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

13,988 篇 会议
7,805 篇 期刊文献
257 册 图书

馆藏范围

22,050 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

13,686 篇 工学
- 10,615 篇 计算机科学与技术...
- 8,789 篇 软件工程
- 3,543 篇 信息与通信工程
- 2,296 篇 控制科学与工程
- 1,910 篇 生物工程
- 1,692 篇 电气工程
- 1,416 篇 生物医学工程（可授...
- 1,190 篇 电子科学与技术（可...
- 1,189 篇 光学工程
- 891 篇 网络空间安全
- 805 篇 机械工程
- 713 篇 安全科学与工程
- 651 篇 动力工程及工程热...
- 628 篇 交通运输工程
- 600 篇 仪器科学与技术
- 561 篇 化学工程与技术
6,957 篇 理学
- 3,534 篇 数学
- 2,101 篇 生物学
- 1,812 篇 物理学
- 1,233 篇 统计学（可授理学、...
- 745 篇 系统科学
- 674 篇 化学
4,128 篇 管理学
- 2,520 篇 管理科学与工程(可...
- 1,821 篇 图书情报与档案管...
- 1,084 篇 工商管理
1,600 篇 医学
- 1,238 篇 临床医学
- 1,127 篇 基础医学(可授医学...
- 844 篇 公共卫生与预防医...
- 656 篇 药学(可授医学、理...
866 篇 法学
- 717 篇 社会学
384 篇 经济学
339 篇 农学
245 篇 教育学
192 篇 文学
33 篇 军事学
28 篇 艺术学
5 篇 哲学
3 篇 历史学

主题

961 篇 deep learning
875 篇 accuracy
542 篇 computational mo...
516 篇 machine learning
472 篇 feature extracti...
451 篇 training
404 篇 real-time system...
385 篇 predictive model...
378 篇 internet of thin...
323 篇 support vector m...
321 篇 convolutional ne...
285 篇 computer science
278 篇 data models
269 篇 semantics
259 篇 optimization
230 篇 neural networks
223 篇 computer archite...
220 篇 reliability
219 篇 scalability
209 篇 image segmentati...

机构

267 篇 department of co...
169 篇 school of comput...
137 篇 school of comput...
137 篇 school of comput...
124 篇 department of co...
118 篇 school of comput...
114 篇 school of comput...
112 篇 department of co...
107 篇 department of co...
103 篇 college of compu...
103 篇 school of comput...
101 篇 school of comput...
101 篇 national enginee...
100 篇 department of co...
96 篇 north-west unive...
95 篇 institut univers...
93 篇 department of co...
90 篇 infn sezione di ...
87 篇 national centre ...
85 篇 tno technical sc...

作者

125 篇 niyato dusit
99 篇 ferrara g.
99 篇 ardid m.
98 篇 calvo d.
95 篇 chen a.
91 篇 gal t.
91 篇 bozza c.
91 篇 díaz a.f.
91 篇 boumaaza j.
89 篇 coleiro a.
87 篇 gozzini s.r.
84 篇 zheng wei-shi
83 篇 anjali t.
83 篇 dallier r.
71 篇 kalekin o.
63 篇 anguita m.
63 篇 boettcher m.
59 篇 kulikovskiy v.
59 篇 huang f.
55 篇 benoit d.m.

语言

15,935 篇 英文
6,011 篇 其他
179 篇 中文
3 篇 法文
2 篇 德文
2 篇 斯洛文尼亚文
1 篇 日文
1 篇 朝鲜文

检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

共 22050 条记录，以下是4091-4100 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：

Comparative Analysis of Deep Learning and Machine Learning models for Network Intrusion Detection 14

Comparative Analysis of Deep Learning and Machine Learning m...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Dharaneish, V.C. Nithin Kumar, S. Hari Varsha, V. Senthil Kumar, T. Gireesh Kumar, T. Vajipayajula, Sulakshan Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Ibm India Pvt Ltd Ibm Security Bengaluru India

ISBN: (纸本)9798350335095

The increasing prevalence of security breaches and malicious software attacks is a major concern in the digital landscape, sparking continued interest in malware detection. Malware attacks have a significant impact on computer users, networks, businesses, organizations, and governments. Despite the development of multiple intrusion detection systems aimed at protecting data and resources from attacks, the frequent emergence of new threats and attacks poses a challenge for these systems to detect and prevent them from penetrating the network. One such attack is Advanced Persistent Threats (APTs) which can cause significant damage to computer network and organizations. To handle these attacks, the study has developed an APT detection system that uses various Machine learning (ML) and Deep Learning (DL) based classifiers, which can more effectively extract data features from huge amounts of complex data and understand patterns to detect anomalies and potential threats. This study compares and evaluate their performance and efficiency on NSL-KDD dataset. By evaluating using various evaluation metrics, it was found that Extreme Gradient Boosting (XGBoost) is the most effective model among all models, followed by Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning Consistent and Identifiable Latent Embeddings for EEG 19th

Contrastive Learning Consistent and Identifiable Latent Emb...

