iosr-JCE   Volume-7 ~ Issue-4

Abstract:Implementation of Secured Customer Relationship Management (CRM) system is the core competitiveness of Small and Medium Enterprises (SME) to build an effective means. Main usage of CRM is to maintain the industry records and customer request. Those records are maintained very confidentially for other industries to increasing their growth. Existing system of CRM's are used to maintain records but they didn't concentrate on security criteria's. The Proposed idea of CRM is to implement security features on it (i.e.) encrypt the confidential data's of the industry using best encryption algorithm. For that best cryptographic algorithm is discovered and then use it over the CRM application. Cryptographic Techniques are evaluated on some points. These points are avalanche effect due to one bit variation in plaintext keeping the key constant, avalanche effect due to one bit variation in key keeping the plaintext constant, memory required for implementation and simulation time required for encryption. The secured CRM system builds a great help in the relationship between employees and companies and provide secured access of system and allow secured transactions..
Keywords: Advanced Encryption Standard (AES), Avalanche Effect, Data encryption standard (DES), CRM, SME.

[1] NeetuSettia.―Cryptanalysis of modern Cryptography Algorithms‖.International Journal of Computer Science and Technology.
December 2010.
[2] Diaasalama, Abdul kader, Mohiy Hadhoud, ―Studying the Effect of Most Common Encryption Alg orithms‖, International Arab
Journal of e-technology, Vol 2, No.1, January 2011.
[3] Kumar, ―Customer Relationship Management‖, Wiley International Encyclopedia of Marketing , 2010: 24 –30.
[4] Yang Jialu, ―Customer Relationship Management of Smail-Medium Enterprises,‖ Fudan University Press, 2004:113–120 (In
Chinere).
[5] Russell S.Winer, ―A framework for Customer Relationship Management ‖ California Management Review, 2001, Vol 43(4).
[6] PC Verhoef, ―Understanding the effect of customer relationship management efforts on customer retention and customer share
development,‖ Journal of marketing, 2003 Vol 74(2).

Abstract:Indigenous Knowledge (IK) can be broadly defined as the knowledge that an indigenous (local) community accumulates over generations of living in a particular environment. This definition encompasses all forms of knowledge – technologies, know-how skills, practices and beliefs – that enable the community to achieve stable livelihoods in their environment. The indigenous communities have always generated, refined and passed the knowledge from generation to generation. Such "traditional knowledge" is an important part of their cultural identity. Traditional knowledge has played a vital role in the daily lives of the vast majority of people. Traditional knowledge is essential to the food security and health of millions of people in the developing country like India and state like Orissa. In many countries, traditional medicines provide the only affordable treatment available to indigenous community.

[1] Botanical Survey of India. 2003. Flora of India, Introductory volume, Part II, 350.
[2] Daes Erica-Irene A. 1994. "Preliminary report of the Special Rapporteur on the protection of the heritage of indigenous people",
Geneva: UN Sub-Commission on Prevention of Discrimination and Protection of Minorities.
[3] Devraj R. 2000. US Corporate Biopirates Still Staking Claim on Basmati Rice, Common Dreams New Center, Available at
http://www.commondreams.org/headlines/ 100900-01.htm.
[4] Dharmananda S. 2003. The Ayurvedic Medicine Industry in India, online paper. Available at:
http://www.itmonline.org/arts/ayurind.htm.
[5] EXIM Bank of India. 2003. Export Potential of Indian Medicinal Plants and Products, pp.11-12.
[6] Gollin M. 1999. New Rules for Natural Products,. Sept. Vol. (17)9:.921.922, Available at:
http://www.nature.com/cgitaf/DynaPage.taf?file=/nbt/journal/v17/n9/full/nbt 0999_921.html
[7] Grain and Kalpavriksh. 2002. "Traditional Knowledge of Biodiversity in Asia Pacific: Problems of Piracy and Protection", Genetic
Resources Action International, October, Available at: http://www.grain.org/publications/tk-asia-2002-en.cfm.
[8] Hansen S and VanFleet J. 2003. "Traditional Knowledge and Intellectual Property: A Handbook on Issues and Options for
Traditional Knowledge Holders in Protecting their Intellectual Property and Maintaining Biological Diversity", Association for the
Advancement of Science, American: Washington, DC Available at: http://shr.aaas.org/tek/handbook.
[9] Jain V. 2008. "Safeguarding the Traditional Knowledge in India" Paper Presented in the Seminar of Asia Pacific Jurist Association
(APJA): Delhi, on 28.04.2008.
[10] Ng'etich K A. 2005. Indigenous Knowledge Alternative Medicine and Intellectual Property Right Concern in Kenya. Paper
Presented at the Council for the Development of Social Science Research in Africa (CODESRIA) General Assembly, Maputo,
Mozambique, 6-10 December, 2005

Abstract:The common problem for data quality is missing data. The real datasets have lot of missing values. Missing values imputation is a challenging issue in machine learning and data mining. Missing data should be carefully handled; otherwise it affects the quality of the mining process or the performance of classification algorithms. Mean method of imputation is the most common method to replace the missing values. In this paper, we address the negative impact of missing value imputation and solution for improvement while evaluating the performance of kNN algorithm for classification of Diabetes data. We selected diabetes dataset because it contains lot of missing values and the impact of imputation is very obvious. To measure the performance, we used Accuracy and Error rate as the metrics.
Keywords: Data Mining, Classification, kNN, Imputation, Data Normalization and Scaling

[1] Roshawnna Scales, Mark Embrechts, "Computational intelligence techniques for medical diagnostics".
[2] World Health Organization. Available: http://www.who.int
[3] G. Ssali, T. Marwala. "Estimation of missing data using computational intelligence and decision trees." Proceedings of IEEE
International Joint Conference On Neural Networks, Hong Kong.
[4] R. J. Little and D. B. Rubin. "Statistical Analysis With Missing Data", Hoboken, NJ: Wiley,(1987).
[5] Gustavo E. A. P. A. Batista and Maria Carolina Monard, An Analysis of Four Missing Data Treatment Methods for Supervised
Learning, Applied Artificial Intelligence 17(5-6): 519-533 , 2003
[6] Alireza Farhangfar, Lukasz Kurgan, Jennifer Dy,"Impact of imputation of missing values on classification error for discrete data",
Pattern Recognition, Volume 41, Issue 12, December 2008, Pages 3692–3705
[7] Qinbo Song, Martin Shepperd."A new imputation method for small software project data sets", Journal of Systems and Software,
Volume 80, Issue 1, January 2007, Pages 51–62
[8] José M. Jerez Ignacio Molina ,, Pedro J. García-Laencina ,Emilio Alba, Nuria Ribelles, Miguel Martín, Leonardo Franco," Missing
data imputation using statistical and machine learning methods in a real breast cancer problem", Artificial Intelligence in Medicine,
Volume 50, Issue 2, October 2010, Pages 105–115
[9] Bhekisipho Twala, Michelle Cartwright,"Ensemble Imputation Methods for Missing Software Engineering Data", 11th IEEE
International Software Metrics Symposium (METRICS'05)
[10] Vidan Fathi Ghoneim, Nahed H. Solouma, Yasser M. Kadah," Evaluation of Missing Values Imputation Methods in cDNA
Microarrays Based on Classification Accuracy", 978-1-4244-7000-6/11 IEEE.