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.

Adaptable Fuzzy Expert System for Ship Lock Control Support

2016 ◽  
Vol 69 (6) ◽  
pp. 1341-1356 ◽  
Author(s):  
Todor Bačkalić ◽  
Vladimir Bugarski ◽  
Filip Kulić ◽  
Željko Kanović
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Specific Area ◽  
Fuzzy Expert System ◽  
Control Logic ◽  
Extensive Experience ◽  
Inference System ◽  
And Control ◽  
Ship Lock

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

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

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

Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses

1992 ◽  
Vol 37 (6) ◽  
Author(s):  
Terhi Siimes ◽  
Mikio Nakajima ◽  
Hideo Yada ◽  
Hajime Asama ◽  
Teruyuki Nagamune ◽  
...  
Keyword(s):  
Expert System ◽  
Object Oriented ◽  
Fuzzy Expert System ◽  
On Line ◽  
And Control

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.

Integrity Assessment and Control of Offshore Topside Piping: An Expert System Based Approach

2018 ◽  
Author(s):  
R. M. Chandima Ratnayake
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Control Program ◽  
Technical Condition ◽  
Practical Significance ◽  
Illustrative Case ◽  
Inference System ◽  
Suggested Approach ◽  
Integrity Assessment ◽  
And Control

Downtime has a significant influence on the productive capability of offshore topside operating systems. Integrity assessment and control (IA&C) disciplines face major challenges in implementing a plant integrity control strategy, due to the lack of a methodology for incorporating fuzziness present in the data. To date, the employed IA&C practices face challenges in maintaining uniform quality from one integrity control program to another, due to the variability present in the technical IA&C process, especially among the different integrity assessment experts. Hence, it is vital to use expert systems-based approaches to sustain IA&C activities at an anticipated level and maintain the performance of operating assets at a target level. This manuscript provides a methodology and an illustrative case for how to perform IA&C activities for offshore topside piping. The illustrative case is demonstrated using a fuzzy inference system (FIS). Technical condition (TC) and relative degradation (RD) are selected as the inputs to the FIS for assessing the likelihood of failure (LoF). Expert system-based calculations, and how to use such results for IA&C, are demonstrated. The practical significance of the suggested approach is also discussed.

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meysam Azimian ◽  
Mahdi Karbasian ◽  
Karim Atashgar ◽  
Golam Kabir
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Real Life ◽  
Fuzzy Model ◽  
Fuzzy Expert System ◽  
Inference System ◽  
Content Type ◽  
Reliability Centered Maintenance ◽  
Expert Team ◽  
Effective Decision Making

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

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.

scholarly journals The Outsourcing Model in the Ministry of Health and Medical Education with Fuzzy Delphi Technique in order to Improve the Quality of Health Services with Emphasis on Hospitals

2021 ◽  
Author(s):  
Hasan Ali Nejad ◽  
Mohamad Ziaaddini ◽  
Amir Rajabi Behjat ◽  
Mohamad Reza Hosseinipour
Keyword(s):  
Medical Education ◽  
Health Services ◽  
Delphi Technique ◽  
Health Centers ◽  
Suitable Model ◽  
Ministry Of Health ◽  
Statistical Population ◽  
Fuzzy Delphi ◽  
Quality Of Health Services

Background: Outsourcing in the Ministry of Health is based on the reforming the structure of the health system and improving the quality of services. Therefore, the present study wanted to present the outsourcing model of services in the Ministry of Health and Medical Education with fuzzy Delphi technique in order to improve the quality of health services with emphasizing on hospitals. Methods: The present research in terms of purpose, data research type, implementation method, was part of the exploratory research which has been done quantitatively and qualitatively in 2021. The statistical population was the outsourcing experts for a study between 59 universities of medical sciences in the country, 50 people were selected by purposive sampling method. They were selected for interviews to collect outsourcing indicators to present a suitable model. The indicators were mined data using the information gain method by Matlab software and the indicators were prioritized according to the scores given. A questionnaire was obtained from the prioritized indicators, which were summarized in 19 dimensions, and the same experts were given to complete in 2 consecutive periods. After completing the questionnaire by experts, fuzzy Delphi method was used for final analysis. Results: According to the obtained model, 44 factors are effective in outsourcing .These factors entered the Delphi poll. During 2 stages of fuzzy Delphi, consensus was reached on all 44 indicators. And the highest degree of expertise agreement with the flexibility component and the lowest level of agreement with the component of the certificate of competency to provide services were outsourced by the company. Conclusion: According to the obtained model, various factors are effective in outsourcing services that need to be considered more in the planning of health centers, especially hospitals. According to the findings, it can be said that the related dimensions with considering the outsourcing services are specifically important .Due to the possibility of outsourcing among many health care units; it can be used for flexibility in providing services and patient satisfaction to manage health centers and hospitals.  

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI

2021 ◽  
Vol 11 (2) ◽  
pp. 73-80
Author(s):  
Maria Yunita Nesi ◽  
Yampi R Kaesmetan ◽  
Meliana O. Meo
Keyword(s):  
Expert System ◽  
Fuzzy Inference System ◽  
Prevention And Control ◽  
Fuzzy Inference ◽  
The Body ◽  
Parasitic Infections ◽  
Inference System ◽  
The Right ◽  
And Control ◽  
Mamdani Fuzzy Inference System

The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.

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