse by third parties which are not responsible in this regard. In view of Health insurance which benefit patients economically should be effective and should also address the issue of providing privacy to their medical record. To address the above issues we are proposing architecture new secure e-health issue system (SEHIS) model which allows to store patients data in Smart e-Health Card (SEHC) which is given to patients by Health Insurance companies in turn used by medical practitioners to access patients data. SEHIS provides expert services to advice patients regarding insurance schemes selection (decision Support system) verify and secure patients data in SEHC. In this model, SEHC contains patients data like finger prints, photo and personnel data is encrypted by using PKI, Digital Image encryption techniques. SEHIS model verifies counterfeit documents, if any of the patient to use insurance facility or an insured person and a physician may deviate from insurance company rules to get benefits illegally etc.Iin our proposed model, SEHIS will benefit insurance companies, patients and works accordance with HIPPA requirements.

[1] "New variant M. J2 – RSA Cryptosystem" : E. Madhusudhan Reddy, B. H. Nagaraiasri, A. B. Rajesh kumar, M. Padmavathamma.
[2] "Threshold Extended ID3 algorithm", A.B. Rajesh Kumar, C. Pani Ramesh, E. Madhusudhan, M. Padmavathamma, ICDIP 2012
International Conference in Malasia.
[3] "A cryptographic key management solution for HIPAA privacy/ security regulations: Wei – Bin Lee, Member, IEEE, and Chien –
Ding Lee, IEEE transaction on information technology in biomedicine, vol 12, No.1, January 2008.
[4] Image encryption using block – based transformation algorithm:Mohammad Ali Bani Younes and Aman Jantan IAENG international
journal of computer science, 35: 1, IJCS _35_1_03.
[5] Design and implementation of a smart card based healthcare information system: Geylani Karda, E. Turhan Tunali* Computer
methods and programs in biomedicine 8I(2006) 66-78.
[6] A novel encryption method for image security: Mohammed Abbas Fadhil A1- Husainy, internation journal of security and its
applications Vol. 6 No. 1 January, 2012.
[7] "Secure E-Health Care Model" IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Volume X,
Issue X (Sep-Oct. 2012), PP 00-00
[8] Rule-based Expert System for Diagnosis and Symptom of Neurological Disorders "Neurologist Expert System (NES)" ICCIT 2012.

Abstract:The aim of the classification task is to discover some kind of relationship between the input attributes and the output class, so that the discovered knowledge can be used to predict the class of a new unknown tuple. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models or classifier while revealing neither their training data nor the tuples to be classified. The Naïve Bayes classifier is a simple but efficient baseline classifier. In this paper, we present a privacy preserving Naïve Bayes classifier for horizontally partitioned data. Our three layer protocol uses an Un-trusted Third Party (UTP). We study how to calculate model parameters for privacy preserving three-layer Naïve Bayes classifier for horizontally partitioned databases and communicate their intermediate results to the UTP not their private data. In our protocol, an UTP allows to meet privacy constraints and achieve acceptable performance.
Keywords: Naïve Bayes classifier, Privacy preserving, Probability, Un-trusted Third Party (UTP).

[1] B. Pinkas, Cryptographic techniques for privacy-preserving data mining, ACM SIGKDD Explorations Newsletter, 4(2), 2006, 12- 19.
[2] C. C. Aggarwal, P. S. Yu., Privacy-Preserving Data Mining:Models and Algorithms(London, Kluwer Academic Publishers Boston).
[3] J. Han, M. Kamber, Data Mining: Concepts and Techniques(India, Elsevier).
[4] M. Kantarcioglu and J. Vaidya, Privacy preserving naive Bayes classifier for horizontally partitioned data, In IEEE ICDM
Workshop on Privacy Preserving Data Mining, Melbourne, FL, November 2003, 3-9.
[5] J. Vaidya and C. Clifton, Privacy preserving naive Bayes classifier on vertically partitioned data, Proc. SIAM International
Conference on Data Mining, Lake Buena Vista, Florida, April 2004, 22-24.
[6] Z. Yang and R. Wright, Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data, IEEE Transactions
on Data Knowledge Engineering, 18(9), April 2006, 1253-1264.
[7] V. Verykios, E. Bertino, State-of-the-art in Privacy preserving Data Mining, SIGMOD Record, 33(1), 2004, 50-57.
[8] R. Agrawal, R. Srikant Privacy preserving data mining, Proc. of the ACM SIGMOD on Management of data, Dallas, TX USA, May
15-18, 2000, 439-450.
[9] A. C. Yao, Protocols for secure computation, Proc. of 23rd IEEE Symposium on Foundations of Computer Science (FOCS), 1982,
160-164.
[10] W. Du, Mikhail J. Attalah, Secure multi-problem computation problems and their applications: A review and open problems, Tech.
Report CERIAS Tech Report 2001-51, Center for Education and Research in Information Assurance and Security and Department of
Computer Sciences, Purdue University, West Lafayette, IN 47906, 2001.

