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

3,204 篇 会议
19 册 图书
9 篇 期刊文献

馆藏范围

3,232 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,933 篇 工学
- 1,731 篇 计算机科学与技术...
- 1,587 篇 软件工程
- 421 篇 信息与通信工程
- 321 篇 控制科学与工程
- 318 篇 生物工程
- 185 篇 生物医学工程（可授...
- 172 篇 电气工程
- 144 篇 机械工程
- 115 篇 电子科学与技术（可...
- 101 篇 光学工程
- 94 篇 安全科学与工程
- 92 篇 化学工程与技术
- 91 篇 动力工程及工程热...
- 79 篇 网络空间安全
890 篇 理学
- 497 篇 数学
- 336 篇 生物学
- 210 篇 统计学（可授理学、...
- 157 篇 物理学
- 154 篇 系统科学
- 84 篇 化学
500 篇 管理学
- 261 篇 管理科学与工程(可...
- 258 篇 图书情报与档案管...
- 160 篇 工商管理
185 篇 医学
- 158 篇 临床医学
- 156 篇 基础医学(可授医学...
- 110 篇 药学(可授医学、理...
- 83 篇 公共卫生与预防医...
133 篇 法学
- 90 篇 社会学
82 篇 经济学
- 82 篇 应用经济学
78 篇 农学
- 75 篇 作物学
66 篇 教育学
4 篇 文学
1 篇 军事学

主题

493 篇 machine learning
103 篇 deep learning
86 篇 software
62 篇 machine learning...
59 篇 support vector m...
52 篇 internet of thin...
49 篇 decision trees
48 篇 artificial intel...
47 篇 forecasting
44 篇 software enginee...
40 篇 training
39 篇 predictive model...
36 篇 feature extracti...
35 篇 neural networks
33 篇 software testing
31 篇 software algorit...
26 篇 reinforcement le...
26 篇 adversarial mach...
25 篇 covid-19
25 篇 object detection

机构

31 篇 college of compu...
31 篇 chitkara univers...
12 篇 karnataka bangal...
12 篇 school of inform...
11 篇 software enginee...
11 篇 department of so...
10 篇 department of so...
10 篇 school of comput...
9 篇 maharishi school...
8 篇 maharishi school...
7 篇 maharishi school...
7 篇 school of comput...
7 篇 daffodil interna...
7 篇 department of co...
7 篇 school of inform...
6 篇 school of comput...
6 篇 school of comput...
6 篇 software enginee...
6 篇 department of co...
6 篇 department of so...

作者

8 篇 patidar sanjay
7 篇 liu han
7 篇 han liu
6 篇 alfurhood badria...
6 篇 ishu sharma
6 篇 sharma ishu
6 篇 badria sulaiman ...
5 篇 bitto abu kowshi...
5 篇 dharshana kasthu...
5 篇 ali raja hashim
5 篇 yibeltal assefa
5 篇 assefa yibeltal
5 篇 ajmeera kiran
5 篇 raja hashim ali
5 篇 sethia divyashik...
4 篇 heyn hans-martin
4 篇 krishna aneesh
4 篇 william p.
4 篇 iqbal muhammad w...
4 篇 zain ul abideen

语言

1,909 篇 英文
1,322 篇 其他
53 篇 中文
1 篇 德文

检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是391-400 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：

Automated mechanism for improving precision irrigation using machine learning

Automated mechanism for improving precision irrigation using...

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Sasi Kala Rani, K. Kalaiselvi, V.K.G. Shanmugasundaram, Hariharan Dwaraknath, A. Swathi, K. Tarrun, R. Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering India Sri Sairam Engineering College chennai India Vardhaman College of Engineering Department of CSE Hyderabad India Sri Sairam Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350348217

To monitor and control the critical components of agricultural farming using the modernized IOT Technology, AGRI - AUTOMATA emphasizes efficiency by increasing the yield by majorly looking through humidity and temperature of the soil by even keeping a track of soil moisture content and sunlight. Through this system, productivity is increased by enhancing the automated maintenance system. The sensors are placed in equitable places in the farm, to sense and convey adequate information to the farmer by comprising IoT technology through the help of required software to the user. This proposed system can be used to improvise the productivity of crops and the supply of water and maintain brightness intensity by even altering the temperature through the automated system with machine learning approach. Between the incorporation of various sensors, the Node MCU microcontroller acts as the decision-making and controlling device for the whole automated system. © 2023 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Towards the Diagnosis of Heart Disease Using an Ensemble learning Approach 22

Towards the Diagnosis of Heart Disease Using an Ensemble Lea...

引用

22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Zhang, Zheng Xu, Liangliang Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches. © 2023 IEEE.

关键词： Autoencoder BERT Heart Disease machine learning Transformer XG-Boost

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Adversarial Attacks Against Natural Language machine learning Models 21

Experimental Evaluation of Adversarial Attacks Against Natur...

