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

70,736 篇 会议
435 篇 期刊文献
108 册 图书

馆藏范围

71,279 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

54,691 篇 工学
- 41,578 篇 计算机科学与技术...
- 32,747 篇 软件工程
- 14,624 篇 信息与通信工程
- 7,650 篇 控制科学与工程
- 5,658 篇 电气工程
- 5,359 篇 生物工程
- 4,640 篇 电子科学与技术（可...
- 4,285 篇 生物医学工程（可授...
- 3,996 篇 机械工程
- 3,234 篇 光学工程
- 2,855 篇 仪器科学与技术
- 2,418 篇 化学工程与技术
- 2,312 篇 动力工程及工程热...
- 2,169 篇 安全科学与工程
- 2,109 篇 网络空间安全
23,764 篇 理学
- 13,444 篇 数学
- 5,875 篇 生物学
- 5,042 篇 物理学
- 4,232 篇 统计学（可授理学、...
- 2,808 篇 系统科学
- 2,422 篇 化学
16,583 篇 管理学
- 9,811 篇 管理科学与工程(可...
- 7,776 篇 图书情报与档案管...
- 4,932 篇 工商管理
3,698 篇 医学
- 3,202 篇 临床医学
- 2,988 篇 基础医学(可授医学...
- 1,973 篇 药学(可授医学、理...
2,571 篇 法学
- 2,018 篇 社会学
2,169 篇 经济学
- 2,139 篇 应用经济学
1,963 篇 教育学
- 1,914 篇 教育学
1,414 篇 农学
711 篇 文学
261 篇 军事学
195 篇 艺术学
11 篇 历史学
7 篇 哲学

主题

1,170 篇 feature extracti...
1,004 篇 computer science
915 篇 deep learning
908 篇 computational mo...
895 篇 data mining
774 篇 convolutional ne...
768 篇 training
742 篇 internet of thin...
737 篇 support vector m...
729 篇 machine learning
670 篇 accuracy
636 篇 semantics
555 篇 computer vision
472 篇 computer archite...
459 篇 neural networks
458 篇 data models
449 篇 predictive model...
444 篇 image segmentati...
431 篇 big data
425 篇 cloud computing

机构

142 篇 school of comput...
64 篇 school of comput...
63 篇 chitkara univers...
61 篇 department of co...
60 篇 university of sc...
53 篇 department of co...
51 篇 college of compu...
46 篇 institute of inf...
45 篇 school of inform...
43 篇 graphic era deem...
42 篇 school of inform...
41 篇 huazhong univers...
39 篇 school of comput...
38 篇 graphic era deem...
37 篇 computer school ...
37 篇 college of infor...
37 篇 college of infor...
36 篇 centre of resear...
35 篇 jiangxi universi...
34 篇 school of inform...

作者

72 篇 li yang
66 篇 wang lei
62 篇 zhang lei
47 篇 wang hui
43 篇 wang jing
40 篇 li xin
37 篇 li li
36 篇 lei wang
34 篇 pareek piyush ku...
33 篇 zhang tao
32 篇 sharma vikrant
31 篇 china
29 篇 wang jian
29 篇 liu yang
26 篇 li peng
26 篇 wang ping
26 篇 yang jun
25 篇 liu bo
24 篇 jian wang
24 篇 wang wei

语言

68,491 篇 英文
1,536 篇 其他
1,474 篇 中文
10 篇 俄文
8 篇 法文
6 篇 土耳其文
4 篇 西班牙文

检索条件"任意字段=2nd IEEE International Conference on Computer Science and Information Technology"

共 71279 条记录，以下是101-110 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：

computer technology for Parametric Research of the Hierarchical Structure of a Multicommodity Communication Network with Discrete Flows 2nd

Computer Technology for Parametric Research of the Hierarchi...

