检索结果-南通市图书馆

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray sana alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to cyberattacks.It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few decades.It is challenging to recognize Botnet since it has excellent carrying and hidden capacities.Various approaches have been employed to identify the source of Botnet at earlier stages.Machine Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection methodology.In spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset.The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)model.The presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter tuning.Initially,the network data is pre-processed to make it compatible for further processing.Besides,RSO algorithm is exploited for effective selection of subset of features.Additionally,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of botnets.Finally,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM model.In order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was conducted.The obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

引用

Computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi sana alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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

Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

引用

Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari sana alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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

Assessing gully erosion and rehabilitation using multi temporal LiDAR DEMs:Case study from the Great Barrier Reef catchments,Australia

引用

International Soil and Water Conservation Research 2024年第1期12卷 184-199页

作者： sana Khan Rebecca Bartley Anne Kinsey-Henderson Aaron Hawdon The Commonwealth Scientific and Industrial Research Organisation(CSIRO) Environment1 James Cook DrDouglas4814Australia The Commonwealth Scientific and Industrial Research Organisation(CSIRO) EnvironmentGPO Box 2583BrisbaneQLD4001Australia

Millions of dollars are being spent on gully rehabilitation to help reduce excess fine sediment delivery to the Great Barrier Reef(GBR).There is an urgent need for(i)prioritisation of active gullies for rehabilitation and(ii)the development of methodologies to inform the effectiveness of remediation.In this study we analyse DEMs of Difference derived from 0.5 m resolution 2-3 year interval multi-temporal LiDAR data collected pre and post rehabilitation at three variable gully morphologies in the Burdekin catchment.Our analysis indicates that the highest annual average fine sediment erosion rates for the untreated control gullies occur at the linear gully(53.38 t ha^(-1)y^(-1))followed by linear-alluvial gully(34.24 t ha^(-1)y^(-1))and least at the alluvial gully(14.41 t ha^(-1)y^(-1)).The proportional loss or export of fine sediment from the gullies in their un-treated condition ranges from∼68 to 90%of what is eroded,and when the gullies are treated the proportion of fine sediment that is retained in the gully proportional to what is eroded increases to∼60%at all sites.Without pre-treatment baseline erosion rates,and additional post treatment LiDAR captures,it is difficult to quantify the treatment effectiveness.Our results offer insights in the erosion mechanisms within different geomorphic gully morphologies and rehabilitation effects in these erosional landforms.This study provides crucial knowledge of gully dynamics that can be coupled with other lines of evidence for better prioritisation of rehabilitation in the GBR catchments.

关键词： Digital elevation models (dis)connectivity Restoration Geomorphic change detection Geomorphic effectiveness GIS

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

Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

引用

Computer Systems Science & Engineering 2023年第9期46卷 3209-3223页

作者： Mesfer Al Duhayyim Fatma S.Alrayes Saud S.Alotaibi sana alazwari Nasser Allheeib Ayman Yafoz Raed Alsini Amira Sayed A.Aziz Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems-College of Computer and Information Sciences King Saud UniversityRiyadhSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

关键词： Remote sensing internet of things image encryption security optimal key generation

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

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

引用

Computer Systems Science & Engineering 2023年第9期46卷 3321-3338页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani sana alazwari Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

关键词： Hate speech offensive speech Arabic corpora natural language processing social networks

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

Promotion of sexual and reproductive health in Pakistan-The role of technology and online awareness

引用

Asian pacific Journal of Reproduction 2024年第1期13卷 1-2页

作者： Mawiya sana Department of Public Health and Community Medicine Shaikh Khalifa Bin Zayed Al Nahyan Medical and Dental CollegeG855+XRM"Sheikh Zayed Medical Complex"Khayaban-e-Jamia PunjabBlock D Muslim TownLahorePunjabPakistan

Digital health innovations such as online webinars,websites,blogs,and health apps have been associated with increased sexual and reproductive health(SRH)among the general population.Many studies have been conducted in developed countries in the West;however,little research has been conducted in low-income countries,especially in Pakistan.The socioeconomic instability and the government's lack of awareness and knowledge have resulted in no regulations being placed to incorporate telehealth in medicine.The healthcare departments must take into consideration the lifechanging impact of telehealth and the barriers that prevent it in becoming known,to reduce the incidence of unsafe abortions and sexual health practices that prove to be a stigma in society.

