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Kumara, S. S.
- Classification and Regression Tree - Based Analysis for the Prediction and Mapping of Funding Pattern
Abstract Views :279 |
PDF Views:12
Authors
Affiliations
1 Department of Human Resource Development, Central Food Technological Research Institute, Mysore 20, IN
2 Department of Planning, Monitoring and Coordination, Central Food Technological Research Institute, Mysore 20, IN
1 Department of Human Resource Development, Central Food Technological Research Institute, Mysore 20, IN
2 Department of Planning, Monitoring and Coordination, Central Food Technological Research Institute, Mysore 20, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 49, No 2 (2012), Pagination: 155-160Abstract
In public R&D institutions, project proposals are submitted by research groups to various government funded bodies for the external financial support. The pattern of funding against various research groups was analyzed in this paper using classification and regression tree (C&RT) technique. The study was aimed to explore and forge an effective networking with funding agencies to supplement the ongoing research programs of the Institute.Keywords
Classification and Regression Tree, Research Groups, Funding Agencies, Data MiningReferences
- Qijia Tian; Jian Ma; Cleve (J.Liang); Ron (C.W. Kwok); Ou Liu; Quan Zhang. An Organizational decision support approach to R&D project selection, In: Proceedings of 35th Hawaii International Conference on System Sciences, 2002.
- Ravichandra Rao (I K). Data Mining and Clustering Techniques. DRTC Workshop on Semantic Web. December 2003; p8-10.
- Clementine 11 Manual, SPSS Inc., Chicago, 2005, p313-325.
- Statsoft Electronic Statistics Textbook, http://www.statsoft.com/TEXTBOOK/stcart.html, (accessed on 3 Feb 2010)
- Massimo Bertolini. Oil pipeline spill cause analysis: A classification tree approach. J. Quality Maint. Eng. Vol. 12(2); 2006; p186-198.
- Thamaraiselvi (G); Kaliammal (A). Data mining: Concepts and Techniques. SRELS Journal of Information Management. Vol. 41(4); 2004; p339-348.
- Manying Qiu. Selecting classification and clustering tools for academic support. Issues in Information System. Vol. 8(2); 2007; p265-272. http://themeasurementgroup.com/definitions/cart.htm, (accessed on 27 Aug 2009)
- Wen-Shuan Tseng; Hang Nguyen; Jay Liebowitz; William Agresti. Distractions and motor vehicle accidents Data mining application on fatality analysis reporting system (FARS) data files. Industrial Management & Data Systems. Vol. 105(9); 2005; p1188-1205.
- The Use of Traditional Knowledge Resource Classification (TKRC) for Institutional Repository: a Study of Indian Cuisines
Abstract Views :251 |
PDF Views:11
Authors
Affiliations
1 Department of Human Resource Development, Central Food Technological Research Institute, Mysore 570020, IN
2 Dept. of Planning, Monitoring and Coordination, Central Food Technological Research Institute, Mysore 570020, IN
1 Department of Human Resource Development, Central Food Technological Research Institute, Mysore 570020, IN
2 Dept. of Planning, Monitoring and Coordination, Central Food Technological Research Institute, Mysore 570020, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 49, No 6 (2012), Pagination: 615-622Abstract
Creating an Institutional Repository (IR) for the protection of traditional Indian cuisines is explored, which would be a supplement to the existing traditional Indian cuisine database in CFTRI. An IR was created in order to bring the communities and practioners together interested in Indian Cuisines. A Traditional Knowledge Resource Classification (TKRC), similar to International Patent Classification (IPC) has been used for organizing and retrieval of the resources.Keywords
Traditional Knowledge, Traditional Knowledge Resource Classification, Metadata, Digitization, Institutional Repository, Indian CuisinesReferences
- Parpia (H A B). Multi-Cultural Heritage of Traditional Indian Foods. Indian Food Industry. Vol. 23(3); May-June 2004; p10-14.
- Vidya Valsaraj; Manilal (P); Varadaraj (M C). International Patent Classification (IPC): A Generation – Next Precision Tool for Patent Search. Indian Food Industry. Vol. 24(3); May-June 2005; p68-74.
- Davis Shotton. Data Webs: new visions for research data on the web. The Closed World of Databases meets the Open World of the Semantic Web. National e-Science Centre, Edinburgh, 12th October 2006.
- Devendra (S Gobbur). Digital Repositories: Concepts and Issues. Gulbarga University, Karnataka Wikipedia (accessed on 19th October 2009).
- James Revell; Dan Dorner. Subject Librarians‟ Perception of the Institutional Repository as an Information Source. World Library and Information Congress: 75th IFLA General Conference and Council, 2009, p1-6.
- Raym Crow. The case for Institutional Repositories: A SPARC position paper. Washington D.C: The Scholarly Publishing & Academic Resources Coalition (SPARC). 2002; p12-37.
- Prem Chand; Murthy (T A V); Prakash K; Umesh Gohel. Institution Repositories, Open Access Movement, OAIPMH Compliant software, 2nd Convention PLANNER, 4-5 November, 2004, p52-64. http://roar.eprints.org/ (accessed on 29th Aug 2009)
- Kanchan Kamila. Institutional Repository Projects in India. 7th International CALIBER-2009, Puducherry, 25-27 February 2009, p128-132.
- Mahakuteshwar (H Y); Padmavathi (T); Sanjailal (K P). EPrints@CFTRI: Institutional Repository of Central Food Technological Research Institute – A Case Study. In Department of Food Science & Technology Information Services (FOSTIS), CFTRI, Shaping the Future of Special Libraries Beyond Boundaries , 2008, p243-254.
- Achaya (K T). A Historical Dictionary of Indian Food. Oxford University Press, New Delhi, 1998.