Abstract:Recently, Content Based Image Retrieval (CBIR) plays a significant role in the image processing field. The construction of large datasets has been facilitated by the developments in data storage and image acquisition technologies. In order to manage these datasets in an efficient manner development of suitable information systems are necessary. Based on image content, CBIR extracts images that are relevant to the given query image from large image databases. Images relevant to a given query image are retrieved by the CBIR system utilizing either low level features such as shape, color, texture and homogeneity or high level features such as human perception. Most of the CBIR systems available in the literature extract only concise feature sets that limit the retrieval efficiency. In this paper, additional feature extraction such as homogeneity based feature extraction is used along with color, shape and texture feature extraction to extract the query image from the database images as well as from the RRI library and also to store the retrieved images in the RRI library. The proposed CBIR technique is evaluated by querying different images and the retrieval efficiency is evaluated by determining precision-recall values for the retrieval results.
Keywords: Content Based Image Retrieval, homogeneity feature, High level feature, Image Segmentation, kmean algorithm, Low level feature.

[1] Wang Xiaoling and Xie Kanglin, "Content based Image retrieval incorporating the AHP method", International Journal of
Information Technology, Vol.11, No.1, pp.25-37,
[2] Irfan and Sumari, "Automated content based image retrieval using wavelets", International Journal of Computational Intelligence,
Vol.1, No.1, pp.1-12, 2004
[3] Chi-Man Pun and Chan-Fong Wong, "Content-Based Image Retrieval Using Rectangular Segmentation", International Journal of
Computers, Vol.2, No.1, pp.72-79, 2008
[4] Hiremath and Jagadeesh Pujari, "Content based image Retrieval based on color, texture and shape features using image and its
complement", International Journal of Computer science and security, Vol.1, No.4, pp.25-35
[5] Arun and Hema P Menon, "Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and
Fourier Descriptors", International Journal of Recent Trends in Engineering, Vol.2, No.2, pp., November 2009
[6] Srinivasa Rao, Srinivas Kumar and Chandra Mohan, "Content Based Image Retrieval using Exact Legendre moments and support
vector machine", The International Journal of Multimedia & its Applications (IJMA), Vol.2, No.2, pp. 69-79,May 2010
[7] Jalil Abbas, Salman Qadri, Muhammad Idrees, Sarfraz Awan and Naeem Akhtar Khan, "Frame Work for Content Based Image
Retrieval(Textual Based) System", Journal of American Science, Vol.6, No.9,pp.704-707, 2010
[8] Christopher C.Yang, "Content based image retrieval: a comparison between query by example and image browsing map
approaches", Journal of Information Sciences, Vol.30, No.3, pp.254-267, 2004
[9] Rajshree S.Dubey, Rajnish Choubey and Joy Bhattacharjee, "Multi Feature Content Based Image Retrieval", International Journal
on Computer Science and Engineering, Vol.2, No.6, pp.2145-2149, 2010
[10] Nandagopalan, Adiga and Deepak, "A Universal Model for Content-Based Image Retrieval", WASET, Vol.46, No.112, pp.644-647, 2008

