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.

scholarly journals Integrating multiple fuzzy expert systems under restricting requirements 

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 187-196
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Business Processes ◽  
New Product ◽  
Decision Problems ◽  
Food Chains ◽  
Problem Context ◽  
Multiple Factors ◽  
Decision Consistency ◽  
Fuzzy Expert Systems

The multiple, different and specific expertises are often needed in making YES-or-NO (YES/NO) decisions for treating a variety of business, economic, and agricultural decision problems. This is due to the nature of such problems in which decisions are influenced by multiple factors, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertise due to its capability to model real world values which are not always exact, but frequently vague, or uncertain. In addition, they are able to incorporate qualitative factors. The problem of integrating multiple fuzzy expert systems involves several independent and autonomous fuzzy expert systems arranged synergistically to suit a varying problem context. Every expert system participates in judging the problem based on a predefined match between problem context and the required specific expertises. In this research, multiple FESs are integrated through combining their crisp numerical outputs, which reflect the degree of bias to the Yes/No subjective answers. The reasons for independency can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. This article presents simple algorithms to integrate multiple parallel FES under specific requirements: preserving the extreme crisp output values, providing for null or non-participating expertises, and considering decision-related expert systems, which are true requirements of a currently held project. The presented results provides a theoretical framework, which can bring advantage to decision making is many disciplines, as e.g. new product launching decision, food quality tracking, monitoring of suspicious deviation of the business processes from the standard performance, tax and customs declaration issues, control and logistic of food chains/networks, etc. 

Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice

2020 ◽  
pp. 155-174
Author(s):  
M. Kalpana ◽  
A. V. Senthil Kumar
Keyword(s):  
Fuzzy Logic ◽  
Expert Systems ◽  
Membership Function ◽  
Disease Incidence ◽  
Assessment Methodology ◽  
Fuzzy Expert Systems ◽  
Fuzzy Assessment ◽  
Triangular Membership Function ◽  
1000 Grain Weight ◽  
Hill Number

Fuzzy expert systems are designed based on fuzzy logic and deal with fuzzy sets. Many fuzzy expert systems have been developed for diagnosis. Fuzzy expert systems are developed using fuzzification interface, enhanced fuzzy assessment methodology, and defuzzification interface. Fuzzification helps to convert crisp values into fuzzy values. By applying the enhanced fuzzy assessment methodology for rice, the yield parameters of rice can be diagnosed with number of tillers per hill, number of grains per panicle, and 1000 grain weight. Pest and disease incidence becomes simple for scientists. Enhanced fuzzy assessment methodology for rice uses triangular membership function with Mamdani's inference and K Ratio. Defuzzification interface is adopted to convert the fuzzy values into crisp values. Performance of the system can be evaluated using the accuracy level. Accuracy is the proportion of the total number of predictions that are correct. The proposed algorithm was implemented using MATLAB fuzzy logic tool box to construct fuzzy expert system for rice.

Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision

Fuzzy Systems ◽  
2017 ◽  
pp. 935-968
Author(s):  
A. B. Bhattacharya ◽  
Arkajit Bhattacharya
Keyword(s):  
Prostate Cancer ◽  
Fuzzy Logic ◽  
Urinary Incontinence ◽  
Expert Systems ◽  
Domestic Animals ◽  
Medical Diagnostics ◽  
Classical Approach ◽  
Medical Work ◽  
Neurological Signs ◽  
Fuzzy Expert Systems

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.

A practical review and taxonomy of fuzzy expert systems: methods and applications

2019 ◽  
Vol 27 (1) ◽  
pp. 81-136 ◽  
Author(s):  
Madjid Tavana ◽  
Vahid Hajipour
Keyword(s):  
Fuzzy Logic ◽  
Conference Proceedings ◽  
Expert Systems ◽  
Future Research ◽  
Specific Domain ◽  
Comprehensive Review ◽  
Content Type ◽  
Fuzzy Expert Systems ◽  
Journal Publications ◽  
Inference Systems

Purpose Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems. Design/methodology/approach The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems. Findings The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field. Originality/value Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

Fuzzy Systems ◽  
2017 ◽  
pp. 418-442
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Rule ◽  
Fuzzy Expert System ◽  
Assessment Methodology ◽  
Correct Decision ◽  
Fuzzy Similarity ◽  
Computer Based ◽  
Fuzzy Assessment

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

DEVELOPMENT OF A FUZZY EXPERT SYSTEM TO PRIORITIZE TRAFFIC CALMING PROJECTS

2016 ◽  
Vol 78 (2) ◽  
Author(s):  
Amir Falamarzi ◽  
Muhamad Nazri Borhan ◽  
Riza Atiq O. K. Rahmat ◽  
Samira Cheraghi ◽  
Hamid Haj Seyyed Javadi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Traffic Safety ◽  
Effective Parameters ◽  
Traffic Calming ◽  
Inference Engines ◽  
Efficiency And Effectiveness ◽  
Decision Making Processes