关键词： income becoming Pakistan

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

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

引用

Computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani sana alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to data.But the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so on.Presently,malware attacks and software piracy pose serious risks in compromising the security of IoT.They can steal confidential data which results infinancial and reputational losses.The advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud environment.This article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud Net-works.The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment.The proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification problem.The EAG-TODL-CTD model pre-processes the input data to transform to a compatible for-mat.For threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class labels.Finally,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our work.The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign.The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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

Insight into small eyes: a practical description from phenotypes presentations to the management

引用

International Journal of Ophthalmology(English edition) 2024年第2期17卷 380-391页

作者： sana Niazi Sorcha NíDhubhghaill Farideh Doroodgar Zisis Gatzioufas Mohammad Hossein Dehghan Translational Ophthalmology Research Center Tehran University of Medical SciencesTehran ***Iran Antwerp University Hospital Edegem 2650Belgium Faculty of Health Sciences Antwerp UniversityAntwerpen 2000EdegemBelgium Negah Aref Ophthalmic Research Center Shahid Beheshti University of Medical SciencesTehran 16666Iran University Eye Hospital Basel Basel 4031Switzerland

This narrative review aimed to have an algorithmic approach to microphthalmos by a systematic search.The definition can be related to a number of special phenotypes.In the more challenging cases of complex microphthalmos,relative anterior microphthalmos,and nanophthalmos,the surgeon can approach these cases more safely if they have a deep understanding of the anatomical variations and ideal formulae for intraocular lens computation and knows how to avoid intra-and post-operative complications.In this article,we review the criteria by which we recognize and describe pre-,intra-,and post-operative considerations,as well as discuss the ideal intraocular lenses for microphthalmos,given the intricate varieties of small eye phenotypes.

关键词： microphthalmos nanophthalmos lenses intraocular anterior chamber axial length eye cataract microcornea syndrome

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

Ionic potency regulation of coagulation bath induced by saline solution to control over the pore structure of PBI membrane for highperformance lithium metal batteries

引用

Journal of Energy Chemistry 2024年第7期94卷 288-298页

作者： Arshad Hussain Waseem Raza Andleeb Mehmood sana Jalees Lihong Ao Yonggui Deng Aymeric Ramiere Xingke Cai Dongqing Liu College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060GuangdongChina Institute for Advanced Study Shenzhen UniversityShenzhen 518060GuangdongChina Institute of Carbon Neutrality Zhejiang Wanli UniversityNingbo 315100ZhejiangChina College of Light Industry and Chemical Engineering Dalian Polytechnic UniversityDalian 116034LiaoningChina College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060GuangdongChina College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060GuangdongChina

In this study,we have explored the use of water as a non-solvent for tuning the microstructure of poly-benzimidazole(PBI)membranes,which are potential separators for lithium metal batteries(LMBs).The traditional method for membrane synthesis called nonsolvent-induced phase separation(NIPS),usually relies on hazardous and costly organic non-solvents.By dissolving sodium chloride(Nacl)in water,we could adjust the water ionic potency and the exchange speed of the non-solvent with the DMAC solution to change the micropore structure of the PBI membrane.With increasing Nacl concentration,the micro-pores in the PBI membrane transitioned from finger-like to sponge-like morphology.Compared to com-mercial separators like the Celgard separator,the PBI membrane with sponge-like micropores exhibited better regulation of lithium deposition and improved Li^(+) transportation capability due to its good wetta-bility with the electrolyte.Consequently,the PBI membrane-based Li/Li symmetric cell and Li/LiFePO_(4) full cell demonstrated superior performance compared to the Celgard-based ones.This research proposes an eco-friendly and scalable synthetic approach for fabricating commercial separators for LMBs,addressing the issue of lithium dendrite growth and improving overall battery safety and performance.

关键词： Lithium metal-based battery Salt-induced Polybenzimidazole Tunablemorphology lonic potency

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

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

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：