FISDeT: Fuzzy Inference System Development Tool

Castellano, Giovanna; Castiello, Ciro; Pasquadibisceglie, Vincenzo; Zaza, Gianluca

doi:10.2991/ijcis.2017.10.1.2

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…A FIS requires rules to determine the appropriate output for different inputs. The usual way to generate rules is to design questionnaires and extract knowledge from experts [ 25 – 29 ]. Given a large number of criteria and sub-criteria in this study, the combination of linguistic values of input and output variables could result in many rules that reduce the speed of decision-making in the system.…”

Section: Methodsmentioning

confidence: 99%

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

et al. 2022

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Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

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Section: Methodsmentioning

confidence: 99%

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

et al. 2022

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“…Another study [2] utilized the ANFIS model for semantic segmentation of RSI. The authors also encountered limitations in reference results due to the method that generates the rulebase.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

et al. 2023

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Fuzzy inference systems, in general, and complex fuzzy inference systems, in particular, play an increasingly important role in many fields, such as change detection, image classification, recognition problems, etc. Despite being the well-known technique to solve with time series data, the rulebase still has the considered limitation because of the directly affecting the results as well as the processing time of these methods. To overcome this limitation, this study proposes an Adaptive spatial complex inference system that can automatically infer and adapt to the new remotely sensed image. In the proposed model, to predict the image of time t + 1, the system will generate a new rulebase according to this expected image. This new rulebase and the previous Co-Spatial-CFIS+ rulebase are evaluated using a complex fuzzy measure. This measure is built by determining the intersection domain between two rule spaces; this intersection value estimates removing, merging, or adding a newly generated rule into the current rulebase. Finally, a more suitable set of rules is obtained for image prediction. To illustrate the efficiency of the proposed approach, it is applied to the remote sensing cloud image data of the U.S. Navy. Our model evaluated the model's effectiveness in comparison to the state-of-the-art along studies in detecting changes in remote sensing cloud images. Moreover, the findings of the experiments revealed that the proposed model could improve the change detection results in terms of R 2 , RMSE, time-consuming, and the number of rules.INDEX TERMS Complex fuzzy inference system; remote sensing images; rule pruning; rule-based system; image change detection.

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“…The resulting fuzzy output is defuzzified to produce a crisp output that corresponds to the suggested level of cardiovascular risk. The FISDeT tool [42] was used to define the knowledge base and apply the fuzzy inference process.…”

Section: Risk Assessment By Fuzzy Rulesmentioning

confidence: 99%

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

et al. 2018

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Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

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FISDeT: Fuzzy Inference System Development Tool

Cited by 13 publications

References 7 publications

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

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