Abstract—The objective of the work is to propose a case based reasoning (CBR) model with semantic intelligence for deciding a fine tuned solution from a number of distributed cases. A case model has been proposed to incorporate the needed data types and certificates through which the local or global authorities can reason out the solution. The n-tier model of reasoning can be performed using a case agency and broker components to file and adjudicate the results for the submitted cases. The machine learning is emphasized through iterative learning process with pre and post filters to fine tune the solution based on the context of the case. The semantic engineering is carried out to categorize the nature of the case in terms of basic informal constructs. An inherent hierarchy of the context is formed and based on the type and importance of the context, the scenario driven solution is identified by this proposed model. An airway passenger guidance system is considered to validate the model of case based reasoning with the help of decision tree technique.
Index Terms—Case agency, case broker, case model, distributed decision, semantic intelligence.
Chandrasekaran Subramaniam is with the Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, 641006, India (email: chandrasekaran.s@msn.com).
V. D. Dhandayudhapani was with Department of Computer Science and Engineering, Ramanujar Engineering College, Chennai, India. (email: dandu_98@hotmail.com).
Niveditha Narendhran is with Department of Computer Science, Polytechnic Institute of New York University, Brooklyn, NY 11201,USA.(e-mail: nivynaren@yahoo.in)
Mohammed Nazim Feroz is with the Department of Computer Science, Texas Tech University, Lubbock, TX 79409 USA, (e-mail: nazimatr@gmail.com).
Cite: Chandrasekaran Subramaniam, V. D. Dhandayudhapani, Niveditha Narendhran, and MohammedNazim Feroz, "Distributed Case Based Reasoning Model with Semantic Intelligence," International Journal of Information and Electronics Engineering vol. 3, no. 2, pp. 136-140, 2013.