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

2,437 篇 会议
49 册 图书
25 篇 期刊文献

馆藏范围

2,511 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,316 篇 工学
- 987 篇 计算机科学与技术...
- 894 篇 软件工程
- 118 篇 电气工程
- 112 篇 信息与通信工程
- 110 篇 土木工程
- 105 篇 控制科学与工程
- 86 篇 材料科学与工程（可...
- 86 篇 生物医学工程（可授...
- 84 篇 机械工程
- 75 篇 生物工程
- 66 篇 安全科学与工程
- 63 篇 建筑学
- 57 篇 动力工程及工程热...
- 50 篇 力学（可授工学、理...
- 43 篇 化学工程与技术
- 35 篇 交通运输工程
- 34 篇 仪器科学与技术
427 篇 理学
- 262 篇 数学
- 99 篇 统计学（可授理学、...
- 96 篇 物理学
- 84 篇 生物学
- 63 篇 系统科学
- 40 篇 化学
- 40 篇 地球物理学
256 篇 管理学
- 178 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 87 篇 工商管理
61 篇 医学
- 50 篇 基础医学(可授医学...
- 43 篇 临床医学
35 篇 经济学
- 35 篇 应用经济学
27 篇 法学
20 篇 农学
18 篇 教育学
8 篇 文学
3 篇 军事学
3 篇 艺术学
1 篇 历史学

主题

686 篇 predictive model...
197 篇 data models
155 篇 accuracy
154 篇 analytical model...
146 篇 computational mo...
141 篇 software
112 篇 software enginee...
106 篇 training
99 篇 prediction algor...
95 篇 machine learning
79 篇 feature extracti...
72 篇 deep learning
71 篇 adaptation model...
64 篇 neural networks
56 篇 machine learning...
52 篇 support vector m...
51 篇 mathematical mod...
47 篇 mathematical mod...
47 篇 forecasting
46 篇 optimization

机构

11 篇 ku leuven depart...
10 篇 chitkara univers...
9 篇 dmms core lab fl...
8 篇 faculty of elect...
5 篇 beijing advanced...
5 篇 nanjing univ sta...
5 篇 school of mechan...
5 篇 school of comput...
4 篇 college of compu...
4 篇 school of comput...
4 篇 computer science...
4 篇 university of ch...
4 篇 univ southampton...
4 篇 school of softwa...
4 篇 school of electr...
3 篇 college of elect...
3 篇 college of infor...
3 篇 school of comput...
3 篇 school of electr...
3 篇 department of ci...

作者

9 篇 menzies tim
8 篇 hall tracy
8 篇 bowes david
7 篇 staron miroslaw
6 篇 yang zhang
5 篇 desmet w.
5 篇 chen chen
5 篇 gang liu
5 篇 biffl stefan
4 篇 tamarozzi t.
4 篇 ashkan safari
4 篇 petric jean
4 篇 hamed kheirandis...
4 篇 jose rodriguez
4 篇 bener ayse
4 篇 bériot h.
4 篇 winkler dietmar
4 篇 rahul chauhan
4 篇 atak o.
4 篇 bencomo nelly

语言

2,464 篇 英文
34 篇 其他
13 篇 中文
1 篇 俄文
1 篇 土耳其文

检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2511 条记录，以下是101-110 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：

Classification of NFR based Importance Level of User Story in Agile software Development 9

Classification of NFR based Importance Level of User Story i...

引用

9th international conference on Signal Processing and Communication, ICSC 2023

作者： Kumar, Bhawnesh Tiwari, Umesh K. Dobhal, Dinesh C. Department of Computer Science and Engineering Graphic Era Deemed To Be University Dehradun India

ISBN: (纸本)9798350383201

the fact that requirement engineering creates effective technology and prevents system failure is very important in the early phases of software development. the final implementation criteria and the selection of many alternative models both heavily depend on non-functional requirements. the term "non-functional requirements" (NFR) refers to a group of qualities that software must possess, such as security, dependability, performance, etc. Extracting and considering NFR from user stories in Agile software Development (ASD) can help developers deliver quality software that meets the expectations of users completely. this paper proposed the classification of NFR based importance level of user story in ASD using machine learning models. At the beginning of this work, requirement converted into user stories, then the weight of NFR type and importance level of each user story calculated and classified using machine learning models. the outcome of this work shows that random forest classifier and XGBoost are achieved the best results with an accuracy of 96.39% and decision tree classifier accuracy is 96.13%, with comparisons of K-nearest neighbor, linear discriminant analysis, support vector classifier, logistic regression, and naïve bayes. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Mutual Learning-Based Framework for Enhancing Robustness of Code models via Adversarial Training 24

Mutual Learning-Based Framework for Enhancing Robustness of ...

