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221-230 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：

Assessment of Cyber Security in Renewable Electricity Market Considering System Reliability Using machine learning 8

Assessment of Cyber Security in Renewable Electricity Market...

引用

8th international conference on Technology and Energy Management, ICTEM 2023

作者： Rizi, Daryoush Tavangar Nazari, Mohammad Hassan Fani, Maryam Hosseinian, Seyed Hossein Amirkabir University of Technology Department of Physics and Energy Engineering Tehran Iran

ISBN: (纸本)9781665452854

The role of cyber security in facing risks and damages is an essential task. The aim of study is first to the effect of a sabotage sample in security on the power market, this is the unavailability of the production or the power outage. This network is a three-bus system consisting of three wind power generation units. This is achieved by running several scenarios in the system. The problem solution approach is considered in minimizing the costs after attacking in the system. The CPLEX solver solves the mixed integer linear programming (MILP) model. Also, the data observed from the output of the GAMS software is used as the input data of the Python software to use data science such as histogram chart analysis and machine learning algorithms for proper prediction and isolation in different attack situations. As a result, the peak load point shifts after the attack, and also between the used machine learning algorithms, the two-layer neural network has significant accuracy for the presented model. The results of this research show that by using this machine learning algorithm, the system's security level has improved in the face of these damages. © 2023 IEEE.

关键词： Electric loads

来源：评论

学校读者我要写书评

暂无评论

Applications of machine learning in software Defect Prediction: A Literature Review 18

Applications of Machine Learning in Software Defect Predicti...

引用

18th IEEE international conference on Computer Science and Information Technologies, CSIT 2023

作者： Liubchenko, Vira Odesa Polytechnic National University Odesa Software Engineering Department Ukraine

ISBN: (纸本)9798350360462

software Defect Prediction (SDP) employs resources to identify defects and improve quality. This study investigates machine learning (ML) techniques in SDP and associated performance measures. Three research questions guide the exploration, covering popular techniques, common performance metrics, and dataset sources. The review highlights a shift towards leveraging rich source code information, particularly Abstract Syntax Trees. Notably, Artificial Neural Networks dominate ML techniques. AUC and G-mean gain prominence for performance evaluation, addressing class imbalance. Dataset scarcity remains a challenge, leading to reliance on external repositories. This study enriches SDP insights and informs future research directions in the evolving landscape of software defect prediction. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Experimentation of Code Smells Using Deep learning Techniques 6

Experimentation of Code Smells Using Deep Learning Technique...

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Fawaz, Omeir Amaan, Mohammad Sahu, Suryansh Adnan, Mohd Gupta, Aakanshi Amity School of Engineering & Technology Amity University Uttar Ptardesh Noida India

ISBN: (纸本)9798350304480

A code smell is a measurable indicator that highlights significant issues in the software development process caused by inadequate programming practices. These are usually introduced in a software program during the design or implementation phases, and may lead to more significant problems during software maintenance. The existing methods to detect smells based on metrics, heuristics or machine learning have certain limitations. In this paper, we suggest two models built on deep learning for predicting code smells: convolutional neural network (CNN) and recurrent neural network (RNN). These models use features extracted from source code to detect six different code smells. We then evaluate and compare their results. The proposed approach performs remarkably well for single code smell identification according to the experimental results obtained. © 2023 IEEE.

关键词： CNN Code smells deep learning RNN software engineering

来源：评论

学校读者我要写书评

暂无评论

Arabic Non-Functional Requirements Extraction Using machine learning 11

Arabic Non-Functional Requirements Extraction Using Machine ...

引用

11th international conference on Information Technology, ICIT 2023

作者： Amro, Reem Althunibat, Ahmad Hawashin, Bilal Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798350320060

Non-Functional Requirements (NFR) are a set of quality attributes that software must have, such as security, reliability, and performance. Extracting NFR from software requirement specifications can help developers deliver quality software that meets users' expectations. However, since functional and non-functional requirements are mixed in the same SRS, it requires a lot of human effort to distinguish them. While many studies have proposed English language requirements extracting techniques, there is a lack of research in Arabic requirements extracting, as well as a lack of publicly available Arabic datasets in this field. In this study, we propose an automatic NFR extraction method for quality software development by combining machine learning and feature extraction techniques. Also, we will collect an Arabic dataset for requirements. This study aims to help software engineers save time, reduce costs and effort in the manual extraction process, and make the requirements engineering phase more efficient. Additionally, it provides new research areas in this field. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

ECG Signals- Early detection of Arrhythmia using machine learning approaches 13

ECG Signals- Early detection of Arrhythmia using Machine Lea...

