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

106,756 篇 会议
687 篇 期刊文献
555 册 图书
4 篇 学位论文

馆藏范围

108,001 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

60,973 篇 工学
- 49,953 篇 计算机科学与技术...
- 39,802 篇 软件工程
- 13,499 篇 信息与通信工程
- 11,482 篇 生物工程
- 8,567 篇 控制科学与工程
- 6,788 篇 电气工程
- 6,343 篇 生物医学工程（可授...
- 3,861 篇 光学工程
- 3,352 篇 机械工程
- 3,006 篇 电子科学与技术（可...
- 2,782 篇 网络空间安全
- 2,726 篇 安全科学与工程
- 2,543 篇 动力工程及工程热...
- 2,342 篇 化学工程与技术
- 2,201 篇 仪器科学与技术
26,033 篇 理学
- 12,115 篇 生物学
- 12,088 篇 数学
- 5,179 篇 物理学
- 4,910 篇 统计学（可授理学、...
- 3,517 篇 系统科学
- 2,348 篇 化学
17,448 篇 管理学
- 11,006 篇 管理科学与工程(可...
- 7,620 篇 图书情报与档案管...
- 5,156 篇 工商管理
13,540 篇 教育学
- 13,339 篇 教育学
6,412 篇 医学
- 5,471 篇 临床医学
- 5,222 篇 基础医学(可授医学...
- 3,407 篇 药学(可授医学、理...
- 2,890 篇 公共卫生与预防医...
3,364 篇 法学
- 2,910 篇 社会学
1,965 篇 文学
1,765 篇 农学
1,660 篇 经济学
267 篇 艺术学
106 篇 军事学
11 篇 历史学
10 篇 哲学

主题

7,181 篇 machine learning
6,982 篇 deep learning
5,351 篇 accuracy
5,035 篇 training
4,829 篇 computational mo...
4,195 篇 education
3,832 篇 students
3,553 篇 feature extracti...
3,029 篇 support vector m...
3,028 篇 predictive model...
3,026 篇 machine learning...
2,972 篇 convolutional ne...
2,489 篇 real-time system...
2,334 篇 data models
2,234 篇 neural networks
1,986 篇 cloud computing
1,817 篇 teaching
1,487 篇 reinforcement le...
1,449 篇 artificial intel...
1,388 篇 internet of thin...

机构

826 篇 chitkara univers...
145 篇 lovely professio...
106 篇 computer science...
98 篇 karnataka bangal...
94 篇 college of compu...
89 篇 school of comput...
80 篇 chitkara univers...
76 篇 school of comput...
72 篇 department of co...
68 篇 department of co...
64 篇 department of co...
60 篇 department of co...
58 篇 school of comput...
50 篇 department of in...
49 篇 chitkara univers...
49 篇 department of co...
43 篇 department of me...
42 篇 school of automa...
41 篇 vellore institut...
41 篇 chennai

作者

70 篇 shiva mehta
70 篇 anjali t.
53 篇 li yang
42 篇 sharma vikrant
40 篇 wang lei
40 篇 kukreja vinay
39 篇 rakesh kumar
37 篇 gill kanwarparta...
35 篇 sharma neha
33 篇 wang jing
32 篇 vinay kukreja
31 篇 lei wang
31 篇 zhang lei
30 篇 huang yueh-min
29 篇 liu bo
29 篇 yu wang
27 篇 li xin
26 篇 amandeep nagpal
25 篇 vinay gautam
25 篇 shang wang

语言

100,155 篇 英文
7,484 篇 其他
773 篇 中文
27 篇 西班牙文
17 篇 葡萄牙文
5 篇 阿拉伯文
3 篇 法文
3 篇 土耳其文
2 篇 俄文

检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 108002 条记录，以下是4611-4620 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：

The Use of Self-Regulation of learning in Recommender Systems: State-of-the-Art and Research Opportunities 16

The Use of Self-Regulation of Learning in Recommender System...

引用

16th international conference on Computer Supported Education, CSEDU 2024

作者： Neo, Alana Viana Borges da Silva Moura, José Antão Beltrão de Araújo, Joseana Macêdo Fechine Régis Neo, Giseldo da Silva Júnior, Olival de Gusmão Freitas Center for Electrical and Computer Engineering Federal University of Campina Grande Campina Grande Brazil Federal Institute of Alagoas Campus Viçosa Viçosa Brazil Institute of Computing Federal University of Alagoas Maceió Brazil

ISBN: (纸本)9789897586972

Self-regulated learning is defined as the degree to which students are metacognitive, motivationally, and behaviorally active participants in their learning. learning can be influenced and improved to achieve successful academic results. This article reviews the literature to analyze and compare learning self-regulation strategies to recommend learning objects in the context of a Virtual learning Environment. The results serve to map the state of the art, main approaches, and characterizations of the topic of recommendation systems that use self-regulated learning strategies to support students’ academic performance;and to identify promising opportunities for future research on the topic. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive learning with Negative Samples Augmentation 21

CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Sty...

