检索结果-南通市图书馆

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

Computers, Materials & Continua 2022年第6期71卷 5311-5325页

作者： Ahmed S.almasoud Siwar Ben Haj Hassine Nadhem NEMRI Fahd N.al-Wesabi Manar Ahmed Hamza Anwer Mustafa Hilal Abdelwahed Motwakel mesfer al duhayyim Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

关键词： 6G network mobile communication uav networks energy efficiency clustering metaheuristics

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

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

引用

Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed Salmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa mesfer al duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

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

Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

引用

Computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： Ahmed S.almasoud Siwar Ben Haj Hassine Fahd N.al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal mesfer al duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially increased.To handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data.So,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information.This paper presents a Deep Learning based Attention Long Short Term Memory(DLalSTM)Model for Multi-document Biomedical Text Summarization.The proposed DL-alSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further processing.Then,the DL-alSTM model gets executed to summarize the contents from the multiple biomedical documents.In order to tune the summarization performance of the DL-alSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is employed.Extensive experimentation analysis is performed to ensure the betterment of the DL-alSTM model and the results are investigated using the PubMed dataset.Comprehensive comparative result analysis is carried out to showcase the efficiency of the proposed DL-alSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter 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

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

引用

Computers, Materials & Continua 2022年第7期72卷 109-124页

作者： Khalil Ibrahim Mohammad Abuzanouneh Fahd N.al-Wesabi Amani Abdulrahman albraikan mesfer al duhayyim M.al-Shabi Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani K.Muthulakshmi Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a61101Yemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Management Information System College of Business and AdministrativeTaibah UniversityMedina42353Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

关键词： Automatic irrigation precision agriculture smart farming machine learning cloud computing decision making internet of things

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

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

引用

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

Design of Intelligent alzheimer Disease Diagnosis Model on CIoT Environment

引用

Computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal Fahd Nal-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad almustafa Nadhem Nemri mesfer al duhayyim Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making

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

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

引用

Computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya Bal-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour mesfer al duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse Pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

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

Fusion-Based Deep Learning Model for Automated Forest Fire Detection

引用

Computers, Materials & Continua 2023年第10期77卷 1355-1371页

作者： mesfer al duhayyim Majdy M.Eltahir Ola Abdelgney Omer ali Amani Abdulrahman albraikan Fahd N.al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.

关键词： Environment monitoring remote sensing forest fire detection deep learning machine learning fusion model

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

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

引用

Computer Systems Science & Engineering 2023年第9期46卷 3583-3598页

作者： Manal Abdullah alohali Muna Elsadig Fahd N.al-Wesabi mesfer al duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj 16278Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University Al-Kharj16278Saudi Arabia

With recent advancements in information and communication technology,a huge volume of corporate and sensitive user data was shared consistently across the network,making it vulnerable to an attack that may be brought some factors under risk:data availability,confidentiality,and integrity.Intrusion Detection Systems(IDS)were mostly exploited in various networks to help promptly recognize intrusions.Nowadays,blockchain(BC)technology has received much more interest as a means to share data without needing a trusted third person.Therefore,this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification(BAOML-CADC)technique.In the BAOML-CADC technique,the major focus lies in identifying cyberattacks.To do so,the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection(TEA-FS)method for the optimal choice of features.The BAOML-CADC technique uses an extreme learning machine(ELM)model for cyberattack recognition.In addition,a BC-based integrity verification technique is developed to defend against the misrouting attack,showing the innovation of the work.The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset.The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

关键词： Cyberattack machine learning blockchain thermal equilibrium 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

Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.al-onazi Mohamed K.Nour Hassan alshamrani mesfer al duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi 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

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for Arabic.Most of the research works are done mainly for English and least amount of effort for non-English language.The study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for Twitter.Therefore,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)model.The presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter data.To attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word embedding.Next,the MSDAE model is exploited for the identification of gender into two classes namely male and female.In the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our work.The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics.The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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

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：