- Mohammad Nazim; Maya Devi. Open Access journals and Institutional Repositories: Practical need and present trends in India. Annals of Library and Information Studies. Vol. 55; March 2008; p27-34.
- Urmila Thaker; Nimesh Oza. Institutional Repository: An Effective tool for Knowledge Management. SRELS Journal of Information Management. Vol. 47(5); Oct 2010; p507-516.
- Regional Food Diversity of Traditional Indian Cuisines-A Perspective based on the Database of Indian Cuisines
Abstract Views :151 |
PDF Views:0
Authors
Affiliations
1 Department of Human Resource Development, CSIR-CFTRl, Mysore 570020, IN
2 Department of Planning Monitoring and Coordination, CSIR-CFTRI, Mysore- 570020, IN
1 Department of Human Resource Development, CSIR-CFTRl, Mysore 570020, IN
2 Department of Planning Monitoring and Coordination, CSIR-CFTRI, Mysore- 570020, IN
Source
Information Studies, Vol 18, No 3 (2012), Pagination: 177-188Abstract
Indian traditional foods are non-spatially mapped to different states and regions of the country. The mapping of region-wise cuisines on major food ingredient groups was carried out. Analysis was made towards exploring the dominant shared cuisines based on selected major ingredients in the intra- - regional states. The study shows the diverse nature of the origin or existence of cuisine traditions in the country.Keywords
Traditional Foods, Shared Cuisines, Food Diversity.References
- Srinivasan K. (2010). Traditional Indian functional foods. In: Functional foods of the East/. Edited by John Shi et al.: CRC Press.
- Larissa S.; Dresher, Silic Thiele; Gert B. M.; and Mensink (2007). A new index to measure healthy food diversity better reflects a healthy diet than traditional measures. J. Nutrition, 137; p. 647-651.
- krahn, Juta (2005). Upland food security and nutritional diversity. In; Improving the livelihoods in the uplands of Lao PDR/ Edited by : Nafri,Nafes and Nudol, p. 107-112.
- Naska A.; Fouskakis D.; Oikonomou E.; Almeida M.D.V.; Berg M.A. et al. Dietary patterns and their socio-demographic determinants in 10 European countries: data from DAFNE databank. European J. Clinical Nutrition, 60; p. 181-190.
- Maria Jose Soto-Mendez B.S.; Raquel Campos B.S.; Liza Hernandez B.S.; Monica Orozco; and Marieke Vossenaar (2011). Food vaiety, dietary diversity, and food characteristics among convenience samples of Guatemalan women. Salud publico de niexico, 53(4); p. 288-298.. http://www.foodvision.gov.uk (last accessed on 20 Nov. 2011
- Manikanta V.; Kumara S.S.; Harsha P.; and Manilal P. (2012). Mapping of shared traditional foods in Southern states using Geographical Information System (GIS) & MATLAB, International Conference on Food Web - A Global Connect, Chennai, India, 17-18th Feb.
- Parpia H.A.B., (2004). Multi-cultural heritage of traditional Indian foods. Indian Food Industry, 23(3): May-June, p. 10-14.
- Arjun Appadurai. (1998). How to make a national cuisine: Cookbooks in contemporary India. Comparative studies in Society and History, 30(1); p. 3-24
- Classification and Regression Tree-Based Analysis for Forecasting and Mapping of Funding Pattern
Abstract Views :151 |
PDF Views:0
Authors
Affiliations
1 Department of Human Resource Development, Central Food Technological Research Institute (CSIR), Mysore- 20, IN
2 Department of Planning Monitoring and Coordination, Central Food Technological Research Institute (CSIR), Mysore- 20, IN
1 Department of Human Resource Development, Central Food Technological Research Institute (CSIR), Mysore- 20, IN
2 Department of Planning Monitoring and Coordination, Central Food Technological Research Institute (CSIR), Mysore- 20, IN
Source
Information Studies, Vol 17, No 1 (2011), Pagination: 39-46Abstract
In public research and development institutions, project proposals are submitted by research groups to various government funded bodies for external financial support. The pattern of funding of various research groups was analyzed using classification and regression tree (C&RT) technique. The study was aimed at exploring and forging an effective networking with funding agencies to supplement the ongoing research programmes of the Institute.Keywords
Research, Funding, Classification and Regression Tree, Data Mining, Funding Agencies.References
- Qijia Tian: Jian Ma; Cleve J. Liang, Ron C.W. Kwok, Ou Liu. Quan Zhang (2002). An Organizational decision .support approach to R&D project selection, In.' Proceedings of the 35th Hawaii International Conference on System Sciences, 2002.
- Ravichandra Rao, I..K. (2003). Data mining and clustering techniques, DRTC Workshop on Semantic Web. 8-10 December.
- Clementine II Manual. (2005). SPSS Inc., Chicago, p. 313-325
- StatSoft Electronic Statistics Textbook. http://www.statsoft.com/TEXTBOOK/stcart.html, (accessed on 3 Feb 2010)
- Massimo, Bertolini, (2006). Oil pipeline spill cause analysis: A classification tree approach, J. Quality Maint. Eng. 12(2); p. 186-198
- Thamaraiselvi, G; Kaliammal, A. (2004). Data mining : Concepts and techniques, SRELS J. Information Management. 41(4); p. 339-348
- Manying Qiu (2007). Selecting classification and clustering tools for academic support. Issues in Information System. 8(2); p. 265-272
- http://themeasurementgroup.com/definitions/cart.htm, (accessed on 27 Aug 2009)
- Wen-Shuan Tseng; Hang Nguyen; Leibowitz, Jay; Agresti, William. (2005). Distractions and motor vehicle accidents: Data mining application on fatality analysis reporting system (PARS) data files, Industrial Management and Data Systems. 105(9); p. 1188-1205