Xiaowei Gu^1,#, Shuwen Chen^1,2,#,*, Huisheng Zhu¹, Mackenzie Brown^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 507-530, 2022, DOI:10.32604/cmes.2022.018948 - 15 June 2022

Abstract Coronavirus disease 2019 brings a huge burden on the medical industry all over the world. In the background of artificial intelligence (AI) and Internet of Things (IoT) technologies, chest computed tomography (CT) and chest X-ray (CXR) scans are becoming more intelligent, and playing an increasingly vital role in the diagnosis and treatment of diseases. This paper will introduce the segmentation of methods and applications. CXR and CT diagnosis of COVID-19 based on deep learning, which can be widely used to fight against COVID-19. More >

Mei Tao¹, Qingwen Ren^1,*, Hanbing Bian², Maosen Cao¹, Yun Jia³

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 681-705, 2022, DOI:10.32604/cmes.2022.019522 - 15 June 2022

Abstract Soil-rock mixture (SRM) filling in fault zone is an inhomogeneous geomaterial, which is composed of soil and rock block. It controls the deformation and stability of the abutment and dam foundation, and threatens the long-term safety of high arch dams. To study the macroscopic and mesoscopic mechanical properties of SRM, the development of a viable mesoscopic numerical simulation method with a mesoscopic model generation technology, and a reasonable parametric model is crucially desired to overcome the limitations of experimental conditions, specimen dimensions, and experiment fund. To this end, this study presents a mesoscopic numerical method… More >

Nisha Mary¹, Bilal Khan^1,*, Abdullah A Asiri², Fazal Muhammad³, Samar Alqhtani⁴, Khlood M Mehdar⁵, Hanan Talal Halwani⁴, Turki Aleyani⁴, Khalaf A Alshamrani²

Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 11-31, 2022, DOI:10.32604/jimh.2022.030161 - 14 June 2022

Abstract Prognosis of HD is a complex task that requires experience and expertise to predict in the early stage. Nowadays, heart failure is rising due to the inherent lifestyle. The healthcare industry generates dense records of patients, which cannot be managed manually. Such an amount of data is very significant in the field of data mining and machine learning when gathering valuable knowledge. During the last few decades, researchers have used different approaches for the prediction of HD, but still, the major problem is the uncertainty factor in the output data and also there is a… More >

Jiabao Shi¹, Zhao Qiu^1,*, Tao Chen¹, Jiale Lin¹, Hancheng Huang², Yunlong He³, Yu Yang³

Journal of New Media, Vol.4, No.2, pp. 97-106, 2022, DOI:10.32604/jnm.2022.027176 - 13 June 2022

Abstract Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point More >

Vianey Mendez-Trujillo¹, Benjamín Valdez-Salas², Mario Curiel-Alvarez², Ernesto Beltran-Partida², Arnoldo Alfaro-Corres³, Esaú Ruiz-Sanchez³, Gerardo Bautista-Trujillo⁴, Olivia Tzintzun Camacho⁵, Onecimo Grimaldo-Juarez⁵, Carlos Ceceña-Duran⁵, Carlos Ail-Catzin⁵, Daniel González-Mendoza^5,*

Journal of Renewable Materials, Vol.10, No.10, pp. 2543-2552, 2022, DOI:10.32604/jrm.2022.020965 - 08 June 2022

Abstract The present study was conducted to evaluate the impact of bimetallic nanoparticles (NPs) of copper (Cu) and zinc (Zn) from Crotalaria longirostrata on the insect mortality and cell viability of Phenacoccus solenopsis. The biosynthesis of bimetallic nanoparticles (Cu/Zn-NPs) from C. longirostrata was confirmed by the presence of a single peak around 378 nm. The hydrodynamic diameter of the Cu/Zn nanoparticles varied from 59 to 119 nm and their zeta potential was found to be −30.90 mV. The EDX analysis showed a strong signal for Cu and Zn (8 and 8.6 keV, respectively). The Cu/Zn-NPs showed an effective insecticide activity against P. solenopsis with More >

Shenglong Wang^1,*, Jing Yang^1,*, Xiaoyu Ding², Jianpei Zhang¹, Meng Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 215-249, 2022, DOI:10.32604/cmes.2022.018050 - 02 June 2022

Abstract In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed… More >

Shouming Hou¹, Chaolan Jia¹, Kai Li¹, Liya Fan², Jincheng Guo^3,*, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 81-94, 2022, DOI:10.32604/cmes.2022.019006 - 02 June 2022

Abstract Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The… More >

Ang Wang^1,*, Hui Wang²

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1293-1306, 2022, DOI:10.32604/fdmp.2022.018561 - 27 May 2022

Abstract This paper investigates the thermal performance of prefabricated exterior walls using the Computational Fluid Dynamics method to reduce energy consumption. The thermal performance of the prefabricated exterior wall was numerically simulated using the software ANSYS Fluent. The composite wall containing the cavity is taken as the research object in this paper after analysis. The simulation suggests that when the cavity thickness is 20 mm and 30 mm, the heat transfer coefficient of the air-sandwich wall is 1.3 and 1.29, respectively. Therefore, the optimal width of the cavity is 20 mm, and the most suitable material More >

Jiaqi Wang¹, Jie Zhang², Genlin Ji^2,*, Bo Sheng³

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1629-1642, 2022, DOI:10.32604/iasc.2022.029535 - 25 May 2022

Abstract The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change… More >

K. Muthulakshmi^1,*, N. Krishnaraj², R. S. Ravi Sankar³, A. Balakumar⁴, S. Kanimozhi⁵, B. Kiruthika⁶

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2083-2096, 2022, DOI:10.32604/iasc.2022.026628 - 25 May 2022

Abstract In recent times, Cyber-physical system (CPS) integrates the cyber systems and physical world for performing critical processes that are started from the development in digital electronics. The sensors deployed in CPS are commonly employed for monitoring and controlling processes that are susceptible to anomalies. For identifying and detecting anomalies, an effective anomaly detection system (ADS) is developed. But ADS faces high false alarms and miss detection rate, which led to the degraded performance in CPS applications. This study develops a novel deep learning (DL) approach for anomaly detection in sensor-based CPS using Bidirectional Long Short… More >