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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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Joint Image and Feature Enhancement for Object Detection under Adverse Weather Conditions

Joint Image and Feature Enhancement for Object Detection und...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mengyu Yin Mingyang Ling Kan Chang Zijian Yuan Qingpao Qin Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection under adverse weather conditions remains a challenging problem to date. To address this problem, a joint image and feature enhancement method called JE-YOLO is proposed. Firstly, a lightweight image enhancement network is used to enhance the low-quality image captured under adverse weather conditions. Secondly, to provide rich information for detection, two detection backbones are applied in parallel to extract features from both the low-quality image and its enhanced result. Afterwards, the extracted features are further enhanced by a foreground-guided feature refinement module (FFRM), which introduces a task-driven attention mechanism and explores inter-layer correlation. Finally, the enhanced features from different branches are fused by the adaptive multi-branch weighting (AMW) strategy, and then fed to the neck and head of detector. Experiments are carried out on both the low-light and foggy conditions, and the results demonstrate that compared with state-of-the-art (SOTA) methods, the proposed JE-YOLO is able to achieve the highest accuracy of detection in all cases. Code will be available at https://***/Murray-Yin/JE-YOLO.

关键词： Head Neural networks Object detection Detectors Feature extraction Neck Reliability

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Hermes: Memory-Efficient Pipeline Inference for Large Models on Edge Devices

Hermes: Memory-Efficient Pipeline Inference for Large Models...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Xueyuan Han Zinuo Cai Yichu Zhang Chongxin Fan Junhan Liu Ruhui Ma Rajkumar Buyya ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Shanghai Aerospace System Engineering Institute Shanghai China Cloud Computing and Distributed Systems (CLOUDS) Laboratory The University of Melbourne Melbourne Australia

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to address this challenge mainly focus on optimizing the model structure and adopting memory swapping methods. However, the former reduces the inference accuracy, and the latter raises the inference latency. This paper introduces PIPELoAD, a novel memory-efficient pipeline execution mechanism. It reduces memory usage by incorporating dynamic memory management and minimizes inference latency by employing parallel model loading. Based on PIPELoAD mechanism, we present Hermes, a framework optimized for large model inference on edge devices. We evaluate Hermes on Transformer-based models of different sizes. Our experiments illustrate that Hermes achieves up to 4.24 x increase in inference speed and 86.7% lower memory consumption than the state-of-the-art pipeline mechanism for BERT and ViT models, 2.58 x increase in inference speed and 90.3% lower memory consumption for GPT-style models.

关键词： Performance evaluation Accuracy Computational modeling Memory management Pipelines Loading Transformers Load modeling

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ECSNeT++: A simulator for distributed stream processing on edge and cloud environments

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Future Generation Computer systems 2020年 111卷 401-418页

作者： Amarasinghe, Gayashan de Assunção, Marcos D. Harwood, Aaron Karunasekera, Shanika School of Computing and Information Systems The University of Melbourne Australia Inria Avalon LIP Laboratory ENS Lyon University of Lyon France

The objective of Internet of Things (IoT) is ubiquitous computing. As a result many computing enabled, connected devices are deployed in various environments, where these devices keep generating unbounded event streams related to the deployed environment. The common paradigm is to process these event streams at the cloud using the available distributed Stream Processing (DSP) frameworks. However, with the emergence of Edge computing, another convenient computing paradigm has been presented for executing such applications. Edge computing introduces the concept of using the underutilised potential of a large number of computing enabled connected devices such as IoT, located outside the cloud. In order to develop optimal strategies to utilise this vast number of potential resources, a realistic test bed is required. However, due to the overwhelming scale and heterogeneity of the edge computing device deployments, the amount of effort and investment required to set up such an environment is high. Therefore, a realistic simulation environment that can accurately predict the behaviour and performance of a large-scale, real deployment is extremely valuable. While the state-of-the-art simulation tools consider different aspects of executing applications on edge or cloud computing environments, we found that no simulator considers all the key characteristics to perform a realistic simulation of the execution of DSP applications on edge and cloud computing environments. To the best of our knowledge, the publicly available simulators lack being verified against real world experimental measurements, i.e. for calibration and to obtain accurate estimates of e.g. latency and power consumption. In this paper, we present our ECSNeT++ simulation toolkit which has been verified using real world experimental measurements for executing DSP applications on edge and cloud computing environments. ECSNeT++ models deployment and processing of DSP applications on edge-cloud environments and i

关键词： Edge computing

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Modeling resource consumption in the US air transportation system via minimum-cost percolation

arXiv

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arXiv 2025年

作者： Kim, Minsuk Diggans, C. Tyler Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Air Force Research Laboratory’s Information Directorate RomeNY13441 United States

