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

1,194 篇 会议
786 篇 期刊文献
8 册 图书

馆藏范围

1,988 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,238 篇 工学
- 884 篇 计算机科学与技术...
- 718 篇 软件工程
- 341 篇 信息与通信工程
- 202 篇 控制科学与工程
- 189 篇 生物工程
- 150 篇 电气工程
- 139 篇 生物医学工程（可授...
- 121 篇 光学工程
- 117 篇 电子科学与技术（可...
- 90 篇 网络空间安全
- 82 篇 机械工程
- 79 篇 化学工程与技术
- 60 篇 交通运输工程
- 59 篇 安全科学与工程
- 50 篇 仪器科学与技术
- 46 篇 材料科学与工程（可...
709 篇 理学
- 325 篇 数学
- 235 篇 物理学
- 206 篇 生物学
- 121 篇 统计学（可授理学、...
- 86 篇 化学
- 51 篇 系统科学
363 篇 管理学
- 231 篇 管理科学与工程(可...
- 151 篇 图书情报与档案管...
- 74 篇 工商管理
121 篇 医学
- 85 篇 基础医学(可授医学...
- 78 篇 临床医学
- 59 篇 公共卫生与预防医...
- 49 篇 药学(可授医学、理...
69 篇 法学
- 58 篇 社会学
34 篇 经济学
28 篇 农学
22 篇 教育学
20 篇 文学
9 篇 军事学
2 篇 艺术学

主题

83 篇 accuracy
75 篇 deep learning
55 篇 feature extracti...
54 篇 training
50 篇 convolutional ne...
44 篇 machine learning
42 篇 real-time system...
38 篇 computational mo...
38 篇 predictive model...
30 篇 optimization
30 篇 artificial intel...
28 篇 semantics
28 篇 security
27 篇 data models
24 篇 support vector m...
23 篇 reliability
23 篇 technological in...
21 篇 medical services
20 篇 scalability
20 篇 cloud computing

机构

55 篇 department of co...
54 篇 vivekananda glob...
45 篇 maharishi school...
44 篇 west university ...
43 篇 department of mo...
43 篇 infn e laborator...
43 篇 faculty of scien...
43 篇 department for p...
43 篇 department of ph...
43 篇 kirchhoff-instit...
42 篇 department of ph...
42 篇 research center ...
40 篇 department of ph...
40 篇 department of ph...
40 篇 graduate school ...
40 篇 department physi...
40 篇 deutsches elektr...
40 篇 department of ph...
39 篇 ochanomizu unive...
39 篇 fakultät für phy...

作者

43 篇 zhang yanning
41 篇 xia yong
37 篇 m. lu
35 篇 c. alexa
35 篇 g. bella
35 篇 d. calvet
35 篇 c. amelung
35 篇 a. grummer
34 篇 j. m. izen
34 篇 j. gao
34 篇 j. strandberg
34 篇 b. konya
34 篇 f. pastore
33 篇 s. barsov
33 篇 n. orlando
33 篇 y. tayalati
33 篇 t. pham
33 篇 g. spigo
33 篇 v. chiarella
33 篇 f. siegert

语言

1,781 篇 英文
184 篇 其他
24 篇 中文
1 篇 德文
1 篇 法文

检索条件"机构=Masters of Computer Application School of Computer Science and Engineering"

共 1988 条记录，以下是511-520 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：

An Improved Lightweight CNN_BiLSTM Model for Network Intrusion Detection Using the UNSW-NB15 Dataset

An Improved Lightweight CNN_BiLSTM Model for Network Intrusi...

引用

computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Licheng Zhuang Jun Hu Qingqing Wang Yanyan Wang Kaikai Zhang Sheng Liu School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learning methods in detection capabilities, a network intrusion detection method based on lightweight convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) is proposed. This method preprocesses data through normalization techniques, extracts data features from both spatial and temporal dimensions using CNN and BiLSTM models, and inputs the extracted spatiotemporal feature vectors into the CNN BiLSTM upper classifier for training, achieving intrusion detection. The proposed model was validated on the dataset UNSW-NB15, and the results showed that the binary and multi classification accuracy of the CNN_BiLSTM model was around 93%. Compared with other methods, the proposed method significantly improves the accuracy of network intrusion detection, and also demonstrates better performance in detection accuracy and false positive rate.

