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Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion

computers, Materials & Continua 2023年第3期74卷 6485-6503页

作者： Jianwei Yuan Xinli Song Huaijian Pu Zhixiong Zheng Ziyang Niu College of Civil Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer&Communication Engineering Changsha University of Science and TechnologyChangsha410114China China Construction Fifth Engineering Bureau Co. Ltd.Changsha410004China

Regular inspection of bridge cracks is crucial to bridge maintenance and *** traditional manual crack detection methods are timeconsuming,dangerous and *** the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and ***,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network ***,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the ***,we introduce the parallel attention mechanism into the encoding and decoding *** attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various ***,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion *** research results are validated on the self-made *** experiments show that our method is more accurate than other *** intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,*** addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/*** results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.

关键词： Crack detection DeeplabV3+ parallel attention mechanism feature fusion

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DoS Attack Detection Based on Deep Factorization Machine in SDN

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computer Systems Science & engineering 2023年第5期45卷 1727-1742页

作者： Jing Wang Xiangyu Lei Qisheng Jiang Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Software-Defined Network(SDN)decouples the control plane of network devices from the data *** alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)*** many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such *** solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in ***,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection ***,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS *** training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is *** results show that our method can effectively detect DoS attacks in *** with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

关键词： Software-defined network denial-of-service attacks deep factorization machine GRMMP

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Secure and Efficient Data Transmission Scheme Based on Physical Mechanism

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computers, Materials & Continua 2023年第5期75卷 3589-3605页

作者： Ping Zhang Haoran Zhu Wenjun Li Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer Science Hunan University of Technology and BusinessChangsha410205China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security *** security technology based on the physicalmechanism has attracted extensive *** to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical *** this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is *** order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same *** analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.

关键词： Data transmission key generation data privacy compressive sensing Internet of Things(IoT)

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Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 421-472页

作者： Tian ZHENG Xinheng WANG Xiaolan PENG Ning SU Tianyi XU Xurong XIE Jin HUANG Lun XIE Feng TIAN Institute of Sofiware Chinese Academy of SciencesBeijing 100190China School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical ScienceBeijing 100005China

The global trend of population aging poses significant challenges to society and healthcare systems,particularly because of neurocognitive disorders(NCDs)such as Parkinson's disease(PD)and Alzheimer's disease(AD).In this context,artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of *** contactless screening technologies,such as speech-language processing,computer vision,and virtual reality,offer efficient and convenient methods for disease diagnosis and progression *** paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms,feature extraction,and modeling ***,the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are *** providing a comprehensive summary and refinement of the extant theories,methodologies,and applications,this study aims to facilitate an in-depth understanding of these technologies for researchers,both within and outside the *** the best of our knowledge,this is the first survey to cover the use of speech-language processing,computer vision,and virtual reality technologies for the detection of NSDs.

关键词： Neurocognitive disorder Speech-language computer vision Virtual reality Artificial intelligence

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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

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computer Systems Science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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NGDcrm:a numeric graph dependency-based conflict resolution method for knowledge graph

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High Technology Letters 2021年第2期27卷 153-162页

作者： Ma Jiangtao Wang Yanjun Chen Xueting Qiao Yaqiong College of Computer and Communication Engineering Zhengzhou Universily of Lighl IndustryZhengzhou 450002P.R.China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002P.R.China School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083P.R.China School of Information Engineering North China University of W ater Resources and Electric PowerZhengzhou 450046P.R.China

Knowledge graph(KG)conflict resolution is to solve knowledge conflicts problem in the construction of *** at the problem of KG conflict resolution,a KG conflict resolution algorithm NGDcrm is proposed,which is a numeric graph dependency-based conflict resolution *** utilizes the dependency graph to perform arithmetic calculation and predicate comparison of numerical entity knowledge in the *** first uses a parallel segmentation method to segment the KG;then,it extracts the features of the KG according to KG embedding;finally,it uses numerical graph dependencies to detect and correct the wrong facts in the KG based on the extracted *** experimental results on real data show that NGDcrm is better than the state-of-the-art knowledge conflict resolution *** them,the AUC value of NGDcrm on the DBpedia dataset is 15.4%higher than the state-of-the-art method.

关键词： dependency graph knowledge conflict resolution knowledge graph(KG) numeric graph dependency(NGD)

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