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

2019 ◽  
Vol 11 (8) ◽  
pp. 27-32
Author(s):  
Johaira U. Lidasan ◽  
◽  
Martina P. Tagacay
Keyword(s):  
Expert System ◽  
Fuzzy Expert System ◽  
Neuro Fuzzy

A Novel Neural Fuzzy Approach for Diagnosis of Potassium Disturbances

2013 ◽  
pp. 208-218
Author(s):  
Mashhour Bani Amer ◽  
Mohammad Amawi ◽  
Hasan El-Khatib
Keyword(s):  
Expert System ◽  
Fuzzy System ◽  
Critical Time ◽  
Diagnostic System ◽  
Accurate Diagnosis ◽  
Fuzzy Expert System ◽  
Fuzzy Approach ◽  
Neuro Fuzzy ◽  
Precise Diagnosis ◽  
Neural Fuzzy

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.

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

CCIT Journal ◽  
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.

scholarly journals Developing and Evaluating a Neuro-Fuzzy Expert System for Improved Food and Nutrition in Nigeria

OALib ◽  
2021 ◽  
Vol 08 (04) ◽  
pp. 1-21
Author(s):  
Oluwatoyin Mary Yerokun ◽  
Moses Okechukwu Onyesolu
Keyword(s):  
Expert System ◽  
Fuzzy Expert System ◽  
Neuro Fuzzy ◽  
Food And Nutrition

scholarly journals Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils

2018 ◽  
Vol 51 ◽  
pp. 147-158
Author(s):  
Богдан Буяк ◽  
Іван Цідило ◽  
Володимир Репський ◽  
Віталій Лялюк
Keyword(s):  
Expert System ◽  
Structural Model ◽  
Factual Knowledge ◽  
Fuzzy Expert System ◽  
Subject Field ◽  
Neuro Fuzzy ◽  
Decision Making System ◽  
Stochastic Data ◽  
Definition Of ◽  
Selection Of

Buyak B.B., Tsidilo I.M., Repskiy V.Í. and Lyalyuk V.P. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowledge, abilities and skills of students. The quality of the system’s operation is determined by the use of various techniques for acquiring knowledge on the basis of which the knowledge base of the neuro-fuzzy system and the combination of the use of fuzzy and stochastic data will be formed.

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