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

5,626 篇 会议
90 篇 期刊文献
10 册 图书

馆藏范围

5,726 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,768 篇 工学
- 2,326 篇 计算机科学与技术...
- 1,324 篇 软件工程
- 566 篇 信息与通信工程
- 466 篇 电气工程
- 200 篇 生物工程
- 176 篇 控制科学与工程
- 174 篇 生物医学工程（可授...
- 109 篇 机械工程
- 84 篇 交通运输工程
- 83 篇 化学工程与技术
- 81 篇 电子科学与技术（可...
- 79 篇 光学工程
- 70 篇 仪器科学与技术
- 65 篇 动力工程及工程热...
- 55 篇 安全科学与工程
- 54 篇 土木工程
- 52 篇 环境科学与工程（可...
899 篇 理学
- 478 篇 数学
- 235 篇 生物学
- 232 篇 物理学
- 191 篇 统计学（可授理学、...
- 119 篇 系统科学
- 67 篇 化学
589 篇 管理学
- 326 篇 图书情报与档案管...
- 307 篇 管理科学与工程(可...
- 164 篇 工商管理
290 篇 医学
- 244 篇 临床医学
- 144 篇 基础医学(可授医学...
- 80 篇 药学(可授医学、理...
76 篇 法学
- 71 篇 社会学
55 篇 经济学
45 篇 教育学
40 篇 农学
26 篇 文学
2 篇 军事学
1 篇 历史学
1 篇 艺术学

主题

1,599 篇 data models
534 篇 predictive model...
529 篇 training
498 篇 machine learning
461 篇 data mining
416 篇 big data
404 篇 computational mo...
343 篇 feature extracti...
333 篇 task analysis
326 篇 prediction algor...
267 篇 deep learning
255 篇 machine learning...
245 篇 classification a...
238 篇 analytical model...
191 篇 clustering algor...
190 篇 support vector m...
182 篇 neural networks
158 篇 natural language...
142 篇 training data
141 篇 mathematical mod...

机构

19 篇 sun yat sen univ...
19 篇 daffodil interna...
12 篇 univ chinese aca...
11 篇 tsinghua univers...
11 篇 university of ch...
11 篇 school of data a...
10 篇 carnegie mellon ...
9 篇 renmin univ chin...
9 篇 lviv polytechnic...
8 篇 shenzhen researc...
7 篇 shanghai jiao to...
7 篇 college of compu...
7 篇 university of el...
7 篇 school of comput...
7 篇 national univers...
7 篇 harbin institute...
7 篇 beijing key lab ...
7 篇 natl univ def te...
7 篇 renmin univ chin...
6 篇 university of sc...

作者

7 篇 li li
7 篇 wang wei
7 篇 xiao yanghua
6 篇 wen ji-rong
6 篇 cui bin
6 篇 zhao wayne xin
6 篇 ying liu
6 篇 syed akhter hoss...
5 篇 yang tong
5 篇 liu yang
5 篇 li zhang
5 篇 jia weijia
5 篇 chen liu
5 篇 yang yang
5 篇 xiao hongbo
5 篇 nguyen trong-the
5 篇 yu jie
5 篇 albert bifet
5 篇 wang bin
5 篇 carlos ordonez

语言

5,682 篇 英文
36 篇 其他
9 篇 中文
2 篇 俄文
1 篇 乌克兰文

检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5726 条记录，以下是291-300 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：

PVitNet:An Effective Approach for Android Malware Detection Using Pyramid Feature processing and Vision Transformer

PVitNet:An Effective Approach for Android Malware Detection ...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Denghui Yang Yifan Ding Hao Zhang Yizhou Li School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University

This presents a significant challenge for detecting and combating malicious software. Users often grant software permissions unknowingly, exposing their devices to risks such as unauthorized access, file manipulation, and malware propagation. Traditional detection algorithms relying on limited permission-based strategies fall short in addressing this issue. To overcome this, we propose PVitNet (Network based On Pyramid Feature processing and Vision Transformer), an Android malware detection method. PVitNet incorporates pyramid feature processing, attention mechanisms, and an automatic feature extraction tool. By leveraging semantic information from feature pyramid models and learning shared characteristics among similar software, we successfully identify Android malware families. Our experiments on the CICMalDroid 2020 dataset demonstrate the effectiveness of our approach, with a 14.96% increase in accuracy and an F1 score of 98.31%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced Darknet Traffic Detection through Minority-Class Augmentation and Multi-Model Fusion

Advanced Darknet Traffic Detection through Minority-Class Au...

