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.

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 A Genetic-Fuzzy System Algorithm Method for the Breast Cancer Diagnosis Problem

2021 ◽  
Vol 8 (2) ◽  
pp. 74-77
Author(s):  
Normalisa
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Medical Diagnosis ◽  
Fuzzy Inference ◽  
Breast Cancer Diagnosis ◽  
Medical Problem ◽  
Inference System ◽  
Genetic Fuzzy System ◽  
Computer Assistance ◽  
High Level

Breast cancer is an important medical problem, especially for women, computer-aided medical diagnosis is very important in terms of prevention and early detection. This paper presents early detection of breast cancer using two methods, namely genetic algorithm and fuzzy inference system which will be used for early detection of breast cancer which will be used by doctors with computer assistance to obtain medical diagnosis of breast cancer in Indonesia. Our research shows that the diagnosis of breast cancer using these two methods has a high level of accuracy.

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.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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.

Low back pain assessment application for construction workers

2015 ◽  
Vol 13 (3) ◽  
pp. 419-434
Author(s):  
H.O. Adeyemi ◽  
S.B. Adejuyigbe ◽  
S.O. Ismaila ◽  
A.F. Adekoya
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Expert System ◽  
Correlation Coefficient ◽  
Fuzzy Inference ◽  
First Aid ◽  
Low Back ◽  
Inference System ◽  
Content Type ◽  
Predicted Risk

Purpose – The purpose of this paper is to develop an expert system capable of assessing risk associated with manual lifting in construction tasks and proffer some first aid advices which are comparable with those obtainable from human experts. Design/methodology/approach – The expert system, musculoskeletal disorders – risk evaluation expert system (MSDs-REES), used Microsoft.Net C# programming language to write the algorithm of the fuzzy inference system with variables load, posture and frequency of lift as inputs and risk of low back pain as the output. The algorithm of the inference engine applied sets of rules to generate the output variable in crisp value. Findings – The result of validation, between the human experts’ calculated risk values and MSDs-REES-predicted risk values, indicated a correlation coefficient of 0.87. Between the predicted risk values generated using MSDs-REES and the existing package (MATLAB version 7.8), there was a strong positive relationship statistically with correlation coefficient of 0.97. Originality/value – The study provided a very simple expert system which has the ability to provide some medical-related injury prevention advice and first aid information for injury management, giving it a unique attribute over the existing applications.

Breast cancer image assessment using an adaptative network-based fuzzy inference system

2010 ◽  
Vol 20 (2) ◽  
pp. 192-200 ◽  
Author(s):  
F. C. Fernandes ◽  
L. M. Brasil ◽  
J. M. Lamas ◽  
R. Guadagnin
Keyword(s):  
Breast Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Image Assessment

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Sign in / Sign up

Export Citation Format

Share Document