scholarly journals On the Development of Neuro-Fuzzy Expert System for Detection of Leghemoglobin (NFESDL) in Legumes

2021 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
O.M. Yerokun ◽  
M.O. Onyesolu
Keyword(s):  
Expert System ◽  
Health Risks ◽  
Rule Base ◽  
Fuzzy Expert System ◽  
Inference System ◽  
Identification Process ◽  
Antecedent Conditions ◽  
Neuro Fuzzy ◽  
Process Metrics ◽  
Base Technique

The regular supply of affordable complete meals most especially protein from animals has been threatened. Protein sourced from animals carry too many health risks. Obesity, cancer, diabetes, etc., have been traced to the consumption of meats, most especially beef. Medical experts claim that some ailments are as a result of the chemically processed feeds given to raise animals. Therefore, an alternative to meat from plants is imperative. This led to the development of a neuro-fuzzy expert system for detection of leghemoglobin in legumes. This work utilized production rule-base technique and forward-chaining mechanisms with linguistic antecedent conditions to detect the presence of leghemoglobin in plants. To further remove clumsiness and ambiguity in the identification process, metrics/weights were obtained and attached to each morphological feature. MATLAB platform was employed for the development of the system. Class and objects were used to model the information elicited. The result is a system that detects the presence of leghemoglobin in plants. Keywords: Expert system, inference system, neuro-fuzzy, dataset, leghemoglobin

scholarly journals A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

2021 ◽  
Author(s):  
Najmeh Fatahi Nafchi ◽  
Adeleh Asemi ◽  
Hamid Tahaei
Keyword(s):  
Expert System ◽  
Delphi Technique ◽  
Rule Base ◽  
Fuzzy Expert System ◽  
Weed Detection ◽  
Inference System ◽  
Statistical Population ◽  
Fuzzy Delphi ◽  
And Control ◽  
Individual Expert

Abstract In this research, the purpose was to design a fuzzy expert system based on fuzzy delphi method to detect and control the rice weed. The statistical population was elites and experts with regard to the science, experience and field of activity; 15 experts were selected as the sample. Two questionnaires were used to design the desired fuzzy expert: i) Fuzzy Delphi Technique Weed Detection Questionnaire, ii) Delphi Technique Weed Control Questionnaire. The design of the desired expert system was done with MATLAB software and the fuzzy logic tool box. That is, after obtaining an appropriate range of factors, through attributing the fuzzy trapezoidal membership functions to these ranges and generating the input functions, designing the rule base of this system and combining the output results of each factor, a system was designed whose input was the weed factor and the output was scores assigned to weeds. MATLAB guide was also used to design the graphical user interface. Then, for validation the designed system was tested. The answers of system and individual expert were then analyzed using paired t-test. Root Mean Square Error and Middle Absolute Value Deviation tests were used to calculate the system errors. The results were 0.12 and 0.01, respectively. This indicates that the designed fuzzy expert system has sufficient accuracy. Finally, given that all but two of the examined rules are the same as the diagnosis of an individual expert, then in 94% of the cases, the diagnosis of the system is the same as the diagnosis of an individual expert.

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 The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

2019 ◽  
Vol 50 (4) ◽  
pp. 991-1001 ◽  
Author(s):  
Mohammad Ashrafi ◽  
Lloyd H. C. Chua ◽  
Chai Quek
Keyword(s):  
Fuzzy Inference System ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Training Dataset ◽  
Rule Base ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
River Routing

Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.

scholarly journals Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

2019 ◽  
pp. 1-14 ◽  
Author(s):  
F. M. Okikiola ◽  
E. E. Aigbokhan ◽  
A. M. Mustapha ◽  
I. O. Onadokun ◽  
O. A. Akinade
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Fuzzy Inference ◽  
Treatment Recommendation ◽  
Fuzzy Expert System ◽  
Cancer Type ◽  
Inference System ◽  
Java Programming ◽  
New Information ◽  
High Level

The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

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
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