Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under...Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.展开更多
The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,...The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.展开更多
Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can ...Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.展开更多
文摘Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.
文摘The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe.Radiologists use X-Rays or Computed Tomography(CT)images to confirm the presence of COVID-19.So,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times.The current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of COVID-19.MBWOCNN model involves four steps such as preprocessing,feature extraction,parameter tuning,and classification.In the beginning,the input images undergo preprocessing followed by CNN-based feature extraction.Then,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of CNN.Finally,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels.The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques.The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.
基金The authors are grateful to Taif University Researchers Supporting Project Number(TURSP-2020/215)Taif University,Taif,Saudi Arabia.This work is also supported by the Faculty of Computer Science and Information Technology,University of Malaya,under Postgraduate Research Grant(PG035-2016A).
文摘Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.
