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

3,290 篇 会议
56 册 图书
26 篇 期刊文献

馆藏范围

3,372 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,752 篇 工学
- 1,347 篇 计算机科学与技术...
- 1,232 篇 软件工程
- 221 篇 控制科学与工程
- 212 篇 信息与通信工程
- 200 篇 电气工程
- 161 篇 机械工程
- 136 篇 材料科学与工程（可...
- 132 篇 安全科学与工程
- 129 篇 生物工程
- 127 篇 生物医学工程（可授...
- 94 篇 土木工程
- 93 篇 交通运输工程
- 87 篇 动力工程及工程热...
- 85 篇 建筑学
- 77 篇 化学工程与技术
- 76 篇 电子科学与技术（可...
- 62 篇 力学（可授工学、理...
717 篇 理学
- 500 篇 数学
- 176 篇 统计学（可授理学、...
- 132 篇 生物学
- 116 篇 物理学
- 113 篇 系统科学
- 64 篇 化学
393 篇 管理学
- 284 篇 管理科学与工程(可...
- 172 篇 图书情报与档案管...
- 128 篇 工商管理
94 篇 医学
- 86 篇 基础医学(可授医学...
- 81 篇 临床医学
56 篇 经济学
- 56 篇 应用经济学
52 篇 教育学
- 51 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

985 篇 predictive model...
294 篇 accuracy
270 篇 data models
242 篇 computational mo...
211 篇 analytical model...
184 篇 prediction algor...
153 篇 training
140 篇 software enginee...
137 篇 feature extracti...
124 篇 software
122 篇 deep learning
121 篇 machine learning
108 篇 neural networks
108 篇 forecasting
105 篇 adaptation model...
85 篇 optimization
83 篇 machine learning...
82 篇 mathematical mod...
77 篇 real-time system...
65 篇 mathematical mod...

机构

10 篇 school of inform...
8 篇 school of electr...
7 篇 national enginee...
7 篇 hubei key labora...
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 hassan ahmed e.
7 篇 menzies tim
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

2,949 篇 英文
412 篇 其他
29 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 3372 条记录，以下是981-990 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：

Performance Evaluation of Deep Learning models in Heating Load Forecasting in Building Environments Under Data Integrity Attacks

Performance Evaluation of Deep Learning Models in Heating Lo...

引用

Smart Power & Internet Energy Systems (SPIES), international conference on

作者： Arash Moradzadeh Atena Motlagh Mohamed Benbouzid Sukumar Kamalasadan S. M. Muyeen Department of Electrical Engineering Qatar University Doha Qatar Department of Aerospace Engineering Tabriz Branch Islamic Azad Uiniversity Tabriz Iran Institut de Recherche Dupuy de Lôme (UMR CNRS 6027) University of Brest Brest France Department of Electrical Engineering Energy Production and Infrastructure Center University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

Recently, the introduction of new thermal regulations has led to a significant improvement in the energy efficiency of buildings. Consequently, a variety of deep learning-based methods have been developed to more accurately forecast the heating load demand of buildings. this paper investigates the performance of various deep learning models in forecasting heating load in building environments, particularly under the impact of data integrity attacks. Various deep learning models such as Convlutional Neural Network (CNN), Long short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), CNN-LSTM, Autoencoder (AE)-BiLSTM, and Variational AE-BiLSTM (VAEBiLSTM) are employed to forecast heating loads using real-world data. the models are evaluated based on variois performance evaluation metrics. Initially, the models are tested under normal conditions, where the VAEBiLSTM and AE-BiLSTM demonstrate superior performance with lower error rates and higher correlation values. Subsequently, a False Data Injection attack (FDIA) is simulated to assess the models' resilience under compromised data conditions. the results reveal a significant degradation in predictive accuracy for all models, with conventional architectures like CNN, LSTM and BiLSTM being most affected. However, models integrating AE-based techniques (AE-BiLSTM and VAEBiLSTM) exhibit greater robustness, maintaining relatively lower error rates and higher correlations.

