Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture

In this paper, a neural fuzzy system for the diagnosis of potassium disturbances is presented. This paper develops an adaptive neuro-fuzzy expert system that can provide accurate diagnosis of potassium disturbances. The proposed diagnostic approach has many attractive features. First, it provides an efficient tool for diagnosis of K+ disturbances and aids clinicians, especially the non-expert ones, in providing fast and accurate diagnosis of K+ disturbances in critical time. Second, it significantly reduces the time needed to accomplish precise diagnosis of K+ disturbances and thus enhances the healthcare standards. Third, it is capable of diagnosing the different types of potassium disturbances using a hybrid neural fuzzy approach. Finally, it has good accuracy (higher than 87%), specificity (100%), and average sensitivity (83%). The performance of the proposed diagnostic system was experimentally evaluated and the achieved results confirmed that the proposed system is efficient and accurate in diagnosing K+ disturbances.

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The Diagnosis of Diabetic Nephropathy using Neuro-Fuzzy Expert System

Indian Journal of Science and Technology ◽

10.17485/ijst/2017/v10i28/113040 ◽

2017 ◽

Vol 10 (28) ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Jimmy Singla ◽

Dinesh Grover ◽

◽

Keyword(s):

Diabetic Nephropathy ◽

Expert System ◽

Fuzzy Expert System ◽

Neuro Fuzzy

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MeDevice: A Mobile – Based Diagnosis of Common Human Illnesses using Neuro – Fuzzy Expert System

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2019.08.04 ◽

2019 ◽

Vol 11 (8) ◽

pp. 27-32

Author(s):

Johaira U. Lidasan ◽

◽

Martina P. Tagacay

Keyword(s):

Expert System ◽

Fuzzy Expert System ◽

Neuro Fuzzy

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SIGNAL CHECKING OF STEGANO INSERTED ON IMAGE DATA CLASSIFICATION BY NFES-MODEL

CCIT Journal ◽

10.33050/ccit.v5i3.158 ◽

2012 ◽

Vol 5 (3) ◽

pp. 312-328

Author(s):

M. Givi Efgivia ◽

Safaruddin A. Prasad ◽

Al-Bahra .LB

Keyword(s):

Neural Network ◽

Expert System ◽

Image Data ◽

Data Classification ◽

Fuzzy Expert System ◽

Classification Result ◽

Transform Method ◽

Secret Information ◽

Neuro Fuzzy ◽

Image Data Classification

Abstract. In this paper, we propose an identification method of the land cover from remote sensing data with combining neuro-fuzzy and expert system. This combining then is called by Neuro-Fuzzy Expert System Model (NFES-Model). A Neural network (NN) is a part from neuro-fuzzy has the ability to recognize complex patterns, and classifies them into many desired classes. However, the neural network might produce misclassification. By adding fuzzy expert system into NN using geographic knowledge based, then misclassification can be decreased, with the result that improvement of classification result, compared with a neural network approximation. An image data classification result may be obtained the secret information with the inserted by steganography method and other encryption. For the known of secret information, we use a fast fourier transform method to detection of existence of that information by signal analyzing technique.

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