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

3,206 篇 会议
56 册 图书
26 篇 期刊文献

馆藏范围

3,288 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,719 篇 工学
- 1,325 篇 计算机科学与技术...
- 1,216 篇 软件工程
- 213 篇 控制科学与工程
- 204 篇 信息与通信工程
- 195 篇 电气工程
- 151 篇 机械工程
- 136 篇 材料科学与工程（可...
- 131 篇 安全科学与工程
- 128 篇 生物工程
- 125 篇 生物医学工程（可授...
- 94 篇 土木工程
- 93 篇 交通运输工程
- 85 篇 建筑学
- 79 篇 动力工程及工程热...
- 75 篇 电子科学与技术（可...
- 70 篇 化学工程与技术
- 62 篇 力学（可授工学、理...
703 篇 理学
- 491 篇 数学
- 167 篇 统计学（可授理学、...
- 132 篇 生物学
- 115 篇 物理学
- 113 篇 系统科学
- 60 篇 化学
391 篇 管理学
- 282 篇 管理科学与工程(可...
- 172 篇 图书情报与档案管...
- 129 篇 工商管理
93 篇 医学
- 85 篇 基础医学(可授医学...
- 80 篇 临床医学
56 篇 经济学
- 56 篇 应用经济学
50 篇 教育学
- 49 篇 教育学
43 篇 法学
32 篇 农学
6 篇 文学
5 篇 军事学
1 篇 艺术学

主题

943 篇 predictive model...
265 篇 accuracy
257 篇 data models
235 篇 computational mo...
203 篇 analytical model...
180 篇 prediction algor...
148 篇 training
135 篇 software enginee...
129 篇 feature extracti...
119 篇 software
117 篇 machine learning
116 篇 deep learning
104 篇 neural networks
103 篇 forecasting
97 篇 adaptation model...
83 篇 mathematical mod...
82 篇 machine learning...
76 篇 optimization
70 篇 real-time system...
62 篇 support vector m...

机构

10 篇 school of inform...
7 篇 national enginee...
7 篇 hubei key labora...
7 篇 school of electr...
7 篇 school of automa...
6 篇 school of comput...
6 篇 school of softwa...
6 篇 school of electr...
6 篇 engineering rese...
5 篇 school of comput...
5 篇 school of transp...
5 篇 nanjing univ sta...
5 篇 chitkara univers...
5 篇 school of softwa...
4 篇 school of inform...
4 篇 naval university...
4 篇 school of manage...
4 篇 school of softwa...
4 篇 software enginee...
4 篇 school of electr...

作者

8 篇 labiche yvan
7 篇 menzies tim
6 篇 hassan ahmed e.
5 篇 yang yang
5 篇 mcintosh shane
5 篇 chen li
5 篇 ziadi tewfik
5 篇 yan wang
4 篇 yu wang
4 篇 ming li
4 篇 liu yang
4 篇 hall tracy
4 篇 xintian liu
4 篇 bowes david
4 篇 monden akito
4 篇 min wu
4 篇 matsumoto ken-ic...
4 篇 yu zhang
4 篇 lei zhang
4 篇 briand lionel c.

语言

3,195 篇 英文
82 篇 其他
21 篇 中文
1 篇 俄文

检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3288 条记录，以下是51-60 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：

Heart Diseases Prediction Using machine learning and Deep learning models 6

Heart Diseases Prediction Using machine learning and Deep le...

引用

6th international conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Himanshi Pattanaik, Srinibas Nayak, Kanishk Department of CSE Computer Science&Engineering Chandigarh University Mohali India Department of CSE UIE University Institute of Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350374629

Heart disorders cause a great deal of illness and mortality, making them a major worldwide health concern. In order to lessen the effects of cardiovascular illnesses, early detection and intervention are essential. In order to improve the precision and effectiveness of heart disease prediction, this study investigates the application of machine learning (ML) and deep learning (DL) approaches. Using a variety of datasets that include clinical measurements, medical histories, and patient demographics, predictive models are created to identify complex patterns that may indicate cardiovascular risk. In addition to sophisticated DL models like neural networks and convolutional neural networks (CNN), traditional machine learning algorithms like logistic regression, decision trees, and ensemble approaches are also used on the job. the study explores the technical nuances of model construction as well as ethical issues, privacy concerns with data, and the relationship between data scientists and medical experts. the aim is to construct precise and comprehensible models that enable doctors to provide proactive insights for individualized patient care, so contributing to the improvement of cardiovascular healthcare. It is expected that the study project's results will have an influence on cardiovascular health, promoting early identification, well-informed choices, and eventually bettering patient outcomes in the field of heart disease treatment. ©2024 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

proceedings of the 19th international conference on software Technologies, ICSOFT 2024

Proceedings of the 19th International Conference on Software...