Abstract:We describe the method for segmentation of Left Ventricle (LV) in short axis cardiac MR Images in order to visibly identify the LV, the endocardium and the epicardium. The interpretation and evaluation of images obtained from Magnetic Resonance Imaging (MRI) is not a trivial task and also it is very timeconsuming if done completely manually. This is especially the case for higher dimensional image data such as volumetric and/or time-dependent data such as the Cardiac Images. The objective of this study was show clearly the LV in particular so that any deviation from the standard dimensions in terms of shape, size or texture, can be unmistakably identified. Early diagnosis of heart abnormalities can potentially decrease the risk of heart attacks as well as benefit people who have just undergone Heart Transplant Surgery. For segmentation of the Cardiac MRI, Principal Component Analysis (PCA) is used in the Active Appearance Model (AAM) building process. The extraction of statistical features with PCA can be regarded as a projection of high-dimensional data vectors into a space of much lower dimensionality. The process of building a computerized model is equated with making the computer learn possible appearances of an object. The basis for such a learning process is the training set which is prepared from the data obtained from a reputed hospital in Pune.
Keywords: Magnetic Resonance Imaging(MRI), Left Ventricle(LV), Principal Component Analysis(PCA), Active Appearance Model(AAM).

[1] Neeti Gore. "Image Segmentation Combining Level Sets and Principal Component Analysis" A Thesis. Department of Electrical
and Computer Engineering, Carnegie Mellon University.
[2] Caroline Petitjean, Jean-Nicolas Dacher. "A review of segmentation methods in short axis cardiac MR images" Science Direct
Medical Image Analysis.
[3] Andy Tsai*, Anthony Yezzi, Jr., William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W. Eric Grimson, and Alan Willsky.
"A Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets" in IEEE Transactions on Medical Imaging,
Vol. 22, No. 2, pp. 137-154 February 2003
[4] Avan Suinesiaputra, Alejandro F. Frangi, A. M. Kaandorp, Hildo J. Lamb, Jeroen J. Bax, Johan H. C. Reiber, and Boudewijn P. F.
Lelieveldt. "Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice
Short-Axis Cardiac MR Images" in IEEE Transactions on Medical Imaging,, Vol. 28, No. 4, pp. 595-607, April 2009
[5] Timothy F. Cootes and Christopher J. Taylor. A mixture model for representing shape variation. Image and Vision Computing, Vol.
17, No.8, pp. 567–573, 1999.
[6] Timothy F. Cootes, Gareth J. Edwards, and Christopher J. Taylor. A comparative evaluation of active appearance model algorithms.
In Proceedings of the British Machine Vision Conference (BMVC), vol. 2, pp 484–498, 1998.
[7] T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol.23, No(6): pp 681-685, June 2001.
[8] G. J. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting face images using active appearance models. In Proc. International
Conference on Automatic Face and Gesture Recognition, pp 300-305, June 1998.
[9] T. Cootes, G. Edwards, and C. Taylor. Active appearance models. In Proceedings of the European Conference on Computer Vision,
volume 2, pages 484-498, 1998.
[10] G. J. Edwards. Learning to Identify Faces in Images and Video Sequences. PhD thesis, University of Manchester, Division of
Imaging Science and Biomedical Engineering, 1999.

Abstract:Knowledge Management is playing a major role in shaping the Organization success. Knowledge management combines the ability of dealing with the data and information under the information technology. The major asset for any organization is the knowledge that any person posses. Knowledge management is used not just to store knowledge but also innovate and discover new knowledge. Knowledge can be of two types – tacit and explicit. The knowledge that can be easily expressed and put on paper is known as explicit knowledge. The major concern in implementing KM is storing the tacit knowledge. Tacit refers to the knowledge that is inherent to any person. Also, the culture and organizational policies will play an important role in shaping up of the future of Knowledge management.
Keywords: codification, explicit, externalization, tacit