关键词： Heating systems Deep learning Load forecasting Error analysis Data integrity Buildings Bidirectional long short term memory predictive models Data models Load modeling

来源：评论

学校读者我要写书评

暂无评论

An Early Warning Method for the Operating Status of Turbine Sliding Pin System Based on Multi-Step Time Series Prediction

An Early Warning Method for the Operating Status of Turbine ...

引用

Energy Systems and Electrical Power (ICESEP), international conference on

作者： Zongjie Li Menghui Li Jiakui Shi Peng Sun Haibo Zhao Jie Wan School of Energy Science and Engineering Harbin Institute of Technology Harbin China Huaneng Shandong Power Generation. Ltd. China Huaneng Group.Ltd. Jinan China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Real-time monitoring of the sliding pin system is essential for ensuring the safe and reliable operation of the turbine generator set, especially under peak loads. this study begins by examining the operational mechanism of the sliding pin system, identifying key monitoring parameters reflecting its operating status, gathering time series data via the distributed control system (DCS), and conducting multi-step predictions using the Long Short-Term Memory (LSTM) model to facilitate indirect early fault detection. the model demonstrates strong performance according to a comprehensive four-dimensional evaluation index assessing the accuracy of the LSTM model in multi-step predictions of individual time series data. these findings hold significant engineering and practical value for steam turbine operation and maintenance practices.

关键词： Accuracy Time series analysis predictive models Power system stability Data models Pins Power system reliability

来源：评论

学校读者我要写书评

暂无评论

Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers

Time Series Modeling for Heart Rate Prediction: From ARIMA t...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Haowei Ni Shuchen Meng Xieming Geng Panfeng Li Zhuoying Li Xupeng Chen Xiaotong Wang Shiyao Zhang Columbia University New York USA Central University of Finance and Economics Beijing China Chongqing University Chongqing China University of Michigan Ann Arbor USA Johns Hopkins University Baltimore USA New York University Brooklyn USA Indepent Researcher Beijing China Cornell University Ithaca USA

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Cardiovascular disease (CVD) is a leading cause of death globally, necessitating precise forecasting models for monitoring vital signs like heart rate, blood pressure, and ECG. Traditional models, such as ARIMA and Prophet, are limited by their need for manual parameter tuning and challenges in handling noisy, sparse, and highly variable medical data. this study investigates advanced deep learning models, including LSTM, and transformer-based architectures, for predicting heart rate time series from the MIT-BIH Database. Results demonstrate that deep learning models, particularly PatchTST, significantly outperform traditional models across multiple metrics, capturing complex patterns and dependencies more effectively. this research underscores the potential of deep learning to enhance patient monitoring and CVD management, suggesting substantial clinical benefits. Future work should extend these findings to larger, more diverse datasets and real-world clinical applications to further validate and optimize model performance.

关键词： Heart rate Deep learning Patient monitoring Accuracy Time series analysis Medical services predictive models Transformers Data models Forecasting

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Traffic Prediction to Optimize Routing Paths and Reduce Latency in SDN

Deep Learning Traffic Prediction to Optimize Routing Paths a...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Rui Xiong Qianchen Yuan Hao Zhang Xiaoming Wang Kun Ma Beijing Institute of Astronautical Systems Engineering (of China Academy of Launch Vehicle Technology) Beijing China Pressure and Vacuum Lab. (of National Institute of Metrology) Beijing China