引用

21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Deng, Yimin Zhang, Xulong Wang, Jianzong Cheng, Ning Xiao, Jing Co. Ltd. China University of Science and Technology of China China

ISBN: (纸本)9798350329223

Better disentanglement of speech representation is essential to improve the quality of voice conversion. Recently contrastive learning is applied to voice conversion successfully based on speaker labels. However, the performance of model will reduce in conversion between similar speakers. Hence, we propose an augmented negative sample selection to address the issue. Specifically, we create hard negative samples based on the proposed speaker fusion module to improve learning ability of speaker encoder. Furthermore, considering the fine-grain modeling of speaker style, we employ a reference encoder to extract fine-grained style and conduct the augmented contrastive learning on global style. The experimental results show that the proposed method outperforms previous work in voice conversion tasks. © 2023 IEEE.

关键词： Speech synthesis

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Detection Using Neural Networks

Breast Cancer Detection Using Neural Networks

引用

2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Preethi, J. Rahul Chiranjeevi, V. Dhanya Lakshmi, A. Hosala Devi, N.K. Rajalakshmi Engineering College Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Cancer is the deadliest disease in the world, and it primarily strikes women. As early cancer detection can aid in the disease's treatment, it is imperative that the primary goal be the scientific discovery of a cancer cure. After analyzing 41 publications, we discovered that there are numerous techniques for detecting cancer. We demonstrated a deep learning neural network method for breast cancer diagnosis in this work using the Breast Cancer database. This study illustrates how deep technology and datasets can be used to learn about breast cancer diagnosis. There are three sections to this work. We started by gathering the dataset, dividing it into training and testing sets, applying a pre-processing technique to scale and filter the data, and making some graphs for data visualization. A training dataset was used to test the model, and it achieved an accuracy of 99.67%. We have shown that deep learning technologies can accurately diagnose breast cancer using the Wisconsin Breast Dataset. The dataset comprises 30 features in total, and the database contains 569 rows. In this paper, we use eleven pre-processed features that we obtained to identify breast cancer. But first, we used several pre-processing techniques including Label Encoder, Normalizer, and Standard Scaler, to apply in the model and achieve good accuracy to enhance the model for breast cancer detection. © 2024 IEEE.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Machine learning Approach for Early Detection and Classification of Hyperthyroidism and Hypothyroidism 2

Comprehensive Machine Learning Approach for Early Detection ...

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Jayadharshini, P. Kumar, A Satheesh Sivasankar, R. Harsine, K.U. Geetha, P. Logu, B. Department of Artificial Intelligence Kongu Engineering College Perundurai Tamilnadu Erode India Department of Cse Nandha Engineering College Erode India Department of Cse Builders Engineering College Tirupur India

ISBN: (纸本)9798350384277

Thyroid disorders, including hyperthyroidism and hypothyroidism, pose significant health risks and necessitate early detection for effective management. In this study, we present a comprehensive approach employing various machine learning algorithms for the prediction of thyroid conditions. The algorithms investigated include Random Forest, Naive Bayes, Decision Tree, Gradient Boosting, Logistic Regression, Adaboost, Support Vector Machines (SVM), and k-Nearest Neighbors (KNN). Using a dataset comprising diverse thyroid-related features, we conducted an extensive comparative analysis of these algorithms. Our findings reveal that Random Forest exhibited the highest predictive accuracy, reaching an impressive 98.46%. This outstanding performance positions Random Forest as a robust model for accurate thyroid disorder classification. The target classes encompass hyperthyroid, hypothyroid, and negative instances, providing a comprehensive framework for diagnosing thyroid conditions. The incorporation of multiple algorithms ensures the robustness of our predictive model, accommodating diverse data patterns associated with thyroid abnormalities. Our research provides a foundation for the development of an intelligent diagnostic system. As the field of machine learning progresses, there is a growing recognition of its capacity to advance healthcare diagnostics, specifically in the early identification of abnormalities related to the thyroid. This has the potential to significantly enhance patient outcomes and overall quality of life. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Uncovering Potential Collusive Behavior of AI Bidders in Future Construction Bidding Market

Uncovering Potential Collusive Behavior of AI Bidders in Fut...

引用

ASCE international conference on computing in Civil engineering (I3CE)

作者： Heo, Chan Park, Moonseo Ahn, Changbum R. Seoul Natl Univ Dept Architecture & Architectural Engn Seoul South Korea

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Artificial intelligence (AI) is becoming more prevalent in the construction bidding process, assisting human decision-makers. However, little is known about the tactical shifts that may arise from AI participation in the market, particularly regarding bid pricing. This study aims to predict the strategic decisions AI bidders may make and their impact on bid pricing when they become dominant players in the construction bidding market. An experiment was conducted in which AI bidders competed repeatedly in an environment that simulates the decision-making process in the construction bidding phase. AI bidders were built with Q-learning algorithms, which is a popular reinforcement learning algorithm in repetitive games. Bid notice data from public construction projects in the Washington Department of Transportation (WSDOT) was given to the AI bidders, who set bid prices based on prior bidding experiences. As a result of repeated competition and learning, it was found that the AI bidders gradually learn to cooperate rather than to compete with each other, sustaining higher bid prices compared to human bidders. The study suggests the possibility of collusive behavior by AI bidders in a scenario where they are the dominant participants in the construction bidding process. These findings highlight the need to monitor and regulate the AI participants to prevent anti-competitive behavior in the construction bidding market.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Deanonymize Tor Hidden Services Using Remote Website Fingerprinting 22

