Application of Fuzzy Expert System for Analyzing Agriculture Data

2020 ◽  
pp. 16-32
Author(s):  
Jharna Majumdar ◽  
Shilpa Ankalaki ◽  
Sabari Prabaaker
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Fruits And Vegetables ◽  
Meat Products ◽  
Fuzzy Expert System ◽  
Indian Economy ◽  
Fuzzy Expert Systems

Agriculture plays a major role in the Indian economy. India is rich in production of crops like rice, cotton, wheat, soybean, sugar; fruits and vegetables like onion, tomatoes, potatoes; dairy; and meat products. India is ranked first worldwide for the production of banana, jute, mango, cardamom, and ranked second worldwide for the production of rice, tomato, potato, and milk. India's agriculture contributed 4759.48 INR billion to the GDP during the first three months of 2018, and it has been reduced drastically during the second three months of 2018 (i.e., it has been reduced to 4197.47 INR billion). The average GDP is 4057.73 INR billion from 2011 until 2018; the agricultural contribution to GDP reached its highest level, that is 5666.82 INR billion, in the last three months of 2017. This chapter explores the application of fuzzy expert systems for analyzing agricultural data.

scholarly journals Toward efficient modeling of fuzzy expert systems: a survey 

2012 ◽  
Vol 52 (No. 10) ◽  
pp. 456-460 ◽  
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Expert System ◽  
Conceptual Modeling ◽  
Maintenance Cost ◽  
Fuzzy Expert System ◽  
Knowledge Modeling ◽  
Modeling Tools ◽  
Knowledge Based ◽  
Application Systems ◽  
Fuzzy Expert Systems ◽  
Efficient Modeling

Efficient modeling of the artificial intelligence tools has become a necessity in order to cut down the development and maintenance cost associated with building application systems in the business, industrial and agriculture sectors that are frequently amendable to sudden unexpected environmental and economic conditions changes. This can be accomplished through developing an efficient modeling language which exploits the beneficial features of the emerging object-oriented technology. This research is aimed at reviewing the recent scientific aspects of the research concerning conceptual modeling of fuzzy knowledge-based system, which exhibits a large extent of applicability in last few decades due to its capability to deal with vagueness, uncertainty and subjectivity, those are inherent in real world problems. The most recent researches and applications of fuzzy expert system are surveyed. The existing knowledge modeling techniques are reviewed and the prominent ones are pinpointed. This paper is intended to identify the main and common bottlenecks of the existing knowledge modeling tools to overcome it in developing a reliable conceptual model of fuzzy expert system. 

A FUZZY DISTRIBUTED EXPERT SYSTEM DESIGN TOOL EXGEN AND ITS APPLICATION TO SENSORY FOODSTUFF EVALUATION

1999 ◽  
Vol 07 (04) ◽  
pp. 383-388
Author(s):  
THANH THUY NGUYEN ◽  
TOAN THANG NGUYEN ◽  
BINH CUONG THAC ◽  
DINH KHANG TRAN
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Bases ◽  
Design Tool ◽  
Rule Base ◽  
Production Rules ◽  
Linguistic Terms ◽  
University Of Technology ◽  
Fuzzy Expert Systems ◽  
And Control

Since the appearance of MYCIN, expert systems have been widely and successfully developed for various scientific and technological researches and applications. These applications require more and more fuzzy information resources because of the uncertainty, inexactness in labeling facts using linguistic terms and expressing human expertise. Sensory foodstuff evaluation is among this kind of fuzzy expert system applications. In the frame of the research project on fuzzy expert systems for science and technology at the Hanoi University of Technology, we have developed an expert system building tool called EXGEN which has the following features: – Knowledge editing in the form of production rules using Vietnamese in the natural language-like syntax. The tool is also capable to verify the consistency of an acquired knowledge base. – Inference engine consisting of two principal inference mechanisms (forward and backward inference) and control strategy module. We proposed also some heuristics for choosing a potential inference trace, allowing to get more information about conclusions. – Possibility of establishing a configuration for a distributed working session. It would be possible to carry out: + a deduction over a shared rule base (RB) in the server, based on information acquired from workstations (common RB and conclusion, distributed fact base (FB)) + a deduction over a shared RB in the server with different cognitive tasks (including hypotheses fact and conclusions) on workstations (common RB and distributed FB) + deductions on workstations with distributed knowledge bases (Distributed RB and FB) We have already implemented an application expert system SENEXSYS for sensory foodstuff evaluation using the building tool EXGEN. Experimental results have shown that qualification given by the expert system is comparable to evaluation results obtained by following up Vietnamese standard TCVN 3215.79

scholarly journals Fuzzy Expert System Generalised Model for Medical Applications

