A NEW ALGORITHM FOR SOLVING FUZZY CONSTRAINED SHORTEST PATH PROBLEM USING INTUITIONISTIC FUZZY NUMBERS
Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, ELECTRONICS AND ELECTRICAL TECHNOLOGY
Author(s): K. K. SHUKLA , MADHUSHI VERMA
Abstract: Constrained shortest path problem (CSPP) is an NP-Complete problem where the goal is to determine the cheapest path bounded by a given delay constraint. This problem finds application in several fields like television and transportation networks, ATM circuit routing, multimedia applications etc. In these kinds of applications it is important to provide quality of service (QoS). Therefore, it is necessary to model the uncertainty involved in the parameters like cost, delay, time etc. The best technique to deal with the imprecise nature of the parameters is to represent them using fuzzy numbers. We propose a solution for the intuitionistic fuzzy version of the problem i.e. constrained intuitionistic fuzzy shortest path problem (CIFSPP) where one of the parameter i.e. cost is represented as a trapezoidal intuitionistic fuzzy number (TIFN) and the other parameter i.e. delay is modelled using real numbers. In this paper, we prefer intuitionistic fuzzy sets (IFS) over ordinary fuzzy set beca
- Publication Date: 21-Dec-2014
- DOI: 10.15224/978-1-63248-034-7-25
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ANALYSIS THE EEG SIGNAL TO DETECT EPILEPSY USING ARTIFICIAL NEURAL NETWORK
Published In: 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, ELECTRONICS AND ELECTRICAL TECHNOLOGY
Author(s): ELNAZ NOMIGOLZAR , MANSOUR ESMAEILPOUR , VAHIDEH NADERIFAR
Abstract: According to the World Health Statistics, the epilepsy is a disease that suffer about one percent of people in the world. The EEG Signals as electrical activity of the brain use to detect type of epilepsy. Epilepsy will be detected by the recurrence of epileptic seizures in EEG signals. In most cases, it can not predict the onset in a short period, but requires a continuous recording of the EEG signal. Conventional way of recording tape that has been recorded for this method is mobile that keeps the EEG data for a very long time, even up to a week holds. Since conventional methods of analysis are very tedious and time consuming, EEG automatic seizure detection methods have been developed in recent years, but the error percentage of them is high. Therefore, this paper presents a method based on artificial neural network for detecting the epilepsy that results demonstrate, good accuracy of the proposed model.
- Publication Date: 21-Dec-2014
- DOI: 10.15224/978-1-63248-034-7-26
- Views: 0
- Downloads: 0