Development of a Fuzzy Logic-Based Model for Monitoring Cardiovascular Risk

2019 ◽  
pp. 172-190
Author(s):  
Peter Adebayo Idowu ◽  
Sarumi Olusegun Ajibola ◽  
Jeremiah Ademola Balogun ◽  
Oluwadare Ogunlade
Keyword(s):  
Heart Failure ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Vital Signs ◽  
Healthcare Services ◽  
Logic Model ◽  
Risk Indicators ◽  
Membership Functions ◽  
Input And Output

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

Development of a Fuzzy Logic-based Model for Monitoring Cardiovascular Risk

2015 ◽  
Vol 10 (4) ◽  
pp. 38-55
Author(s):  
Peter Adebayo Idowu ◽  
Sarumi Olusegun Ajibola ◽  
Jeremiah Ademola Balogun ◽  
Oluwadare Ogunlade
Keyword(s):  
Heart Failure ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Vital Signs ◽  
Healthcare Services ◽  
Logic Model ◽  
Risk Indicators ◽  
Membership Functions ◽  
Input And Output

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

scholarly journals Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Sri Redjeki
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Original Data ◽  
Poverty Rate ◽  
Poor People ◽  
Membership Functions ◽  
Test Results ◽  
Inference System ◽  
Poverty Relief ◽  
Correct Data

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results

scholarly journals APPLICATION OF FUZZY LOGIC TOOLBOX FOR MODELLING FUZZY LOGIC CONTROLLERS

2017 ◽  
Vol 3 ◽  
pp. 539
Author(s):  
Krzysztof Olesiak
Keyword(s):  
Fuzzy Logic ◽  
Computer Technology ◽  
Control Surface ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Logic Controllers ◽  
Input And Output ◽  
Matlab Package ◽  
And Robotics ◽  
Logic Controllers

Computer technology, which has been developing very fast in the recent years, can be also fruitfully applied in teaching. For example, the software package Matlab is highly useful in teaching students at Bachelor Programs of Electrical Engineering and Automatics and Robotics. Fuzzy Logic Toolbox of the Matlab package can be used for designing and modelling controllers. Thanks to a large number of pre-defined elements available in the libraries, it is possible to create even highly complicated models of systems without much effort. Fuzzy Logic Toolbox is especially useful for exploring the basic rules of designing fuzzy logic controllers. The rules involve selecting input and output membership functions, determining their location with respect to one another and defining their ranges. When the membership functions are introduced, a rule base is defined and a defuzzification method is selected. For any defuzzification method, a control surface is obtained, which can be modified by changing the rule base and/or the input and output parameters of the membership function.

scholarly journals PENENTUAN KECEPATAN PUTAR KIPAS ANGIN DAN INTENSITAS LAMPU DENGAN FUZZY LOGIC MAMDANI

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Chyquitha Danuputri
Keyword(s):  
Fuzzy Logic ◽  
Light Intensity ◽  
Intelligent Systems ◽  
Smart Home ◽  
Room Temperature ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Temperature And Humidity ◽  
The Republic

<p><em>Intelligent systems are one of the most important branches of the computer world. Computers are expected to be able to solve various problems in the real world, not just a tool for doing calculations. To make this system, algorithms are needed that are in accordance with the problems faced so that they can solve or produce the decisions needed to solve these problems appropriately. Mamdani fuzzy logic algorithm is one of the algorithms that can be applied in intelligent systems. Fuzzzy mamdani algorithm, is one part of the Fuzzy Inference System which is useful for making the best conclusion or decision in an uncertain problem. This research focuses on the calculation of the fuzzy logic algorithm in providing answers to the uncertainties found in smart home systems used to control the speed of a fan and lights, while the factors that become uncertain in controlling a fan are room temperature and humidity and For lamps, they have a factor of light intensity and time of the region, for these factors, the researchers use the Humanity Guide Hygiene standard reference for humidity and the Regulation of the Minister of Health of the Republic of Indonesia Number 1077 / Menkes / Per / V / 2011 concerning Guidelines for Air Sanitation in Home Spaces. Through this research, it can be seen that using the mamdani fuzzy logic algorithm can provide a result in the form of a decision to determine how fast a fan should rotate based on the temperature and humidity factors in the room as well as the level of light intensity that the lights must emit.</em><strong><em></em></strong></p>

scholarly journals An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 132 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
DoHyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Environmental Temperature ◽  
Fuzzy Inference ◽  
Control Method ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Inference Method ◽  
Logic Control

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

scholarly journals Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1676
Author(s):  
Grzegorz Dec ◽  
Grzegorz Drałus ◽  
Damian Mazur ◽  
Bogdan Kwiatkowski
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Analytical Models ◽  
Prognostic Models ◽  
Membership Functions ◽  
Mathematical Methods ◽  
Pv Panel ◽  
Daily Energy ◽  
Inference Systems ◽  
Prototype Software

This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.