引用

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Liang, Feng Zhang, Zhen Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Extracting informative EEG data into low-dimension latent embeddings is important for storing and analyzing these neuron signals and applying them to various applications, such as modern human-computer interaction (HCI) techniques. We use the contrastive learning algorithm on time-domain features of EEG in both discovery-driven (self-supervised) and hypothesis (supervised) manners to encode the EEG data into latent embeddings that are proven consistent and identifiable. The self-supervised embeddings have the potential to be used for a range of downstream tasks, while the supervised embeddings have very high decoding accuracy for specific tasks. With embeddings encoded from EEG features collected within every 0.5-s window, the accuracy of recognizing the identities of persons by decoding the self-supervised and supervised embeddings is as high as 96.2% and 99.6%, respectively. Our method and results can promote new HCI techniques, e.g., automatically connecting users to their roles in AR games once they wear EEG-capable devices. The source code is available at: https://***/liangfengsid/timeEegContrastive. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Differential Privacy-based Avatar Update in Cooperative Intelligent Transportation System Metaverse 10

Differential Privacy-based Avatar Update in Cooperative Inte...

引用

10th International Conference on computer and Communications, ICCC 2024

作者： Khan, Salabat Luo, Fei Khan, Mansoor Ullah, Insaf Amin, Farhan Ullah, Shamsher Rahim, Mussadiq Abdul School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Yeungnam University Korea Republic of Shenzhen University National Engineering Laboratory for Big Data Shenzhen China Dhahran31261 Saudi Arabia

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative Intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates. © 2024 IEEE.

关键词： Advanced driver assistance systems

来源：评论

学校读者我要写书评

暂无评论

Stationary Vehicles Detection on Smart Highways and Roads using Spatio-temporal Tracking 16

Stationary Vehicles Detection on Smart Highways and Roads us...

引用

16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Khan, Wasiq Kelly, Jessica Al-Kafri, Ala Kleanthous, Natasa Khayam, Umar Khan, Bilal Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom Teesside University School of Computing Engineering and Digital Technologies United Kingdom Khalifa University Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Comsats University Pakistan California State University School of Computer Science & Eng San BernardinoCA United States

ISBN: (纸本)9798350381344

Where traditional motorways contain hard shoulders to provide refuge for broken-down vehicles, smart motorways instead use live outer lane to ease congestion. The live lanes can be closed due to accidents or breakdowns which is communicated to other road users through overhead gantry signs. This can only occur if the traffic management control center is made aware of the stationary vehicle(s), through either notification via phone call or Motorway Incident Detection and Automatic Signaling (MIDAS) induction loop technology. Alternatively, radar-based stopped vehicle detection is used to identify non-moving objects on the highway. However, this technology is unable to recognize objects or distinguish between congestion and break down etc., which leads to generate false alarms. For the first time, we propose a fully autonomous computer vision and deep learning-based solution to detect stationary vehicles on highways and local roads. We employ deep transfer learning to build a custom-trained vehicle detection model using a newly prepared dataset comprising over 105,000 annotated vehicle instances. DeepSort algorithm is employed for real-time vehicle tracking through associating instances between time series frames, followed by a rule-based algorithm to identify the current state of detected vehicles. Experimental outcomes show our approach as outperforming the state-of-the-art methods in terms of efficient and reliable detection of stationary vehicles (with 98.3% accuracy) as well as distinguish them from congestions when evaluated over video streams captured in realistic dynamic and diverse conditions. © 2023 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Transforming High-Frequency Trading Predictions with CNN-LSTM Networks

Transforming High-Frequency Trading Predictions with CNN-LST...

引用

Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： S. Baghavathi Priya Priyanga S Sri Surya Shobith Kamisetty Venkata Sai Vinay Neelapala Nithila G Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics & Communication Engineering School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Computer Science and Engineering (SCOPE) Vellore Institute of technology Vellore India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets.

关键词： Fluctuations Noise reduction Finance Predictive models Feature extraction Convolutional neural networks Indexes Noise measurement Predictive analytics Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Real-time Influencer Detection in Twitter Using A Hybrid Approach. 4th

Real-time Influencer Detection in Twitter Using A Hybrid App...

