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Online Swine-flue detection system

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Online Swine-flue detection system
ABSTRACT

“ONLINE SWINEFLU DETECTION SYSTEM” is a computerized system. It will interact with the user (patient). This facility is very helpful to the users. According to the symptoms has been given by the user to the expert system, it will suggest the required tests. After analyzing the test reports and disease, it will diagnosis the problem and also displays the help line centers list regarding that particular disease.

2. Requirements Elicitation: Requirements elicitation is the process of collecting all the necessary functionalities of the system from the client and users. The requirements elicitation helps us to depict the requirements of the system in the form of UML diagrams Requirements elicitation focuses only on the user’s view of the system. A requirements elicitation is a feature that a system must have or a constraint that satisfy to be accepted by the client. It is about communication among user, client, and developer for defining a new system. Errors introduced during requirements elicitation are expensive to correct, as they are usually discovered late in the process. Requirements elicitation aims at improving communication among users, clients and developers. Requirements elicitation focuses on describing a purpose of the system. The client, the developers and the users identify a problem area and define a system that addresses the problem. Such a definition is called a system specification and serves as a contract between the client and the developer
2.1. Introduction: The “ONLINE SWINEFLU DETECTION SYSTEM” allows the user to interact with the expert system. The user will register by giving their user name and password. The system will maintain all the registered members details.
2.2. Problem Statement: Before this application the patient had to go to the doctor and he had to get treated by him.
2.2.1. Current System: The systems



Bibliography: [1] S.Parthsarthy and C.C. Aggarwal, “On the Use of Conceptual Reconstruction for Mining Massively Incomplete Data Sets [2] Ian H. Witten and Eibe Frank , “Data Mining: Practical Machine Learning Tools and Techniques” Second Edition, Morgan Kaufmann Publishers. ISBN: 81-312-0050-7. [3] I.T. Jolliffe. Principal Component Analysis, Springer-Verlag, New York, 1986. [4] R.J.A.Little, D.Rubin. Statastical Analysis with Missing Data. Wiley Series in Prob. And Stat., 2002. [5] K. V. Ravikanth, D. Agrawal, A. Singh. “Dimensionality Reduction for Similarity Searching in Dynamic Databases.”In ACM SIGMOD Conference, 1998.

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