关键词： Training Accuracy Bidirectional long short term memory Network intrusion detection Feature extraction Vectors Spatiotemporal phenomena Convolutional neural networks Data mining Security

来源：评论

学校读者我要写书评

暂无评论

Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework

Oddity Based Botnet Recognition Utilizing AI Model in the In...

引用

International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Rupa Rani Sudeshna Chakraborty Pooja Singh Neha Varshney Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Banasthali Vidyapith Rajasthan School of Computer Application and Technology Galgotias University UP India Department of Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with <0.01% actual regrettable.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases

Hybrid Transformer-CNN Model for Automated Diagnosis of Pome...

引用

International Conference on Electrical Energy Systems (ICEES)

作者： Raj Gaurang Tiwari Alok Misra Himani Maheshwari Ambuj Kumar Agarwal Manish Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda University Sharda School of Engineering and Technology India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Improving crop quality and output relies on early and precise disease diagnosis in pomegranate fruits. This study presents a new Feature Fusion Network for disease classification in pomegranate fruits, which combines Transformer and CNN modules. The proposed model classifies pomegranate fruit into five categories that are Healthy, Bacterial Blight, Anthracnose, Cercospora Fruit Spot, and Alternaria Fruit Spot. While the CNN module is great at collecting local visual aspects, the Transformer module is used to gather global contextual information. The model uses both global and local information for robust disease categorization by combining two complimentary feature representations. Adam, Adadelta, Gradient Descent, and RMSProp optimization algorithms are used to fine-tune the suggested Feature Fusion Network. After extensive training with Adam Optimizer, the suggested method attained a maximum accuracy of 96.45% in disease classification of pomegranate fruits. In addition, when the performance of the proposed model is compared with other popular deep learning architectures like ResNet, Inception, VGG, and MobileNet, it outperforms.

关键词： Training Visualization Accuracy Microorganisms Sensitivity and specificity Transformers Multitasking Feature extraction Optimization Diseases

来源：评论

学校读者我要写书评

暂无评论

3-D Near-Field Localization by Jointly Exploiting Spatial and Temporal Information Based on a Nonuniform Cross Array

3-D Near-Field Localization by Jointly Exploiting Spatial an...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zelong Yi Hua Chen Zhiwei Jiang Wei Liu Qing Wang Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Electronic Informatin Engineering Tianjin University Tianjin China

In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, the fourth-order cumulant of the near-field observations with multiple delay lags is exploited to construct virtual far-field pseudo-observations, leading to increased degrees of freedom (DOF); then, 2-D angles of the nearfield sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the one-dimensional (1-D) MUSIC algorithm is applied by resorting to the conjugate symmetry property of the signal's autocorrelation function. Numerical results are provided to demonstrate the superiority of our method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Brain Tumor Diagnosis with Convolutional Neural Networks 6

Revolutionizing Brain Tumor Diagnosis with Convolutional Neu...

引用

6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Singh, Aryan Shuaib, Mohd Diwakar, Manoj Joshi, Aditya Singh, Prabhishek Pandey, Neha Department of Computer Application Graphic Era Deemed to Be University Dehradun248001 India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun248001 India School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Biotechnology Graphic Era Deemed to Be University Dehradun248001 India

Historically, the diagnosis of brain tumors from magnetic resonance imaging (MRI) images has been a careful and extended endeavor that relies heavily on the expertise of radiologists and neurologists. In addition to being time-consuming, the complex nature of brain image analysis makes it vulnerable to cognitive biases and human error, which can affect the precision and effectiveness of diagnosis. Moreover, manual MRI scan interpretation might result in discrepancies between practitioners, which makes diagnosis even more difficult. These difficulties highlight the need for innovative approaches to improve the accuracy and speed of brain tumor identification as the need for quicker and more accurate medical evaluations keeps rising. To meet these difficulties, an innovative method based on image-driven Convolutional Neural Networks (CNNs) has been developed. Meningiomas, pituitary, gliomas, and absence tumors are the four major brain cancer groups that this research focuses on. With the basis of the EfficientNetB3 framework which had been pre-trained on the large-scale ImageNet dataset, the model uses a transfer learning method using a brain tumor dataset of 7023 images by adding extra layers that are exclusively meant to enhance classification and improved performance. The proposed method secures a notable accuracy of 99.84% and a precision of 99.33%. This advanced approach not only improves diagnostic precision but also significantly reduces the time required for neurologists to examine scans, enabling them to focus more on patient care and the development of preventive strategies. The efficiency of the proposed model underlines that it can become a breakthrough in clinical diagnostics and stimulate the progress of medical disciplines' development. For a comprehensive overview of the model architecture and implementation, please visit the GitHub repository https://***/Aryansin1234/***. Additionally, a live demonstration of

