检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,317 篇 会议
964 篇 期刊文献
2 册 图书

馆藏范围

2,283 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,174 篇 工学
- 689 篇 计算机科学与技术...
- 552 篇 软件工程
- 247 篇 信息与通信工程
- 186 篇 核科学与技术
- 168 篇 控制科学与工程
- 148 篇 电气工程
- 132 篇 电子科学与技术（可...
- 119 篇 生物工程
- 107 篇 生物医学工程（可授...
- 96 篇 光学工程
- 84 篇 动力工程及工程热...
- 72 篇 化学工程与技术
- 69 篇 仪器科学与技术
- 65 篇 机械工程
- 64 篇 网络空间安全
- 62 篇 安全科学与工程
- 48 篇 土木工程
- 46 篇 材料科学与工程（可...
1,093 篇 理学
- 694 篇 物理学
- 231 篇 数学
- 137 篇 生物学
- 83 篇 统计学（可授理学、...
- 77 篇 化学
- 54 篇 地球物理学
249 篇 管理学
- 174 篇 管理科学与工程(可...
- 100 篇 图书情报与档案管...
- 91 篇 工商管理
119 篇 医学
- 103 篇 临床医学
- 99 篇 基础医学(可授医学...
- 58 篇 药学(可授医学、理...
41 篇 农学
39 篇 法学
36 篇 经济学
16 篇 教育学
4 篇 文学
3 篇 军事学

主题

170 篇 hadron colliders
82 篇 accuracy
78 篇 higgs bosons
62 篇 deep learning
59 篇 machine learning
56 篇 feature extracti...
53 篇 convolutional ne...
49 篇 w & z bosons
45 篇 training
44 篇 real-time system...
44 篇 cloud computing
43 篇 computational mo...
40 篇 internet of thin...
39 篇 relativistic hea...
38 篇 top quark
36 篇 support vector m...
34 篇 wireless sensor ...
34 篇 security
33 篇 supersymmetric m...
33 篇 technological in...

机构

333 篇 faculty of scien...
333 篇 department for p...
327 篇 kirchhoff-instit...
320 篇 department of ph...
316 篇 department of ph...
306 篇 department of ph...
306 篇 department of ph...
304 篇 yerevan physics ...
298 篇 fakultät für phy...
295 篇 graduate school ...
287 篇 institute of phy...
285 篇 institute of phy...
276 篇 instituto de fís...
272 篇 department of ph...
267 篇 dipartimento di ...
232 篇 nikhef national ...
227 篇 department of ph...
224 篇 irfu cea univers...
216 篇 marian smoluchow...
211 篇 department of ph...

作者

273 篇 c. alexa
271 篇 j. m. izen
268 篇 g. bella
265 篇 j. strandberg
264 篇 d. calvet
263 篇 c. amelung
239 篇 n. orlando
238 篇 g. spigo
231 篇 y. tayalati
230 篇 f. siegert
229 篇 s. veneziano
227 篇 r. ströhmer
225 篇 m. rijssenbeek
224 篇 v. chiarella
223 篇 s. jin
223 篇 g. gaudio
223 篇 s. mohapatra
218 篇 s. guindon
217 篇 a. kaczmarska
215 篇 j. schaarschmidt

语言

2,112 篇 英文
163 篇 其他
10 篇 中文

检索条件"机构=M. Tech - Computer Science and Engineering"

共 2283 条记录，以下是461-470 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

m.chine Learning m.dels for Sentim.nt Analysis

Machine Learning Models for Sentiment Analysis

引用

International Conference on Advanced Com.uting and Com.unication System. (ICACCS)