[1] Nonaka I, A dynamic theory of Organizational knowledge creation, Organization Science, 5(1), 1994, 14-37.
[2] Wu Kebao, Dai Junxum,, Knowledge management technologies in education, International symposium on knowledge acquisition and
modeling, 2008.
[3] Marc Henselewski, Evaluation of knowledge management technologies for the support of technology forecasting, proceedings of the
39th Hawaii international conference on system sciences, 2006
[4] M Polanyi, The tacit dimension (Doubleday & Company, Inc, Garden City, New York, 1966)
[5] J Casstillo, A note on the concept of tacit knowledge, Journal of Management Inquiry, vol 11, Mar 2002, pp 46-57
[6] Novak J D, A J Canas, The theory underlying Concept Maps and how to construct and use them, Technical report on IHMC CMap tools 2006-0 Rev 2008-01, Florida institute for Human and machine cognition, 2008

Abstract:In distributed system, electing a leader for the various coordination activities is an important issue. This paper present different election algorithm with different approach. The coordinating activities can be a directory search, balancing the load of the distributed system etc. The major coordinating activity is to manage the use of a shared resource in an optimal manner. The goal of a leader election in distributed system of autonomous processes is to select one of the currently alive processes as a leader so as to manage the coordination activities of the other processes in the system. This paper proposes a comparative analysis of the various election algorithms in distributed system.
Keywords: Election, Coordinator, Priority, Status table, Resource characteristics

[1] Sinha P.K, Distributed Operating Systems Concepts and Design, Prentice-Hall of India private Limited, 2008.
[2] Tanenbaum A.S Distributed Operating System, Pearson Education, 2007.
[2] H.Garcial Molina "Election in a distributed Computing System". IEEE Trans. Comp, 1982, vol31, no. 1, pp. 48 -59. 1982
[3] An Efficient Approach of Election Algorithm in Distributed Systems" - Sandipan Basu / Indian Journal of Computer Science and
Engineering (IJCSE), Vol 20, No 1, 2010
[4] Fredrickson and Lynch, Fredrickson, and Lynch, "Electing a Leader in a synchronous Ring", journal of the ACM, Vol 34, pp 98 -
115, 1987.
[5] "A Dynamic Election Strategy in Distributed System" - Prof. Sanjay Shah & Heta Jasmin Jhaveri/ International Journal of
Engineering Research and Technology (IJERT), Vol. 1 Issue 3, May - 2012
[6] GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing –
Rajkumar Buyya and Manzur Murshed

Abstract:In software development, managers increasingly focus on process improvement which in turn has increased the demand for software metrics. To turn the business application of a software project to system specification is a big challenge in business environment. Since business analysts and system analysts have their own perspective, the modeling of business process is necessary to facilitate both the perspectives and for a better coordination. An attempt is made to resolve inconsistencies and to develop model that verifies process consistency described in Petri net graph. From this model it has been confirmed that the consistency ranges from 0.0 to 1.0. In addition to the above Support Vector Machines (SVM) usage is helpful to improve consistency with greater confidence to evaluate behavioral and structural consistency.
Keywords: behavioral profiles, behavioral equivalence, change propagation, consistency checking, process model.

[1] M. Weidlich, M. Weske and J. Mendling,―Efficient Consistency Measurement Basedon Behavioral Profiles of Process Models‖,
IEEE Trans. Software Eng.,vol. 37, no. 3, pp. 410-429, May 2011.
[2] C. Rolland and N. Prakash, ―Bridging the Gap between Organisational Needs and ERP Functionality,‖ Requirements Eng., vol. 5,
no. 3, pp. 180-193, Oct. 2000.
[3] G. Rull, C. Farre and E. Teniente ―Validation of Mappings between Schemas,‖ Data and Knowledge Eng., vol. 66,no. 3, pp. 414-
437, 2008.
[4] W.M.P. van der Aalst, ―The Application of Petri Nets to Workflow Management,‖J.Circuits Systems and Computers, vol. 8, no.
1,pp. 21-66, 1998.
[5] C. Rolland and N. Prakash, ―Bridging the Gap BetweenOrganisational Needs and ERP Functionality,‖ RequirementsEngineering,
vol. 5, no. 3, pp. 180–193, October 2000.
[6] M. Weidlich, M. Weske and J. Mendling, ―Change Propagation in Process Models Using Behavioural Profiles,‖ Proc. IEEE Int'l
Conf.Services Computing, 2009.
[7] M. Weidlich, R. Dijkman, and J. Mendling, ―The ICoP Framework:Identification of Correspondences between Process Models,‖
Proc.22nd Int'l Conf. Advanced Information Systems Eng., 2010.
[8] M. Weidlich, J. Mendling and Sergey Smirnov ―Business Process Model Abstraction based on Behavioral Profiles‖ , Proc. IEEE
Int'l Conf. Services Computing, 2009.
[9] N. Lohmann, E. Verbeek, and R.M. Dijkman, ―Petri Net Transformations for Business Processes—A Survey,‖ Trans. PetriNets and Other Models of Concurrency, vol. 2, pp. 46-63, Springer-Verlag, 2009.
[10] R. M. Dijkman, ―Diagnosing Differences between Business Process Models,‖ in BPM, ser. Lecture Notes in Computer Science, M. Dumas, M. Reichert, and M.-C. Shan, Eds., vol.5240. Springer, 2008, pp. 261–277.