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

引用

Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

来源：评论

学校读者我要写书评

暂无评论

Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

引用

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

来源：评论

学校读者我要写书评

暂无评论

Towards Hierarchical Categorical Named Entity Recognition via Injective Multi-task Learning Strategy 8

Towards Hierarchical Categorical Named Entity Recognition vi...

引用

8th International Conference on Big Data and Information Analytics, BigDIA 2022

作者： Xiaoguang, Yuan Chaofan, Dai Youchen, Wang Yaokai, Cheng Zengwenpro, Yu Tianfeng, Du National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China School of Computer Science and Technology Xidian University Xi'an China School of Management and Engineering Captital University of Economics and Business Beijing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

The Named Entity Recognition (NER) task aims to locate meaningful entities in a text and classify them into pre-defined categories. NER is a fundamental task in the field of natural language processing. In practice, entities often have multiple types at different levels of granularity, and it is essential to identify various levels of categories of entities simultaneously. A deep learning-based approach treats NER as a sequence annotation problem by giving each character a unique entity label, which significantly improves the accuracy of NER. Deep learning-based NER performs well on single-category and coarse-grained NER but does not perform well on hierarchical category NER problems. This paper proposes a multi-task learning-based hierarchical category named entity recognition model to solve this problem. The model uses a multi-task learning mechanism to simultaneously learn multiple levels of information of named entity recognition tasks. Named entity recognition results at higher levels are directly constructed from lower-level recognition structures. Experiments show that both systems can significantly improve the recognition performance at each level compared to the basic multi-tasking model. The model is also compared with each level's traditional named entity recognition method. The hierarchical category named entity recognition model can sig-nificantly improve the performance of named entity recognition tasks at each level. © 2022 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Text2NKG: fine-grained N-ary relation extraction for N-ary relational knowledge graph construction 24

Text2NKG: fine-grained N-ary relation extraction for N-ary r...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoran Luo Haihong E. Yuhao Yang Tianyu Yao Yikai Guo Zichen Tang Wentai Zhang Shiyao Peng Kaiyang Wan Meina Song Wei Lin Yifan Zhu Luu Anh Tuan School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available https://***/LHRLAB/Text2NKG.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Cloud Security with CNN and XGBoost Models for Intrusion Detection Systems 3

Optimizing Cloud Security with CNN and XGBoost Models for In...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mogal, Amit Karbhari Anitha, V. Bhatt, Preeti Nitin Srivasatava, Kriti Devi, S. Rukmani Perada, Anvesh Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India School of Engineering and Technology Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Trichy India Iimt College of Engineering Department of Electronics & Communication Engineering Greater Noida India Vile Parle India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Chennai India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Cloud computing and Internet growth have simplified certain formerly difficult activities. This advancement has also revealed many security weaknesses. Due to cyberattacks, organizations need Intrusion Detection Systems (IDS) to protect their data and networks. Preprocessing, feature selection, and model training comprise the suggested strategy. Preprocessing includes data cleansing, standardization, and labeling. Information gain, chi-square, and PSO are used to choose features. CNN-XGBoost trains the model. The CNN-XGBoost model outperforms solo CNN and XGBoost with an average accuracy of 92.08%. This accuracy proves the hybrid method's cloud computing breach detection usefulness. The study underlines the importance of enhanced IDS models in cyber risk reduction. CNN's feature extraction and XGBoost's classification power provide a powerful, efficient, and reliable cloud intrusion detection model. Modern organizations benefit from this network security method. © 2025 IEEE.

关键词： Network intrusion

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199页 << < 48 49 50 51 52 53 54 55 56 57 > >>

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：