K. Muthumayil¹, M. Buvana², K. R. Sekar³, Adnen El Amraoui^4,*, Issam Nouaouri⁴, Romany F. Mansour⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.017178

Abstract 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., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing… More >

Phyu Hnin Thike^{1, 2}, Zhaoyang Zhao¹, Peng Liu¹, Feihu Bao¹, Ying Jin¹, Peng Shi^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2091-2109, 2020, DOI:10.32604/cmc.2020.011608

Abstract The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning… More >