引用

2nd international conference on information Technologies and Their Applications

作者： Vasyanin, Volodymyr Ushakova, Liudmyla Inst Telecommun & Global Informat Space NASU Kyiv Ukraine

ISBN: (纸本)9783031734168;9783031734175

The article is devoted to the study of the problem of optimizing the hierarchical structure of a multicommodity communication network with discrete flows when changing its important parameters, such as the capacity of network arcs in transport blocks and the size of the transport block for transportation of the discrete small-lot cargo or of the data transmission in a digital communication network. The network has three levels of hierarchy - a backbone, a zonal and an internal and four types of nodes - backbone nodes of the first, second and third types, forming the backbone and zonal levels of the network, and nodes of the fourth type, which subordinate to each backbone node and forming the internal levels of the network. Types of nodes differ from each other in terms of functionality. The main task of the study is to establish how the structure of the backbone network changes (the number and location of backbone nodes of the first, second, and third types), the flow processing and distribution scheme, and the technical and economic indicators of the network's functioning for different values of its parameters. The principles of organization of sorting and distribution of flows in a three-level network and its mathematical model are given. A mathematical model of the optimization problem of the backbone network structure and flow sorting and distribution scheme is formulated. computer modeling of the problem on a network containing 10 nodes at the specified change in the both parameters is carried out.

关键词： Hierarchical Communication Networks Discrete Flows and Parameters Optimization Problems computer Modeling

来源：评论

学校读者我要写书评

暂无评论

Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

引用

2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. Shabaz, Mohammad Kollu, Archana Sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based Support Vector Machine (KSVM) optimized by Whales algorithm. The combination kernel SVM shows better accuracy and less computational time compared to single kernel of SVM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

computer network information security monitoring system 2

Computer network information security monitoring system

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Lin, Hongkai Wuhan Business University Hubei Wuhan430056 China

ISBN: (纸本)9781510689053

Although traditional protection methods can improve the security of the network to a certain extent, its effect is often limited in the face of complex network environment and various attacks. Therefore, it is an important problem to be solved in the field of communication network security to find a more efficient and intelligent communication network link data security protection method. This paper proposes a computer network information security monitoring system combined with knowledge mapping technology, which aims to improve the data security protection effect of computer communication network. this paper designs and implements an information security situation awareness system, which can find and deal with network security threats in time by monitoring and analyzing network data traffic and security event information in real time, and improves the efficiency and level of information security protection. The results show that the information security situational awareness system based on artificial intelligence technology has high detection accuracy and strong real-time response capability, and can effectively deal with various network security threats. It has a good application prospect in data security protection of communication network link. © 2025 SPIE.

关键词： Network security Data communications computer security Defense and security information security Data modeling Machine learning computer networks Evolutionary algorithms Education and training

来源：评论

学校读者我要写书评

暂无评论

Evaluation of jet energy determination in heavy-ion collisions using Machine Learning 2

Evaluation of jet energy determination in heavy-ion collisio...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhao, Yangguang Wang, Yuhui Zhou, Yujie Bao, Qihan College of Computer Science and Technology Qingdao University Qingdao266071 China College of Food Science Sichuan Agriculture University Chengdu610000 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781510689053

In high-energy physics experiments, the precise reconstruction of the transverse momentum of jets is challenging. The primary obstacle is the interference caused by background noise particles overlapping with the jet and the limitations of the sensors currently being used. A traditional noise reduction method is area-based subtraction, which calculates the mean value of certain events. In previous studies, some noise reduction methods utilizing machine learning techniques have been proposed. In this study, we evaluate the performance of various machine learning models in reconstructing the jet transverse momentum in simulated heavy-ion events, based on Pythia events embedded in a thermal background distribution. The performance of the models at the particle level is compared to that using pseudo-towers of various resolutions in eta, phi to evaluate the impact of the sensor performance limitations. The results demonstrate that as the resolution decreases, the performance of the machine learning models also degrades, but still performs better than the traditional method. This provides a reference for the application of machine learning methods. © 2025 SPIE.

