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Enhancing Collaborative and Geometric Multi-Kernel Learning Using deep neural network

Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Hybridized Wrapper Filter Using deep neural network for Intrusion Detection

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Computer Systems Science & Engineering 2022年第7期42卷 1-14页

作者： N.Venkateswaran K.Umadevi Information and Communication Engineering Anna UniversityChennai600025India Department of Electronics and Communication Engineering Sengunthar Engineering CollegeTiruchengodu637205India

Huge data over the cloud computing and big data are processed over the *** data may be stored,send,altered and communicated over the network between the source and *** data send by source to destina-tion,before reaching the destination data may be attacked by any intruders over the *** network has numerous routers and devices to connect to *** may attack any were in the network and breaks the original data,*** of attack in the network became interesting task for many *** are many intrusion detection feature selection algorithm has been suggested which lags on performance and *** our article we pro-pose new IDS feature selection algorithm with higher accuracy and performance in detecting the *** combination of wrapperﬁltering method using Pearson correlation with recursion function is used to eliminate the unwanted *** feature extraction process clearly extracts the attacked *** the deep neural network is used for detecting intruders attack over the data in the *** hybrid machine learning algorithm in feature extraction process helps toﬁnd attacked information using recursive *** of proposed method is compared with existing *** traditional feature selection in IDS such as differential equation(DE),Gain ratio(GR),symmetrical uncertainty(SU)and artiﬁcial bee colony(ABC)has less accuracy than proposed *** experimented results are shown that our proposed PCRFE-CDNN gives 99%of accuracy in IDS feature selection process and 98%in sensitivity.

关键词： deep neural network intrusion detection machine learning

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Cooperative Content Caching and Delivery in Vehicular networks: A deep neural network Approach

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China Communications 2023年第3期20卷 43-54页

作者： Xuelian Cai Jing Zheng Yuchuan Fu Yao Zhang Weigang Wu State Key Laboratory of Integrated Services networks Xidian UniversityXi’an 710071China Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Northwestern Polytechnical University Xi’an 710072China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

The growing demand for low delay vehicular content has put tremendous strain on the backbone *** a promising alternative,cooperative content caching among different cache nodes can reduce content access ***,heterogeneous cache nodes have different communication modes and limited caching *** addition,the high mobility of vehicles renders the more complicated caching ***,performing efficient cooperative caching becomes a key *** this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to ***,we devise the communication link and content caching model to facilitate timely content *** at minimizing transmission delay and cache cost,an optimization problem is ***,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal *** simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

关键词： dynamic content delivery cooperative content caching deep neural network vehicular net-works

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Dual-domain and Multiscale Fusion deep neural network for PPG Biometric Recognition

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Machine Intelligence Research 2023年第5期20卷 707-715页

作者： Chun-Ying Liu Gong-Ping Yang Yu-Wen Huang Fu-Xian Huang School of computer Heze UniversityHeze 274015China School of Software Shandong UniversityJinan 250101China

Photoplethysmography(PPG)biometrics have received considerable *** deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential *** address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric *** DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency ***,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition *** addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG *** on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

关键词： Photoplethysmography(PPG)signal biometric recognition multiple scale deep neural network dual-domain attention.

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Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden's virtual scenario

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International Journal of Digital Earth 2023年第1期16卷 1491-1509页

作者： Shuai Hong Jie Shen Guonian Lü Xiaoyan Liu Yirui Mao Nina Sun Long Tang Key Laboratory of Virtual Geographic Environment of Ministry of Education Nanjing Normal UniversityNanjingPeople’s Republic of China Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China School of Geography Nanjing Normal UniversityNanjingPeople’s Republic of China School of Earth Sciences Zhejiang UniversityHangzhouPeople’s Republic of China Jiangsu Second Normal University NanjingPeople’s Republic of China

Most of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical *** the aesthetic commonality between traditional Chinese landscape paintings and classical private gardens and referring to image style transfer,here,a deep neural network was proposed to transfer the aesthetic style from landscape paintings to the virtual scenario of classical private *** network consisted of two parts:style prediction and style *** style prediction network was used to obtain style representation from style paintings,and the style transfer network was used to transfer style representation to the content *** pre-trained network was then embedded into the scenario rendering pipeline and combined with the screen post-processing method to realise the stylised expression of the virtual *** verify the feasibility of this methodology,a virtual scenario of the Humble Administrator’s Garden was used as the content scenario andfive garden landscape paintings from different time periods and painting styles were selected for the case *** results demonstrated that this methodology could effectively achieve the aesthetic style transfer of a virtual scenario.