引用

19th international conference on software Technologies, ICSOFT 2024

ISBN: (纸本)9789897587061

the proceedings contain 51 papers. the topics discussed include: a literature survey on pitfalls of open-source dependency management in enterprise;feature extraction, learning and selection in support of patch correctness assessment;AsmDocGen: generating functional natural language descriptions for assembly code;towards an approach for integra ng risk management practices into agile contexts;the game of one more idea: gamification of managerial group decision-making in software engineering;detecting and resolving bad organizational smells for microservices;an evaluation of risk management standards and frameworks for assuring data security of medical device software AI models;automated generation of web application front-end components from user interface mockups;and six-layer industrial architecture applied to predictive maintenance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

6th international conference on Intelligent Robotics and Control engineering, IRCE 2023

6th International Conference on Intelligent Robotics and Con...

引用

6th international conference on Intelligent Robotics and Control engineering, IRCE 2023

ISBN: (纸本)9798350326338

the proceedings contain 40 papers. the topics discussed include: breast cancer prediction using classical machine learning models;spray cooling control based on model predictive control: status, challenges and prospects;segmented prediction model of lithium-ion battery remaining useful life based on LSTM and SVR;simultaneous localization and mapping with fiducial markers based on extended Kalman filter;randomness-enhanced grey wolf optimizer for inverse kinematics solution of redundant robotic manipulators;modeling and simulation for a novel semi-active seat-suspension with a cam-roller-spring mechanism;prediction of NOx emission concentration based on weighted optimal combination model;a multi-domain feature fusion method for NOx concentration prediction in SCR systems;and skill-dependent representations for object navigation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Demand Forecasting Model for Big Marts using Seasonal Decomposition, ARIMA, and Gradient Boosting Regressor 6

Hybrid Demand Forecasting Model for Big Marts using Seasonal...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Balaji, S. Manikavelan, D. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai602 105 India

ISBN: (纸本)9798331522667

Demand forecasting is a pivotal factor of the retail enterprise, permitting businesses to successfully optimize stock management and meet client demand. this paper affords a hybrid model that integrates 3 key methodologies—Seasonal Decomposition (SD), Auto Regressive integrated shifting common (ARIMA), and Gradient Boosting Regressor (GBR)—to decorate forecasting accuracy for large marts. the version is constructed on ancient income facts, where Seasonal Decomposition first separates the time collection into seasonal, trend, and residual components, revealing underlying patterns. ARIMA is then implemented to the detrended records, accounting for time-dependent autocorrelations and shifting averages, while GBR is hired to model and predict the residuals, thereby refining the overall forecast. the performance of the SD-ARIMA-GBR version is empirically tested on income statistics from 1559 products throughout 10 stores, yielding an impressively low implied Mean Absolute percentage error (MAPE) of 3.0%. When in comparison to standard models like ARIMA and Exponential Smoothing, the hybrid technique proves substantially extra correct. the combination of machine mastering, especially GBR, performs a crucial function in improving predictive precision. this complete version gives huge marts an effective tool for awaiting calls for fluctuations, using more accurate stock-making plans, and enhancing operational efficiency. © 2025 IEEE.

关键词： Inventory control

来源：评论

学校读者我要写书评

暂无评论

Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. this study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. this approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. this research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality predictive models Protein Levels Sustainability

来源：评论

学校读者我要写书评

暂无评论

Momentum Prediction Method Based on LSTM and LightGBM 6

Momentum Prediction Method Based on LSTM and LightGBM

引用

6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Li, Jianzhe Zhu, Jianqiang School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350353174

this paper presents a comprehensive study on momentum prediction using a combination of LSTM and LightGBM models. the research utilizes a detailed dataset from the 2023 Wimbledon Championships, covering 1701 matches, to create a momentum prediction framework that incorporates various statistical data points such as serve speeds and match outcomes. the methodology includes data correction steps for misrecorded scores and speeds, followed by the application of LSTM for analyzing sequential data dependencies and LightGBM for enhancing predictive accuracy through decision tree algorithms. the paper delves into feature engineering, adjusting input variables to better reflect match dynamics, and employs a sensitivity analysis to optimize model parameters. Results indicate that the combined model effectively predicts turning points in matches, providing insights into player performance dynamics. the study advances understanding of momentum factors in tennis, highlighting the potential of machine learning in sports analytics. © 2024 IEEE.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Comparing Word-Based and AST-Based models for Design Pattern Recognition 19

Comparing Word-Based and AST-Based Models for Design Pattern...