引用

2025 2nd international conference on algorithms, Software Engineering and Network Security (ASENS)

作者： Zikang Liu School of Cyber Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The Tor network is an anonymous communication technology that can protect user privacy and break through network censorship, but it can also be used by malicious actors and is a focus of network security research. Because its traffic is encrypted multiple times and is similar to ordinary traffic in some characteristics, traditional rule-based matching or anomaly detection methods are difficult to accurately distinguish, resulting in high false positive and false negative rates. To address this problem, this paper proposes an anonymous traffic detection method that combines data enhancement and ensemble learning. Logarithmic transformation, quantile truncation and normalization methods are used for data processing, and minority class samples are supplemented based on data enhancement strategies to alleviate the data imbalance problem, and its impact is evaluated through visualization. This paper constructs an ensemble learning framework based on LightGBM and RandomForest, and uses the Soft Voting strategy to fuse classifier prediction results, significantly improving detection accuracy and stability. Experiments on the CIC-Darknet2020 dataset show that the model can effectively improve the accuracy of anonymous traffic detection, has good stability, and plays a positive role in optimizing the performance of existing network traffic classification models.

关键词： Privacy Dark Web Accuracy data enhancement Software algorithms Telecommunication traffic Network security Stability analysis Ensemble learning Software engineering

来源：评论

学校读者我要写书评

暂无评论

Lightweight Federated Learning and Blockchain System for Industrial IoT Big data Security 15

Lightweight Federated Learning and Blockchain System for Ind...

引用

15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Amadi, Chimeremma Sandra Ajakwe, Simeon Okechukwu Igboanusi, Ikechi Saviour Kim, Dong-Seong Jun, Taesoo Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of Information and Communication Technology Convergence Research Centre Gumi Korea Republic of

ISBN: (纸本)9798350364637

The vast amounts of data produced by industrial IoT devices have recently attracted considerable attention across various scientific disciplines. While big data provides many benefits and applications, it also introduces several challenges that must be resolved to enhance service quality. Artificial intelligence (AI) and blockchain are recognized for their significant potential in strengthening big data security. This study addresses the security of lightweight devices that store large volumes of data but have limited memory and processing power. A lightweight framework for detecting zero-day attacks on resource-constrained yet data-intensive industrial devices was implemented. The performance of the proposed model was assessed using various metrics: Accuracy, Precision, Recall, F1-score, Evaluation time, Gas cost, Loss function, Transaction cost, and its performance compared to the state-of-the-art models. Federated learning (FL) simulation was conducted over 20 rounds, resulting in an overall global model learning accuracy of 97.7%, an F1-score of 97.1 %, a precision of 97.1 %, and a recall value of 97.7%, with a total evaluation time of 899.3 minutes. Additionally, all global model trainable parameters were efficiently deployed over the blockchain public network on the Ethereum smart contract. A security audit carried out showed an average score of 91 %. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence for Urban Safety: A Case Study for reducing road accident in Genoa 8

Artificial Intelligence for Urban Safety: A Case Study for r...

引用

8th international conference on Smart data and Smart Cities, SDSC 2024

作者： Marceddu, Alessandro Miccoli, Massimo Amicone, Alessandro Marangoni, Luca Risso, Alessandra GFT Italy Data & AI and European Project Department Padua Italy GFT Italy Data & AI and European Project Department Turin Italy GFT Italy Innovation & European Project Department Genoa Italy GFT Italy Innovation & European Project Department Milan Italy Genoa's Municipality Genoa Italy

This study explores the application of Machine Learning (ML) and citizen engagement in improving road safety for vulnerable populations (pedestrians, cyclists) in Genoa, Italy. Aligned with the UN's 2030 Agenda for Sustainable Development, the project aims for a 50% reduction in traffic accidents by *** AI4PublicPolicy initiative introduces the Virtual Policy Management Environment (VPME) platform. VPME utilizes ML, Deep Learning (DL), Natural Language processing (NLP), and chatbots to empower the policy development lifecycle. Citizen feedback is integrated through workshops and surveys, fostering a citizen-centric approach. The Genoa pilot program demonstrates VPME's capabilities. ML models analyse historical accident and Geographic Information Systems (GIS) data to predict future high-risk areas. These predictions inform resource allocation and targeted interventions for pedestrian crossings and school walking routes ("Pedibus"). Dashboards visualize the model outputs, allowing users to assess risk levels and predict accident occurrences. Future improvements include incorporating additional data sources (demographics, real-time traffic) for enhanced model accuracy. Citizen engagement played a vital role. Co-creation workshops facilitated stakeholder participation in defining Use Cases, User Stories, and project objectives. Discussions focused on integrating data from environmental, traffic, and citizen reporting systems with VPME solutions. Participants evaluated the project approach and provided valuable feedback. The project highlights the potential of AI and citizen collaboration for data-driven policymaking. This approach empowers municipalities to make informed decisions that prioritize public safety and well-being. © Author(s) 2024. CC BY 4.0 License.

