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A Decentralized and TCAM-Aware Failure Recovery Model in Software Defined Data Center networks

Computers, Materials & Continua 2025年第1期82卷 1087-1107页

作者： Suheib Alhiyari Siti Hafizah AB Hamid Nur Nasuha Daud department of Computer system and Technology University of MalayaKuala Lumpur50603Malaysia

Link failure is a critical issue in large networks and must be effectively *** software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive *** schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow *** SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes *** particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade *** paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource *** DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory *** employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and *** employs flow entry aggregation techniques to reduce switch memory *** of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC *** reduces the switches’Ternary Content-Addressable Memory(TCAM)***,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network *** performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN *** different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed vario

关键词： Software defined networking failure detection failure recovery restoration protection TCAM size

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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea department of information Convergence system Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation systems

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Computers, Materials & Continua 2025年第1期82卷 1377-1399页

作者： Ibrar Afzal Noor ul Amin Zulfiqar Ahmad Abdulmohsen Algarni department of Computer Science and information Technology Hazara UniversityMansehra21300Pakistan department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation *** computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end *** of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud *** smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system *** address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog *** framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation *** FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud *** simulation-based executions,tasks are allocated to the nearest available nodes with minimum *** the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of *** successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

关键词： Fog computing smart cities smart transportation data management fault tolerance resource scheduling

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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Computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Decision making module based on stochastic magnetic tunnel junctions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 202-209页

作者： Yifan Miao Li Zhao Yajun Zhang Zhe Yuan Center for Advanced Quantum Studies and department of Physics Beijing Normal University Institute for Nanoelectric Devices and Quantum Computing Fudan University Interdisciplinary Center for Theoretical Physics and information Sciences Fudan University

In biological neural systems, noise is ubiquitous but does not affect the correct decisions made in the complex cognitive ***-making in biological neural system is typically achieved by accumulating input information over a period of *** by recent developments in neurosciences, we design a decision-making module based on spintronic devices, utilizing superparamagnetic tunnel junctions as artificial neurons. The feasibility of this decision-making module is verified through circuit simulations. Taking a multi-layer perceptron as an example, the module significantly improves the accuracy of the perceptron in the handwritten digit recognition task. Furthermore, the spintronic decision-making module offers advantages over the conventional pooling methods, such as adaptive decision time, high performance and the absence of analog-to-digital conversion. The decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.

关键词： neuromorphic computing spintronics magnetic tunnel junction neural networks

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Decentralized fault diagnosis of discrete-event systems with unreliable sensors using linear temporal logic

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Science China(information Sciences) 2025年第1期 322-341页

作者： Weijie DONG Shaoyuan LI Xiang YIN Key Laboratory of system Control and information Processing Ministry of EducationDepartment of Automation Shanghai Jiao Tong University

In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of possible *** studies on decentralized robust diagnosis can only deal with different types of sensor failures separately,e.g.,all sensors suffer from the same type of sensor failures such as intermittent sensor failures or permanent sensor ***,since sensors of different local diagnosers may face different external environments and have different internal characteristics,sensors corresponding to different local diagnosers may also have their own fault *** this paper,we propose a flexible framework of decentralized diagnosis for DES subject to unreliable sensors such that sensors of different local diagnosers are permitted to have different types of sensor *** this end,we extend the existing decentralized diagnosis framework to the case where there exist common sensors broadcasting their observations to all local *** apply linear temporal logic(LTL) to constrain infinite behaviors of private sensors of local diagnosers and common ***,a new notion of φ-codiagnosability is proposed as the necessary and sufficient condition for the existence of a decentralized diagnoser that works correctly under sensors,satisfying LTL-based sensor ***,we provide an effective approach to verify the φ-codiagnosability.

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of information Engineering and Automation Kunming University of Science and TechnologyKunming650500China department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Automatic radar antenna scan type recognition based on limited penetrable visibility graph

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Journal of systems Engineering and Electronics 2021年第2期32卷 437-446页

作者： LIU Songtao LEI Zhenshuo GE Yang WEN Zhenming department of information system Dalian Naval AcademyDalian 116018China

To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is ***,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic ***,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are ***,the recognition result is obtained by using a support vector machine(SVM)*** experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.

关键词： antenna scan type limited penetrable visibility graph(LPVG) support vector machine(SVM) cognitive electronic warfare

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An Efficient Ensemble Model for Various Scale Medical Data

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Computers, Materials & Continua 2022年第10期73卷 1283-1305页

作者： Heba A.Elzeheiry Sherief Barakat Amira Rezk information system department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Electronic Health Records(EHRs)are the digital form of patients’medical reports or *** facilitate advanced analytics and aid in better decision-making for clinical *** data are very complicated and using one classification algorithm to reach good results is *** this reason,we use a combination of classification techniques to reach an efficient and accurate classification *** model combination is called the Ensemble *** need to predict new medical data with a high accuracy value in a small processing *** propose a new ensemble model MDRL which is efficient with different *** MDRL gives the highest accuracy *** saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in *** implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 *** four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR *** our experiments,we conclude that our ensemble model has the best accuracy value for most *** reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy *** accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.

关键词： Electronic health records(EHRs) Random forest(RF) Decision tree(DT) linear model(LR) Multi-layer Perceptron(MLP) MDRL correlation feature selection(CFS)

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