Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, Arif Hussain Magsi³, Cong Hu¹, Hussain Shah¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

Habib Dhahri^1,2,*, Besma Rabhi³, Slaheddine Chelbi⁴, Omar Almutiry¹, Awais Mahmood¹, Adel M. Alimi³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3259-3274, 2021, DOI:10.32604/cmc.2021.018449

Abstract The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, More >