引用

39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Wang, Yangsen Chen, Yizhou Zhao, Yifan Gong, Zhihao Chen, Junjie Hao, Dan Peking Univ Key Lab MOE HCST PKU Beijing Peoples R China Peking Univ SCS Beijing Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Deep code models (DCMs) have achieved impressive accomplishments and have been widely applied to various code-related tasks. However, existing studies show that some DCMs have poor robustness, and even small noise in the input data can lead to erroneous outputs. this phenomenon can seriously hinder the application of these DCMs in real-world scenarios. To address this limitation, we propose MARVEL, a mutual learning-based framework for enhancing the robustness of DCMs via adversarial training. Specifically, MARVEL initializes two identical DCMs, one of which receives Gaussian-distorted data and performs adversarial training, and the other receives the clean data. then these two DCMs work together to not only fit the true labels but also fit each other's internal parameters. Our intuition is that the DCM can enhance robustness by training noisy data, while the DCM achieves accurate prediction performance by learn the clean data. their mutual learning enables the DCM to balance both robustness and predictive performance. We selected three popular DCMs, five open-source datasets, and three state-of-the-art attack methods to evaluate the performance of MARVEL on 45 (3x5x3) downstream tasks composed of their combinations. Additionally, we set two of the state-of-the-art robustness enhancement techniques as baselines. the experimental results show that MARVEL significantly enhances the robustness of DCMs across all 45 tasks. In 43 out of 45 tasks, MARVEL outperforms the two baselines with an average improvement of 15.33% and 31.88%, respectively. At the same time, MARVEL can maintain the inherent accuracy with an error margin within +-2.43% compared to the original DCMs.

关键词： Code Model Deep Mutual Learning Model Robustness Adversarial Training

来源：评论

学校读者我要写书评

暂无评论

Investigation of Risk Factors of Students' Healthy Lifestyle Using Correlation and Regression Analysis and Prospects of Artificial Intelligence Application 17

Investigation of Risk Factors of Students' Healthy Lifestyle...

引用

17th IEEE international conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Ilkanych, Kateryna Lototska, Lesia Maikher, Viktoria Riznychok, Svitlana Kolach, Tetyana Chaban, Olesia Danylo Halytsky Lviv National Medical University Medical Informatics Department Lviv Ukraine Lviv Polytechnic National University Software Department Lviv Ukraine

this study aims to investigate the main important points related to students9; healthy lifestyle behaviours and self-rated health of medical students. In this paper, the factors that directly or indirectly determin... 详细信息

ISBN: (纸本)9798331520564

this study aims to investigate the main important points related to students' healthy lifestyle behaviours and self-rated health of medical students. In this paper, the factors that directly or indirectly determine the health status of the respondents are investigated using correlation and regression analysis. the proposed model will make it possible to predict the score in the self-assessment of the health of each individual student and determine the most significant risk factors for him. the designed model can be used to develop predictive models with the further aim of continuing research using artificial intelligence technologies. © 2024 IEEE.

关键词： artificial intelligence correlation and regression analysis healthy lifestyle factors

来源：评论

学校读者我要写书评

暂无评论

Just-in-time software defect prediction based on feature selection

Just-in-time software defect prediction based on feature sel...