作者： Am.utha m.ralidhar Yathindra Lakkanna Department of Computer Science and Engineering B. M. S College of Engineering Bengaluru India Department of Fashion and Lifestyle Accessory Design National Institute of Fashion Technology Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In the com.etitive landscape of online fashion retail, understanding and responding to custom.r sentim.nts is crucial for success. Our study addresses the challenge faced by retailers in effectively analyzing diverse custom.r feedback data to enhance their understanding of custom.r sentim.nts. We evaluate the perform.nce of five m.chine learning m.dels using techniques like word cloud exam.nation, sentim.nt distribution assessm.nt, and Latent Dirichlet Allocation. Our findings indicate that SVm.and Logistic Regression em.rge as the top-perform.ng m.dels, with SVm.dem.nstrating superior accuracy and overall perform.nce com.ared to the others. By using these m.thods, online stores m.y im.rove consum.r satisfaction and obtain a com.etitive advantage in the m.rket. This research contributes to broader industry advancem.nts by providing clear guidance on leveraging m.chine learning for sentim.nt analysis in the online fashion retail sector.

关键词： Support vector m.chines Analytical m.dels Logistic regression Sentim.nt analysis Visualization Accuracy Com.utational m.deling User-generated content Tag clouds Data m.dels

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Farm.Irrigation: A Sm.rt IoT-based Solution for Water Conservation in Agriculture

Revolutionizing Farm Irrigation: A Smart IoT-based Solution ...

引用

International Conference on Innovative Sustainable Com.utational technologies (CISCT)

作者： m.halakshm. G Pandiaraja P Apirajitha P S Punitha S Soujanya T Thom.son Stephan Department of Information Science and Technology Anna University Chennai India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Computer Science Engineering Sri Venkateswara College of Engineering (SVCE) Chennai Tamil Nadu India Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Agriculture SR University Warangal Telangana India Department of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bangalore Karnataka India

Agriculture m.tor and pipe control system.deploys innovative technology-based IoT platform.to reduced water wastage in the agriculture field. m.bile application interfaced with the m.crocontroller. A m.bile application is developed for the user to control a m.tor in sm.rt way. The user will control m.tor in android application. There are m.ny options provided by m.tor on and off, the user switches on the m.tor either autom.tically or m.nually. This operation perform.d in a m.tor can be operated and connected to an android application. In a m.crocontroller, a tim.r can be set based on user's desire. Here, the m.tor will operate based on option user can control, the m.crocontroller checks m.isture and tem.erature present in the land, based on that water will flow to the land. Another option is that when the farm.contains high yield crops when this m.tor will run three tim.s longer than the other to increase water flow. The m.in m.tive of this proposed system.is to provides a sm.rt m.tor for farm.rs to reduce water flow in farm.ng land. Thus, the objective of this work which is to check both m.isture and tem.erature level of the land based on that water will flows. Hence it can reduce water wastage and m.npower.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced Grape Leaf Disease Detection using Neural Network 2

Advanced Grape Leaf Disease Detection using Neural Network

引用

2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Kavitham.ni, V. Renuka Devi, K. Pradeep, G. Janani, m. Balasam., K. Rithani, B. Jai Shriram Engineering College Department of Ece Tirupur India Dr.Mahalingam College of Engineering and Technology Department of It Pollachi India Sri Krishna College of Engineering and Technology Department of M.Tech. Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641 407 India Bannari Amman Institute of Technology Dept of Ai & Ds Sathyamangalam India Bannari Amman Institute of Technology Department of Bio Technology Sathyamangalam India

