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Fulltext Solving fuzzy fractional differential equations using Zadeh's extension principle

Ahmad MZ, Hasan MK, Abbasbandy S

ScientificWorldJournal, 2013;2013:454969.
PMID: 24082853 DOI: 10.1155/2013/454969

We study a fuzzy fractional differential equation (FFDE) and present its solution using Zadeh's extension principle. The proposed study extends the case of fuzzy differential equations of integer order. We also propose a numerical method to approximate the solution of FFDEs. To solve nonlinear problems, the proposed numerical method is then incorporated into an unconstrained optimisation technique. Several numerical examples are provided.

Matched MeSH terms: Fuzzy Logic
Fulltext Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder

Shamshirband S, Banjanovic-Mehmedovic L, Bosankic I, Kasapovic S, Abdul Wahab AW

PLoS One, 2016;11(5):e0155697.
PMID: 27219539 DOI: 10.1371/journal.pone.0155697

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Matched MeSH terms: Fuzzy Logic
A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases

Lim CK, Yew KM, Ng KH, Abdullah BJ

Australas Phys Eng Sci Med, 2002 Sep;25(3):144-50.
PMID: 12416592 DOI: 10.1007/BF03178776

Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.

Matched MeSH terms: Fuzzy Logic*
Fulltext Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS

Zaidan AA, Zaidan BB, Al-Haiqi A, Kiah ML, Hussain M, Abdulnabi M

J Biomed Inform, 2015 Feb;53:390-404.
PMID: 25483886 DOI: 10.1016/j.jbi.2014.11.012

Evaluating and selecting software packages that meet the requirements of an organization are difficult aspects of software engineering process. Selecting the wrong open-source EMR software package can be costly and may adversely affect business processes and functioning of the organization. This study aims to evaluate and select open-source EMR software packages based on multi-criteria decision-making. A hands-on study was performed and a set of open-source EMR software packages were implemented locally on separate virtual machines to examine the systems more closely. Several measures as evaluation basis were specified, and the systems were selected based a set of metric outcomes using Integrated Analytic Hierarchy Process (AHP) and TOPSIS. The experimental results showed that GNUmed and OpenEMR software can provide better basis on ranking score records than other open-source EMR software packages.

Matched MeSH terms: Fuzzy Logic
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.

Comput Methods Programs Biomed, 2018 Jul;161:133-143.
PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Matched MeSH terms: Fuzzy Logic
Fulltext Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Wan Ahmad WS, Zaki WM, Ahmad Fauzi MF

Biomed Eng Online, 2015;14:20.
PMID: 25889188 DOI: 10.1186/s12938-015-0014-8

Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

Matched MeSH terms: Fuzzy Logic
Fulltext Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

Rahman HA, Harun SW, Arof H, Irawati N, Musirin I, Ibrahim F, et al.

J Biomed Opt, 2014 May;19(5):057009.
PMID: 24839996 DOI: 10.1117/1.JBO.19.5.057009

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Matched MeSH terms: Fuzzy Logic*
Fulltext Minimal solution of singular LR fuzzy linear systems

Nikuie M, Ahmad MZ

ScientificWorldJournal, 2014;2014:517218.
PMID: 24737977 DOI: 10.1155/2014/517218

In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated.

Matched MeSH terms: Fuzzy Logic*
Fulltext Robust H∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

Ahmad NS

Sensors (Basel), 2020 Jun 30;20(13).
PMID: 32630046 DOI: 10.3390/s20133673

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.

Matched MeSH terms: Fuzzy Logic
Fulltext An innovative probabilistic hesitant fuzzy set MCDM perspective for selecting flexible packaging bags after the prohibition on single-use plastics

Jeon J, Krishnan S, Manirathinam T, Narayanamoorthy S, Nazir Ahmad M, Ferrara M, et al.

Sci Rep, 2023 Jun 23;13(1):10206.
PMID: 37353615 DOI: 10.1038/s41598-023-37200-2

The probabilistic hesitant elements (PHFEs) are a beneficial augmentation to the hesitant fuzzy element (HFE), which is intended to give decision-makers more flexibility in expressing their biases while using hesitant fuzzy information. To extrapolate a more accurate interpretation of the decision documentation, it is sufficient to standardize the organization of the elements in PHFEs without introducing fictional elements. Several processes for unifying and arranging components in PHFEs have been proposed so far, but most of them result in various disadvantages that are critically explored in this paper. The primary objective of this research is to recommend a PHFE unification procedure that avoids the deficiencies of operational practices while maintaining the inherent properties of PHFE probabilities. The prevailing study advances the hypothesis of permutation on PHFEs by suggesting a new sort of PHFS division and subtraction compared with the existing unification procedure. Eventually, the proposed PHFE-unification process will be used in this study, an innovative PHFEs based on the Weighted Aggregated Sum Product Assessment Method-Analytic Hierarchy Process (WASPAS-AHP) perspective for selecting flexible packaging bags after the prohibition on single-use plastics. As a result, we have included the PHFEs-WASPAS in our selection of the most effective fuzzy environment for bio-plastic bags. The ranking results for the suggested PHFEs-MCDM techniques surpassed the existing AHP methods in the research study by providing the best solution. Our solutions offer the best bio-plastic bag alternative strategy for mitigating environmental impacts.