引用

13th international conference on Cloud Computing, Data Science and engineering, confluence 2023

作者： Nandanwar, Jahnavi Singh, Jasmeet Patidar, Sanjay Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9781665462631

Heartbeats can be recorded in the form of electrical signals using a machine called an electrocardiogram. The cardiovascular system's performance can be tracked using an electrocardiogram (ECG), which can also be used to diagnose various cardiac diseases. The paper by Mohammad Kachuee et al., entitled 'ECG Heartbeat Classification: A Deep Transferable Representation' classifies heartbeats into five different classes according to AAMI EC57 standards. In this paper, we continue the work on their preprocessed dataset and train the model to detect arrhythmia, i.e., abnormalities in the heartbeat. We also propose a comparative study between different machine learning and deep learning models, namely random forest classification, support vector machines, and convolutional neural networks, for detecting abnormalities in heartbeats. The results we got after the application of all our classifiers showed the best accuracy score of 98.26% for the MIT-BIH dataset and a 100% accuracy score for the PTB Diagnostic dataset. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Bugsplainer: Leveraging Code Structures to Explain software Bugs with Neural machine Translation 39

Bugsplainer: Leveraging Code Structures to Explain Software ...

引用

39th IEEE international conference on software Maintenance and Evolution (ICSME)

作者： Mahbub, Parvez Rahman, Mohammad Masudur Shuvo, Ohiduzzaman Gopal, Avinash Dalhousie Univ Halifax NS Canada Metabob Inc Palo Alto CA USA

ISBN: (纸本)9798350327830

software bugs cost the global economy billions of dollars each year and take up approximate to 50% of the development time. Once a bug is reported, the assigned developer attempts to identify and understand the source code responsible for the bug and then corrects the code. Over the last five decades, there has been significant research on automatically finding or correcting software bugs. However, there has been little research on automatically explaining the bugs to the developers, which is essential but a highly challenging task. In this paper, we propose Bugsplainer, a novel web-based debugging solution that generates natural language explanations for software bugs by learning from a large corpus of bug-fix commits. Bugsplainer leverages code structures to reason about a bug and employs the fine-tuned version of a text generation model - CodeT5 - to generate the explanations. Tool video: https://***/xga-ScvULpk

关键词： software bug bug explanation software maintenance software engineering natural language processing deep learning neural text generation

来源：评论

学校读者我要写书评

暂无评论

Classification of software Bugs using Support Vector machine 14

Classification of Software Bugs using Support Vector Machine

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kusumaniswari, V. Chinta, Mukesh Shareff, Sk. Nawaz Vr Siddhartha Engineering College Computer Science & Engineering Department Andhra Pradesh India

ISBN: (纸本)9798350335095

Support Vector machine (SVM) is a most widely used classification technique in machine learning as it gives a better accuracy than most of the existing techniques. These days, users frequently run across glitches and malfunctions when using software or applications. A lot more understanding is required by the developer to identify the type of bug, which takes time. In this system, to find the type of a bug a machine learning classification technique SVM. Here, bug report is fed as an input to a natural language processing algorithm. In this method, a dataset containing various types of bug reports is used to analyse the description in the given bug report using text processing and keyword extraction in natural language processing. This model can identify three types of bugs: memory bugs, concurrency bugs and semantic bugs using SVM. This system proposes a SVM model that gives better accuracy than the existing models which use several other techniques like decision tree, random forest and naive bayes. © 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

Intrusion Detection for Internet of Vehicles using machine learning 14

Intrusion Detection for Internet of Vehicles using Machine L...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Bajpai, Soumya Sharma, Kapil Chaurasia, Brijesh Kumar Amity University Madhya Pradesh Computer Science and Engineering MP Gwalior India Pranveer Sing Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350335095