关键词： Heavy ions

来源：评论

学校读者我要写书评

暂无评论

Enhancing Flood Severity Prediction through a Combined Threshold-Based Alert Algorithm and Random Forest Classifier 2

Enhancing Flood Severity Prediction through a Combined Thres...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Govind, Aksshat Gupta, Aishwarya Katiyar, Aviral Tyagi, Chirag Gaur, Sreesh Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9798331530389

Flooding is a significant and recurrent is- sue that poses severe risks to communities, economies, and environments. Traditional flood prediction methods, which often rely on fixed thresholds, may lack adaptability to changing conditions. Meanwhile, machine learning (ML) models like Random Forest Classifiers (RFCs) offer advanced predictive capabilities, but may not provide straightforward alert mechanisms. This research proposes a novel approach that integrates a Threshold-Based Alert Algorithm (TBA) with an RFC to enhance flood severity prediction. The combined model leverages the strengths of both methodologies: immediate, interpretable alerts from the TBA and adaptive, data-driven insights from the RFC. Using historical and real-time data from river basins in Wisconsin and New York, the hybrid model demonstrates improved accuracy and reliability over traditional methods and standalone RFCs. The results indicate that this integrated approach offers a more effective tool for flood management and disaster mitigation. © 2025 ieee.

关键词： data integration disaster risk reduction environmental data Flood forecasting flood management hybrid models machine learning (ML) predictive analytics random forest classification (RFC) threshold-based alert algorithm (TBA)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Weather Prediction Accuracy with Gradient Boosting: A Machine Learning Approach 2

Enhancing Weather Prediction Accuracy with Gradient Boosting...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Jain, Abhinav Sharma, Gargi School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India

ISBN: (纸本)9798331530389

This research focuses on the integration of Machine Learning (ML) in predicting the climate with the help of daily recorded weather dataset of Seattle [33] which we obtained from the Kaggle. This research focuses on implementing and analyzing multiple ML techniques such as Naive Bayes, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Gradient Boosting, XGBoost, and Decision Tree. XGBoost able to get to the top position with an accuracy rate of 87.90% which speaks volume of its performance. This integration of ML techniques is useful in understanding the weather prediction which is highly complex due to multiple atmospheric changes. Such modern methods help in recognizing the more complex non-linear features and trends within weather data. Machine learning algorithms makes it possible to detect such tendencies from complex datasets over time. ML enhances weather prediction approaches by learning and analyzing patterns from historical data and real time data. This paper highlights the importance of extreme gradient boosting in weather forecasting by providing examples of its successful applications within the interdisciplinary field of meteorology and ML. This study compares different algorithms for best accuracy and also points to the increasing significance of ML in the enhancement of weather forecast models. © 2025 ieee.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Approach to Detect Adulteration in Turmeric Using RGB and Thermal Images 2nd

Machine Learning Based Approach to Detect Adulteration in Tu...

引用

2nd international conference on Agriculture-Centric Computation

作者： Kaur, Rupinder Singh, Simrandeep Saini, Mukesh Indian Inst Technol Ropar Rupnagar 140001 Punjab India Chandigarh Univ Dept Elect & Commun Engn UCRD Gharuan India

ISBN: (纸本)9783031744396;9783031744402

Turmeric is a widely used spice due to its medicinal, culinary, and dyeing properties. However, its frequent adulteration poses significant health risks, including heart disease, metabolic disorders, and even death. Therefore, a swift, dependable, and non-destructive method is imperative. This research paper aims to detect adulterants in turmeric using thermal and RGB images. Pure turmeric powder is blended with 20% chalk powder, chickpea powder, and starch, along with 10% tartrazine adulterants. The prepared samples undergo heating at 50 and 70 degrees C, while thermal and RGB images are captured. Applying CNN and VGG16 models, we achieve accuracies of 77.11% and 63.46% on CNN, and 93.08% and 95.08% on VGG16 for thermal images at 50 and 70 degrees C, respectively. For RGB, we attain accuracies of 68.18% on CNN and 95.45% on VGG16. These results indicate the viability of detecting adulteration using thermal images, with VGG16 demonstrating superior performance.