引用

2024 international conference on Computer Network Security and software engineering, CNSSE 2024

作者： Cai, Shipeng Ren, Hongmin Shanghai Maritime University School of Information Engineering Shanghai China

ISBN: (纸本)9781510680234

At the present stage, just-time software defect prediction has garnered significant attention from researchers due to its granularity and immediacy. Primarily utilizing machine learning classifiers, these models are trained on information from code repositories to predict whether future changes may lead to defects. However, a current challenge with these classifiers lies in the vast number of features, leading to decreased prediction efficiency. these features not only impact model performance but can sometimes result in a decline in predictive accuracy. this paper explores a feature selection technique that combines random forests and self-attention to discard less important features without compromising performance. through this approach, the number of features required for training is significantly reduced, often to less than 50% of the original features. In our study across six software projects, we observed that using feature selection in the KNN model led to a 9% improvement in the F1 metric and a 6% improvement in the AUC metric compared to logistic regression and Bayesian models. Finally, we applied SHAP for interpretability analysis of the model. this research contributes to enhancing the accuracy and efficiency of just-in-time software defect prediction, providing valuable insights for research and practice in related fields. © 2024 SPIE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Detecting student depression on Weibo based on various multimodal fusion methods 4

Detecting student depression on Weibo based on various multi...

引用

4th international conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Luo, Yiming Ye, Zhanghao Lyu, Rui Business school University of Sydney SydneyNSW2150 Australia School of Software Zhengzhou University Zhengzhou450000 China School of Engineering University of New South Wales Sydney2030 Australia

ISBN: (纸本)9781510674721

this study uses a multimodal fusion model for early student depression detection by analysing student data from Sina Weibo. It compares early and late fusion methods with traditional Natural Language Processing models and achieves a 3% accuracy improvement over 100 cycles. the study shows that standardising only structured data without neural network mapping reduces predictive performance. It was also found that while both fusion methods exhibited similar predictive capabilities, the late fusion model exhibited overfitting, suggesting that there is potential for the late fusion strategy to further improve model performance performance. this study summarises the ability of multimodal fusion models to effectively detect early signs of student depression and lays the foundation for future research on model interpretability for early student depression detection and future research on student behaviour analysis. © 2024 SPIE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Earthquake Resilient Management in Northeast Algeria: A Multivariate Time Series Forecasting based on LSTM-Attention Neural Network 6

Earthquake Resilient Management in Northeast Algeria: A Mult...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Abdallah, Khababa Drif, Ahlem Khababa, Ghizlane Harous, Saad University of Ferhat Abbas Setif 1 Serif 19000 Algeria Department of Computer Science University of Ferhat Abbas Setif 1 Serif 19000 Algeria Department of Computer Science College of Computing and Informatics University of Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

Forecasting methods are critical to earthquake management by providing early warning systems and predictive insights into potential hazards. this work proposes a prediction framework for forecasting earthquake magnitude. the methodology involves collecting historical data from the seismic catalog, identifying geophysical characteristics of northeast Algeria, specifically the Setif (Algeria) region, and proposing a multivariate time series forecasting model based on LSTM with an attention mechanism. the attention neural network learns deeply the most relevant weights representing the subduction zone characteristics that influence earthquake prediction. We validate the proposed model based on real data-sets, and the results show an accurate magnitude forecasting for the northeast Algeria zone. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Detection and Monitoring Using LLM 9

Vulnerability Detection and Monitoring Using LLM

引用

9th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Akuthota, Vishwanath Kasula, Raghunandan Sumona, Sabiha Tasnim Mohiuddin, Masud Reza, Md Tanzim Rahman, Md Mizanur Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh BRAC University Department of Computer Science and Engineering 66 Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350319651

Large Language models (LLMs) have evolved as a cornerstone for intricate code evaluations in the modern realm of artificial intelligence and machine learning. the prioritizing of rigorous security requirements is a crucial requirement for the business in the dynamic and ever-changing world of software development. the current study has used the capabilities of the GPT-3.5- Turbo model to conduct a detailed assessment of various code snippets to find any vulnerabilities. the main objective of the experiment was to introduce continuous monitoring technologies to enhance software security and release control. To obtain reliable results, we used a classification report and a confusion matrix. Out of these validation methods we choose accuracy as an important metric for this validation because in this experiment we need our model to predict the vulnerabilities that are present in the 2740 test cases and we would need our model to focus more on true positives(TP). the ideal goal of this experiment was to predict any kind of vulnerability from the real-world data. Out of all test cases, we were able to have an accuracy of 0.77. this demonstrates the approach's potential efficacy in discovering vulnerabilities. Nonetheless, the study found certain parts that require improvement, emphasizing the importance of continual refinement in the model's methodology to ensure more thorough security assessments. this study lays the groundwork for future research into the use of powerful machine learning models in the assessment of software vulnerabilities. the findings not only highlight the effectiveness of the existing approach but also offer light on prospective future research directions, paving the way for the next generation of models and evaluation techniques. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. this paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. the suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. the study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 IEEE.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Biomarker Trajectory Prediction and Causal Analysis of the Impact of the Covid-19 Pandemic on CVD Patients using Machine Learning

Biomarker Trajectory Prediction and Causal Analysis of the I...