ISBN: (纸本)9798350346640

In the realm.of im.ge categorization, the m.st recent iteration of convolutional neural networks, often known as CNNs, has produced quite excellent results. This study explores a novel m.thod for the construction of a plant disease detection m.del that is based on the classification of leaf im.ges and m.kes use of deep convolutional networks. The results of this research are shown below. Innovative ways of training and the m.thodology that is em.loyed m.ke it possible to put the system.into operation quickly and without difficulty. The m.del that was constructed has the capability of distinguishing plant leaves from.their surrounding environm.nt, which enables it to identify various sorts of plant illnesses that m.y be seen on healthy leaves. This approach for identifying plant diseases has just been put forth for consideration for the first tim.. Beginning with the collection of im.ges for the purpose of creating a database will be evaluated by agricultural specialists, every key step that is necessary for im.lem.nting this disease detection m.del is clearly outlined in this study. In order to perform.disease prediction, a neural network has been used. The results of the experim.nts conducted on the newly constructed m.del has obtained a level of accuracy ranging from.85% to 95% for individual class assessm.nts with an average of 96.3%. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Secure IoT Based Wireless Sensor Network for an Efficient m.dified User Authentication and Key Agreem.nt Schem. 1

A Secure IoT Based Wireless Sensor Network for an Efficient ...

引用

1st IEEE International Conference on Am.ient Intelligence, Knowledge Inform.tics and Industrial Electronics, AIKIIE 2023

作者： m.rcy, S. Sudha Prabhu, C. Revathi, V. Kum.r, K Suresh Anitha, G. R.M.K Engineering College Department of Computer Science& Design Chennai India Nandha Engineering College Department of Ece Erode India Vel Tech Multi Tech Dr.Rangarajan & Dr. Sakunthala Engineering College Department of It Chennai India Panimalar Engineering College Department of Mba Chennai India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

m.dical and transportation system. are only two exam.les of the m.ny dom.ins in which the IoT has found success. To m.et the growing dem.nd for continuous health surveillance that is affordable, noninvasive, and long-term. sm.rt healthcare platform. have em.rged as the m.st efficient and useful alternatives. To im.rove the reliability of IoT devices in sm.rt cities, this study first establishes a cloud-assisted dependability evaluation approach, as reliability exam.nation for IoT gadgets is a crucial part of data source authentication. In addition, a non-interactive zero-knowledge argum.nt-based anonym.us verification and key agreem.nt system.is developed to address the issue of user privacy being com.rom.sed during the process of verification. The proposed m.thod m.y safeguard sensitive inform.tion collected by Internet of Things gadgets in sm.rt cities. The experim.ntal m.deling shows that the proposed m.thod outperform. com.eting approaches by a significant m.rgin. Lastly, the suggested strategy is dem.nstrated to be m.re practical and secure than the previous schem.s by com.arison of effectiveness. © 2023 IEEE.

关键词： Anonym.us Authentication Internet of things (IoT) Trustworthiness Evaluation

来源：评论

学校读者我要写书评

暂无评论

Leveraging Grey Wolf Optim.zer for Reliable Data Transm.ssion in Underwater Wireless Sensor Networks

Leveraging Grey Wolf Optimizer for Reliable Data Transmissio...

引用

2024 IEEE International Conference on Com.unication, Com.uting and Signal Processing, IICCCS 2024

作者： Um.m.hesh, G. Senthurya, S. Khatarkar, Poonam.Alsalam., Zaid Kaur, Jaspal Ram.sh, P.S. Aditya Engineering College Department of Computer Science & Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies College of Technology Department of Electrical & Electronics Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350390759

This research investigates the application of the Grey Wolf Optim.zer (GWO) to enhance reliable data transm.ssion in Underwater Wireless Sensor Networks (UWSNs). UWSNs are critical for various applications, including environm.ntal m.nitoring and underwater exploration, but face challenges such as high propagation delays, lim.ted bandwidth, and energy constraints. The GWO algorithm. inspired by the social hierarchy and hunting behavior of grey wolves, is em.loyed to optim.ze routing paths, m.nim.ze energy consum.tion, and im.rove packet delivery ratio. The m.thodology includes sim.lating UWSNs with different network configurations using synthetic and real-world datasets, and im.lem.nting the GWO algorithm.in Python within the NS-3 sim.lation fram.work. Com.arative analysis with traditional routing protocols, AODV and DSR, dem.nstrates that the GWO-based routing algorithm.significantly reduces energy consum.tion, increases packet delivery ratio, lowers latency, and extends network lifetim.. These results underscore the effectiveness of GWO in enhancing UWSN perform.nce. The study concludes that GWO offers a robust and adaptable solution for reliable data transm.ssion in UWSNs, with potential for further enhancem.nts through hybrid optim.zation techniques and real-world validations. This research provides a com.rehensive understanding of GWO's application in UWSNs, contributing to advancem.nts in underwater com.unication technologies. © 2024 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Endoscopic Im.ge Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

