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

4,137 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,181 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,738 篇 工学
- 2,465 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 587 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 216 篇 电气工程
- 211 篇 光学工程
- 148 篇 安全科学与工程
- 136 篇 机械工程
- 124 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 107 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,369 篇 理学
- 675 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 307 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
157 篇 教育学
- 155 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

526 篇 machine learning
243 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
75 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
58 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 neural networks

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

3,930 篇 英文
250 篇 其他
34 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4181 条记录，以下是1351-1360 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：

A web application to demonstrate the properties of Label Propagation algorithms 29

A web application to demonstrate the properties of Label Pro...

引用

Demonstration Track of the 29th international conference on Cooperative Information Systems, CoopIS-D 2023

作者： Ceravolo, Paolo Maghool, Samira Talpalaru, Andrei Georgiani Department of Computer Science Università Degli Studi di Milano Via Celoria 18 Milano Italy

Vector Label Propagation (VLP) is a machine learning technique used for tasks like semi-supervised learning and graph-based analysis. It is valuable in various fields, including social network analysis and recommendation systems, where each data point has multiple attributes. this paper discusses the development of a web application aimed at demonstrating the Agent-based vector-label propagation algorithm's (AVPRA) operation. Prior to this application, AVPRA execution and visualization were challenging for non-experts, relying on scripts. the web app offers a user-friendly, graphical interface, enabling real-time tracking and transparent visualization of the algorithm's iterations. © 2023 CEUR-WS. All rights reserved.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems 4

Cognitive Twins for Predictive Maintenance and Security in I...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Swamy, B. Venkata Barmola, Pareshwar Prasad thangavel, Senthil Kaliappan, S. Patel, Hardik Abhyankar, Girish B V Raju Institute of Technology Department of Chemistry Medak India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Paypal Inc CA United States Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

the increasing complexity and interconnectedness of Internet of things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. this paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. the framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. the framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Automatic Classification and Analysis of Spam Based on machine learning 4

Automatic Classification and Analysis of Spam Based on Machi...

引用

4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Xu, Jiacheng School of Cyberspace Security Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350341690

With the proliferation of Internet use and online communication, the issue of spam, characterized by the inundation of unsolicited or unwanted messages in large numbers, has become an enduring challenge. Spam classification, the automated task of identifying and filtering out spam, has been a subject of extensive research. this paper evaluates the performance of five machine learning models, namely Naive Bayes, Support Vector machines (SVM), Logistic Regression, Random Forest, and LightGBM, through comprehensive training using confusion matrices and accuracy ratings. the evaluation process is carried out following three main steps. First, the spam dataset is meticulously analyzed and subjected to pre-processing, incorporating feature extraction techniques to adapt its data format. Second, the five machine learning algorithms are applied to construct classification models for data training. Lastly, the strengths, weaknesses, and comparative performance of these algorithms are rigorously assessed. the experimental findings highlight the effectiveness of Naive Bayes and SVM in spam classification. this methodology is instrumental in the development of efficient spam classifiers and enhancing email filtering processes. Moreover, the paper discusses potential future directions in the field aimed at advancing and refining spam detection techniques. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Construction of a computer Network Performance Prediction Optimization Model Based on machine learning Algorithms 4

Construction of a Computer Network Performance Prediction Op...

引用

4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Su, Jianhao College of Integrated Circuits and Optoelectronic Chips Shenzhen Technology University Guangdong Shenzhen518118 China

computer networks have been widely used in critical infrastructure for businesses, schools, governments, and individuals. the development of computer networks is an irreversible historical trend. this article studies computer network performance prediction optimization models through machine learning, which can improve the intelligence and efficiency of network management. Existing research has made some progress in machine learning based network security and performance optimization. However, good results have not been achieved in real-time performance prediction and optimization. this article uses Long Short Term Memory Network (LSTM) to establish an effective predictive optimization model. Firstly, the data is processed, such as missing values, duplicates, and device time redundancy. then, features are selected to select the corresponding input features of the model. Finally, the model is optimized to obtain the optimized model. the performance of the LSTM model is compared with the random forest (RF) and support vector machine (SVM) models. Model performance indicators include mean absolute error (MAE), coefficient of determination, F1 score, and model computation time. the experimental results show that the LSTM model has higher accuracy than RF and SVM models in all evaluation metrics. Meanwhile, the LSTM model has significant advantages in computational time compared to the other two models. the LSTM based computer network performance prediction optimization model can be optimized in terms of prediction accuracy, generalization ability, and computational efficiency. It has the ability to effectively process time series data. the LSTM model used for network performance prediction has a minimum average absolute error of 8% and a maximum coefficient of determination of 0.95. It achieves higher F1 scores and faster calculation speed. the results reflect the potential and practicality of the LSTM model in optimizing computer network performance prediction. © 2024

