Iterative Learning and Fractional Order Control for Complex Systems

Bouakrif, Farah; Azar, Ahmad Taher; Volos, Christos; Muñoz‐Pacheco, Jesús M.; Pham, Viet–Thanh

doi:10.1155/2019/7958625

Cited by 5 publications

(2 citation statements)

References 24 publications

(19 reference statements)

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“…The median filter considerably decreases noise by substituting noisy color numbers with the neighborhood's average significance, producing smoother images with improved visual clarity. (Bouakrif et al, 2019;ElBedwehy et al, 2014;Inbarani et al, 2018;Kumar et al, 2017, Sundaram et al, 2021Zhu & Azar, 2015). This technique effectively removes noise while preserving crucial image structures, ultimately improving the overall image quality and facilitating subsequent analysis and interpretation.…”

Section: Preprocessingmentioning

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: Preprocessingmentioning

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 a subset of artificial intelligence that creates dynamic algorithms capable of generating data-driven decisions (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Fekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Bouakrif et al, 2019;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Vaidyanathan & Azar, 2016;Zhu & Azar, 2015;Malek et al, 2015a,b;Ding et al, 2015;Elshazly et al, 2013a,b,c).…”

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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