关键词： Turmeric Adulteration Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Flood prediction near Yamuna express way a case study on Delhi 2

Flood prediction near Yamuna express way a case study on Del...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Verma, Shikha Mishra, Priya ABES Engineering College Ghaziabad India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798331530389

The area lies near Yamuna River in Delhi faces a frequent flood event, and with the increasing frequency and intensity of flood events highly impacted the human lives, infrastructure, and agriculture. Hence, there is a great need of accurate and timely flood prediction models. Traditional hydrological models are useful but often inaccurate for effective disaster management. The new technologies like machine learning are showing a prominent solution for the complex problems like flood. Therefore, this study conducts a comprehensive analysis of advanced ML(machine learning) techniques: Logistic Regression or named as LR, Support Vector Machines named as SVM, Random Forest named as (RF)and XG-Boost, in predicting floods in the area lies near Yamuna River by using GIS, Remote sensing data and seven flood influencing factors. The performance of the models is tested by using performance metrics such as accuracy, recall, F1-score and precision. Further, the models were also validated using curve of AUC-ROC. The outputs reveal that XG-Boost out performs the other models in prediction accuracy. Logistic Regression and SVM also perform well, giving them viable options depending on the application scenario. This study examines the benefits and weaknesses of each model and recommends their use in flood-prone area near Yamuna River. © 2025 ieee.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Simplifying code translation: Custom syntax language to C language transpiler 2

Simplifying code translation: Custom syntax language to C la...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Lalith, Pecheti Charan Goel, Sujal Kakkar, Misha Sharma, Seema Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

In today's world, programming has become a very essential skill as it is used everywhere, from creating software's for operating systems to analysis of patients' health care data in biomedical engineering. Programmers usually face a lot of difficulties to understand beginner level coding languages having low-level syntax. The proposed study "Simplifying Code Translation: Custom Syntax Language to C Language Transpiler", targets the specified problem and provides the solution in a form of transpiler that converts the newly developed custom language (made up of high-level syntax) to C language. The developed tool minimizes the overall cost incurred during the code designing process. This transpiler consists of 4 main parts: preprocessor, lexer, parser and emitter. It first starts by creating preprocessor which handles preprocessor directives such as 'define' and 'include'. This preprocessed code is then passed onto lexer which tokenizes the code. The parser is responsible for creating Abstract Syntax Tree (AST) using the tokens provided. The final AST is then transformed to C program by the emitter. Then output C code maintains proper syntactic and semantic structure of the original custom syntax language code. © 2025 ieee.

关键词： C (programming language)

来源：评论

学校读者我要写书评

暂无评论

Hybrid Machine Learning for Feature-Based Spam Detection 2

Hybrid Machine Learning for Feature-Based Spam Detection

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Gupta, Sandeep Barigidad, Susmith Hussain, Shadab Dubey, Santosh Kanaujia, Sandeep Uttarakhand Dehradun India Computer Science Santa Clara University United States MathCo DallasTX United States Department of Electronics and Communication Engineering United College of Engineering & Research Naini Prayagraj India Naini Prayagraj India

ISBN: (纸本)9798331530389

Researchers have been paying significant attention to the increasing need for and prevalence of unwanted spam on networks. Multiple studies have extensively examined the fundamental origins of spam in networks. Previous research has shown that spam-related problems are one of the main dangers to network systems, affecting both ongoing operations and past data. To tackle and reduce the increase in spam, researchers have put forward different methodologies. Although certain techniques have received approval from experts, every method has its own set of constraints. Furthermore, the persistent issue of inadequate precision in identifying spam poses a significant obstacle for numerous current predictive models. Therefore, the task of finding a reliable prediction method continues to be a challenging and extensively studied field, attracting significant attention from researchers in recent decades. This paper introduces a machine learning approach for detecting spam in emails. The proposed model demonstrates improved accuracy compared to previous studies. © 2025 ieee.

关键词： Adversarial machine learning

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500页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：