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 followed by CNN-based feature… More >

Mahmoud Ragab^1,2, Ali Algarni³, Adel A. Bahaddad⁴, Romany F. Mansour^5,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2277-2294, 2021, DOI:10.32604/cmc.2021.018000

Abstract Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning… More >

J. Banumathi¹, A. Muthumari², S. Dhanasekaran³, S. Rajasekaran⁴, Irina V. Pustokhina⁵, Denis A. Pustokhin⁶, K. Shankar^7,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2393-2407, 2021, DOI:10.32604/cmc.2021.015605

Abstract Due to the advancements in remote sensing technologies, the generation of hyperspectral imagery (HSI) gets significantly increased. Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis. The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials. Presently, deep learning (DL) models particularly, convolutional neural networks (CNNs) become useful for HSI classification owing to the effective feature representation and high performance. In this view, this paper introduces a new DL based Xception model for HSI analysis and classification, called Xcep-HSIC model. Initially, the… More >

Meera Radhakrishnan^1,*, Anandan Panneerselvam², Nandhagopal Nachimuthu³

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1185-1199, 2020, DOI:10.32604/iasc.2020.012069

Abstract Image segmentation is a crucial stage in the investigation of medical images and is predominantly implemented in various medical applications. In the case of investigating MRI brain images, the image segmentation is mainly employed to measure and visualize the anatomic structure of the brain that underwent modifications to delineate the regions. At present, distinct segmentation approaches with various degrees of accurateness and complexities are available. But, it needs tuning of various parameters to obtain optimal results. The tuning of parameters can be considered as an optimization issue using a similarity function in solution space. This paper presents a new Parametric… 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 is performed to develop multiple… More >

Yunping Liu^{1, 2, *}, Xingxing Yan¹, Fei Yan¹, Ze Xu¹, Weiyan Shang³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 469-487, 2020, DOI:10.32604/cmc.2020.05746

Abstract Due to the coupled motion between the rotor unmanned aerial vehicle (UAV) and the manipulator, the underactuation characteristics of the system itself, and the influence of external uncertainties, the stability of the rotor UAV’s manipulator control system is difficult to control. Based on the dynamic model of the rotor UAV, the stability of the whole UAV manipulator control system is improved by using the piecewise cost function, the compression factor particle swarm optimization (PSO) algorithm and the sliding mode PID to establish the sliding mode PID control stability method based on the PSO. Compared with the sliding mode PID control… More >