Nowadays, due to the constraints of budget and time, the prioritization of traffic calming projects before installation of traffic calming measures is vital for transportation engineers and urban planners. The purpose of this study is to develop an expert system for prioritizing streets that are affected by problems associated with traffic safety using Fuzzy Logic. Expert systems have been used widely and globally for facilitating decision-making processes in various fields of engineering. Due to the uncertainty and vagueness in traffic and transportation related problems, the use of fuzzy logic in the inference engines and decision-making processes of expert systems, is effective. In the proposed expert system, effective parameters in prioritizing traffic calming projects in residential streets including traffic volume, residential density, differential speed and number of accidents are investigated. The Fuzzy Logic toolbox, which is embedded in MATLAB (R2010b), is employed to design and simulate this expert system on the basis of Fuzzy Logic. A specific GUI was developed for this purpose. By developing this system, engineers and decision-makers will be able to rank projects according to their importance. This expert system was tested through prioritizing a number of residential streets in the city of Tehran. The output of the tests showed that the proposed system is helpful in prioritizing different traffic calming projects. Finally, the evaluation of the system was conducted. According to the assessment, most evaluators acknowledged the efficiency and effectiveness of the system. 

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.

scholarly journals Control de Velocidad de un auto empleando Sistemas Difusos

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Edwin Mejía Peñafiel ◽  
Alberto Leopoldo Arellano Aucancela ◽  
Geovanny Vallejo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Expert Systems ◽  
Automatic Control ◽  
Linear Systems ◽  
Control Area ◽  
Traditional Logic ◽  
Toma De Decisiones ◽  
Non Linear Systems

En los últimos años la inteligencia artificial ha ido aumentando su nivel en cuanto a investigación se refiere, los sistemas difusos se han venido consolidando como una herramienta útil para modelar sistemas complejos y no lineales. Las técnicas de la inteligencia artificial se han convertido en una herramienta fundamental para abordar problemas complejos incluyendo el área de control automático. A diferencia de la lógica tradicional que solo utiliza dos valores de verdadero o falso, la lógica difusa permite definir valores intermedios en un intento por aplicar un modo de pensamiento similar al del ser humano. En esta situación los sistemas expertos tienen mucho que ver con lo que significa inferir conocimiento, utilizando las famosas reglas de inferencia o también conocidas como reglas de producción, dentro de la lógica difusa se utilizará el método de inferencia de Mandani que hace uso de las reglas Si X Entonces Y, si premisa entonces conclusión. En este artículo se ha desarrollado un algoritmo difuso para controlar la velocidad de un auto y evitar que el mismo choque cuando el conductor sufre cualquier alteración de su cuerpo, el prototipo recoge información de su entorno para la toma de decisiones, se presenta un modelo como prototipo a seguir en este caso para la construcción, se hace un análisis de los diferentes dispositivos y se presentan los conceptos.  Abstract In recent years artificial intelligence has been increasing its level in terms of research, diffuse systems have been consolidated as a useful tool for modeling complex and non-linear systems. Artificial intelligence techniques have become a fundamental tool for addressing complex problems including the automatic control area. Unlike traditional logic that uses only two values ​​of true or false, fuzzy logic allows defining intermediate values ​​in an attempt to apply a mode of thinking similar to that of the human being. In this situation, the expert systems have much to do with what it means to infer knowledge, using the famous rules of inference or also known as rules of production, within the fuzzy logic will be used the method of inference of Mandani that makes use of the rules If X Then Y, if premise then conclusion. In this article we have developed a diffuse algorithm to control the speed of a car and prevent the same shock when the driver suffers any alteration of his body, the prototype collects information from its environment for decision making, a model is presented as Prototype to follow in this case for the construction, is made an analysis of the different devices and the concepts are presented.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

2015 ◽  
pp. 280-301
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Rule ◽  
Fuzzy Expert System ◽  
Assessment Methodology ◽  
Correct Decision ◽  
Fuzzy Similarity ◽  
Computer Based ◽  
Fuzzy Assessment

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

A Generalization of the Theory of Expertons

2018 ◽  
Vol 26 (Suppl. 1) ◽  
pp. 121-139 ◽  
Author(s):  
Joan Carles Ferrer-Comalat ◽  
Salvador Linares-Mustarós ◽  
Dolors Corominas-Coll
Keyword(s):  
Social Sciences ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Data Fusion ◽  
Expert Systems ◽  
Data Gathering ◽  
New Approach ◽  
The Social ◽  
Support Element ◽  
Decision Making Processes

With the advent of fuzzy logic applications in the field of economics and in the context of expert systems we are witnessing a new approach to data-gathering methods as the aggregation of data provided by various experts brings with it new data fusion techniques. In 1987, the exploration of these techniques gave rise to the experton concept as an integrating element that allows the collection of all information expressed by a group of experts relating to the level or degree of truth of a statement or the degree of fulfilment of a certain vague or imprecise characteristic. Over the thirty years since its formulation, the experton concept has been applied as a support element in decision-making processes in many areas of the social sciences. The aim of this article is to present a generalization of the experton concept for both the discrete and continuous cases, which respects known properties and has the potential to be practically applied in various situations where there is a need to perform a simulation of various opinion scenarios relating to a characteristic or statement, and thus explore new approaches to decision-making models.

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