A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization

Pintea, Camelia-Mihaela; Matei, Oliviu; Ramadan, Rabie A.; Pavone, Mario; Niazi, Muaz A.; Azar, Ahmad Taher

doi:10.1007/978-3-319-62524-9_8

Cited by 21 publications

(4 citation statements)

References 24 publications

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“…Here, ρ is the "weight penalty coefficient", W k denotes the "connection weight", k in layer l, and the Layer l's connections are represented by the letters L and K which stand for the total number of layers. Table 2 depicts the extraction of features in CNN architecture (Humaidi et al, 2020b;Kumar et al, 2014b, Pintea et al, 2018. the feature maps further.…”

Section: Mathematical Model For Cnnmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic approaches are a family of algorithms that seek to find the optimal values for uncertain problems with several implications in optimization and classification problems. An automated system for recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization Algorithm-based feature selection) method as a way to choose the features for several machine learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just important features, feature selection approaches might improve performance. The proposed approach employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.

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Section: Mathematical Model For Cnnmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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“…Machine Learning (ML) is now widely used in research and implemented in a wide range of applications, including control systems, robotics, medical diagnostics, fraud detection, autonomous driving, image and audio classification, and multimedia concept retrieval (Abed et al, 2022;Lavanya et al, 2022Lavanya et al, , 2021Azar, & Banu, 2022;Ananth et al, 2021;Sain et al, 2022;Saidi et al, 2022;Panda & Azar, 2021;Acharyulu et al, 2021;Ajeil et al, 2020a,b;Ibraheem et al, 2020a, b;Najm et al, 2020;Liu et al, 2020Liu et al, , 2022Sekhar et al, 2022;Mustafa et al, 2020;Ghoudelbourk et al, 2021;Cheema et al, 2020;Kamal et al, 2020;Humaidi et al, 2020a,b;Elfouly et al, 2021;Khan et al, 2021;Elkholy et al, 2020a;Barakat et al, 2020;Pilla et al, 2021aPilla et al, ,b, 2020Pilla et al, , 2019Amara et al, 2019;Babajani et al, 2019;Habibifar et al, 2019;Gorripotu et al, 2019;Ammar et al, , 2019Ammar et al, , 2018Pintea et al, 2018;Ben Smida et al, 2018;Ghazizadeh et al, 2018;Meghni et al, 2023Meghni et al, , 2017aGiove et al, 2013;…”

Section: Deep Trs Architecture (Dtrsa)mentioning

confidence: 99%

Novel Architecture for Image Classification Based on Rough Set

Nivetha

Inbarani

2023

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

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The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

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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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A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization

Cited by 21 publications

References 24 publications

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Novel Architecture for Image Classification Based on Rough Set

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

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