引用

9th IEEE/ACM international conference on Connected Health - Applications, Systems and engineering Technologies (CHASE)

作者： Inekwe, Trusting Mkandawire, Winnie Wee, Brian Agu, Emmanuel Colubri, Andres Worcester Polytech Inst Comp Sci Dept Worcester MA 01609 USA Univ Massachusetts Chan Med Sch Genom & Computat Biol Worcester MA 01655 USA Harvard Univ Chem & Phys Biol Cambridge MA 02138 USA

ISBN: (纸本)9798350345025;9798350345018

Background: the COVID-19 pandemic disrupted healthcare services, increasing the susceptibility of high-risk patients including those with cardiovascular Diseases (CVDs), to adverse outcomes. Biomarkers provide insights into patients' underlying health status. However, few studies have investigated the effects of the COVID-19 pandemic on CVD biomarker trajectories using predictive modeling and causal analyses frameworks. Prior research explored the impacts of the COVID-19 pandemic on CVD severity and prognosis but did not investigate biomarker trajectories using Machine Learning (ML), which can discover complex multivariate relationships in multi-modal data. Objective: this study aimed to compare six ML regression models to select the best performing models for predicting biomarker trajectories in CVD patients using retrospective data. Subsequently, these models were used to assess the COVID-19 pandemic's impact on CVD patients and for causal analyses Approach: Using ML regression and causal inference, this study investigated the pandemic's impact on biomarker values of 80,917 CVD patients and 77,332 non-CVD controls, treated at two hospitals in Central Massachusetts between May 2018 and December 2021. ML regression algorithms, including Neural Networks (NN), Decision Trees (DT), Random Forests (RF), XGBoost, CATBoost and ADABoost, were trained and compared. Important CVD biomarkers (HbA1c, LDL cholesterol, BMI, and BP) were predicted as outcome variables with patients' risk factors (age, race, gender, socioeconomic status) as input variables. Shapley feature importance analyses identified the most predictive features, which were then utilized in Causal Analysis. A Difference-in-Differences (DID) approach within a Double/Debiased Machine Learning (DML) method isolated the pandemic's impact on biomarkers, while minimizing the effects of confounding factors. Results: CATBoost and XGBoost were the most predictive ML models for LDL cholesterol and HbA1c, yielding R-2 val

关键词： COVID-19 Cardiovascular Diseases biomarkers machine learning regression predictive modeling causal inference Difference-in-Differences SHAP value Double/Debiased machine learning

来源：评论

学校读者我要写书评

暂无评论

SHAP-based Interpretable models for Credit Default Assessment Using Machine Learning 14

SHAP-based Interpretable Models for Credit Default Assessmen...

引用

14th international conference on software Technology and engineering, ICSTE 2024

作者： Xu, Qingyang Liao, Yunlong Li, Qiutong Zhang, Jiaqi Song, Zhilan Wang, Linjun Yuan, Xiaochen Macao Polytechnic University Faculty of Applied Sciences China

ISBN: (纸本)9798350378955

In recent years, the issue of credit fraud risk has garnered increased attention from the banking and financial sectors. However, prevailing credit assessment models predominantly focus on predictive outcomes, often overlooking the imperative of model interpretability. Understanding the contributions of model features and their interactions is paramount for elucidating model behavior and furnishing vital insights for model enhancement and optimization. To address this gap, this paper proposes a machine learning model leveraging SHAP for explicating credit assessment. Utilizing publicly available datasets from Lending Club, this study validates the proposed model against four industry-standard machine learning approaches: SVM, MLP, XGBoost, and LightGBM. Experimental findings unveil feature importance rankings and elucidate relationships between features and target variables. Notably, the study identifies the predominant roles of loan interest rates and credit policies in credit fraud assessment within the dataset and endeavors to uncover interactions within individual key features. the SHAP framework, as demonstrated, holds promise for informing the design and construction of future credit risk assessment models, thereby bolstering support for financial decision-making and risk management endeavors. © 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

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252页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：