关键词： Chinese classical private garden virtual scenario Chinese traditional landscape painting deep neural network aesthetic style transfer

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An Optimal Framework for SDN Based on deep neural network

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Computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： deep neural network computer networks data security optimization

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Multi-Scale Attention-Based deep neural network for Brain Disease Diagnosis

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Computers, Materials & Continua 2022年第9期72卷 4645-4661页

作者： Yin Liang Gaoxu Xu Sadaqat ur Rehman Faculty of Information Technology College of Computer Science and TechnologyBeijing Artificial Intelligence InstituteBeijing University of TechnologyBeijing100124China Department of Natural and Computing Science University of AberdeenAberdeenU.K

Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease ***,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease *** this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD *** model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous *** model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model *** performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out *** showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction *** also localized important FC features and brain regions associated with ASD ***,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.

关键词： Autism spectrum disorder diagnosis resting-state fMRI deep neural network functional connectivity multi-scale attention module

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Wireless Sensor networks Routing Attacks Prevention with Blockchain and deep neural network

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Computers, Materials & Continua 2022年第3期70卷 6127-6140页

作者： Mohamed Ali Ibrahim A.Abd El-Moghith Mohamed N.El-Derini Saad M.Darwish Higher Institute for Tourism Hotels and ComputerAl-SeyoufAlexandria21533Egypt Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University21526Egypt Department of Computer and Systems Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

Routing is a key function inWireless Sensor networks(WSNs)since it facilitates data transfer to base *** attacks have the potential to destroy and degrade the functionality ofWSNs.A trustworthy routing system is essential for routing security andWSN *** methods have been implemented to build trust between routing nodes,including the use of cryptographic methods and centralized ***,the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node *** the moment,there is no effective way to avoid malicious node *** a consequence of these concerns,this paper proposes a trusted routing technique that combines blockchain infrastructure,deep neural networks,and Markov Decision Processes(MDPs)to improve the security and efficiency of WSN *** authenticate the transmission process,the suggested methodology makes use of a Proof of Authority(PoA)mechanism inside the blockchain *** validation group required for proofing is chosen using a deep learning approach that prioritizes each node’s *** are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting *** to testing data,our routing system outperforms current routing algorithms in a 50%malicious node routing scenario.

关键词： Wireless sensor networks trusted routing deep neural network blockchain markov decision

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Prediction of second-order rate constants between carbonate radical and organics by deep neural network combined with molecular fingerprints

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Chinese Chemical Letters 2022年第1期33卷 438-441页

作者： Peizhe Sun Huixin Ma Shangyu Li Hong Yao Ruochun Zhang School of Environmental Science and Engineering Tianjin UniversityTianjin 300072China School of Civil Engineering Tianjin UniversityTianjin 300072China Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard Department of Municipal and Environmental EngineeringSchool of Civil EngineeringBeijing Jiaotong UniversityBeijing 100044China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Tianjin UniversityTianjin 300072China

Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined,and quantitative structural-activity relationships(QSARs)have not been established for rate *** study applied Max Min data processing method and used molecular fingerprints(MF)as the input of a deep neural network(DNN)to predict the rate constants between carbonate radical and organic *** parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate *** vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best *** optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods,including random data splitting,molecular descriptors,supporting vector machine,decision tree,*** showed that the MF-DNN model out-performed the other methods by more than 10%increase in prediction *** this MF-DNN model,we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine(1576)and veterinary practice(390).Among them,46 drugs were identified as fast-reacting compounds,suggesting the important relations of their environmental fate with carbonate radical.

关键词： deep neural network Carbonate radical Molecular fingerprints QSAR Pharmaceuticals

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High Order deep neural network for Solving High Frequency Partial Differential Equations

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Communications in Computational Physics 2022年第2期31卷 370-397页

作者： Zhipeng Chang Ke Li Xiufen Zou Xueshuang Xiang Qian Xuesen Laboratory of Space Technology China Academy of Space TechnologyBeijing 100875P.R.China School of Mathematics and Statistics Wuhan UniversityWuhan 430072P.R.China Information Engineering University Zhengzhou 450001P.R.China

This paper proposes a high order deep neural network(HOrderDNN)for solving high frequency partial differential equations(PDEs),which incorporates the idea of“high order”from finite element methods(FEMs)into commonly-used deep neural networks(DNNs)to obtain greater approximation *** main idea of HOrderDNN is introducing a nonlinear transformation layer between the input layer and the first hidden layer to form a high order polynomial space with the degree not exceeding p,followed by a normal *** order p can be guided by the regularity of solutions of *** performance of HOrderDNNis evaluated on high frequency function fitting problems and high frequency Poisson and Helmholtz *** results demonstrate that:HOrderDNNs(p>1)can efficiently capture the high frequency information in target functions;and when compared to physics-informed neural network(PINN),HOrderDNNs(p>1)converge faster and achieve much smaller relative errors with same number of trainable *** particular,when solving the high frequency Helmholtz equation in Section 3.5,the relative error of PINN stays around 1 with its depth and width increase,while the relative error can be reduced to around 0.02 as p increases(see Table 5).

关键词： deep neural network high order methods high frequency PDEs

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