Matched MeSH terms: Fuzzy Logic
Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

Mohd Azlan NNI, Abdul Malek M, Zolkepli M, Mohd Salim J, Ahmed AN

Environ Sci Pollut Res Int, 2021 Apr;28(16):20261-20272.
PMID: 33405154 DOI: 10.1007/s11356-020-11908-4

Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at Kenyir Lake, Terengganu, using a fuzzy inference system (FIS). The analysis is widened by comparing FIS with the multiple linear regression (MLR) method. FIS applied as an analysis tool provides good generalization capability for optimum solutions and utilizes human behavior influenced by expert knowledge in water resources management for fuzzy rules specified in the system, whereas MLR can simultaneously adjust and compare several variables as per the needs of the study. The water demand dataset of Kenyir Lake was analyzed using FIS and MLR, resulting in total forecasted water consumptions at Kenyir Lake of 2314.38 m3 and 1358.22 m3, respectively. It is confirmed that both techniques converge close to the actual water consumption of 1249.98 m3. MLR showed the accuracy of the water demand values with smaller forecasted errors to be higher than FIS did. To attain sustainable water demand management, the techniques used can be examined extensively by researchers, educators, and learners by adding more variables, which will provide more anticipated outcomes.

Matched MeSH terms: Fuzzy Logic*
Fulltext Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition

Al-batah MS, Isa NA, Klaib MF, Al-Betar MA

Comput Math Methods Med, 2014;2014:181245.
PMID: 24707316 DOI: 10.1155/2014/181245

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

Matched MeSH terms: Fuzzy Logic*
Fulltext Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients

Albahri OS, Zaidan AA, Albahri AS, Alsattar HA, Mohammed R, Aickelin U, et al.

J Adv Res, 2022 Mar;37:147-168.
PMID: 35475277 DOI: 10.1016/j.jare.2021.08.009

INTRODUCTION: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.
OBJECTIVES: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.
METHODS: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.
RESULTS: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.
CONCLUSION: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Matched MeSH terms: Fuzzy Logic
Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller

Naderipour A, Abdul-Malek Z, Davoodkhani IF, Kamyab H, Ali RR

Environ Sci Pollut Res Int, 2023 Jun;30(28):71677-71688.
PMID: 34241794 DOI: 10.1007/s11356-021-14799-1

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.

Matched MeSH terms: Fuzzy Logic*
Fulltext Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator-Fuzzy Logic Algorithms and Sensor Fusion

Ali MAH, Mailah M, Jabbar WA, Moiduddin K, Ameen W, Alkhalefah H

Sensors (Basel), 2020 Jul 01;20(13).
PMID: 32630340 DOI: 10.3390/s20133694

A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator-fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road's roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot's path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.

Matched MeSH terms: Fuzzy Logic
Fulltext Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Naveed QN, Qureshi MRN, Tairan N, Mohammad A, Shaikh A, Alsayed AO, et al.

PLoS One, 2020;15(5):e0231465.
PMID: 32365123 DOI: 10.1371/journal.pone.0231465

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

Matched MeSH terms: Fuzzy Logic
Fulltext A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Safaei M, A Sundararajan E, Asadi S, Nilashi M, Ab Aziz MJ, Saravanan MS, et al.

Int J Environ Res Public Health, 2022 Nov 22;19(23).
PMID: 36497509 DOI: 10.3390/ijerph192315432

Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.

Matched MeSH terms: Fuzzy Logic
Fulltext An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

Nguyen HT, Dawal SZ, Nukman Y, Rifai AP, Aoyama H

PLoS One, 2016;11(4):e0153222.
PMID: 27070543 DOI: 10.1371/journal.pone.0153222

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts' uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.

Matched MeSH terms: Fuzzy Logic*
Fulltext An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment

Shukla S, Hassan MF, Khan MK, Jung LT, Awang A

PLoS One, 2019;14(11):e0224934.
PMID: 31721807 DOI: 10.1371/journal.pone.0224934

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

Matched MeSH terms: Fuzzy Logic
Fulltext Identifying the uncertain model parameter of a steam turbine system

Wan Munirah, W.M., Tahir, A., Azmirul, A.

Pertanika Journal of Science & Technology, 2017;25(2):545-560.
MyJurnal

The transformation method (TM) of fuzzy arithmetic is aimed at simulation and analysis of a system. The aim of this paper is to use fuzzy arithmetic based on the TM on a state space of a steam turbine system. The model is then used to identify the degree of influence of each parameter on the system. Simulation and analysis of the system are presented in this paper.

Matched MeSH terms: Fuzzy Logic

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