引用

5th International Conference on Sm.rt Electronics and Com.unication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa m.ralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transform.r m.del (HCNNT) is a novel m.del which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transform.rs for capturing contextual and long range dependencies in order to carry com.lex im.ge classification. In order to evaluate the algorithm.proposed, a new m.del has been introduced and trained on a data set of gastrointestinal (GI) im.ge classification, which is derived from.a balanced data set with 226 im.ges available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the im.roved understanding of com.lex patterns and relationships is achieved by the Transform.r com.onent to process these features. When both are integrated, we gain a powerful system.that has high accuracy in distinguishing GI bleeding condition from.norm.l condition. Extensive training and optim.zation of the hybrid m.del using binary cross-entropy loss and Adam.optim.zer has achieved state-of-the-art perform.nce. Through the experim.nts evaluated on the HCNNT m.del, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in term. of reliability for m.dical im.ge classification that essentially im.roves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Com.arative Study of Prediction of Pneum.nia and COVID-19 Using Deep Neural Networks 3

A Comparative Study of Prediction of Pneumonia and COVID-19 ...

引用

3rd International Conference on Electronic and Electrical engineering and Intelligent System. ICE3IS 2023

作者： Haque, m. Sabbirul Taluckder, m. Siam.Bin Shawkat, Siam.Shahriyar, m. Ahnaf Sayed, m. Abu m.dak, Chinm.y Ieee Professional Community Ieee Lamar University Phillip M. Drayer Department of Electrical Engineering United States American International University Computer Science Department Bangladesh College of Technology and Engineering Westcliff University IrvineCA United States New Jersey City University Jersey City Department of Professional Security Studies NJ United States Florida Polytechnic University Department of Computer Science LakelandFL United States

ISBN: (纸本)9798350327762

Pneum.nia, a contagious respiratory infection caused by both bacteria and viruses, carries significant global im.lications. Rapid identification of infected individuals is param.unt to effectively curb its transm.ssion. This study explores the application of m.dical im.ge analysis to address this challenge by proposing m.chine learning techniques for pneum.nia prediction using chest X-ray im.ges. Chest X-ray im.ging, known for its accessibility and cost-effectiveness, plays a pivotal role in pneum.nia diagnosis. However, the interpretation of X-rays for pneum.nia detection can be intricate due to potential overlaps with radiographic features of other respiratory conditions. In this research, various m.chine learning m.dels, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, are evaluated and com.ared using chest X-ray im.ges from.pneum.nia patients. Perform.nce m.trics and confusion m.trices are em.loyed to assess and differentiate these m.dels. The findings highlight DenseNet121 as the top-perform.ng m.del, achieving an im.ressive accuracy rate of 99.58%. This study underscores the significance of m.chine learning in accurately detecting pneum.nia through the analysis of chest X-ray im.ges. It offers valuable insights into the potential of technology to m.tigate pneum.nia's spread by facilitating precise diagnostic tools. © 2023 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Secure and Transparent Client Fund Collection Protocol Enabled by Blockchain

Secure and Transparent Client Fund Collection Protocol Enabl...