Fuzzy Logic System in Quantifying Human Driving Skill for Human Adaptive Mechatronics

2015 ◽  
Vol 735 ◽  
pp. 304-310 ◽  
Author(s):  
Mohamad Hafis Izran Ishak ◽  
A.W.A. Aziz ◽  
M.F.A.M. Kasai
Keyword(s):  
Computer Simulation ◽  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Simulation Software ◽  
Membership Functions ◽  
Human Skill ◽  
Input And Output ◽  
Driving Skill ◽  
Human Skills ◽  
Logic System

Human Adaptive Mechatronic (HAM) is an enhance system for Human Machine System (HMS). Instead of one-way relationship between human and machine, HAM system provides two-way relationship between human and machine in order to assist human and to improve human skills in operating the machine. Driving a car is an example of applications where HAM system can be applied. One of the problems of HAM is to quantify the human skill for operating the machine. Therefore, this paper proposed a method to quantify human driving skill using Fuzzy Logic System (FLS). In order to get the best design of FLS for quantifying human driving skill, twelve designs of FLS were designed and tested using computer simulation software. The best design from all twelve designs is then been compared with other method of quantifying human skill for verification. Results show that the design of membership functions for input and output have big impact to the accuracy of the output.

scholarly journals Analysis of The Impact of Fuzzy Logic Algorithm On Handover Decision in A Cellular Network

2017 ◽  
Vol 5 (5) ◽  
pp. 46-62 ◽  
Author(s):  
Emily Teresa Nyambati ◽  
Vitalice K. Oduol
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Handover Decision ◽  
Fuzzy Logic Algorithm ◽  
Gsm Network ◽  
Work Done ◽  
The Impact

Fuzzy logic is one of the intelligent systems that can be used to develop algorithms for handover. For success in handing over, the decision-making process is crucial and thus should be highly considered. The performance of fixed parameters is not okay in the changing cellular system environments. The work done on this paper aims to analyse the impact of utilising the fuzzy logic system for handover decision making considering the Global System for Mobile communication (GSM) network. The results from the different simulations show that the need to handover varies depending on the input(s) to the Fuzzy Inference System (FIS). By increasing the number of data, thus the criteria parameters used in the algorithm, an Optimised Handover Decision (OHOD) is realised.

scholarly journals Development of pre-time intersection isolated signal using a fuzzy logic model

2019 ◽  
Vol 270 ◽  
pp. 03002
Author(s):  
Moch D. Studyana ◽  
Ade Sjafruddin ◽  
Iwan P. Kusumantoro ◽  
Yudi Soeharyadi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Final Analysis ◽  
Logic Model ◽  
Time Signal ◽  
Large Cities ◽  
Performance Improvements ◽  
Research Survey ◽  
Fuzzy Logic Model ◽  
Model Study

We investigate the development of pre-time signal intersection operating systems for isolated intersections using Fuzzy Logic models. The traffic signal system setting in Indonesia has been using the Indonesia Road Capacity Manual model 1997, for example it is installed at the intersection in large cities in Indonesia. The development of the Fuzzy Logic model is focused on improving the performance of the signaled intersection, using a combination of numerical variable analysis used by IRCM 1997, and the linguistic or traffic behavior variable as the basis of the Fuzzy Logic model study. The combination of the two variables in the Fuzzy Logic model analysis is expected to improve the intersection performance. The Fuzzy Logic model process involves the Membership Function theory as the basis for the confidence level of the traffic variable to be surveyed, and the Fuzzy Inference Engine to measure the choice of combinations of variables that will be selected to make the best performance of the intersection. The geometric of intersection must be control as it involves the input of research data, especially on the condition of the intersection legs and markers of motor cycle-special stopping places, which is a potential of a traffic violation by traveller. The model is verified with fuzzified data from 2017 traffic research survey in Bandung. As an illustration of the majority of intersection setting with an isolated pre-time operating system, there are 60 intersection points or 85% of the total 71 intersections available. This would be a potentially major problem when performance improvements is not carried out. The final analysis shows that the number of vehicles queues decreases while the traffic flows passing through the intersection increases, therefore fuzzy logic model is expected to contribute and to give alternative handling for intersection performance with pre-time operational.

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