Improvement of fuel cell MPPT performance with a fuzzy logic controller

Fekik, Arezki; Azar, Ahmad Taher; Denoun, Hakim; Kamal, Nashwa Ahmad; Bahgaat, Naglaa K.; Gorripotu, Tulasichandra Sekhar; Pilla, Ramana; Serrano, Fernando E.; Mittal, Shikha; Rana, K.P.S.; Kumar, Vineet; Vaidyanathan, Sundarapandian; Hamida, Mohamed Lamine; Yassa, Nacera; Amara, Karima

doi:10.1016/b978-0-12-820004-9.00023-1

Cited by 7 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;Ramadan et al, 2022;Aslam et al, 2021). Machine Learning (ML) is an artificial intelligence subset that generates dynamic algorithms capable of making data-driven judgments (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Inbarani et al, 2022Inbarani et al, , 2020Inbarani et al, , 2018Inbarani et al, , 2015aInbarani et al, ,b, 2014aFekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;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;Malek et al, 2015a,b;…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

View full text Add to dashboard Cite

Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Improvement of fuel cell MPPT performance with a fuzzy logic controller

Cited by 7 publications

References 56 publications

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Novel Architecture for Image Classification Based on Rough Set

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

Contact Info

Product

Resources

About