引用

21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Li, Jonathan Pugh, Steven Zhou, Honghe Deng, Lin Dehlinger, Josh Chakraborty, Suranjan Howard County Public School System River Hill High School MD United States Towson University Department of Computer and Information Sciences MD United States

ISBN: (纸本)9798350345889

machine learning models are being increasingly relied on for many natural language processing tasks. However, these models are vulnerable to adversarial attacks, i.e., inputs designed to target models into making a wrong prediction. Among different methods of attacking a model, it is important to understand what attacks are effective, so that we can design countermeasures to protect the models. In this paper, we design and implement six adversarial attacks against natural language machine learning models. Then, we evaluate the effectiveness of these attacks using a fine-Tuned distilled BERT model and 5,000 sample sentences from the SST-2 dataset. Our results indicate that the Word-replace attack affected the model the most, which reduces the F1-score of the model by 34%. The Word-delete attack is the least effective, but still reduces the model's accuracy by 17%. Based on the experimental results, we discuss our insights and provide our recommendations for building robust natural language machine learning models. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Research on Network Traffic Feature Map Generation and Evaluation Methods 4

Research on Network Traffic Feature Map Generation and Evalu...

引用

4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Jiang, Anxing Shi, Kai Wang, Mengshu Zhang, Zeri Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

With the continuous development of machine learning, it has been widely used in the fields of image processing and computer vision. However, it needs to be transformed into images before feeding network traffic data into a deep learning model for training. The existing methods for directly converting network traffic data into images demonstrate unfavorable results. Therefore, this paper proposes a method for generating network traffic feature maps. This method first uses information entropy and classical k-nearest neighbors estimation algorithm to calculate the entropy values for discrete and continuous variables respectively, then the entropy values are used to filter network traffic. Finally, feature maps are generated through an encoder. Experiments on the UNSW_NB15 and CIC-IDS2017 datasets show that the proposed method outperforms other methods in terms of metrics such as PSNR and SSIM. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Prediction of software Bugs from Textual Description 18

Towards Automated Prediction of Software Bugs from Textual D...

引用

18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Shukla, Suyash Kumar, Sandeep Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9789897586477

Every software deals with issues such as bugs, defect tracking, task management, development issue to a customer query, etc., in its entire lifecycle. An issue-tracking system (ITS) tracks issues and manages software development tasks. However, it has been noted that the inferred issue types often mismatch with the issue title and description. Recent studies showed machine learning (ML) based issue type prediction as a promising direction, mitigating manual issue type assignment problems. This work proposes an ensemble method for issue-type prediction using different ML classifiers. The effectiveness of the proposed model is evaluated over the 40302 manually validated issues of thirty-eight java projects from the SmartSHARK data repository, which has not been done earlier. The textual description of an issue is used as input to the classification model for predicting the type of issue. We employed the term frequency-inverse document frequency (TF-IDF) method to convert textual descriptions of issues into numerical features. We have compared the proposed approach with other widely used ensemble approaches and found that the proposed approach outperforms the other ensemble approaches with an accuracy of 81.41%. Further, we have compared the proposed approach with existing issue-type prediction models in the literature. The results show that the proposed approach performed better than existing models in the literature.

关键词： Issue Tracking System machine learning Term Frequency Inverse Document Frequency Smartshark

来源：评论

学校读者我要写书评

暂无评论

software engineering as the Linchpin of Responsible AI 23

Software Engineering as the Linchpin of Responsible AI

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Zhu, Liming Univ New South Wales Sydney NSW Australia CSIROs Data61 Sydney NSW Australia

ISBN: (纸本)9781665457019

From humanity's existential risks to safety risks in critical systems to ethical risks, responsible AI, as the saviour, has become a major research challenge with significant real-world consequences. However, achieving responsible AI remains elusive despite the plethora of high-level ethical principles, risk frameworks and progress in algorithmic assurance. In the meantime, software engineering (SE) is being upended by AI, grappling with building system-level quality and alignment from inscrutable machine learning models and code generated from natural language prompts. The upending poses new challenges and opportunities for engineering AI systems responsibly. This talk will share our experiences in helping the industry achieve responsible AI systems by inventing new SE approaches. It will dive into industry challenges (such as risk silos and principlealgorithm gaps) and research challenges (such as lack of requirements, emerging properties and inscrutable systems) and make the point that SE is the linchpin of responsible AI. But SE also requires some fundamental rethinking - shifting from building functions into AI systems to discovering and managing emerging functions from AI systems. Only by doing so can SE take on critical new roles, from understanding human intelligence to building a thriving human-AI symbiosis.

