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

3,214 篇 会议
19 册 图书
10 篇 期刊文献

馆藏范围

3,243 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,942 篇 工学
- 1,740 篇 计算机科学与技术...
- 1,594 篇 软件工程
- 423 篇 信息与通信工程
- 329 篇 控制科学与工程
- 318 篇 生物工程
- 185 篇 生物医学工程（可授...
- 176 篇 电气工程
- 145 篇 机械工程
- 116 篇 电子科学与技术（可...
- 101 篇 光学工程
- 99 篇 安全科学与工程
- 93 篇 化学工程与技术
- 92 篇 动力工程及工程热...
- 79 篇 网络空间安全
893 篇 理学
- 499 篇 数学
- 336 篇 生物学
- 210 篇 统计学（可授理学、...
- 158 篇 物理学
- 154 篇 系统科学
- 84 篇 化学
505 篇 管理学
- 266 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...
134 篇 法学
- 91 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

499 篇 machine learning
104 篇 deep learning
86 篇 software
62 篇 machine learning...
59 篇 support vector m...
52 篇 internet of thin...
52 篇 artificial intel...
49 篇 decision trees
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 liu xin

语言

1,802 篇 英文
1,440 篇 其他
69 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 3243 条记录，以下是701-710 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：

MLOps: Automatic, Zero-Touch and Reusable machine learning Training and Serving Pipelines

MLOps: Automatic, Zero-Touch and Reusable Machine Learning T...

引用

2023 IEEE international conference on Internet of Things and Intelligence Systems, IoTaIS 2023

作者： Panchal, Deven Baran, Isilay Musgrove, Dan Lu, David AT&T Middletown NJ United States AT&T Dallas TX United States

ISBN: (纸本)9798350319040

Due to increased interest in machine learning, Deep learning and AI and easy availability of ML toolk-its/libraries/ frameworks as well as talent that has ML skillsets and know-how to use these ML tools, machine learning model building has become easier and is now being employed to solve a variety of problems across different fields. But when the time comes to be able to take these ML/DL models to production i.e., integrate them with business applications, it is often a challenge. This is due to the many skillsets in software development, cloud, DevOps, system design, data engineering, that this requires. Furthermore, we see that ML engineers or data scientists and software developers often work in silos to make locally optimum decisions without thinking about how the model is going to be used in production, how it is going to be trained on fresh new incoming data and how and when it is going to be replaced with a new better model. In short, most of the time, there is no plan for how models will behave when in production nor any automatic pipelines to manage them. This leads to lots of difficulty in productionalizing the ML models and leads to wasted time and effort. We we will demonstrate how we can take an ML model to production very easily using components from the Acumos AI project and do much more by creating zero-touch i.e., automatic and reusable ML model training and ML model serving pipelines and reusable model infrastructures using Acumos and Nifi. We will create a Cloud Forensics ML model, productionalize it and show how it can be consumed in a business application. © 2023 IEEE.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Real Time Object Detection using CNN

Design and Implementation of Real Time Object Detection usin...

引用

2023 international conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Palanivel, N. Madhan, K. Venkatvamsi, A. Madhavan, G. Sindhuja, B. Priya G, Lakshmi Manakula Vinayagar Institute of Technology Department of CSE Puducherry India Mailam Engineering College Mailam Department of CSE Tamil Nadu India Manakula Vinayagar Institute of Technology Department of CSE Puducherry India

ISBN: (纸本)9798350315127

The study of computer systems and software that can recognize and understand scenes and images is known as computer vision. Image recognition, object detection, image synthesis, image super resolution and many other computer vision altering features. For, security systems and selfdriving cars, object detection is commonly used. In this study, we use R-CNN, RetinaNet and fast and highly accurate object recognition algorithms and methods such as SSD and YOLO. Using these deep learning methods and algorithms, also based on machine learning, requires a deep understanding of deep learning frameworks including Tensor Flow, Open CV, image, and more. By placing objects in the selected rectangular blocks, everything in the image can be recognized, named, and labeled. This includes the accuracy of each object recognition method. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Using machine learning Techniques for Assessing Media Text Readability 24

Using Machine Learning Techniques for Assessing Media Text R...

