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

3,259 篇 会议
56 册 图书
24 篇 期刊文献

馆藏范围

3,339 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,736 篇 工学
- 1,337 篇 计算机科学与技术...
- 1,225 篇 软件工程
- 212 篇 控制科学与工程
- 210 篇 信息与通信工程
- 197 篇 电气工程
- 153 篇 机械工程
- 135 篇 材料科学与工程（可...
- 132 篇 安全科学与工程
- 127 篇 生物工程
- 126 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...
709 篇 理学
- 498 篇 数学
- 173 篇 统计学（可授理学、...
- 131 篇 生物学
- 115 篇 物理学
- 113 篇 系统科学
- 60 篇 化学
390 篇 管理学
- 281 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

971 篇 predictive model...
282 篇 accuracy
268 篇 data models
240 篇 computational mo...
208 篇 analytical model...
182 篇 prediction algor...
149 篇 training
138 篇 software enginee...
136 篇 feature extracti...
121 篇 software
120 篇 machine learning
118 篇 deep learning
108 篇 forecasting
105 篇 neural networks
103 篇 adaptation model...
84 篇 optimization
83 篇 machine learning...
82 篇 mathematical mod...
75 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 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

3,043 篇 英文
285 篇 其他
25 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 3339 条记录，以下是881-890 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：

Deep methods based on GAN for face-spoofing 5

Deep methods based on GAN for face-spoofing

引用

5th international conference on Advanced Electronic Materials, Computers and software engineering, AEMCSE 2022

作者： Chen, Lianghong Li, Wenkai Zhang, Leyi Western University 1151 Richmond St LondonN6A 3K7 Canada Northeastern University Baita Shenyang110167 China The University of Melbourne Grattan Street ParkvilleVIC3010 Australia

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

To prevent illegal access to users' privacy by using face-spoofing, many researchers attempt to develop CNN models to identify it. However, only by working with high-quality face images, the CNN model can precisely report illegal accesses but is unreliable when the images are taken in bad conditions. To make up for the defect, this paper compares the performance of two kinds of more advanced neuron network models under the self-attention mechanism dealing with face-spoof issues. the first method is the Self-Attention GAN (SAGAN) model. Under the GAN framework, the SAGAN model introduces a self-attention mechanism to enable generator and discriminator to model the relationship between widely separated spatial regions. Based on this feature, SAGAN can be used to operate distant pixel points and then generate clear face images. the second research method is to apply the ViTGAN model to generate clear face images. Compared with developing the SAGAN model, general the ViTGAN model is a new approach, which can elegantly deal with the face images taken in the dark light conditions. this helps to CNN model cannot report face-spoofing issues with the input face images in the dark environment. To sum up, it is better to use the ViTGAN model to help to solve the face-spoofing issue. © 2022 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

A UAV Sensor Fault Detection Model Based on Dual-threshold Method and LSTM

A UAV Sensor Fault Detection Model Based on Dual-Threshold M...

引用

international conference on Communications, Information System and Computer engineering (CISCE)

作者： Yifan Li Miami College Henan University Kaifeng Henan China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

With the popularization and application of drone technology in many industries, ensuring the safety of drone flights and detecting drone faults has become an important challenge. Sensors are an important part of UAV flight safety. Ensuring the normal operation of sensors is crucial to ensure UAV flight safety. However, due to the risk of sensor loss or failure when UAVs perform extreme missions, UAVs pose significant challenges to flight safety and mission completion. this study proposes a UAV sensor failure analysis method that combines a dual-threshold model and a data-driven deep neural network. the method employs a long short-term memory (LSTM) network for sensor fault prediction. this method combines the advantages of a fast and direct fixed model with strong plasticity of a data-driven model, achieving good performance. the study conducted experimental verification using the BASiC data set. the results demonstrate that this method exhibits high adaptability and high accuracy in UAV sensor fault detection, thereby providing a high-precision UAV sensor fault judgment method for the future.

关键词： Adaptation models Analytical models Accuracy Fault detection predictive models Autonomous aerial vehicles Sensors

来源：评论

学校读者我要写书评

暂无评论

1D-Convnet Model for Detection of Antidepressant Drugs 6

1D-Convnet Model for Detection of Antidepressant Drugs

引用

6th IEEE international conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Pasfica, Gracia Rizka Ramadhan, Nur Ghaniaviyanto Adhinata, Faisal Dharma Institut Teknologi Telkom Purwokerto Department of Software Engineering Purwokerto Indonesia