2019 ◽  
Vol 24 (2) ◽  
pp. 128-133
Author(s):  
Osée Muhindo Masivi
Keyword(s):  
Expert System ◽  
Exponential Growth ◽  
Fuzzy Systems ◽  
Medical Application ◽  
Medical Applications ◽  
Fuzzy Expert System ◽  
Fuzzy Variables ◽  
The Past ◽  
Proposed Model ◽  
Fuzzy Expert Systems

Abstract Over the past two decades an exponential growth of medical fuzzy expert systems has been observed. These systems address specific forms of medical and health problems resulting in differentiated models which are application dependent and may lack adaptability. This research proposes a generalized model encompassing major features in specialized existing fuzzy systems. Generalization modelling by design in which the major components of differentiated the system were identified and used as the components of the general model. The prototype shows that the proposed model allows medical experts to define fuzzy variables (rules base) for any medical application and users to enter symptoms (facts base) and ask their medical conditions from the designed generalised core inference engine. Further research may include adding more composition conditions, more combining techniques and more tests in several environments in order to check its precision, sensitivity and specificity.

scholarly journals Implementation constraints and opportunities in developing expert system solutions for diagnosis of common diseases found in Taraba State

2018 ◽  
Vol 4 (3) ◽  
pp. 18
Author(s):  
Aliyu Sani Ahmad
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Clinical Observation ◽  
Fuzzy Expert System ◽  
Medical Field ◽  
Common Diseases ◽  
Information And Communication

Digital age has reform decision making especially in medical field through information and communication technology which become inevitable part of our lives. this paper illustrates the implementation constraint that encompasses developing Fuzzy Expert System (FES) for diagnosis of common diseases usually found in Taraba State. The paper, shows how fuzzy expert works through four distinct phases. It is discovered that the ratio of doctors to patients and the ratio of hospitals to doctors in Taraba is too low. Different literature that discussed how expert systems for diagnosing various diseases were reviewed; Interview, clinical observation, asking question and internet services were used as methodology for accomplishing this paper.  Result were illustrated and finally conclusion was drowned which shows that e-medical solution for diagnosing disease would do well in Taraba because of the opportunities it offers but it loaded with challenges and implementation constraint.

Fault Diagnosis with Fuzzy Expert System

2011 ◽  
Vol 48-49 ◽  
pp. 519-522 ◽  
Author(s):  
Yang Lan Ou
Keyword(s):  
Expert System ◽  
Numerical Models ◽  
Energy Systems ◽  
Sensor Data ◽  
Fuzzy Expert System ◽  
Sensor Fault ◽  
Knowledge Based ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge based on fuzzy expert system.

scholarly journals Fuzzy Expert System for Earthquake Prediction in Western Himalayan Range

2020 ◽  
Vol 26 (3) ◽  
pp. 4-12
Author(s):  
Rabia Tehseen ◽  
Muhammad Shoaib Farooq ◽  
Adnan Abid
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Quantitative Determination ◽  
Earthquake Prediction ◽  
Rule Base ◽  
Fuzzy Expert System ◽  
Human Expert ◽  
Magnitude Scales ◽  
Earthquake Data

Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reproduce the performance of a human expert in earthquake prediction using expert systems. This research aims to predict future earthquakes having a magnitude 5.5 or greater. Previous earthquake data from 2000 to 2019 have been collected for this purpose. Since the earthquake data for the specified region have been reported on different magnitude scales, suitable relationships were determined to obtain uniform data. The uniform data have been used to calculate seismicity indicators according to the guidelines provided by Gutenberg-Richter’s scale for quantitative determination of earthquake features. The relationships among these seismic indicators have been used by the human expert to set the rule base of Fuzzy expert system. These rules have been mathematically validated and tested on instrumentally recorded earthquake data. The results obtained from the proposed FES presented 47 % accuracy in predicting future earthquakes that may occur in the 100 km radial area from 34.708 ° N, 72.5478 ° E.

