Fuzzy firefly clustering for tumour and cancer analysis

Banu, P. K. Nizar; Azar, Ahmad Taher; Inbarani, H. Hannah

doi:10.1504/ijmic.2017.082941

Cited by 40 publications

(9 citation statements)

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“…Fuzzy clustering is a delicate clustering method that is better than the firm method by enabling each quality to be put in every one of the groups, which also converges to the local optima because it arbitrarily chooses the underlying focuses (Banu, Azar, & Inbarani, ). PSO is computationally productive and less demanding to execute when contrasted and other numerical calculations and transformative calculations (Selvakumar, Inbarani, & Shakeel, ).…”

Section: Methodsmentioning

confidence: 99%

Text analytics for big data using rough–fuzzy soft computing techniques

Al‐Maitah

2019

Expert Systems

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Text mining or analytics is important for various applications such as market analysis and biomedical purposes because it enables the efficient retrieval of information from large datasets. During the analysis, increasing the dimensionality of the data reduces the performance of an entire system because doing so may retrieve irrelevant text, which creates errors. Therefore, this paper introduces big data and data mining techniques to analyse large volumes of information while mining texts, emails, blogs, online forums, news, and call centre documents. Initially, the data are collected from various sources that contain noise, which is removed by applying normalization techniques. Data mining techniques eliminate the irrelevant information and noise, and the relevant features are selected using the rough set‐based particle swarm optimization algorithm. The selected features are formed as a cluster using a fuzzy set with the particle swarm optimization algorithm, which improves the efficiency of the mining process. Then, the efficiency of the system is evaluated using the University of California Irvine Machine Learning Repository knowledge process mining database, along with the sum of the intra cluster distances, the mean squared error rate, and the accuracy.

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Section: Methodsmentioning

confidence: 99%

Text analytics for big data using rough–fuzzy soft computing techniques

Al‐Maitah

2019

Expert Systems

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“…It focuses on the process that governs how the components and system change over time. In the literature, there are many applications of dynamic modeling in machine learning, deep learning, computational intelligence, control systems, robotics, sensor network and cyber-security (Ben Smida et al, 2018;Lamamra et al, 2017;Grassi et al, 2017;Mohanty et al, 2021 ;Ghoudelbourk et al, 2022Ghoudelbourk et al, , 2021Ghoudelbourk et al, , 2016Mekki et al, 2015;Dudekula et al, 2023 ;Hussain et al, 2023 ;El-Shorbagy et al, 2023 ;Ramadan et al, 2022 ;Ashfaq et al, 2022a,b;Waleed et al, 2022 ;Jothi et al, 2022Jothi et al, , 2020Jothi et al, , 2019Jothi et al, , 2013Lavanya et al, 2022 ;Inbarani et al, , 2018Inbarani et al, , 2014Inbarani et al, , 2015Boulmaiz et al, 2022 ;Fouad et al, 2021 ;Elfouly et al, 2021 ;Khan et al, 2021 ;Aslam et al, 2021 ;Nasser et al, 2021 ;Hussien et al, 2020 ;Kumar et al, 2019Kumar et al, , 2015aMjahed et al, 2020 ;Banu et al, 2017 ;Ben Abdallah et al, 2016Emary et al, 2014a,b;Anter et al, 2015Anter et al, , 2013Elshazly et al, 2013a,b ;Azar et al, 2013…”

Section: Modeling Of the Suggested Approachmentioning

confidence: 99%

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

Fekik

Hamida

Kais

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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This article introduces a novel and effective diagram based on direct instantaneous power control (DPC) of a PWM-controlled rectifier connected to the grid without a switching table. An optimum control vector of the PWM rectifier's input voltage, which depends on the switching states determined by a Petri nets controller, is adopted. This approach limits the instantaneous detection errors of reactive and active powers, maintains the DC bus voltage at a reference level, and ensures current close to a sinusoidal wave, guaranteeing operation at a unit power factor. The instantaneous tracking errors of active and reactive powers and the angular position of the voltage are used as input variables for the proposed controller, which then selects the best control vector for the converter based on the transition of a Petri net. The significant advantages of DPC based on Petri nets compared to traditional switching tables are that hysteresis comparators are not required, and the classical regulation of active and reactive powers is achieved in all sectors. Simulation and testing findings demonstrated excellent performance, supporting the viability of the suggested control approach using Petri nets.

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“…The concept of fuzzy logic control is not new; it arises from efforts to develop artificially intelligent decision-making and inference systems (Sain et al, 2022 ;Shalaby et al, 2021;Ghoudelbourk et al, 2021;Humaidi et al, 2021;Nasser et al, 2021;Mohanty et al, 2021;Fekik et al, 2021dFekik et al, , 2020bAhmadian et al, 2021;Abdelmalek et al, 2021;Ananth et al, 2021;Azar et al, 2013;Mjahed et al, 2020;Djeddi et al, 2019;Amara et al, 2019;Vaidyanathan and Azar, 2016;Kumar et al, 2018;Khettab et al, 2018;Pintea et al, 2018;Banu et al, 2017;Emary et al, 2014;Gharbia et al, 2014;Giove et al, 2013;Elshazly et al, 2013;Jothi et al, 2013). The structure of fuzzy inference is comparable to that of a human judgment process: incoming signals are evaluated subjectively and in a fuzzy manner.…”

Section: Design Of the Proposed Controlmentioning

confidence: 99%

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

Saidi¹,

Mellah²,

Fekik

et al. 2022

International Journal of Service Science, Management, Engineering, and Technology

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In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.

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Fuzzy firefly clustering for tumour and cancer analysis

Cited by 40 publications

References 0 publications

Text analytics for big data using rough–fuzzy soft computing techniques

Text analytics for big data using rough–fuzzy soft computing techniques

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

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