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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Computer Systems Science & Engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour wamda nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

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Computer Systems Science & Engineering 2023年第9期46卷 3729-3748页

作者： Babangida Isyaku Kamalrulnizam Bin Abu Bakar wamda nagmeldin Abdelzahir Abdelmaboud Faisal Saeed Fuad A.Ghaleb Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Computing and Information Technology Sule Lamido UniversityKafin HausaP.M.B.048Nigeria College of Computer Engineering and Sciences PrinceSattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia Taibah University P.O.Box 344Medina41411Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUK

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data ***,communication reliability is the desired property in computer *** frequency of communication link failure degrades network performance,and service disruptions are likely to *** network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in ***,these schemes have various weaknesses,which may not always guarantee service *** paths differ in their roles;some paths are critical because of the higher frequency *** paths frequently share links between primary and *** the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system ***,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link *** paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery *** affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery *** the former and latter path with a minimal score is *** simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

关键词： SDN OpenFlow failure restoration critical path Bayesian probability

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