Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

Emerging Application of Fuzzy Expert System in Medical Domain

Fuzzy Systems ◽  
2017 ◽  
pp. 1667-1689
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

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.

Research on the Expert System in the Transmitter Monitor

2013 ◽  
Vol 722 ◽  
pp. 212-216
Author(s):  
Lu Ying Jia ◽  
Jian Zhou ◽  
Chen Hui Xie
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Stable Operation ◽  
Monitor System ◽  
Working Process ◽  
Inference Mechanism ◽  
Fuzzy Inference Mechanism ◽  
Radio Broadcast ◽  
Fault Information

Safe and stable operation of the transmitter determines the quality of the radio broadcast. In order to make the transmitter safe and stable operation in the working process, authors developed a transmitter monitor system. In the monitor system, authors designed the expert system to diagnose fault for the transmitter. The expert system is composed of several modules, through the establishment of knowledge base, the application of fuzzy inference mechanism and the interpreter thus can diagnose transmitter fault information. This system cans timely diagnosis and debugging in practical application, it also provides a good security to improve the transmitter safety and continuous broadcast.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

scholarly journals Hybrid Technique for Data Scrutiny using Artificial Intelligence

2020 ◽  
Vol 8 (5) ◽  
pp. 4456-4459
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Uncertain Data ◽  
Genetic System ◽  
Search Space ◽  
Fuzzy Expert System ◽  
Hybrid Technique ◽  
Hybrid Techniques ◽  
Fuzzy Genetic

Today’s time is of artificial intelligence. We are trying to reduce the search space by some methods as uncertain data is also used in the system to solve problems. To manage our database we use hybrid techniques. Like Fuzzy expert system, fuzzy genetic system, in this paper we will see how can we use such system in solving an application.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Fuzzy Logic Based Approach for Power System Fault Section Analysis

Fuzzy Systems ◽  
2017 ◽  
pp. 987-1002
Author(s):  
Neeti Dugaya ◽  
Smita Shandilya
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Power System ◽  
Fuzzy Expert System ◽  
Circuit Breakers ◽  
Membership Grade ◽  
Protective Relays ◽  
Basic Network ◽  
The One ◽  
Maximum Selection

In this chapter, a fuzzy expert system is developed to assist the operators in fault detection. It requires much less memory to store the database (power system topology and the post fault status of circuit breakers and protective relays). The fuzzy expert system identifies two basic network section sets, Shealthy for the healthy sub network and Sisland for the fault islands, using the post fault status of circuit breakers and relays. It then calculates membership function for each possible fault section. The objective of this calculation is to determine the likelihood of each candidate fault section as the actual fault section. Moreover membership functions provide a convenient means of ranking among possible (or candidate) fault sections, and are the most important factors in decision making. During decision making, the most possible fault section is determined by maximum selection method. In this method most possible fault section is the one which is having highest membership grade. MATLAB code for the proposed scheme is developed and the results obtained in four cases for a power- system network.

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