引用

4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Siji Rani, S. Baby, Sangeeth Sajan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kollam Amritapuri690525 India

Social media is attaining popularity day by day across domains. Approximately about 40% of the world's population uses social media. Automatically these people look the influencers in social media to guide them with their decision-making in a day-to-day life. An influencer in social media is someone who can influence others. Finding the best influencer as a seed node among social media users is relevant to research. This paper proposes a methodology that can get the most suitable influencer list based on context analysis. A contextual analysis analyzes a text in different media that helps us assess that text within the context. The hybrid way of using the graph networks and the machine learning algorithms like LSTM and Naive Bayes together as suggested in this article helps us assess that text and find the influencer significantly better than the existing approaches. We prove that the hybrid model can produce more accurate results in such a way that we only get the users who can positively influencer other users. Based on our result, we are sure that this hybrid model can produce more accurate results for funding the best set of influencers. Along with that, the combined use of LSTM and Naive Bayes can produce better results. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Scheduling Ev Battery Swap/Charge Operations

Scheduling Ev Battery Swap/Charge Operations

引用

IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Jaeheon Kwak Seongtae Lee Kang G. Shin Jinkyu Lee School of Computing KAIST Republic of Korea Department of Computer Science and Engineering Sungkyunkwan University Republic of Korea Department of Electrical Engineering and Computer Science The University of Michigan USA

ISBN: (数字)9798331543402

ISBN: (纸本)9798331543419

With the increasing popularity of electric vehicles (EVs), drivers want their vehicle batteries to be charged in a few minutes; at present, this is feasible only if battery service stations replace the EV battery pack with a fully charged battery pack. In this paper, we formulate the scheduling problem for battery swap stations, aiming to provide the drivers timing guarantees for different types of EVs, each with its own sporadic/periodic arrival pattern and deadline constraint. To solve this problem, we analyze its unique characteristics from a real-time scheduling perspective, with the main challenge being the circular timing dependency between two distinct scheduling processes: the swapping operation and the charging operation. We first derive a sufficient condition that decouples the dependency and then develop scheduling policies and timing guarantee techniques, designed for not only being specialized for the problem but also accommodating the sufficient condition in a time-predictable and resource-efficient manner. While the problem formulation and solution hold significance as the first attempt to establish real-time scheduling principles for battery swap stations, we also address how to accommodate real-world EV arrivals at a swapping station that do not necessarily follow a sporadic/periodic pattern. Finally, we evaluate the effectiveness of the proposed principles not only in addressing the formulated problem but also in accommodating real-world EV arrival patterns via simulation and a case study.

关键词： Sufficient conditions Electric vehicles Real-time systems Batteries Timing

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Deepfake Image Detection Using Deep Learning Approach 22

A Novel Framework for Deepfake Image Detection Using Deep Le...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Jaiswal, Ambika Chamatkar, Aruna Kharat, Akash Prakash Sharma, Bhisham Dhaou, Imed Ben Chaurasia, Suhashini Awadhesh Sant Gadge Baba Amravati University P.G. Department of Computer Science Maharashtra Amravati India Kamala Nehru College Maharashtra Nagpur India Saraswati College Shegaon Department of Computer Science Shegaon India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Hekma School of Engineering Computing Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

Deepfake, fast growing field in the age of multimedia and AI, have been attracted attention in the recent years. Deep learning algorithms are used to create real digital content which are difficult to distinguish from authentic content. Deepfakes can serve multiples purposes, including educational content, academic research, social entertainment so it spread wrong information, information manipulation, damage to reputation, many frauds. Day to day deepfake crime is increasing. Detection of deepfake is big challenging issue in the digital forensics. A strong approach needs to be created from protecting against the media deepfake. The main work of this paper is to build a efficient framework for deepfake detection. The paper discusses deepfakes, detection techniques, datasets, proposed model. Novel proposed approach has been created that are based on the integration of CNN and VGG16. The deepfake dataset has been used to utilized the network architecture. Proposed model has been achieved the 95% accuracy and 94% precision score. The developed model is superior than the other state of art developed methods. © 2025 IEEE.

关键词： Dataset Deep Learning Deepfake Image Processing

来源：评论

学校读者我要写书评

暂无评论

Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

引用

computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Vaishnavi Shivaji Mohite Krishna Gayatri Patra CH Hari Sankar Sarada Jayan Department of Computer Science Amrita School of Computing Bengaluru Department of Mathematics Amrita School of Engineering Bengaluru

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering.

关键词： Image analysis Fourier transforms Accuracy Heuristic algorithms Clustering algorithms Linear algebra Feature extraction Optimization Astronomy Tuning

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 << < 406 407 408 409 410 411 412 413 414 415 > >>

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：