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

A drug is a substance or mixture of materials to be used in determining the diagnosis, preventing, reducing, eliminating, curing disease or symptoms of disease, bodily or spiritual injury or disorder in humans or animals, including to beautify the body or parts of the human body. Problems begin to arise when a patient is wrong in consuming the target drug used, which is not by the type of disease suffered. For example, suppose a person suffers from a psychological disorder that requires taking different types of drugs, if it turns out that the type of drug consumed is not by the disease, it is very dangerous. this problem is certainly very dangerous because it can cause death for those who consume it. Currently, many researchers are using the deep learning Convolutional Neural Network (CNN) model for drug detection problems. the CNN model has a higher level, namely 1D-Convolutional Neural Network (1D-Convnet) which is still rarely used for drug detection problems. So, the purpose of this study was to detect the classification of atypical antidepressants and SSRIs antidepressants using a deep learning model of the 1D-Convolutional Network (1D-Convnet) type. the results obtained using this model are 98.3% with the most influential parameter, namely dropout. the proposed research model also produces higher accuracy than the Naive Bayes supervised learning model. © 2022 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Improved Distributed Algorithms for Random Colorings 27

Improved Distributed Algorithms for Random Colorings

引用

27th international conference on Principles of Distributed Systems, OPODIS 2023

作者： Carlson, Charlie Frishberg, Daniel Vigoda, Eric Department of Computer Science University of California Santa BarbaraCA United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

ISBN: (纸本)9783959773089

Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic tool for sampling from high-dimensional distributions, a notable example is the equilibirum distribution of graphical models. the Glauber dynamics, also known as the Gibbs sampler, is the simplest example of an MCMC algorithm;the transitions of the chain update the configuration at a randomly chosen coordinate at each step. Several works have studied distributed versions of the Glauber dynamics and we extend these efforts to a more general family of Markov chains. An important combinatorial problem in the study of MCMC algorithms is random colorings. Given a graph G of maximum degree ∆ and an integer k ≥ ∆ + 1, the goal is to generate a random proper vertex k-coloring of G. Jerrum (1995) proved that the Glauber dynamics has O(n log n) mixing time when k > 2∆. Fischer and Ghaffari (2018), and independently Feng, Hayes, and Yin (2018), presented a parallel and distributed version of the Glauber dynamics which converges in O(log n) rounds for k > (2 + Ε)∆ for any Ε > 0. We improve this result to k > (11/6 − δ)∆ for a fixed δ > 0. this matches the state of the art for randomly sampling colorings of general graphs in the sequential setting. Whereas previous works focused on distributed variants of the Glauber dynamics, our work presents a parallel and distributed version of the more general flip dynamics presented by Vigoda (2000) (and refined by Chen, Delcourt, Moitra, Perarnau, and Postle (2019)), which recolors local maximal two-colored components in each step. © Charlie Carlson, Daniel Frishberg, and Eric Vigoda

关键词： Coloring

来源：评论

学校读者我要写书评

暂无评论

GNN and Encoder Integrated Model for Distributed Solar and Wind Power Forecasting

GNN and Encoder Integrated Model for Distributed Solar and W...

引用

software engineering and Computer Science (CSECS), international conference on

作者： Qiuyu Wu Yu Wang Jie Cao Jin Huang Weiwei Miao College of Information Engineering Nanjing University Of Finance and Economics Nanjing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

the integration of large amount of distributed power generation from renewable resources like solar and wind brings new challenges to power forecasting in both short-term and long-term time scales. Distributed power generating units characterized by increased variability and uncertainty among different sites, necessitates collaborative data analysis. And regional variations and simultaneous predictions for multiple sites should also be accounted. this paper proposes a time series forecasting model based on the combination of graph neural network (GNN) and Transformer-Encoder, which is used for long-term and short-term accurate forecasting of renewable energy. It combines temporal convolutional models, graph convolutional neural network, and Transformer-Encoder. this scheme first uses the temporal convolution model to extract short-term and time-dimensional features of time series data, and then replaces the ordinary graph learning layer with Transformer's Encoder layer to further extract the long-term and global features of time series data, and then converts the generated the adjacency matrix is input into the graph convolution module to extract information about its spatial dimensions. Simulation results on an open-source dataset verify that the model is effective and accurate in short-and long-term renewable energy forecasting.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Feature Selection Algorithm Based on Counterfactuals

Efficient Feature Selection Algorithm Based on Counterfactua...

引用

international conference on Communications, Information System and Computer engineering (CISCE)

作者： Diwen Liu Xiaodong Yue School of Computer Eng. and Sci. Shanghai University Shanghai China School of Future Technology Institute of Artificial Intelligence Shanghai University Shanghai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Feature selection is crucial for model construction, involving the identification and selection of the most predictive subset of features from the original set. While information theory-based feature selection algorithms are broadly utilized, they primarily assess correlations and fail to uncover causality, which is essential for pinpointing key decision-influencing features. Additionally, the computational efficiency of these algorithms is often challenged by vast, high-dimensional datasets prevalent today. this chapter introduces a counterfactual-based feature selection algorithm that incorporates counterfactual reasoning to analyze and evaluate features. this innovative approach not only integrates causal analysis into the computation process but also enhances the selection effectiveness of the feature subset. the proposed algorithm includes submodular properties, allowing it to be optimized using greedy techniques, thereby significantly boosting computational efficiency. Experimental results confirm the efficacy of the algorithm, demonstrating its potential to improve feature selection outcomes in complex data environments.