We introduce a dynamic percolation model aimed at describing the consumption, and eventual exhaustion, of resources in transportation networks. In the model, rational agents progressively consume the edges of a network along demanded minimum-cost paths. As a result, the network undergoes a transition between a percolating phase where it can properly serve demand to a non-percolating phase where demand can no longer be supplied. We apply the model to a weighted, directed, temporal, multi-layer network representation of the air transportation system that can be generated using real schedules of commercial flights operated by US carriers. We study how cooperation among different carriers could improve the ability of the overall air transportation system in serving the demand of passengers, finding that unrestricted cooperation could lead to a 30% efficiency increase compared to the non-cooperative scenario. Cooperation would require major airlines to share a significant portion of their market, but it would allow also for an increased robustness of the system against perturbations causing flight cancellations. Our findings underscore some key benefits that could emerge by simply promoting code-share arrangements among US airlines without altering their current cost of operation. © 2025, CC BY.

关键词： Air transportation

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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Joint Super-Resolution and Classification Based on Bidirectional Mapping and Multiple Constraints

Joint Super-Resolution and Classification Based on Bidirecti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zijian Yuan Kan Chang Zhiquan Liu Xinjie Wei Boning Chen School of Computer and Electronic Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

Since discriminant features are insufficient in the low-resolution (LR) images, it is challenging to accurately classify them. To address this issue, this paper proposes a joint super-resolution (SR) and classification network (JSRCN) based on bidirectional mapping and multiple constraints. In JSRCN, there is a SR sub-network containing a forward mapping path and several backward mapping paths. In the forward mapping path, high resolution (HR) features are progressively reconstructed. On the other hand, the backward mapping paths are used to alleviate the hardship of directly learning a nonlinear mapping from the LR space to the HR space. To effectively restore discriminant features for the image classification sub-network, multiple perceptual loss and multi-scale feature loss are presented to enhance the representation ability for the multi-scale features in images with different resolutions. Experiments show that compared with other competing methods, the proposed method achieves the highest accuracy.

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An algorithm for solving satisfiability problem based on the structural information of formulas

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Frontiers of Computer Science 2021年第6期15卷 191-193页

作者： Zaijun ZHANG Daoyun XU Jincheng ZHOU College of Computer Science and Technology Guizhou UniversityGuiyang 550025China Key Laboratory of Complex Systems and Intelligent Computing and School of Mathematics and Statistics Qiannan Normal University for NationalitiesDuyun 558000China School of Computer and Information Qiannan Normal University for NationalitiesDuyun 558000China

1 Introduction The propositional satisfiability(SAT)problem is an important and prototypical NP-hard problem in theoretical computer science[1].Many efforts have been made for designing high-performance SAT *** existing practical techniques for solving SAT problems are mainly divided into two categories:complete search technique and stochastic local search technique.

关键词： satisfiability technique computer

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Deep Learning Control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh Saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud Mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

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Encoding Temporal Change of Longitudinal EBV DNA for Dynamic Risk Prediction of Nasopharyngeal Carcinoma 7

Encoding Temporal Change of Longitudinal EBV DNA for Dynamic...

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7th International Conference on Biological information and Biomedical Engineering, BIBE 2024

作者： Yang, Fangfang Zhen, Zicheng Lin, Li Liu, Yuyu Gao, Rui Zhang, Taoyu Lu, Lijun Lv, Wenbing School of Information Yunnan University Yunnan Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650504 China Department of Radiation Oncology Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou510060 China School of Software Yunnan University Yunnan Kunming650504 China School of Biomedical Engineering Southern Medical University 1023 Shatai Road Guangzhou510515 China

ISBN: (纸本)9783800762910

—To accurately predict the risk of recurrence and metastasis of nasopharyngeal carcinoma (NPC) patients, an image-encoded risk prediction model was proposed. 752 patients (training vs. testing: 527 vs. 225) were retrospectively collected, and the plasma EBV DNA copies were tested 13 times during treatment. Based on 13 times plasma EBV DNA copies, three kinds of image encoding methods were employed to characterize the linear and nonlinear dynamic change of longitudinal EBV DNA, i.e. Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Recurrence Plot (RP). The Cox proportional hazard regression model was constructed by incorporating both image and clinical features as covariates to determine the risk factors and prediction models for disease-free survival (DFS). On the test set, the proposed model combining image and clinical features achieved C-index and AUC of 0.777 and 0.776, respectively outperforming the model relying solely on clinical features (C-index = 0.667, AUC = 0.675). In conclusion, the dynamic risk prediction model with image encoding can monitor changes in plasma EBV DNA copies during treatment, aiding doctors in assessing whole-body tumor burden, accurately predicting the dynamic risk of recurrence and metastasis, and planning individualized treatments in NPC patients. © VDE VERLAG GMBH.

关键词： Gene encoding

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