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

14,989 篇 会议
6,297 篇 期刊文献
260 册 图书

馆藏范围

21,546 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

13,006 篇 工学
- 10,181 篇 计算机科学与技术...
- 8,423 篇 软件工程
- 2,661 篇 信息与通信工程
- 1,804 篇 控制科学与工程
- 1,402 篇 生物工程
- 1,254 篇 电气工程
- 984 篇 生物医学工程（可授...
- 901 篇 电子科学与技术（可...
- 852 篇 机械工程
- 716 篇 光学工程
- 544 篇 安全科学与工程
- 500 篇 仪器科学与技术
- 480 篇 动力工程及工程热...
- 447 篇 化学工程与技术
- 417 篇 建筑学
- 394 篇 交通运输工程
5,817 篇 理学
- 3,350 篇 数学
- 1,564 篇 生物学
- 1,099 篇 物理学
- 952 篇 统计学（可授理学、...
- 801 篇 系统科学
- 471 篇 化学
3,950 篇 管理学
- 2,456 篇 管理科学与工程(可...
- 1,666 篇 图书情报与档案管...
- 1,213 篇 工商管理
956 篇 医学
- 837 篇 临床医学
- 746 篇 基础医学(可授医学...
- 461 篇 药学(可授医学、理...
627 篇 法学
- 489 篇 社会学
407 篇 经济学
- 404 篇 应用经济学
401 篇 教育学
210 篇 农学
104 篇 文学
63 篇 军事学
36 篇 艺术学
3 篇 哲学
1 篇 历史学

主题

1,253 篇 software enginee...
1,086 篇 computer science
408 篇 deep learning
407 篇 application soft...
355 篇 computational mo...
338 篇 computer archite...
332 篇 software
313 篇 machine learning
306 篇 accuracy
272 篇 software systems
271 篇 data mining
257 篇 feature extracti...
255 篇 training
247 篇 internet of thin...
223 篇 programming
220 篇 support vector m...
190 篇 artificial intel...
187 篇 testing
181 篇 optimization
174 篇 semantics

机构

358 篇 college of compu...
199 篇 department of co...
121 篇 state key labora...
118 篇 department of co...
116 篇 department of co...
115 篇 school of softwa...
105 篇 department of co...
91 篇 department of co...
82 篇 school of inform...
74 篇 department of co...
73 篇 department of co...
61 篇 school of comput...
61 篇 department of co...
53 篇 department of co...
51 篇 department of co...
50 篇 department of co...
49 篇 software enginee...
46 篇 department of so...
46 篇 department of co...
44 篇 school of comput...

作者

67 篇 rajkumar buyya
47 篇 baowen xu
43 篇 man ka lok
37 篇 stuckey peter j.
33 篇 jiacun wang
30 篇 shen furao
25 篇 muhammad waseem ...
25 篇 rehman attique u...
25 篇 sylvain martel
24 篇 kotagiri ramamoh...
23 篇 wang lei
23 篇 xiao qin
22 篇 niyato dusit
22 篇 khomh foutse
22 篇 parizi reza m.
22 篇 shihab emad
22 篇 bailey james
22 篇 christopher leck...
21 篇 qusay h. mahmoud
21 篇 fattahi jaouhar

语言

18,815 篇 英文
2,587 篇 其他
174 篇 中文
11 篇 德文
4 篇 日文
3 篇 法文
3 篇 土耳其文
2 篇 俄文
2 篇 斯洛文尼亚文
1 篇 阿拉伯文
1 篇 捷克文
1 篇 朝鲜文

检索条件"机构=Department of Computer Engineering and Software Engineering"

共 21546 条记录，以下是1361-1370 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：

A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 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

Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

引用

27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

Weighted Poisson-disk Resampling on Large-Scale Point Clouds 39

Weighted Poisson-disk Resampling on Large-Scale Point Clouds

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiao, Xianhe Lv, Chenlei Zhao, Junli Yi, Ran Wen, Yu-Hui Pan, Zhenkuan Wu, Zhongke Liu, Yong-Jin College of Computer Science and Technology Qingdao University China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science & Technology Beijing Jiaotong University China School of Artificial Intelligence Beijing Normal University China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

ISBN: (纸本)157735897X

For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Prediction of Antimicrobial Peptides Using Deep Neural Networks

Prediction of Antimicrobial Peptides Using Deep Neural Netwo...

引用

16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Gülsüm Söylemez, Ümmü Yousef, Malik Bakir-Gungor, Burcu Department of Software Engineering Faculty of Engineering Muş Alparslan University Muş Turkey Department of Information Systems Zefat Academic College Zefat13206 Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

Antimicrobial peptides (AMPs) are crucial elements of the innate immune system;and they are effective against bacteria that cause several diseases. These peptides are investigated as potential alternatives of antibiotics to treat infections. Since wet lab experiments are expensive and time-consuming, computational methods become crucial in this field. In this study, we suggest a computational technique for AMP prediction using deep neural networks (DNN). We trained a DNN classifier using physicochemical features that include a sequential model;and evaluated the model with 10-fold cross-validation on a benchmark dataset. We compared our method with other machine learning approaches and demonstrated that the method we developed generates higher performance (accuracy: 92%, precision: 92%, recall: 93%, f1: 93%, AUC: 98%). In our experiments, we have realized that there is a strong positive correlation between the ‘Normalized Hydrophobic Moment’ feature and ‘Angle Subtended by the Hydrophobic Residues’ feature;and strong negative correlations between ‘Normalized Hydrophobicity’ feature and ‘Disordered Conformation Propensity’ feature, and between ‘Amphilicity Index’ - ‘Disordered Conformation Propensity’ features. We believe that the approach we proposed could guide further experimental studies and could facilitate the prediction of other types of AMPs having anticancer, antivirus, antiparasitic activities. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 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

Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Sorting Algorithms: A Review 11

Comparative Analysis of Sorting Algorithms: A Review

引用

11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Ala'anzy, Mohammed Alaa Mazhit, Zhanar Ala'anzy, Alaa Fadhil Algarni, Abdulmohsen Akhmedov, Ramis Bauyrzhan, Abdiyev SDU University Department of Computer Science Almaty Kazakhstan Al-Hikma University College Department of Business Administration Baghdad Iraq King Khalid University Department of Computer Science Abha61421 Saudi Arabia Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan

ISBN: (纸本)9798331518127

In the realm of computer science, sorting algorithms play a pivotal role in optimising data organisation and retrieval processes across various applications. This paper presents a comprehensive examination and comparative analysis of the most commonly used sorting algorithms, focusing on their time, space complexity, and efficiency. By scrutinising both traditional methods like bubble sort, selection sort, and insertion sort, and advanced techniques such as merge sort, quick sort, heap sort, and radix sort, this study sheds light on the performance metrics of each algorithm. Through meticulous implementation and evaluation across datasets of varying magnitudes, ranging from 100 to 100,000 integers, the research provides valuable insights into the strengths and weaknesses of these sorting techniques. The outcomes of this study offer practical guidance for professionals in data engineering and software development, aiding in the informed selection of sorting algorithms tailored to specific application needs. Utilising a rigorous experimental setup with the pseudocode, the study meticulously measures the time complexity of each algorithm, recording CPU time in seconds. The analysis reveals significant performance differentials among the algorithms, showcasing the efficiency of quick sort, shell sort, and heap sort on small datasets, while highlighting the scalability of merge sort, radix sort, and quick sort as dataset sizes increase. This research contributes to the advancement of algorithmic knowledge and provides a valuable resource for practitioners seeking to optimise sorting processes in their computational tasks. © 2024 IEEE.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Effects of un targeted Adversarial Attacks on Deep Learning Methods 15

Effects of un targeted Adversarial Attacks on Deep Learning ...

引用

15th International Conference on Information Security and Cryptography, ISCTURKEY 2022

作者： Degirmenci, Elif Ozcelik, Ilker Yazici, Ahmet Eskisehir Osmangazi University Computer Engineering Department Eskisehir Turkey Software Engineering Department Eskisehir Osmangazi University Eskisehir Turkey

ISBN: (数字)9781665456036

ISBN: (纸本)9781665456036

The increasing connectivity of smart systems opens up new security vulnerabilities. Since smart systems are used in various sectors such as healthcare, smart cities, and the intelligent industries, security becomes a fundamental concern. For this reason, security measurements are often added to security systems, such as authorization, authentication, encryption, Intrusion Prevention System (IPS) and Intrusion Detection Systems (IDS). However, these systems are still vulnerable to attacks such as DoS, DDoS. Recently, Deep Learning methods have been used to detect these attacks as early as possible. This method, however, has its own vulnerabilities to adversarial attacks. This paper presents three types of adversarial effects on the deep learning models analysis. In this study we use the open dataset CICIDS2017. Initially, DDoS attacks are detected using deep learning methods. Then, adversarial examples are created using adversarial attack methods. The untargeted adversarial attacks are used in this study, these being;FGSM, PGD and BIM. The results show that all adversarial attacks are effective on Deep Learning models. However, the results show that attacks from PGD and BIM are more effective. In addition, the deep learning models training is evaluated with K-fold cross-validation. The results show that, deep learning models that occur during each fold could be get different accuracy results against adversarial attacks. Adversarial attacks may be used in a K-fold cross validation process as a parameter for best model selection. © 2022 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

EfficientNet B7 based Deep Learning Technique for Adaptive Gait Recognition under Challenging Covariate Conditions

EfficientNet B7 based Deep Learning Technique for Adaptive G...

引用

2024 International Conference on Recent Progresses in Science, engineering and Technology, ICRPSET 2024

作者： Hasan, Md. Mehedi Aman, Nakib Jahan, Md Wadud Software Engineer I Developer Experience Hub Rajshahi Bangladesh Pabna University of Science and Technology Department of Computer Science and Engineering Pabna Bangladesh Varendra University Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331509477

Gait recognition is a crucial biometric tool for surveillance and security, but its effectiveness is hindered by factors like clothing, carried obj ects, and environmental conditions. This study introduces an adaptive framework that assigns weights to gait components to counter these issues. Using EfficientNet_B7, a deep learning technique, the framework reduces intra-class variations and improves recognition accuracy. The experimental results show a 98.7% accuracy rate, proving the method's robustness in various conditions. This advancement significantly enhances the reliability of gait recognition systems in real-world applications. © 2024 IEEE.

关键词： Biometric Deep Learning Gait Recognition Security Applications

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 << < 133 134 135 136 137 138 139 140 141 142 > >>

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：