software-Defined Networking (SDN) decouples the control and data planes to enrich the network control, management and functionality. By collecting the status of each switch through the Southbound, SDN provides a global view for the controller to attain flexible packet forwarding by a proactive mode or a reactive mode. However, a proactive mode is not suitable for highly dynamic networks and reactive mode needs to take a while termed as control delay from the collection state to the start of schedule. In large-scale and dynamic networks, control delay can cause network congestion when the network state changes more severely with high loads. In this paper, an M/D/1 queuing model is proposed to analyze the influence of this control delay, and then deep learning methods combining the advantages of proactive mode and reactive mode are adopted to predict the traffic to manage the traffic routing in advance. To reduce the prediction inaccuracy caused by the randomness and suddenness of traffic, we modify the loss function in deep learning models considering the time correlation and spatial correlation of traffic sequences and this proposed method has the potential to effectively reduce the predicted root mean square error (RMSE) by over 30%. Next, according to the predicted results, we propose a method of adjusting routing paths in advance in SDN to balance the load of overlapping links. Finally, simulation results show that using a deep learning method with a modified loss function can effectively reduce the average end-to-end delay by over 10% under a high load.

关键词： Deep learning Analytical models Correlation Switches predictive models Control systems Routing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Graph Based models for Automatic Program Repair 6th

Enhancing Graph Based Models for Automatic Program Repair

引用

6th international conference on Soft Computing and its engineering Applications, icSoftComp 2024

作者： Singh, Vaibhav Srivastava, Jyoti Hamirpur India

ISBN: (纸本)9783031880384

software development teams are confronted with substantial problems when it comes to software flaws, which can result in reduced user experience, compromised security, and lower reliability. For software systems to be robust and mitigated, effective bug detection and patching are essential. the goal of this research is to improve bug detection and repair in software development processes by using an effective way for code representation and then fine-tuning the model on real bugs. the paper explores and highlights the mechanisms, applications, and implications of several bug localization techniques, including machine learning-based approaches and graph based methods. this research adds to the continuing conversation on software quality assurance and reliability by clarifying the nuances of bug localization and patching. this research helps in improving the false positive rate by identifying the real bug correctly. It highlights the graph-based model, which works on finding short distance in graph for patching up the bug, as the best method which can effectively use the real bugs for automatic program repair. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Short-term photovoltaic power prediction based on similar day clustering and VMD-SSA-LSTM

Short-term photovoltaic power prediction based on similar da...

引用

IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Juntao Kuang College of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

To mitigate the high variability and unpredictability in photovoltaic power generation, this paper introduces a prediction method for photovoltaic models using similar day clustering combined with the variational mode decomposition-sparrow search algorithm-long short-term memory neural network (VMD-SSA-LSTM). First, the Kmeans algorithm is used for clustering the photovoltaic data, and the main meteorological factors are selected using the Pearson correlation coefficient. then, on the basis of selecting the historical similar days of the day to be predicted using a weighted comprehensive evaluation index based on grey correlation analysis and cosine similarity, the variational mode decomposition (VMD) is used to decompose the photovoltaic signal. Finally, the sparrow search algorithm-long short-term memory neural network (SSA-LSTM) model is used to predict each subsequence, and the results are aggregated to obtain the final prediction. Simulation results demonstrate that the proposed VMD-SSA-LSTM model significantly enhances the accuracy of photovoltaic output prediction under similar day clustering, outperforming single models and other combined models.

关键词： Photovoltaic systems Accuracy Correlation Meteorological factors Neural networks Clustering algorithms predictive models Prediction algorithms Indexes Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Network Based Control System Design Optimization for Buck Circuits Using Genetic Algorithm

Network Based Control System Design Optimization for Buck Ci...

引用

international conference on Industrial Artificial Intelligence (IAI)

作者： Ziwei Wang Xudong Gao Wenjie Cao School of electronic and information engineering Anhui Jianzhu University Hefei China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Optimization of control systems for power electronic systems using evolutionary algorithms has gained significant attention in current research. However, the effectiveness of such methods heavily relies on the accuracy of initial data collection, often leading to prolonged time consumption with higher precision. this paper presents an approach for optimizing control systems of Buck circuits by integrating BP neural networks to address both data accuracy and time efficiency using genetic algorithm. Initially, data corresponding to controller parameters and system indicators are collected. Subsequently, BP neural networks are utilized to model the relationship between multiple controller parameters and control indicators. After that, the genetic algorithm is employed to optimize system controller parameters continuously. Finally, the proposed method is implemented in a Matlab/Simulink control system, and its efficacy is validated through simulation results.

