检索结果-南通市图书馆

Forest composition and red oak (Quercus sp.) response to elevation gradients across greentree reservoirs

Forest Ecosystems 2023年第5期10卷 607-617页

作者： Cassandra Hug pradip saud Keith McKnight Douglas C.Osborne College of Forestry Agriculturaland Natural ResourcesUniversity of Arkansas at MonticelloMonticelloAR71656USA USFWS/Lower Mississippi Valley Joint Venture TylerTX75707USA Five Oaks Ag Research and Education Center HumphreyAR72073USA

Elevation gradients within forested wetlands have long been recognized for their role in defining species composition through factors such as hydrology and soil *** reservoirs(GTRs)are leveeimpounded tracts of bottomland hardwood forest flooded throughout the winter months to provide habitat for overwintering *** flooding of GTRs alters the forest composition due to flood frequency,depth,and duration in combination with slight changes in *** evaluate the effect of elevation gradients,soil properties,and management techniques in the overstory species composition and red oak(Quercus spp.)species abundance,we inventoried 662 plots across 12 independent GTRs in eastern *** the lower elevations ranging from 50.98 to 54.99 m above sea level,the importance value index(IVI)was highest for nuttall oak(Quercus texana)and overcup oak(Quercus lyrata),whereas IVI shifted to cherrybark oak(Quercus pagoda)in the higher elevations ranging from 54.99 to 58.00 *** diversity did not differ by elevation gradient,soil property,or management technique within *** diversity,using non-metric multi-dimensional scaling(NMDS)analysis,indicated site-specific variability significantly correlated with the environmental predictors,including elevation(R^(2)=0.57),easting(R^(2)=0.47),soil texture(R^(2)=0.21),and pH(R^(2)=0.12).Red oak species-specific mixed-effects modeling of abundance response using Poisson distribution suggested an inverse correlation of nuttall oak and a direct correlation of cherrybark oak abundance with ***,willow oak(Quercus phellos)abundance was not significantly affected by elevation but was by silt loam soil texture and restoration management *** findings will aid management efforts to reduce the dominance of less desirable species that are prominent under specific environmental conditions and promote the dominance of more desirable *** GTR sustainability is increas

关键词： Bottomland hardwoods Diversity Forested wetland Overstory Red oak Soil Thinning and management

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

引用

Computer Modeling in Engineering & Sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

引用

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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

引用

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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

引用

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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Effect of rare earth(Nd^(3+))metal doping on structural,morphological,optical and magnetic traits of Zn-Mg nano-ferrites

引用

Journal of Rare Earths 2023年第11期41卷 1763-1770,I0004页

作者： Rohit Jasrotia Nisha Kumari Ritesh Verma Suman Sachin Kumar Godara Jahangeer Ahmed Saad M.Alshehri Bidhan Pandit Sanjay Kumar Himanshi Shubham Sharma Kirti pradip K.Maji School of Physics&Materials Science Shoolini University of Biotechnology and Management SciencesSolanHimachal Pradesh173229India Himalayan Centre of Excellence in Nanotechnology Shoolini UniversityBajholSolanH.P173229India Department of Physics Baddi University of Emerging Science and TechnologyMakhnumajraBaddiHimachal Pradesh173205India Department of Physics Amity University HaryanaGururgram122413India Department of Mathematics School of Basic and Applied SciencesMaharaja Agrasen UniversityBaddiH.P174103India Department of Apparel and Textile Technology Guru Nanak Dev UniversityAmritsar143005India Department of Chemistry College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Materials Science and Engineering and Chemical Engineering Universidad Carlos III de MadridAvenida de la Universidad 3028911LeganésMadridSpain School of Advanced Chemical Sciences Shoolini University of Biotechnology and Management SciencesSolanHimachal Pradesh173229India Dept.of Polymer&Process Engineering Indian Institute of Technology RoorkeeSaharanpurUttar Pradesh247001India

Herein,we report the synthesis of Zn_(0.7)Mg_(0.3)Nd_(x)Fe_(2-x)O_(4)(where,x=0,0,0,01,0,02)ferrite nanoparticles by employing the sol-gel auto-combustion *** X-ray diffraction(XRD)pattern suggests the formation of a pure cubic structure,without any impurity phase,with an Fd3m space group at room *** increasing doping amount,the crystallite size is reported as 35-41 nm,while the lattice parameters rise from 0.8381 to 0.8395 *** emission scanning electron microscopy(FESEM)images show the formation of spherical grains with agglomerated morphology in all the samples,with grain sizes ranging from 49 to 103 *** dispersive X-ray spectroscopy(EDX)and elemental mapping investigation confirm the chemical purity of all the *** transform infrared(FTIR)analysis shows the presence of two prominent peaks around 440 and 560 cm^(-1)that correspond to the octahedral and tetrahedral *** addition,the existence of five Raman active vibratio nal modes in all produced specimens again confirms the structural purity of all the *** M-H curve shows that saturation magnetization(M_(s))first increases from 14.98 to 28.22 emu/g and then decreases to 18.98emu/g with increasing doping *** is due to the A-B type super-exchange interaction for the synthesized *** variation in coercivity(H_(c))and magnetic anisotropy(K_(1))suggest the thermal stability of all the samples and can be utilized in transformers and solenoids.

关键词： Magnetic nanoparticles Saturation magnetization Sol-gel auto-combustion synthesis Super-exchange interaction Cubic crystal structure Rare earths

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：