Abstract:Data sense and recovery in wireless sensor systems have a general request in areas such as defense and inspection monitoring, and command and control in battlefields. In query-based wireless sensor systems, a user would subject a question and expect a answer to be returned within the limit. While the use of fault acceptance devices through idleness improves query reliability in the presence of changeable wireless message and sensor faults, it could reason the energy of the system to be quickly tired. Therefore, there is an inherent trade-off between query reliability versus energy consumption in query-based wireless sensor systems. In this paper, develop fault-tolerant quality of service (QoS) control algorithms based on hop-by-hop data delivery utilizing "source" and "path" redundancy, with the goal to satisfy application QoS requirements while prolong the duration of the sensor system. To develop a mathematical model for the lifetime of the sensor system as a function of structure parameters including the "source" and "path" redundancy levels utilized and discover that there exists optimal "source" and "path" redundancy below which the lifetime of the system is maximized while satisfying application QoS requirements. Numerical data are presented and validated through extensive simulation, with bodily understandings given, to show the possibility of our algorithm design.
Keywords: Wireless sensor networks, reliability, timeliness, query processing, redundancy, energy conservation, QoS, mean time to failure.

[1] O. Younis and S. Fahmy, "HEED: A Hybrid Energy Efficient, Distributed Clustering Approach for Ad Hoc Sensor Network", IEEE
Transaction on Mobile Computing, Vol. 3, No. 3, October-December 2004,pp. 366-379.
[2] W. Heinzelman, C. Chandrakasan and H.Balakrishnan, "An Application-Specific Protocol Architecture for Wireless Micro sensor
Networks,"IEEE Transactions on Wireless communication, Vol.1, No. 4, 2002, pp. 660-670.
[3] P. Mhatre, et al. "A Minimum Cost Heterogeneous Sensor Network with a Lifetime Constraint," IEEE Transactions on Mobile
Computing, Vol. 4, No. 1,2005, pp. 4-15.
[4] D. Chen, and P. Varshney, "QoS Support in Wireless Sensor Networks: A Survey", International Conference on Wireless Networks
(ICWN 2004), LasVegas, Nevada, USA, June 21-24, 2004
[5] K Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie,"Protocol for Self-Organization of a wireless Sensor Network", IEEE Personal
Communications, pp. 16-27, October 2000
[6] B. Deb, S. Bhatnagar and B. Nath, "ReInForM:Reliable Information Fowarding using Multiple Paths in Sensor Networks", 28th
Annual IEEE Conferenceon Local Computer Networks (LCN 2003), Bonn,Germany, October 2003
[7] M Perilo, and W. Heinzelman, "Providing Application QoS through Intelligent Sensor Management", Proceedings of the 1st IEEE
International Workshop on Sensor Network Protocols and Applications (SNPA 03), May 2003.
[8] E. Felemban, C. Lee, E. Ekici, R. Boderm, and S.Vural, "Probabilistic QoS Guarantee in Reliability and Timeliness Domains in
Wireless Sensor Networks", Proceedings of IEEE INFOCOM 2005,vol. 4, March 2005, pp.2646-2657.
[9] R. Iyer, and L. Kleinrock, "QoS Control for Sensor Network", IEEE International Conference on Communications (ICC 2003), May
2003