关键词： Accuracy Simulation Neural networks Circuits predictive models Control systems Prediction algorithms Mathematical models Sliding mode control Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Multi-Class Missense Variant Prediction Based on Asymmetric Bagging and Tabular Generation Combined with Extreme Gradient Boosting

Multi-Class Missense Variant Prediction Based on Asymmetric ...

引用

6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Cong Liu Jing Liu Information engineering College Shanghai Maritime University Shanghai China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Existing mis-sense variant classification models are primarily built upon binary classifiers and conventional data- level imbalance handling methods, thereby, as tools for the prediction of mis-sense mutations, often overlook the diversity and complexity of mis-sense variant data in clinical significance categorization. To enhance the accuracy and practicality of missense variant prediction, this study introduces a novel multi-class mis-sense variant classification prediction model employing asymmetric bagging algorithms (AB), Conditional Tabular Generative Adversarial Networks (CTGAN), and Extreme Gradient Boosting (XGBoost). A series of empirical experiments on the ClinVar database's multi-class imbalanced mis-sense variant dataset show that the proposed model, with an average recall rate (M-R) of 0.668 and an accuracy (Acc) of 0.725, outperforms the benchmark models. the benchmark models include combinations of random undersampling, random oversampling, Synthetic Minority Over-sampling Technique (SMOTE), AB ensemble algorithms, and CTGAN data augmentation algorithms with Random Forest, XGBoost, and the deep learning model FT-Transformer. this research not only overcomes the limitations of existing studies, enriching the theoretical and methodological system for multi-class mis-sense variant prediction, but also provides a more accurate and efficient computational tool for precision medicine research.

关键词： Deep learning Adaptation models Accuracy Computational modeling predictive models Prediction algorithms Data augmentation Boosting Data models Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers

Machine Learning Techniques for Predicting Ovarian Cancer in...

引用

international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Mir Jafikul Alam Jannat-E Anawar Khandaker Mohammad Mohi Uddin Md. Hasibur Rahman Md. Mahbubur Rahman Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

the deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. the key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. the proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning.

关键词： Measurement Logistic regression Statistical analysis Forestry predictive models Reliability Random forests

来源：评论

学校读者我要写书评

暂无评论

Interpreting Multiclass Lung Cancer from CT Scans using Grad-CAM on Lightweight CNN Layers

Interpreting Multiclass Lung Cancer from CT Scans using Grad...

引用

international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Fahim Muntasir Ayon Datta Shakib Mahmud Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Lung cancer is one of the most prevalent and deadly cancer kinds. CT scan imaging has made it easier to observe in-depth at different lung cancer conditions. Convolutional Neural Networks have already been utilized for detecting objects in images. this research implements a lightweight CNN architecture in classifying two types of cancerous images and distinguishing them from healthy ones. AlexNet, ResNet101 V2, DenseNet201, MobileNet V2, and other sizable deep learning and transfer learning models were tested against this unique architecture with global max pooling. the proposed CNN model overlaps in performance with much bigger models whilst using about eight times fewer parameters, thus being efficient. Apart from making the architecture smaller, Grad-CAM was used to explain the convolutional flow over different convolution layers. this integration of the Grad-CAM technique into the analysis adds a crucial layer of transparency to the deep-learning models. By vi-sualizing the regions within lung cancer images that significantly influence predictions, we aim to enhance the interpretability of our model, fostering trust and better understanding among healthcare professionals.

关键词： Deep learning Visualization Computed tomography Transfer learning Lung cancer Medical services predictive models

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338页 << < 95 96 97 98 99 100 101 102 103 104 > >>

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：