引用

19th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Chand, Sivajeet Pandey, Sushant Kumar Horkoff, Jennifer Staron, Miroslaw Ochodek, Miroslaw Durisic, Darko Chalmers Univ Gothenburg Gothenburg Sweden Poznan Univ Poznan Poland Volvo Cars Gothenburg Sweden

ISBN: (纸本)9798400703751

Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. the models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. this study demonstrates how two language models-Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. the results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.

关键词： Programming Language models Design Patterns NLP

来源：评论

学校读者我要写书评

暂无评论

Smart Healthcare: Harnessing the Power of Machine Learning for predictive Analysis 6

Smart Healthcare: Harnessing the Power of Machine Learning f...

引用

6th Parul international conference on engineering and Technology, PICET 2024

作者： Barhate, Aditya Kumar, Praveen Verma, Prateek Jikar, Nayan Tale, Abhay Hikre, Vaibhav Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Smart healthcare employs cloud computing, the Internet of things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine Learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. the trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. this study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry. © 2024 IEEE.

关键词： Disease Detection Machine Learning Medical Imaging predictive Analytics Smart Healthcare

来源：评论

学校读者我要写书评

暂无评论

Some Investigations of Machine Learning models for software Defects 23

Some Investigations of Machine Learning Models for Software ...

引用

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

作者： Bhutamapuram, Umamaheswara Sharma Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Andhra Pradesh India

ISBN: (纸本)9798350322637

software defect prediction (SDP) and software defect severity prediction (SDSP) models alleviate the burden on the testers by providing the automatic assessment of a newlydeveloped program in a short amount of time. the research on defect prediction or defect severity prediction is primarily focused on proposing classification frameworks or addressing challenges in developing prediction models;however, the primary yet significant gap in the literature is interpreting the predictions in terms of project objectives. Furthermore, the literature indicates that these models have poor predictive performance. In this thesis, we investigate the use of a diversity-based ensemble learning mechanism for the cross-project defect prediction (CPDP) task and self-training semi-supervised learning for the software defect severity prediction, respectively, for obtaining better prediction performances. We also propose a few project-specific performance measures to interpret the predictions in terms of project objectives (such as a reduction in expenditure, time, and failure chances). through the empirical analysis, we observe that (1) the diversity-based ensemble learning mechanism improves the prediction performance in terms of both the traditional and proposed measures, and (2) the self-training semi-supervised learning model has a positive impact on predicting the severity of a defective module. Once a potential prediction model is developed, any software organisation may utilise its services. How can an organisation showcase their trust in the developed prediction model? To this end, we investigate the feasibility of SDP models in real-world testing environments by providing proofs using the probabilistic bounds. the proofs summarised show that even if the prediction model has a lower failure probability, the probability of obtaining fewer failures in SDP-tested software than in similar but manually tested software is still exponentially small. this result enables the researchers

关键词： software Defect Prediction Cross-Project Defect Prediction software Defect Severity Prediction software Reliability Performance Measures Feasibility Study

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Turbulence models for Flow and Heat Transfer of Drop-Shaped Tubes 6

Evaluation of Turbulence Models for Flow and Heat Transfer o...

引用

6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Deeb, Rawad Sidenkov, D.V. Damascus University Department of General Mechanics Damascus Syria Moscow Power Engineering Institute Department of Theoretical Bases of Heat Engineering Moscow Russia

ISBN: (纸本)9781665405775

Heat transfer and turbulent flow passing over a single tube and a bundle of drop-shaped tubes have been simulated by Ansys Fluent package. Five two-transport-equation turbulence models were considered. the strengths and weaknesses of the studied models have been studied. the numerical results were compared with the available experimental data. In addition, the capability of turbulence models to predict a flow pattern around the tubes was presented. © 2022 IEEE.

关键词： Training Heat treatment software packages Computational modeling Heat engines predictive models Electron tubes

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329页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：