Developing a fuzzy expert system for determining the levels of students' eHealth literacy (Preprint)

2019 ◽  
Author(s):  
Roghayeh Eskrootchi ◽  
Masoud Zavari ◽  
Chetan Kumar ◽  
Mohammadreza Alibeyk ◽  
Amir Ramezani
Keyword(s):  
Health Literacy ◽  
Expert System ◽  
Expert Systems ◽  
Rough Set Theory ◽  
Fuzzy Expert System ◽  
Ehealth Literacy ◽  
Significant Difference ◽  
Literacy Level ◽  
Electronic Health

BACKGROUND The concept of eHealth literacy refers to the ability of a person to access electronic health information, evaluate the information and apply the resulting knowledge in order to address or solve a health problem. In a society with higher levels of e-health literacy, health and aid in health care can be promoted by using electronic health tools. The first step of promoting eHealth literacy is to assess the current situation of society and determine its health literacy level. Although there are different methods for determination of the level of eHealth literacy in the existing studies, there is no way to measure the level of e-health literacy more precisely and realistically due to its subjective concept. OBJECTIVE This research aims to develop and implement a fuzzy expert system to determine the level of eHealth literacy. The system must be able to identify the weakness of students' e-health literacy in order to tailor services and information to the needs of the target group. In addition, the system could be a help for responsible organizations such as the Ministry of Health or the university to suggest intervention programs for improving the students' eHealth literacy based on the results. METHODS In this paper, different ways of measuring the individual’s literacy level were extracted. Due to the experts’ opinion, the Digital Health Literacy Instrument was selected and used to develop a rule-based fuzzy expert system to determine the levels of eHealth literacy. The reliability and validity of the expert system were evaluated based on the experts’ judgment and by asking for the participation of 50 students of Mashhad University of Medical Sciences. In order to decrease the calculation time and make the system easier to use, the fuzzy expert system was modified based on rough set theory, which caused a reduction in the number of rules from 300 to 159. RESULTS The comparison between the two fuzzy expert systems indicated that no significant difference was detected and both systems were succeeded in around 90% of the cases. CONCLUSIONS Determination of the levels of students’ electronic health literacy is a complex problem that includes uncertainty and inaccuracy. Due to the accuracy and agility of expert systems, it is recommended to use the fuzzy-rough expert system in order to overcome this problem.

Fuzzy Expert System in Agriculture Domain

2020 ◽  
pp. 1-15
Author(s):  
M. Kalpana ◽  
A. V. Senthil Kumar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert Systems ◽  
Weed Management ◽  
Irrigation Management ◽  
Uncertain Data ◽  
Fuzzy Expert System ◽  
Land Preparation ◽  
Fuzzy Expert Systems ◽  
Insufficient Knowledge

Agriculture is an important source of livelihood and economy of a country. Decision making plays an important role in various fields. Farmers are the backbone of agriculture. They need expert systems to make decisions during land preparation, sowing, fertilizer management, irrigation management, etc. for farming. Expert systems may suggest precisely suitable solutions to farmers for all the activities. Uncertainty deals with various situations during sowing, weed management, diagnosis of disease, insect, storage, marketing of product, etc. Uncertainty is compounded by many facts that many decision-making activities in agriculture are often vague or based on perception. Imprecision, vagueness, and insufficient knowledge are handled using the concept of fuzzy logic. Fuzzy logic with expert systems helps find uncertain data. Fuzzy expert systems are oriented with numerical processing.

Methodology for integrating fuzzy expert systems and discrete event simulation in construction engineering

2009 ◽  
Vol 36 (9) ◽  
pp. 1478-1490 ◽  
Author(s):  
Ahmed A. Shaheen ◽  
Aminah Robinson Fayek ◽  
Simaan M. AbouRizk
Keyword(s):  
Expert System ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Simulation Models ◽  
Integrated System ◽  
Fuzzy Expert System ◽  
Factors Affecting ◽  
Construction Engineering ◽  
Event Simulation ◽  
Fuzzy Expert Systems

This paper demonstrates how fuzzy expert systems can be integrated within discrete event simulation models to enhance their modeling and predictive capabilities for construction engineering applications. A proposed methodology is presented for extracting the information from experts to develop the fuzzy expert system rules. The developed fuzzy expert system is integrated within a discrete event simulation model to enhance its modeling capability by explicitly accounting for the different factors affecting some of the simulation activities. A tunneling case study is used to illustrate the features of the integrated system. The outputs generated from the integrated system are very comparable to those from the original probabilistic simulation model. The integrated system represents a more realistic modeling scenario, since it thoroughly accounts for the different factors affecting the tunnel boring machine (TBM) advance rate. This paper is relevant to researchers because it provides an advance in combining artificial intelligence techniques with simulation models to yield better tools for construction modeling. It is of relevance to practitioners because it provides a useful tool for modeling construction engineering problems.

A FUZZY EXPERT SYSTEM FOR SOLVING REAL-OPTION DECISION PROCESSES

2001 ◽  
Vol 06 (02) ◽  
Author(s):  
C.A Magni ◽  
G. Mastroleo ◽  
G. Facchinetti
Keyword(s):  
Expert System ◽  
Real Option ◽  
Decision Processes ◽  
Fuzzy Expert System
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