关键词： Roads and streets

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile 16

Machine Learning-Based Classification of Mental Health State...

引用

16th Biomedical Engineering international conference, BMEiCON 2024

作者： Duangchaemkarn, Khanita Khammarew, Patiphan Aramvith, Supavadee School of Pharmaceutical Sciences University of Phayao Department of Pharmacy Practice Phayao56000 Thailand School of Pharmaceutical Sciences University of Phayao Pharmacy Research Extern Under the Doctor of Pharmacy Program Phayao56000 Thailand Chulalongkorn University Multimedia Data Analytic and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9798331505431

This study aimed to determine the most effective machine learning model for classifying emotional states of depression, anxiety, and stress based on the DASS-21 profile. Six machine learning models were developed and tested using 2,602 records from the University of Phayao Counselling Center, collected between 23 July and 16 August 2020. The results demonstrate that the deep learning model provided the highest accuracy and precision, exceeding 90% for all three emotional categories, with an AUC greater than 99%. These findings introduce an innovative approach for classifying emotional states of depression, anxiety, and stress using the DASS-21 profile as a benchmark. Incorporating additional features into the model could further enhance its utility in clinical decision-making, improving the accuracy of mental health screening and diagnosis. © 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

Exploratory data Analysis in Cloud Computing Environments for Server Consolidation via Fuzzy Classification models 26

Exploratory Data Analysis in Cloud Computing Environments fo...

引用

26th international conference on Enterprise Information Systems, ICEIS 2024

作者： Bastos, Rafael R. Seibert, Vagner A. Maia, Giovani P. de Moura, Bruno M.P. Lucca, Giancarlo Yamin, Adenauer C. Reiser, Renata H.S. Pelotas Brazil Pelotas Brazil Bagé Brazil

ISBN: (纸本)9789897586927

The present work addresses the challenges of flexible resource management in Cloud Computing, emphasizing the critical need for efficient resource utilization. Precisely, we tackle the problem of dynamic server consolidation, supported by the capacity of Fuzzy Logic to deal with uncertainties and imprecisions inherent in cloud environments. In the preprocessing step, we employ a feature selection strategy to perform attribute selection and, better understand the problem. data classification was performed by fuzzy rule learning approaches. Comparative evaluations of algorithm classification highlight the remarkable accuracy of FURIA, with IVTURS as a close alternative. While FURIA generates 41 rules, indicating a comprehensive model, IVTURS produces only six, introducing an abstract level to model uncertainties as interval-valued fuzzy membership degrees. The study underscores the relevance of parameter adaptation in mapping feature selection and membership functions to achieve optimal performance for flexible algorithms in the Cloud Computing environment. Our results underlie the structure of a fuzzy system adapted to CloudSim, integrating energy optimization and Service Level Agreements assurance through different server consolidation strategies. This research contributes valuable perspectives to decision-making processes in the Cloud Computing environment. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Membership functions

来源：评论

学校读者我要写书评

暂无评论

Exploring Synthetic Noise algorithms for Real-World Similar data Generation: A Case Study on Digitally Twining Hybrid Turbo-Shaft Engines in UAV/UAS Applications 12th

Exploring Synthetic Noise Algorithms for Real-World Similar...

引用

12th international conference on Model and data Engineering, MEDI 2023

作者： Aghazadeh Ardebili, Ali Longo, Antonella Ficarella, Antonio Khalil, Adem Khalil, Sabri Data Lab Dept. of Engineering for Innovation University of Salento LE Lecce7100 Italy CRISR Research Center University of Salento LE Lecce7100 Italy Green Engine Lab Dept. of Engineering for Innovation University of Salento LE Lecce7100 Italy Engineering and Architecture Faculty Istanbul Gelisim University Istanbul Turkey Bologna Italy

ISBN: (纸本)9783031493324

An emerging technology for automating Unmanned aircraft is digitally twining the system, and employing AI-based data-driven solutions. Digital Twin (DT) enables real-time information flow between physical assets and a virtual model, creating a fully autonomous and resilient transport system. A key challenge in DT as a Service (DTaaS) is the lack of Real-world data for training algorithms and verifying DT functionality. This article focuses on data augmentation using Real-world Similar Synthetic data Generation (RSSDG) to facilitate DT development in the absence of training data for Machine Learning (ML) algorithms. The main focus is on the noise generation step of the RSSDG for a common Hybrid turbo-shaft engine because there is a significant gap in transforming synthetic data to Real-world similar data. Therefore we generate noise through 6 different noise generation algorithms before Rolling Linear Regression and Filtering the noisy predictions through Kalman Filter. The primary objective is to investigate the sensitivity of the RSSDG process concerning the algorithm that is used for noise generation. The study’s results support the potential capacity of RSSDG for digitally twining the engine in a Real-world operational lifecycle. However, noise generation through Weibull and Von Mises distribution showed low efficiency in general. In the case of Normal Distribution, for both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters, except the engine torque;however, Students-T, Laplace, and log-normal show better performance for engine torque RSSDG. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