引用

24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Laputenko, Andrey Evtushenko, Tatiana Klochkova, Elena Yevtushenko, Nina National Research Tomsk State University Department of Information Technologies Tomsk Russia Graduate School of Applied Linguistics and Translation Studies Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Ivannikov Institute for System Programming of the Russian Academy of Sciences Department of Software Engineering Moscow Russia

ISBN: (纸本)9798350336870

The paper addresses the problem of assessing media text readability depending on the correlation of objective and subjective text complexity. Objective text complexity refers to a set of measurable characteristics such as baseline, morphological, syntactic and lexical characteristics of a media text (a piece of news published on university websites), which is calculated automatically for each text. In total, 34 parameters are considered. Subjective text complexity score is assigned to each text by human experts who assess the clarity, structure, cohesion and coherence of the text. The correlation between subjective and objective text complexity is studied using machine learning models. The readability level is measured using machine learning methods and regression-correlation analysis. Namely, an artificial neural network and regression models are used. It is demonstrated that the use of polynomial features and lasso regularization allows to get a compact regression model of a high quality. The use of the chosen machine learning techniques made it possible to estimate the impact of distinct linguistic features on the readability level. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Name Prediction Based on Enhanced Multi-Source Domain Adaptation 22

Vulnerability Name Prediction Based on Enhanced Multi-Source...

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： Xing, Ying Zhao, Mengci Yang, Bin Zhang, Yuwei Li, Wenjin Gu, Jiawei Yuan, Jun Xu, Lexi Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Safety Crit Software Dev & Verificat Nanjing 211106 Peoples R China Yunnan Key Lab Software Engn Kunming 650091 Yunnan Peoples R China China Unicom Res Inst Beijing 100048 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China NSFOCUS Technol Grp Co Ltd Beijing 100089 Peoples R China

ISBN: (纸本)9798350381993;9798350382006

software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

关键词： vulnerability name prediction multi-source domain adaptation data augmentation adversarial training attention mechanism

来源：评论

学校读者我要写书评

暂无评论

A review: Wireless sensor network location 3

A review: Wireless sensor network location

引用

3rd international conference on Signal Processing and machine learning, conf-SPML 2023

作者： Xiao, Qing School of Materials Science and Engineering Northeastern University Liaoning province Shenyang110819 China

Node localisation is one of the important technologies in wireless sensor networks, and localisation algorithms are the core elements of localisation technology. This paper first lists the evaluation criteria of localization performance, the related terminology used in localization technology, and the classification and analysis of localization algorithms. The paper introduces various node localisation algorithms based on both ranging and non-ranging aspects, and finally presents the shortcomings of the current wireless sensor network localisation technology and provides an outlook on future research prospects. © Published under licence by IOP Publishing Ltd.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Performance Improvement in Time Series Prediction through PECNET Framework 22

Performance Improvement in Time Series Prediction through PE...

引用

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

作者： MacIt, Serkan Ustundag, Burak Berk İstanbul Technical University Department of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350345346

machine learning algorithms have garnered con-siderable attention and recognition in the context of addressing time series prediction challenges. However, constructing a ma-chine learning model with optimal architecture and hyperpa-rameters that effectively captures the intricacies inherent in the time series data can present challenges. If the data encompasses multivariable characteristics with chaotic or stochastic proper-ties and has missing parts, the task of constructing an accurate machine learning model with appropriate structure and hy-perparameters becomes further challenging. Addressing the challenge of overfitting, which is a common machine learning problem encountered in such cases where the data exhibits these characteristics, a cascaded neural network, named PECNET (Predictive Error Compensated Wavelet Neural Networks), is a highly favorable alternative to mitigate this issue. PECNET ad-dresses the problem by training separate neural networks in a cascaded manner for each different frequency band of input data features, starting from the input feature most correlated with the target data and applying wavelet transformation to the input data prior to training and utilizing the remaining errors of the prior network as the target labels of the next network. This approach enhances the orthogonality of data characteris-tics across time windows and subsequently reduces the likeli-hood of overfitting as additional networks are added, thereby improving prediction performance. In this study, to tackle the escalation in computational complexity and preempt the occur-rence of implementation errors in time synchronization man-agement, the previously experimentally tested PECNET is transformed into a modular and parametric framework soft-ware, considering the prevalent utilization of off-the-shelf framework software in the majority of artificial intelligence studies. The performance of the developed framework software is initially demonstrated on financial data, an

关键词： data fusion deep learning discrete wavelet transformation earthquake prediction ml frame-work design neural networks time series prediction

来源：评论

学校读者我要写书评

暂无评论

Exploring machine learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka 9

Exploring Machine Learning Models to Predict the Diamond Pri...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Amzad Basha, Md Shaik Martha Sucharitha, M. Devi, A. Uma Ashok, M. Oveis, Peerzadah Mohammad GITAM School of Business Bengaluru India Deemed to be University Department of Professional Studies Christ Bengaluru India SRM Valliammai Engineering College Department of Management Studies Chengalpattu India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

