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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

computer Systems science & Engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

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computers, Materials & Continua 2020年第12期65卷 2591-2605页

作者： Tahir Abbas Khan Sagheer Abbas Allah Ditta Muhammad Adnan Khan Hani Alquhayz Areej Fatima Muhammad Farhan Khan Department of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and *** deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in *** expert system has two input variables in layer 1,and seven input variables in layer *** layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are ***,advanced lab tests are conducted to identify the actual current status of the *** major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for ***-2019a tool is used to conduct the *** COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

关键词： IoMT MERS-COV Ct-chest ESR/CRP ABD(lgG) Fuzzy logic HMFIS WHO

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A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

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computers, Materials & Continua 2021年第5期67卷 2123-2139页

作者： Muhammad Kashif Ayyaz Hussain Asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi Muhammad Aakif Arif Jamal Malik Fayez Eid Alazemi Oh-Young Song Department of Computer Science&Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Capital University of Science&TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait Department of Software Sejong UniversitySeoul05006Korea

Expression detection plays a vital role to determine the patient’s condition in healthcare *** helps the monitoring teams to respond swiftly in case of *** to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression *** consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering *** representation is encoded from local patch statistics using these *** and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based *** images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature *** vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected *** experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA *** demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关键词： Detection expressions gestures analytics pain patch-based local binary descriptor discrete cosine transform healthcare

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Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

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computers, Materials & Continua 2020年第2期62卷 695-711页

作者： Jin Wang Yu Gao Chang Zhou R.Simon Sherratt Lei Wang School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Information Engineering Yangzhou UniversityYangzhou225009China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Department of Biomedical Engineering University of ReadingBerkshire RG66AYUK School of Civil Engineering Changsha University of Science&TechnologyChangsha410114China

Wireless Sensor Networks(WSNs)are usually formed with many tiny sensors which are randomly deployed within sensing field for target *** sensors can transmit their monitored data to the sink in a multi-hop communication ***,the‘hot spots’problem will be caused since nodes near sink will consume more energy during ***,mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data *** though it is difficult to consider many network metrics such as sensor position,residual energy and coverage rate etc.,it is still very important to schedule a reasonable moving trajectory for the mobile *** this paper,a novel trajectory scheduling method based on coverage rate for multiple mobile sinks(TSCR-M)is presented especially for large-scale *** improved particle swarm optimization(PSO)combined with mutation operator is introduced to search the parking positions with optimal coverage *** the genetic algorithm(GA)is adopted to schedule the moving trajectory for multiple mobile *** simulations are performed to validate the performance of our proposed method.

关键词： WSNs mobile sink trajectory scheduling network performance

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A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

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computers, Materials & Continua 2020年第3期62卷 1423-1443页

作者： Jieren Cheng Junqi Li Xiangyan Tang Victor SSheng Chen Zhang Mengyang Li Key Laboratory of Internet Information Retrieval of Hainan Province Hainan UniversityHaikouChina College of Information Science&Technology Hainan UniversityHaikouChina Department of Computer Science University of Central ArkansasConwayAR 72035USA

Distributed Denial of Service(DDoS)attack has become one of the most destructive network attacks which can pose a mortal threat to Internet *** detection methods cannot effectively detect early *** this paper,we propose a detection method of DDoS attacks based on generalized multiple kernel learning(GMKL)combining with the constructed parameter *** super-fusion feature value(SFV)and comprehensive degree of feature(CDF)are defined to describe the characteristic of attack flow and normal flow.A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm.A DDoS attack detection classifier is generated by using the trained GMKL model with R *** experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection,and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.

关键词： DDoS attack detection GMKL parameter optimization

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

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computers, Materials & Continua 2020年第10期65卷 139-151页

作者： Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Allah Ditta Hani Alquhayz Muhammad Farhan Khan Atta-ur-Rahman Rizwan Ali Naqvi Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Government College UniversityLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science College of ScienceMajmaah UniversityMajmmah11952Saudi Arabia Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea

The innovation in technologies related to health facilities today is increasingly helping to manage patients with different *** most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood *** real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a *** developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile *** health records(EHRs)are used to manage the huge volume of data using cloud *** reduces the storage,processing,and retrieval cost as well as ensuring the availability of *** learning procedures are used to extract hidden patterns and data *** this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart *** results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.

关键词： Cloud computing machine learning healthcare

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Topological defects on solutions of the non-relativistic equation for extended double ring-shaped potential

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Communications in Theoretical Physics 2024年第8期76卷 29-43页

作者： Badredine Boudjedaa Faizuddin Ahmed Natural Sciences and Materials Laboratory University Center Abdelhafid BoussoufMilaAlgeria Department of Mathematics Institute of Mathematics and Computer ScienceUniversity Center Abdelhafid BoussoufMilaAlgeria Department of Physics University of Science&Technology MeghalayaRi-Bhoi793101India

In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string,resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting ***, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.

关键词： non-relativistic wave-equation:Schrödinger equation topological defect:cosmic string point-like global monopole potential:extended ring-shaped potentials Bethe ansatz method

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