Empowering Search and Rescue Operations with Big data Technology: A Comprehensive Study of YOLOv8 Transfer Learning for Transportation Safety

Empowering Search and Rescue Operations with Big Data Techno...

引用

2023 IEEE international conference on Big data, Bigdata 2023

作者： Lima, Luciano Andrade, Fabio Djenouri, Youcef Pfeiffer, Carlos Moura, Marcos University of South-Eastern Norway Vestfold Norway University of South-Eastern Norway Porsgrunn Norway

ISBN: (纸本)9798350324457

In this research work, we demonstrate the important role of object detection technology and how to optimize it for elevating the efficiency of accident rescue missions in the maritime transportation industry. In this context, the use of unmanned aerial vehicles in search and rescue missions is a promising research topic. However, the processing power limitations and the lack of data focused on sea operations present some challenges in this area. The mix of synthetic and real data during the training and even the total replacement of real data with virtual generated ones can lead to a good and flexible solution for the dataset challenges. Another strategy for dealing with the lack of data is the use of transfer learning for leveraging the knowledge in a domain with an abundance of data when compared to a new domain of interest. In this work, the use of transfer learning and synthetic and real mixed datasets is explored for the field of search and rescue. The YOLOv8 is trained in different configurations of regular learning and transfer learning, with fine-tuning and 4 and 7 frozen layers, using both synthetic and real data. Finally, the models and the set of data are evaluated based on mAP50-95 showing some possible reasons for a performance difference between real and synthetic data in the training process. © 2023 IEEE.

关键词： SAR operations Synthetic dataset Transfer learning UAV YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Abnormal data Based on Deep Learning and Improved algorithms

Detection and Classification of Abnormal Data Based on Deep ...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Ming Li Wuhan Meteorological Observatory Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Given the rapid growth of emerging systems and technologies, cloud systems of computing, IoT, and other massive data applications, as viewed in this case, are trying to cross thresholds in value realization and real-time analysis. This makes the element of data streams ubiquitous within different industries. Given this, the need for the detection of anomalies within these cohorts of data streams may play a fundamental role in avoiding the risks that come with limiting decision-making. The traditional methods of anomaly and abnormal data algorithms widely relied on acquiring information from a pool of data globally, a factor that made it significantly challenging to streamline and apply the data to different scenarios. However, significant reviews on algorithms within the domain of abnormal or anomaly data detection widely focus on using and exposing data anomalies through learning and improved algorithms, establishing the rationale behind this article. Therefore, this article focuses on detecting and classifying abnormal data based on deep learning and improved algorithms in static and different data environments.

关键词： Deep learning Industries Cloud computing Limiting Reviews Decision making Real-time systems Classification algorithms Internet of Things Streams

来源：评论

学校读者我要写书评

暂无评论

An Extensible Parsing Pipeline for Unstructured data processing 24

An Extensible Parsing Pipeline for Unstructured Data Process...

引用

24th international conference on Advanced Communication Technology, ICACT 2022

作者： Jain, Shubham Buitleir, Amy De Fallon, Enda Software Research Institute Athlone Institute of Technology Athlone Ireland Network Management Lab Ericsson Athlone Ireland

ISBN: (纸本)9791188428090

Network monitoring and diagnostics systems depict the running system's state and generate enormous amounts of unstructured data through log files, print statements, and other reports. It is not feasible to manually analyze all these files due to limited resources and the need to develop custom parsers to convert unstructured data into desirable file formats. Prior research focuses on rule-based and relationship-based parsing methods to parse unstructured data into structured file formats;these methods are labor-intensive and need large annotated datasets. This paper presents an unsupervised text processing pipeline that analyses such text files, removes extraneous information, identifies tabular components, and parses them into a structured file format. The proposed approach is resilient to changes in the data structure, does not require training data, and is domain-independent. We experiment and compare topic modeling and clustering approaches to verify the accuracy of the proposed technique. Our findings indicate that combining similarity and clustering algorithms to identify data components had better accuracy than topic modeling. © 2022 Global IT Research Institute-GiRI.

关键词： Pipelines

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500页 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：