In contrast to gold and platinum, whose values may be fairly determined, determining a diamond's worth involves a far more complex set of considerations. The appropriate rate is based on many factors, not just one of the stones. Diamonds are graded based on their appearance, carat weight, cut quality, and how well they have presented dimensions like a table's surface, depth, and breadth. In order to accurately forecast diamond prices, this study seeks to develop the most effective approaches possible. Different machine learning classifiers are trained on the diamond dataset to forecast diamond prices based on the features. This article shows how to analyze diamond prices using WEKA's data mining software. Diamond data have been utilized for this study. These methods include M5P, Random Forest, Multilayer perceptron, Decision Stump, REP Trees, and M5Rules. For the purpose of estimating the cost of a diamond, different machine learning classifiers are compared and contrasted. Performance measures and analysis showed that Random Forest was the best-performing classifier. Experimental findings show, as shown by the coefficient of correlation that Random Forest is better than other classification methods. © 2023 IEEE.

关键词： Diamonds

来源：评论

学校读者我要写书评

暂无评论

Requirements Elicitation in the Age of AI: A Tool's Multi-system Journey 1

引用

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

作者： Ahmad, Khlood Arora, Chetan Abdelrazek, Mohamed Grundy, John Vasa, Rajesh Deakin Univ Geelong Vic Australia Monash Univ Clayton Vic Australia

ISBN: (数字)9783031641824

ISBN: (纸本)9783031641817;9783031641824

Traditional Requirements engineering (RE) practices have introduced new tools to elicit and model requirements. Applying these tools to building AI software solutions has raised new issues and challenges. Also, most AIbased software solutions ignore human-centred values and focus on technical aspects. Existing tools and RE methods must extend to consider building more human-centred AI solutions. Recognizing this, we present a novel tool to support requirements elicitation and modelling for human-centred AI software. This paper details the tool's multi-system journey as it was applied across three diverse real-world projects: a mHealth app, a Virtual Reality 360 video enhancer, and a supermarket compliance app. The first two projects were in the later stages of software development, and the third was conducted during the early stages of building the software solution. The tool was used to elicit and model requirements for the three case studies in collaboration with eight experts. The tool helped to understand what requirements must be captured at the initial stages vs later stages in RE for AI (RE4AI).

关键词： Requirements engineering software engineering Artificial intelligence machine learning Human-centered Conceptual modeling

来源：评论

学校读者我要写书评

暂无评论

An Assessment of machine learning Algorithms and Models for Prediction of Change-Prone Java Methods 23

An Assessment of Machine Learning Algorithms and Models for ...

引用

37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Farah, Paulo Roberto Silva, Rogério Vergilio, Silvia Santa Catarina State University SC Ibirama Brazil PR Curitiba Brazil

ISBN: (纸本)9798400707872

Identifying which parts of code are prone to change during software evolution allows developers to prioritize and allocate resources efficiently. Having as focus a smaller scope makes easier change management and allows monitoring the type of modification and its impact. However, existing change-proneness prediction approaches are focused mainly on system classes. But the problem is that classes contain many characteristics of different software attributes and some software behaviors are more granular and better captured at the method-level. Motivated by these facts, in this paper, we empirically assess the performance of four machine learning algorithms for change-prone method prediction in seven open-source software projects. We derived and compared models obtained with three sets of independent variables (features): a set composed of structural metrics, a second set composed of evolution-based metrics, and a third that includes a combination of both kinds of metrics. The results show that, Random Forest presents the best general performance, independently of the used indicator and set of features. The model composed by both sets of metrics outperforms the other two. Two features based on the frequency of changes that happened in the evolution history of the method are point out as the most important for our problem. © 2023 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications 17

The GitHub Recent Bugs Dataset for Evaluating LLM-based Debu...

引用

17th IEEE international conference on software Testing, Verification, and Validation (ICST)

作者： Lee, Jae Yong Kang, Sungmin Yoon, Juyeon Yoo, Shin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350308198;9798350308181

While Large Language Models (LLMs) have demonstrated strong natural language and code processing capabilities, concern has been raised as to whether existing bug benchmarks are included in their training data. We examine the training data of the open-source LLM StarCoder, and find it likely that data from the widely used Defects4J benchmark was included, raising the possibility of its inclusion in the training data of the GPT model as well. This makes it difficult to tell how well LLM-based results on Defects4J would generalize, as for any results it would be unclear whether a technique's performance is due to LLM generalization or memorization. To remedy this issue and facilitate continued research on LLM-based SE, we present the GitHub Recent Bugs (GHRB) framework, which continuously gathers real-world Java bugs for use in evaluation of LLM-based techniques. To date, we have gathered 89 bugs reported after the GPT-3.5 training data cutoff point of September 2021.

关键词： Benchmark Debugging 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325页 << < 67 68 69 70 71 72 73 74 75 76 > >>

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：