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

AI Based Optimization of Fault Tolerance in Multipath Routing for Medical application 4

AI Based Optimization of Fault Tolerance in Multipath Routin...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Prasad, Lakkakula Hari Shobana, G. Bhardwai, Aniul Lakshmi Sireesha, C.H. Bais, Parikshit Kumar Kothapalli, Balakrishna Anurag Engineering College Department of ECE India Loyola Icam College of Engineering and Technology Department of It India Uic - University Institute of Computing Chandigarh University India Cbit Hyderabad Department of Mathematics Hyderabad India LNCT University Bhopal Computer Application Faculty of Engineering India School of Engineering Sr University Department of Eee Warangal India

ISBN: (纸本)9798350300888

Artificial Intelligence (AI) has had a substantial influence on the process of pharmaceutical research and development over the course of the last decade. Several applications that are connected to drug discovery, such as virtual screening and drug design, have made use of a number of AI's varied approaches. One of these applications is drug discovery. In the first section of this study, we are going to present an overview of drug discovery and discuss applications that are relevant to this field. these applications may be divided into two major tasks, which are synthesizing new compounds and making predictions about the characteristics of existing molecules. After that, this study will discuss about the standard data resources, different representations of molecules, and benchmark platforms. As a crucial component of the investigation, the methodologies of artificial intelligence are dissected into a variety of model architectures and learning paradigms. the reviewed papers have been arranged in a chronological manner to demonstrate the development of the technical features of AI in drug discovery throughout the course of time. According to our anticipations, the results of this study should provide a comprehensive analysis of the use of AI in drug development. An additional instructional resource that we make accessible to users is a repository that can be found on Github and is comprised of a variety of publications (and codes, if relevant). this archival storage space is regularly upgraded. © 2023 IEEE.

关键词： Expert systems

来源：评论

学校读者我要写书评

暂无评论

PHISHING DETECTION USING machine learning ALGORIthMS 4

PHISHING DETECTION USING MACHINE LEARNING ALGORITHMS

引用

4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Mohammed, Moulana Prasanth, K. Koteswara Subhash, S. Venkata Sai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (纸本)9781665401180

Phishing attacks are one of the most serious hazards to individuals and businesses in today's digital world. Many attacks are performed each month with the goal of convincing consumers that they are visiting a reputable website or online application in order to obtain account information. A strategy is provided for detecting these types of assaults by modifying existing Document Object Model (DOM) comparison tools such as Proportional Distance, false positive and false negative, and the favicon image recognition algorithm. there may be a different impact, misclassifying a phishing website than misclassifying a legitimate website if the proportional distance approach is used. To solve this, the proportional distance method is used. © 2022 IEEE

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

the application Landscape and Research Status of Artificial Intelligence in Language learning: A Visual Analysis 15

The Application Landscape and Research Status of Artificial ...

引用

15th international conference on Education Technology and computers (ICETC)

作者： Chen, Xiaojiao Zhu, Keke Jing, Yuhui Wang, Chengliang Zhejiang Univ Technol Coll Educ Sci & Technol Hangzhou Peoples R China Zhejiang Univ Technol Coll Foreign Languages Hangzhou Peoples R China East China Normal Univ Dept Educ Informat Technol Shanghai Peoples R China

ISBN: (纸本)9798400709111

the integration of Artificial Intelligence (AI) in the education industry has emerged as a dominant trend, particularly in the field of language education and learning. this study employs advanced visualization tools, such as VOSviewer, to conduct a comprehensive analysis of the current research landscape and key areas of focus pertaining to the amalgamation of foreign language learning with AI. the findings reveal the following insights: 1) Most articles in this domain are published in journals affiliated with the educational technology and computer education disciplines, with a notable prevalence of open-access publications. 2) Prominent research themes encompass the continuous advancements in AI technology and the dynamic adaptation of language learning to embrace these transformative innovations. 3) Investigations into the application of AI in language learning primarily involve researchers from China and the United States, although collaborative networks among these scholars remain to be firmly established. 4) Employing clustering analysis, this study delineates two overarching categories within the field, namely "intelligent tutoring systems" and "natural language processing." By elucidating the current state of research and discerning future trends, this study provides valuable insights and guidance to scholars and practitioners, facilitating further empirical investigations and practical implementations. Additionally, we anticipate that the findings presented herein will serve as a theoretical benchmark for the advancement of language learning and education within the realm of AI-driven technological transformations.

