Fine-Grained Knowledge in Agriculture System
Abstract
phase to acquire information. It is possible that in a combined
environment, more than one person may try to obtain similar
information in one domain. One person may like to solve a
problem using an unfamiliar Apache Tomcat which he had
studied by another person before. Connecting and then
sharing with that persons will be more beneficial to get there
learned knowledge. Fine-grained knowledge sharing is
proposed for this combined environment. The system is
proposed to classify the surfed data into clusters and
summarize the details in fine grained details. For any system
the efficiency depends upon the surfing. The framework of
proposed work includes: (1) Data which is surfed, clustered
into tasks. (2)Then task is mined in fine grained output. To get
proper result, the search method is applied to the output
(mined results).The concept of Data Mining in fine grained
knowledge is combined with the information gathering and
classification to produce efficient data searching technique in
agriculture system.
Full Text:
PDFReferences
Boser, B. E., I. Guyon, and V. Vapnik (1992), A training
algorithm for optimal margin classifiers, . In Proceedings
of the Fifth Annual Workshop on Computational
Learning Theory, pages. 144 -152. ACM Press 1992.
V. Vapnik, The Nature of Statistical Learning Theory,
NY: Springer-Verlag. 1995.
Meijuan Gao, Jingwen Tian, Shiru Zhou, Research of
Web Classification Mining Based on Classify Support
Vector Machine SECS International Colloquium on
Computing, Communication, Control, and Management -
Wang, P., Hu, J., Zeng, H. J., & Chen, Z. (2009). Using
Wikipedia knowledge to improve text classification.
Knowledge and Information Systems,9-13.
WeiWu & Zhengdong Lu Hang Li ,Learning Bilinear
Model for Matching Queries and Documents, Krishi-
Mitra: Expert System for Farmers ,IJCSMC, Vol. 4,
Issue. 4, April 2015, pg.893 899
Cambazoglu B. B., Aykanat C.: Performance of query
processing implementations in ranking-based text
retrieval systems using inverted indices. International
Journal of Food and Agricultural Economics ISSN 2147-
Vol. 1 No.1 pp. 63-74.
Federal Committee on Statistical Methodology (1980),
Statistical Policy Working Paper 5: Report on Exact and
Statistical Matching Techniques Washington, DC: Office
Federal Statistical Policy and Standards, U.S.
Department of Commerce. Organizational History of the
Department of Agriculture & Cooperation. Retrieved 5
July 2012.
International Journal of Agricultural Science and
Technology (IJAST), Dr. Yanbo Huang United States
Department of Agriculture, USA.
Z. Pawlak, Rough sets and intelligent data analysis,
Information Sciences 147 (2002) 1 12.
K.I. Kim, K. Jung, S.H. Park, H.J. Kim, Support vector
machines for texture classification, IEEE Transactions on
Pattern Analysis and Machine Intelligenc 24(11) (2002)
A Novel Face Recognition Algorithm with Support
Vector Machine Classifier, Latha Parthiban, International
Jouranal of Mathemetics and Scientific Computing, Vol.
,no. 1,2011.
Object Recognition Using Support Vector Machine
Augmented by RST Invariants, R.Muralidharan , and
Dr.C.Chandrasekar, IJCSI International Journal of
Computer Science Issues, Vol. 8, Issue 5, No 3,
September 2011.
Bio-Medical Image Retrieval Using SVM, S.Nithya and
G.ShineLet, International Journal of Advanced Research
in Computer Engineering & Technology (IJARCET)
Volume 1, Issue 10, December 2012.
R. Baker and K. Yacef, The State of Educational Data
Mining in 2009: A Review and Future Visions J.
Educational Data Mining, vol. 1, no. 1, pp. 3-17, 2009.
Question Classification using Semantic, Syntactic and
Lexical features, Megha Mishra ,Vishnu Kumar Mishra
and Dr. H.R. Sharma, International Journal of Web &
Semantic Technology (IJWesT) Vol.4, No.3, July 2013.
Sentiment Analysis Using Support Vector Machine,
Aamera Z. H. Khan, Dr. Mohammad Atique, Dr. V. M.
Thakare, International Journal of Advanced Research in
Computer Science and Software Engineering, Volume 5,
Issue 4, April 2015 ISSN: 2277 128X.
Binder, J., Murphy, K., & Russell, S. (1997b). Spaceefficient
inference in dynamic probabilistic networks. In
Proceedings of the Fifteenth International Joint
Conference on Artificial Intelligence (IJCAI-97) Nagoya,
Japan. Morgan Kaufmann.
A. M. Turing, Intelligent Machinery, in Machine
Intelligence, B. Meltzer and D. Michie, Eds. Edinburgh:
Edinburgh University Press, 1969, vol. 5, National
Physical Laboratory Report (1948).
Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin, A
Practical Guide to Support Vector Classification. Ziyu
Guan, Shengqi Yang, Huan Sun, Mudhakar Srivatsa,
Xifeng Yan,Fine-Grained Knowledge Sharing in
Collaborative Environments , IEEE Transactions on
Knowledge and Data Engineering,2015.
Pradipsinh K. Chavda and Jitendra S.Dhobi , proposed
the A Survey of Model Used for Web Users Browsing
Behavior Prediction in Computer Engineering and
Intelligent Systems, Vol.6, No.3, 2015.
Cong Wang , Kui Ren , Wenjing Lou and Shucheng Yu
proposed the Achieving secure, scalable and fine-grained
data access control in cloud computin.,INFOCOM, 2010
preceedings IEEE.
Iftikhar, N, Integration, Aggregation, and Exchange of
arming Device Data: A high level perspective,
Application of digital information and web
Technologies, 2009. ICADIWT09.
A.Parameshwari, B.Rasina Begum proposed intitled Fine
Grained Data Access Control in Cloud Computing..
Ding, W. and Marchionini, G. 1997 A Study on Video
Browsing Strategies. Technical Report. University of
Maryland at College Park, 2004.
Refbacks
- There are currently no refbacks.
Copyright © IJETT, International Journal on Emerging Trends in Technology