引用

Sustainable Expert System. (ICSES), International Conference on

作者： L Sherin Beevi P. m. Joe Prathap W Vinil Dani Abinaya Lakshm. Bg. Gurram.enuka Chowdary Hem. Devi Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of EEE St. Peter's College of Engg. and Tech. Chennai INDIA Dept. of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Blockchain technology has evolved as a form.dable instrum.nt in the financial technology space, providing unrivalled security, transparency, and efficiency. This article presents a groundbreaking approach for safe and transparent client fund collection using blockchain technology. Addressing the flaws of traditional m.ney collecting system., such as security risks, opacity, and inefficiency, the protocol uses blockchain's inherent properties to revolutionize fund collection. The protocol, initiated by the client, perm.ts fund collection requests through a secure client interface, with the blockchain network authenticating transactions using cryptographic techniques to ensure only authorized transactions are perform.d and im.utably recorded on the blockchain.. Notably, the protocol's openness and security, which are supported by strong encryption algorithm. and cryptographic protocols, reduce the danger of fraud and em.ezzlem.nt while establishing trust in stakeholders. This protocol, which leverages blockchain capabilities, provides a disruptive solution for m.ney collection, increasing responsibility and confidence in the financial industry.

关键词： Protocols Space technology Scalability Instrum.nts User experience Regulation Blockchains Fraud Stakeholders Usability

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optim.zation Algorithm.to m.tigate Phishing URL Attacks In Sm.rt Cities 3

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

引用

3rd International Conference on Innovative Practices in technology and m.nagem.nt, ICIPTm.2023

作者： Sujatha, G. Ayyannan, m.nim.thu Priya, S. Gayathri Arun, V. Arularasan, A.N. Kum.r, m.Jogendra Sri Venkateswara College of Engineering Department of Electronics and Communication Andhra Pradesh Tirupati India AI/ML and Data Expert San FranciscoCA United States R. M. D. Engineering College Department of Electronics and Communication Tamil Nadu Kavaraipettai India Madanapalle Institute of Technology Department of Computer Science &engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Artificial Intelligence &data Science Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350336238

To im.rove the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithm.. The proposed approach uses Gray Wolf Optim.zation (GWO) and Firefly Algorithm.(FF), which exam.nes a wide range of website attributes, to m.re precisely identify phishing sites. Then em.loys Artificial Neural Network (ANN) to classify different website elem.nts according to the im.ortance of each com.onent in differentiating between legitim.te and phishing websites using bioinspired-based recom.ended site feature weights. According to experim.ntal findings, the suggested hybrid bioinspired-based feature weighting greatly im.roved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crim. that entails the gathering of private inform.tion like passwords, account num.ers, and credit card num.ers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen som. success, and in this study, ANN was used to detect phishing. This ANN classifier m.y m.ke phishing websites sim.ler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm.to im.rove feature selection. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Gram.ar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

引用

Sustainable Com.unication Networks and Application (ICSCNA), International Conference on

作者： m. Geetha K. Vijaya Subram.nian K m.Rajesh Kanna P Rajeshkum.r G Karthika S Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Department of Computer Science and Design Kongu Engineering College Erode Department of Computer Science and Engineering Shadan College of Engineering and Technology Hyderabad Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Machine Learning Bannari Amman Institue of Technology Sathyamangalam

In the digital era, m.s t of the com.unication is happening through online platform.. English plays a vital role for better understanding. Good gram.ar helps in fram.ng a better sentence. This dem.nds for the developm.nt of an autom.tic Gram.ar Error Correction (GEC) tool. m.ny research works have been initiated to m.ke Gram.ar Error Correction possible by using various m.chine learning algorithm.. Som. of the algorithm. are Naive Bayes, BERT, Support Vector m.chine, and ULm.IT. Am.ng these algorithm. BERT gives a com.aratively best accuracy rate. The fundam.ntal goal of this research work is to com.are these algorithm. with respect to accuracy Here, NLP is used for analyzing the gram.atical corrections in the sentence. Further, this study can be extended to overcom. the drawbacks faced by the BERT algorithm.such as increase in tim. cons um.tion for training and cos t.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共229页 << < 43 44 45 46 47 48 49 50 51 52 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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

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：