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  • Open Access


    Optimized Model Based Controller with Model Plant Mismatch for NMP Mitigation in Boost Converter

    R. Prasanna1,*, Uma Govindarajan1, N. S. Bhuvaneswari2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1961-1979, 2023, DOI:10.32604/csse.2023.032424

    Abstract In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter λ. Also, More >

  • Open Access


    An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

    Daniyal M. Alghazzawi1, Osama Bassam J. Rabie1, Surbhi Bhatia2, Syed Hamid Hasan1,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1173-1193, 2022, DOI:10.32604/cmc.2022.022748

    Abstract The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved… More >

  • Open Access


    An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

    Anas Basalamah1, Shadikur Rahman2,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 375-388, 2022, DOI:10.32604/csse.2022.016949

    Abstract This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for More >

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