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.

Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security

2015 ◽  
pp. 1052-1071 ◽  
Author(s):  
Miltiadis Alamaniotis ◽  
Jason Young ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Gamma Ray ◽  
Detection Sensitivity ◽  
False Alarms ◽  
Low Resolution ◽  
Fuzzy Membership Functions ◽  
Analysis Methodology ◽  
Automated Processing ◽  
Triangular Membership Function

Analysis of acquired nuclear detector gamma-ray signals for recognition of present radioisotopic signatures is crucial to national security and security applications. Identification algorithms must be accurate and rapid. Artificial intelligence is a scientific field with a variety of tools suitable to implement automated processing of nuclear signals. The use of low resolution portable detectors to measure gamma-ray signals has found a wide use in security and safeguards applications. In this paper, the fuzzy logic based analysis methodology that has been previously developed is applied and assessed on a variety of nuclear signals obtained with a low resolution scintillation detector, and more particularly a sodium iodide (NaI) detector. Various types of fuzzy membership functions are employed and their performance is assessed with regard to the number of positive detections, misses, and false alarms. Furthermore, recorded results from the set of low resolution gamma ray signals are used to estimate the detection sensitivity for each membership function. Results demonstrate the overall effectiveness of the fuzzy logic based identifier, and consist of the main course for the assessment of each membership function. Furthermore, comparison of results designates the triangular membership function as the best membership shape for this type of detector signals.

Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security

2014 ◽  
Vol 2 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Miltiadis Alamaniotis ◽  
Jason Young ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Gamma Ray ◽  
Detection Sensitivity ◽  
False Alarms ◽  
Low Resolution ◽  
Fuzzy Membership Functions ◽  
Analysis Methodology ◽  
Automated Processing ◽  
Triangular Membership Function

Analysis of acquired nuclear detector gamma-ray signals for recognition of present radioisotopic signatures is crucial to national security and security applications. Identification algorithms must be accurate and rapid. Artificial intelligence is a scientific field with a variety of tools suitable to implement automated processing of nuclear signals. The use of low resolution portable detectors to measure gamma-ray signals has found a wide use in security and safeguards applications. In this paper, the fuzzy logic based analysis methodology that has been previously developed is applied and assessed on a variety of nuclear signals obtained with a low resolution scintillation detector, and more particularly a sodium iodide (NaI) detector. Various types of fuzzy membership functions are employed and their performance is assessed with regard to the number of positive detections, misses, and false alarms. Furthermore, recorded results from the set of low resolution gamma ray signals are used to estimate the detection sensitivity for each membership function. Results demonstrate the overall effectiveness of the fuzzy logic based identifier, and consist of the main course for the assessment of each membership function. Furthermore, comparison of results designates the triangular membership function as the best membership shape for this type of detector signals.

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.

scholarly journals The Sunn [Eurygaster spp. (Hemiptera: Scutelleridae)] damage in wheat and the effect of seed amount used on yield by fuzzy logic method

2021 ◽  
Vol 11 (1) ◽  
pp. 239-246
Author(s):  
Mustafa Ilcin ◽  
Senol Celik
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Inference ◽  
Wheat Yield ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Sunn Pest ◽  
Herbal Products ◽  
Fuzzy Inference Mechanism ◽  
Triangular Membership Function

Sunn pest (Eurygaster spp.) is a highly harmful insect species for Wheatgrass. Especially with the emphasis it makes in herbal products, it causes the wheat to lose both its bread and pasta qualities. This study presents an example of a model that approximates the wheat yield in the irrigated field in Batman province according to the criteria selected through fuzzy logic. In the modelling, firstly the parameters affecting the wheat yield were determined and input and output variables were defined. In the next step, the membership functions are determined by doing the blurring process. The triangular membership function has been selected for the membership function. Later, fuzzy rule base was determined and fuzzy rules were formed. In the next step, the fuzzy inference mechanism was created. For the rinsing process, the "weight average" method was used. In the study, fuzzy logic toolbox was used in Matlab and the results obtained were seen to be useful in determining wheat yield per decare.

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