Xiang Li¹, Guangyan Huang^1,2,*, Zhiwei Guo^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-3, 2023, DOI:10.32604/icces.2023.09269

Abstract SPH method is widely used to study the dynamic response of metal casing under explosive loading because of its superiority in simulating metal fracture phenomenon [1-3]. The distribution of the fragment from uncoupled charge structures with segmental shaped explosive were studied. The X-ray photographic images of fragmentation obtained from explosion experiment were compared with the numerical results based on SPH method and Lagrange method. The fragmentation shows that the numerical results based on the Lagrange method are in good agreement with the experimental results while some errors appear in results based on SPH method. The velocity of the fragments at… More >

Abdul Haseeb¹, Muhammad Attique Khan^1,*, Faheem Shehzad¹, Majed Alhaisoni², Junaid Ali Khan¹, Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical… More >

Ahmad Almadhor^1,*, Arfat Ahmad Khan², Chitapong Wechtaisong^3,*, Iqra Yousaf⁴, Natalia Kryvinska⁵, Usman Tariq⁶, Haithem Ben Chikha¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.039007

Abstract Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a… More >

Mohammed A. Al Ghamdi^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 881-894, 2023, DOI:10.32604/cmc.2023.037826

Abstract Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively available nor very reliable. In… More >

Fangzhou Wang^a,* , Gennifer A. Riley^b, Munonyedi Egbo^c, Melanie M. Derby^b, Gisuk Hwang^c, Xianglin Li^a,†

Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-8, 2020, DOI:10.5098/hmt.15.1

Abstract This study conducts pore-scale simulations and experiments to estimate the permeability of two different types of porous materials: metal foams and sintered copper particles with porosities of approximately 0.9 and 0.4, respectively. The integration of micro X-ray computed tomography with pore-scale computational fluid dynamics simulations develops a unique tool to capture the pore-scale geometry of porous media and accurately predict non-isotropic permeability of porous media. The pore-scale simulation not only results in improved prediction accuracy but also has the capability to capture non-isotropic properties of heterogeneous materials, which is a huge challenge for empirical correlations, volume averaged simulations, and simulations… More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The… More >

Ki-Yeol Eom¹, Byungseok Min^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336

Abstract Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors, autonomous vehicles, and artificial intelligence devices. However, there are few solutions to segment occluded objects in the X-ray inspection efficiently. In particular, in the Ball Grid Array inspection of X-ray images, it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls. In this paper, we present a novel automatic inspection algorithm that segments solder balls, and detects defects fast and efficiently when solder balls are occluded. The proposed algorithm consists of two stages. In the first stage, the… More >

José Escorcia-Gutierrez^1,*, Margarita Gamarra¹, Roosvel Soto-Diaz², Safa Alsafari³, Ayman Yafoz⁴, Romany F. Mansour⁵

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5255-5270, 2023, DOI:10.32604/cmc.2023.033731

Abstract A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This… More >

Ali Akbar Siddique¹, S. M. Umar Talha¹, M. Aamir¹, Abeer D. Algarni², Naglaa F. Soliman^2,*, Walid El-Shafai^3,4

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3883-3901, 2023, DOI:10.32604/cmc.2023.037413

Abstract The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and… More >

Bo Wang^1,*, Yongxin Zhang¹, Shihui Ji², Binbin Zhang¹, Xiangyu Wang¹, Jiyong Zhang¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3625-3642, 2023, DOI:10.32604/cmc.2023.036754

Abstract A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract the COVID-19 feature information by… More >