关键词： Greedy algorithms Correlation Computational modeling Cause effect analysis predictive models Feature extraction Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Cancer Prediction Using Clinical and Genomic Data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

引用

international conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

this systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. the review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. the studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. the results show significant improvements in model prediction performance accuracy across multiple studies. the review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services predictive models Data models Bioinformatics Cancer Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

A Neocognitron Based on Multi-objective Optimization for Few-Shot Learning 1

引用

6th EAI international conference on Robotics and Networks, EAI ROSENET 2022

作者： Pan, Hao Guo, Guiyu Shi, Daming College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

In recent years, with the continuous development of deep learning, more and more network models have been proposed to solve practical problems. However, most models often need a large number of labeled samples to train the network to achieve better recognition results. Because a large number of samples need to be labeled, it is labor-intensive and prone to human error labels. For this reason, a large number of researchers have created the concept of few-shot learning, which entails that the model acquires new information from a limited amount of labeled samples in order to get improved recognition outcomes. In this chapter, we learn from the structure of the visual cortex of the human brain and propose new shape templates to improve the traditional neural network. First, we extract the global characteristics of the image to form a 0-1 mask. As the network hierarchy increases and the field of perception expands, the mask is cut to form shape templates. then evolutionary computation selects some excellent shape templates, which provide a non-linear constraint for the convolution layer. Moreover, the convolution layer introduces inhibition signals for training, and a new function is proposed to make it more in line with the biomimetic principles of the human visual cortex. the results show that the proposed method can improve the accuracy of few-shot learning. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Worksho...

引用

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

the proceedings contain 34 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Boosting Data Monetisation with60;DATAMITE;Boosting Digitalization Across European Regions: ...

ISBN: (纸本)9783031632266

the proceedings contain 34 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Boosting Data Monetisation with DATAMITE;Boosting Digitalization Across European Regions: the AMBITIOUS Approach;Data Monetization Opportunities and Challenges: the European Landscape by DATAMITE;Innovative Digital Forensic and Investigation Tools for Law Enforcement: the EMPOWER & TRACY Approach;market Analysis of a Data Platform in the European Data Ecosystem;OCTAPUS: Empowering Next Generation Central Offices with Cross-Layer Network Intelligence Towards a Reconfigurable Data Plane;On 6G-Enabled SDN-Based Mobile Network User Plane with DRL-Based Traffic engineering;on the Efficient Architecture for 6G System;Promoting Deployment of Innovative Use Cases in Market Verticals for the Support of 6G Evolution: the 6G-PAth Context;scalable Data Profiling for Quality Analytics Extraction;seamless Integration of Efficient 6G Wireless Technologies for Communication and Sensing Enabling Ecosystems;testing Plan Description and Field Measurements for Real-Time Wide Area Monitoring of Interconnected Power Systems in the Smart5Grid Project;comparative Analysis of Time Series and Machine Learning models for Air Quality Prediction Utilizing IoT Data;Efficient Energy Disaggregation Using DBSCAN: A Novel Approach for Enhanced Energy Management;integrating Machine Learning and Biological Context for Single-Cell Gene Regulatory Network Inference;museum Education: Integration of Cultural Heritage and Educational Metadata Schemas;net Zero Strategies: Empowering Climate Change Solutions through Advanced Analytics and Time Series;New Perspectives in e-Learning: EEG-Based Modelling of Human Cognition Individual Differences;optimizing Vehicle-to-Vehicle Energy Sharing with predictive Modeling;predicting Song Popularity through Machine Learning and Sentiment Analysis on Social Networks;transformer-Based Anomaly Detection in Energy Consumption Data;Advancing Agricultural

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ensemble Attention Bidirectional Recurrent Neural Network-based RUL Prediction Under Variable Working Condition

Ensemble Attention Bidirectional Recurrent Neural Network-ba...

引用

System Reliability and Safety engineering (SRSE), international conference on

作者： Heng Zhang Chengjie Wang Jun Wu School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Predicting the remaining useful life (RUL) of mechanical equipment under variable operating conditions poses a significant challenge in prognostics. Existing methods often focus solely on monitoring data from the equipment itself, neglecting crucial operating condition data. this limitation leads to reduced prediction accuracy in fluctuating operating conditions. To address this gap, this paper proposes an Ensemble Attention Bidirectional Recurrent Neural Network (EABRNN). the EABRNN model processes multi-sensor monitoring and operational context data separately. It leverages parallel bidirectional Long Short-Term Memory (Bi-LSTM) and bidirectional Gated Recurrent Unit (Bi-GRU) architectures to extract comprehensive features. Furthermore, it integrates Temporal Pattern Attention to enhance remaining useful life prediction accuracy, particularly in variable aircraft engine operating conditions. By combining data integration and advanced model architecture, the EABRNN aims to improve the robustness and accuracy of RUL predictions in practical scenarios.

关键词： Employee welfare Training Recurrent neural networks Accuracy Atmospheric modeling predictive models Feature extraction Data models Safety Monitoring

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334页 << < 85 86 87 88 89 90 91 92 93 94 > >>

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：