关键词： artificial intelligence intelligent tutoring systems language learning natural language processing visualization analysis

来源：评论

学校读者我要写书评

暂无评论

Near-infrared spectroscopy based on transfer learning, detection of tobacco chemical composition 4

Near-infrared spectroscopy based on transfer learning, detec...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Wang, Lili Tian, Lei Gao, Pengcheng Liu, Yong Wang, Yongqian Shandong Linyi Tobacco Co. Ltd. Shandong Linyi276001 China Shandong Institute of Commerce and Technology Shandong Jinan250103 China Shandong Deyun Information Technology Co. Ltd. Shandong Jinan250101 China

ISBN: (纸本)9781510674448

the use of near-infrared spectroscopy enables rapid and efficient detection of tobacco leaf chemical components. However, changes in environmental and climatic factors can affect the accuracy of the originally established prediction model. To reduce the workload involved in re-sampling and modeling, a more stable prediction model was established using transfer learning, transferring valuable information from fragmented leaf spectra to enhance the stability of the whole-leaf spectral measurement model. New prediction models were developed for total sugars, reducing sugars, total alkaloids, potassium, and chlorine contents. Experimental results show that the mathematical model established through transfer learning provides highly consistent predictions with results obtained through continuous flow methods. It is more stable than existing models and can be directly applied for model transfer without the need for re-sampling. It can be used for the analysis of tobacco leaf chemical components in the tobacco production process, meeting the requirements for rapid detection of routine chemical components in tobacco leaves. the model exhibits high accuracy in predicting new samples and can meet the demands of tobacco production companies. © 2024 SPIE.

关键词： Near infrared spectroscopy

来源：评论

学校读者我要写书评

暂无评论

A Robust Deep learning-based Approach for Network Traffic Classification using CNNs and RNNs 4

A Robust Deep Learning-based Approach for Network Traffic Cl...

引用

4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Jenefa, A. Sam, Shebin Nair, Varun thomas, Boney George George, Anson Saju thomas, Rino Sunil, Alwin Dany Karunya Institute of Tech. Dept. of Cse Coimbatore India

ISBN: (纸本)9798350300772

the application of deep learning has become prevalent in the area of network traffic classification. Deep learning has acquired widespread use in network traffic classification, providing a powerful and flexible approach to identify different types of traffic in a network. this research provides a novel deep learning-based technique for network traffic classification. the proposed method leverages both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify network traffic. this study aims to demonstrate the efficiency of deep learning in classifying network traffic and emphasize the potential of using a combination of CNNs and RNNs for this task. the proposed approach first uses CNNs to extract features from the raw network traffic data, and then uses RNNs to classify the extracted features. the proposed approach was evaluated using a recent dataset of network traffic from different sources, such as the CIC-IDS 2022 dataset. the findings suggest that the proposed technique can attain a high classification accuracy rate (98%), and surpasses traditional machine learning-based methods. Additionally, the study demonstrates the ability of the proposed approach to manage extensive and intricate datasets, making it suitable for real-world network traffic classification tasks. Overall, this paper demonstrates the effectiveness of deep learning-based approaches for network traffic classification, and highlights the potential of using a combination of CNNs and RNNs for this task. In future work, the proposed approach could be further improved by incorporating additional deep learning architectures, such as auto encoders and generative models, to extract more meaningful features from the raw network traffic data. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Engage Learn: An AI-Based English Proficiency Improviser 4

Engage Learn: An AI-Based English Proficiency Improviser

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Jadhav, Balasaheb Jain, Manas Jajoo, Adhip Kadam, Devika Kadam, Harshvardhan Kakkad, Toshish Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331529635

In this research, the new and highly effective Web-based English learning and teaching platform called Engage Learn is introduced with the main purpose of avoiding the mistakes of the traditional models and to help users gain the ability to write and speak English more effectively. Engage Learn employs a marked-defined approach, that involves the application of several advanced technologies. Generative models including ChatGPT 3.5 turbo and Gemini 1.5 flash get enhanced presence when trained using Retrieval-Augmented Generation (RAG) technique. First, the ability for users to speak English makes for an almost informal procedure when learning since the user and the computer are communicating with one another by use of words. In addition to this, targeted feedback modules make an assessment of the user's input and give an immediate-feedback of what is required in terms of improving a user's vocabulary, grammar, and fluency. It offers another learning approach since it presents the content in a manner that is suited more to the current ability level of the user and the usage of preferred learning objectives. Engage Learn also provides flexibility of use based on learning modes as encompassed by general conversation practice, focused grammar correction and vocabulary buildup. this paper reviews the system architecture, explaining features of the different parts of the system, including the speech recognition, LLM with RAG, output analyzing, and the feedback-giving modules on grammar, vocabulary, and reading preferences. Further the paper discusses the tuning of the LLMs in a carefully selected set for language acquisition. this fine-tuning makes sure that the LLMs shall have adequate capability and knowledge required so as to get a feel about how spoken English is and about the particular feedbacks that different user may require out of them. In the last section of the abstract, the planned evaluation methods are presented with the focus on the responsibility of user te

关键词： Contrastive 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419页 << < 132 133 134 135 136 137 138 139 140 141 > >>

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：