Deanonymize Tor Hidden Services Using Remote Website Fingerp...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Meiqi Chen, Muqian Li, Zeyu Wang, Xuebin Shi, Jinqiao Fang, Binxing Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Natl Engn Res Ctr Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Guangzhou Univ Sch Cyberspace Secur Guangzhou Guangdong Peoples R China Natl Internet Emergency Ctr CNCERT CC Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Website Fingerprinting (WF) is commonly used to deanonymize the Tor users. Meanwhile, WF can also be applied to the hidden service (HS) side, to destroy the anonymity of the HS operator. However, the attacker faces the problem of no available data for training. To solve this problem, previous researchers build mirror sites and use the data collected on them to train the classifier. However, this approach still has limitations: The attacker cannot tell whether the target HS is some HS that is not in the set of sites he mirrors, i.e, an HS in the wild. To address this, we propose a novel two-phase approach to deanonymize HSs that using the website fingerprint collected on the remote node, i.e., a node on the same RP circuit with HS. Our approach does not rely on mirror sites because we decouple feature extraction from classification, so that mirror sites are only involved in the training of the feature extractor and are not related to the classification process. The experimental results show that our attack is effective in both closed-world and open-world scenarios.

关键词： Tor hidden service website fingerprinting deep learning

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Optimization of Machine learning Model in Prostate Cancer Detection 5

An Enhanced Optimization of Machine Learning Model in Prosta...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Yennapusa, Haritha Ramakrishnan, Rakesh Muniandi, Balakumar Gupta, Ketan Logeshwaran, J. Gla University Dept. of Computer Engineering and Applications Mathura India University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350383522

the present study goals to optimize the overall performance and accuracy of a device-mastering model for prostate most cancers detection. Prostate most cancers is a malignancy which regularly leaves little to no clue of its presence. Early detection is, therefore, the important thing to a hit remedy. Gadget mastering models are increasingly more being applied in fitness care for prognosis and diagnosis. However, those fashions frequently require good sized quantities of records and well-crafted machines to gain ultimate accuracy. The observe proposed a more desirable Optimization of machine mastering (EOML) method to enhance the accuracy of a prostate cancer detection version. First, a gadget learning model changed into educated the usage of publicly available records from the cancer Genome Atlas. These facts set become preprocessed, and a feature extraction and choice method were executed the usage of a classical ensemble function selection approach. After that, a pass-validation method become used to optimize the version further. Eventually, an ensemble gaining knowledge of approach became adopted to enhance the model's accuracy and performance further. The ensemble getting to know technique blended the predictions of some of device getting to know models to create a more robust and dependable version. © 2024 IEEE.

关键词： Application Ensemble learning Machine-learning Publicly Technique

来源：评论

学校读者我要写书评

暂无评论

Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

引用

4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study on the Effectiveness of Various Machine learning Paradigms in Image Recognition and Predictive Modelling

A Comparative Study on the Effectiveness of Various Machine ...

引用

2023 IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Upadhyay, Deepak Gupta, Mridul Verma, Aditya Mittal, Saksham Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9781665474924

The efficiency of several machine learning algorithms in picture identification and predictive modelling is thoroughly compared in this research article. The necessity to find the best algorithms for certain use cases and the rising need for reliable and effective image recognition systems across a variety of areas are the driving forces behind the study. In order to assess how well these algorithms, perform in terms of accuracy, precision, and recall, the study uses a wide range of machine learning techniques, including classical and deep learning approaches. The study's findings show that convolutional neural networks, in particular, outperform conventional algorithms in image identification tests while standard and deep learning techniques perform similarly in tasks requiring predictive modelling. The study also emphasises the value of transfer learning and fine-tuning strategies in obtaining cutting-edge performance on picture datasets, and finally we will see results at the end. © 2023 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

2024 IEEE 11th international conference on E-learning in Industrial Electronics, ICELIE 2024

2024 IEEE 11th International Conference on E-Learning in Ind...

引用

11th IEEE international conference on E-learning in Industrial Electronics, ICELIE 2024

ISBN: (纸本)9798350363654

The proceedings contain 19 papers. The topics discussed include: emerging technologies for water quality monitoring and assessment: a systematic review;experiments and project-based enhancements for STEM learning;an open-source hardware platform to advance educational multi-sensor environmental monitoring;educational high power photovoltaic curve tracer using an IoT DC to DC power converter with smartphone integration;can AI be helpful for teaching engineering subjects?;enhancing educational methods for electric machines and drives through open-source robotics;a comprehensive multi-stage validation approach for development of power electronic systems;and embedded artificial intelligence in education: bibliometric analysis.

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500页 << < 458 459 460 461 462 463 464 465 466 467 > >>

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：