关键词： Responsible AI Ethical AI Trustworthy AI AI engineering SE4AI

来源：评论

学校读者我要写书评

暂无评论

Overall Survial Prediction from Brain MRI in Glioblastoma

Overall Survial Prediction from Brain MRI in Glioblastoma

引用

2023 international conference on Robotics and Automation in Industry, ICRAI 2023

作者： Yousaf, Sobia Ibrar, Nimra Majid, Muhammad Anwar, Syed National University of Modern Languages Department of Software Engineering Rawalpindi Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan Sheikh Zayed Institute Children's National Hospital WashingtonDC20010 United States

ISBN: (纸本)9781665464727

Tumor segmentation using radiological images, particularly in the brain region, is a challenging task due to the heterogeneous nature of the tissue representing the brain lesions. In computerized diagnostic systems, this method is an essential step in isolating tumor regions for visualization and examination. Recently, deep learning (DL) technology has resulted in major breakthroughs in computer vision and artificial intelligence. This has impacted clinical tasks such as brain tumor segmentation, where deep learning allows learning hierarchical and distinctive characteristics from radiographs. A major paradigm shift has evolved, when compared with traditional machine learning approaches, where pathological and healthy tissue can be differentiated without relying on extracting features that required a significant expertise. Herein, with our proposed method, we aim to help radiologists in assisting them to detect tumor regions and further predict the overall patient survival rate using magnetic resonance images effectively. Towards this, we use U-Net based architecture to perform the segmentation. We achieve acceptable segmentation accuracy, 82% and 75% on training and validation datasets, respectively. We further used our segmentation results for survival predication task. We computed and selected 16 most significant 3D and 2D radiomic features from the segmented regions. By combining age with radiomics features, we trained Convolutional Neural Network (CNN) model and five different machine learning (ML) models and achieved 65.57% and 63% accuracy in survival rate prediction when using CNN and support vector machine (SVM) classification model. © 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

2023 1st international conference on Advanced engineering and Technologies, ICONNIC 2023 - Proceeding

2023 1st International Conference on Advanced Engineering an...

引用

1st international conference on Advanced engineering and Technologies, ICONNIC 2023

ISBN: (纸本)9798350306484

The proceedings contain 67 papers. The topics discussed include: FLUENT: factoid retrieval based chatbot using LSTM;secure mobile application for uniform resource locator (URL) phishing detection based on deep learning;CNN-based detection of SARS-CoV-2 variants using spike protein hydrophobicity;conserved sequence : progressive alignment and binary Boolean logic algorithm;Indonesia white sugar supply and demand forecast using machine learning;a hybrid machine learning enabled tourism recommender system providing a context-aware experience to tourists in Ireland;sentiment-score analysis of P2P lending industry on android applications in Indonesia;forecasting energy consumption in the Chimborazo Province, Ecuador, using random forest and XGBoost Algorithms;and towards optimal efficiencies in software defined network SDN-edge cloud: performance evaluation of load balancing algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Weak Labelling for File -level Source Code Classification 30

Weak Labelling for File -level Source Code Classification

引用

30th IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Sas, Cezar Capiluppi, Andrea Univ Groningen Bernoulli Inst Groningen Netherlands

ISBN: (纸本)9781665452786

software repository hosting services contain large amounts of open-source software, with GitHub hosting over 200 million repositories, from new to established ones. However, these repositories are not easy to find, calling for various attempts to classify their application domains automatically. However, most proposed approaches use artifacts, like README files, as a proxy for the project, losing the information in the source code and the interaction between files. Furthermore, they all focus on the project-level, ignoring the decomposition of software projects into components and modules. This work presents a weak labelling approach based on keyword extraction to annotate source files in a software project. Our findings suggest that using keywords to perform file-level annotations is an effective approach that can capture enough information from the source file so that new labels can be predicted. The long-term goal of our research is to classify source code files and use these annotations to identify semantic components in software projects. In addition, these annotations can be used for semantic reverse engineering, software reuse, and more. We plan to train machine learning models that use our proposed weak supervision to better annotate source files inside software projects.

关键词： software classification software categories keywords semantic reverse engineering

来源：评论

学校读者我要写书评

暂无评论

Bug Resolution Prediction for Open-Source software Using Ensembles of Instance Selection Algorithms 9

Bug Resolution Prediction for Open-Source Software Using Ens...

引用

9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Miloudi, Chaymae Cheikhi, Laila Idri, Ali Abran, Alain Ensias Mohammed v University Software Project Management Team Rabat Morocco Ecole de Technologie Superieure Department of Software Engineering & Information Technology Montreal Canada

ISBN: (纸本)9798350311402

Studies about maintenance effort estimation of open-source software have investigated the impact of single instance selection on the performance machine learning techniques. However, Ensemble of Instance Selection (EIS) has not been investigated for this topic, especially for bug resolution prediction. This empirical study considers the impact of EIS on the performance of k-Nearest Neighbor, Support Vector machine, and Multinomial Naïve Bayes techniques. A set of 27 classifiers are built using Bagging and Random Feature Subset Ensembles based on AllkNN single instance selection algorithm on three datasets. The results are presented, together with a comparative analysis of the built classifiers' performance. The results show that the investigated EIS algorithms improve the performance of ML classifiers and outperform the single instance selection algorithm-based classifiers. © 2023 IEEE.

关键词： Open source software

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324页 << < 36 37 38 39 40 41 42 43 44 45 > >>

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：