The Internet of Vehicles (IoV) has replaced vehicular networks as the preferred paradigm as a result of the enormous expansion in computer and network capabilities. Because of the dynamic IoV's diverse nature necessitates effective resource management, which calls for cutting-edge technologies like software Defined Networking (SDN), machine learning (ML), and others. In software Defined-IoV (SD-IoV) networks, Road Side Units (RSUs) are in charge of network effectiveness and provide a number of safety features. However, it is not practical to deploy enough RSUs, and the current RSU placement does not provide complete coverage of an area. Furthermore, any lapse in network security or performance has a negative influence on driving. Thus, the objective of this study is to increase security in an IoV network by using different types of machine learning Algorithm to increase network efficiency. As a result, it is suggested to use XG-BOOST learning Algorithm placement method to decrease communication time while expanding coverage among IoV devices. Along with this method, this paper is works on the IoV network by using CAN-OITDS Dataset. The comparative study of conventional ML algorithms shows that the IDS detects the malicious attack on IoV with the help of XGBOOST with high accuracy of 96.04%. © 2023 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Flexible and Secure Code Deployment in Federated learning using Large Language Models: Prompt engineering to Enhance Malicious Code Detection 14

Flexible and Secure Code Deployment in Federated Learning us...

引用

14th IEEE international conference on Cloud Computing Technology and Science (CloudCom)

作者： Seo, Jungwon Zhang, Nan Rong, Chunming Univ Stavanger Stavanger Norway

ISBN: (纸本)9798350339826

Federated learning is a machine learning methodology that emphasizes data privacy, involving minimal interaction with each other's systems, primarily exchanging model parameters. However, this approach can introduce challenges in system development and operation because it inherently faces statistical and system heterogeneity issues. The diverse data storage formats and system environments across clients limit the feasibility of training with a uniform code. To distribute a new code to each environment, active participation of Federated learning collaborators is necessary, incurring time and cost. Moreover, it impedes adopting modern automated development and deployment paradigms such as DevOps or MLOps. This study investigates how Large Language Models (LLMs) can automatically tailor a single code to individual client environments in heterogeneous scenarios without human intervention. Moreover, to enable the automatic adaptation of the deployed code for conducting new experiments within the system, it is imperative to assess the presence of potentially malicious code that could jeopardize data security. To address this challenge, we introduce a novel prompt engineering technique to enhance LLMs' detection capabilities, thereby bolstering our ability to detect malicious code effectively.

关键词： software engineering Federated learning Large Language Models Security

来源：评论

学校读者我要写书评

暂无评论

HOBAT: Batch Verification for Homogeneous Structural Neural Networks 38

HOBAT: Batch Verification for Homogeneous Structural Neural ...

引用

38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Li, Jingyang Li, Guoqiang Shanghai Jiao Tong Univ Sch Software Shanghai 200240 Peoples R China

ISBN: (纸本)9798350329964

The rapid development of deep learning has significantly transformed the ecology of the software engineering field. As new data continues to grow and evolve at an explosive rate, the challenge of iteratively updating software built on neural networks has become a critical issue. While the continuous learning paradigm enables networks to incorporate new data and update accordingly without losing previous memories, resulting in a batch of new networks as candidates for software updating, these approaches merely select from these networks by empirically testing their accuracy;they lack formal guarantees for such a batch of networks, especially in the presence of adversarial samples. Existing verification techniques, based on constraint solving, interval propagation, and linear approximation, provide formal guarantees but are designed to verify the properties of individual networks rather than a batch of networks. To address this issue, we analyze the batch verification problem corresponding to several non-traditional machine learning paradigms and further propose a framework named HOBAT (BATch verification for HOmogeneous structural neural networks) to enhance batch verification under reasonable assumptions about the representation of homogeneous structure neural networks, increasing scalability in practical applications. Our method involves abstracting the neurons at the same position in a batch of networks into a single neuron, followed by an iterative refinement process on the abstracted neuron to restore the precision until the desired properties for verification are met. Our method is orthogonal to boundary propagation verification on a single neural network. To assess our methodology, we integrate it with boundary propagation verification and observe significant improvements compared to the vanilla approach. Our experiments demonstrate the enormous potential for verifying large batches of networks in the era of big data.

关键词： Batch verification Neural networks Homogeneous structure Abstraction

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 << < 19 20 21 22 23 24 25 26 27 28 > >>

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：