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

107,183 篇 会议
678 篇 期刊文献
565 册 图书
2 篇 学位论文

馆藏范围

108,427 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

60,908 篇 工学
- 49,897 篇 计算机科学与技术...
- 39,812 篇 软件工程
- 13,426 篇 信息与通信工程
- 11,482 篇 生物工程
- 8,512 篇 控制科学与工程
- 6,739 篇 电气工程
- 6,349 篇 生物医学工程（可授...
- 3,866 篇 光学工程
- 3,332 篇 机械工程
- 2,984 篇 电子科学与技术（可...
- 2,763 篇 网络空间安全
- 2,719 篇 安全科学与工程
- 2,516 篇 动力工程及工程热...
- 2,319 篇 化学工程与技术
- 2,188 篇 仪器科学与技术
25,996 篇 理学
- 12,113 篇 生物学
- 12,065 篇 数学
- 5,172 篇 物理学
- 4,919 篇 统计学（可授理学、...
- 3,515 篇 系统科学
- 2,330 篇 化学
17,450 篇 管理学
- 11,016 篇 管理科学与工程(可...
- 7,614 篇 图书情报与档案管...
- 5,174 篇 工商管理
13,621 篇 教育学
- 13,420 篇 教育学
6,408 篇 医学
- 5,469 篇 临床医学
- 5,220 篇 基础医学(可授医学...
- 3,406 篇 药学(可授医学、理...
- 2,887 篇 公共卫生与预防医...
3,377 篇 法学
- 2,923 篇 社会学
1,965 篇 文学
1,766 篇 农学
1,661 篇 经济学
267 篇 艺术学
106 篇 军事学
11 篇 历史学
10 篇 哲学

主题

7,214 篇 machine learning
7,071 篇 deep learning
5,537 篇 accuracy
5,104 篇 training
4,929 篇 computational mo...
4,222 篇 education
3,838 篇 students
3,623 篇 feature extracti...
3,070 篇 support vector m...
3,068 篇 predictive model...
3,061 篇 machine learning...
3,029 篇 convolutional ne...
2,594 篇 real-time system...
2,397 篇 data models
2,245 篇 neural networks
2,010 篇 cloud computing
1,840 篇 teaching
1,497 篇 reinforcement le...
1,472 篇 artificial intel...
1,412 篇 internet of thin...

机构

826 篇 chitkara univers...
145 篇 lovely professio...
105 篇 computer science...
98 篇 karnataka bangal...
94 篇 college of compu...
89 篇 school of comput...
79 篇 chitkara univers...
76 篇 school of comput...
72 篇 department of co...
70 篇 department of co...
67 篇 department of co...
63 篇 department of co...
58 篇 school of comput...
58 篇 department of co...
52 篇 department of in...
49 篇 chitkara univers...
43 篇 department of me...
42 篇 school of automa...
41 篇 chennai
40 篇 vellore institut...

作者

70 篇 shiva mehta
70 篇 anjali t.
53 篇 li yang
42 篇 sharma vikrant
40 篇 wang lei
40 篇 kukreja vinay
39 篇 rakesh kumar
36 篇 gill kanwarparta...
35 篇 sharma neha
33 篇 wang jing
32 篇 vinay kukreja
31 篇 lei wang
31 篇 zhang lei
30 篇 huang yueh-min
29 篇 yu wang
28 篇 liu bo
27 篇 li xin
26 篇 amandeep nagpal
25 篇 vinay gautam
25 篇 shang wang

语言

91,086 篇 英文
16,987 篇 其他
949 篇 中文
23 篇 西班牙文
12 篇 葡萄牙文
5 篇 阿拉伯文
3 篇 法文
3 篇 土耳其文
2 篇 俄文
1 篇 捷克文

检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 108428 条记录，以下是4321-4330 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Deep learning-Based Personalized Course Content Push Algorithm for Embedded Professional teaching 1

引用

3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Hu, Zikun Dai, Hongxiu Lei, Yang Liu, Yan Jiujiang Vocational and Technical College Jiangxi Jiujiang332007 China Shandong Xiehe University Shandong Jinan250109 China

ISBN: (数字)9783031631399

ISBN: (纸本)9783031631382

This paper explores and studies the embedded system teaching reform from four aspects: the construction of embedded system curriculum system, the application of comparative education and learning transfer teaching methods, the organization and content setting of practical teaching links, and scientific research practice, and emphatically expounds the importance of practical teaching links to embedded system teaching. With the advent of the Internet of Things era, embedded system, as an important part of the Internet of Things, plays an important role. The development of society puts forward higher requirements for the training of embedded talents and course teaching in colleges and universities. The author analyzes the characteristics and present situation of embedded system courses, and puts forward a series of reform measures from the aspects of course system, content setting, teaching methods, practice teaching, etc., aiming at the existing problems in current teaching and combining with the characteristics of students in computer department of Yuncheng University. Practice shows that the curriculum reform has a good effect. Among them, the theoretical teaching part emphasizes the learning and application of students’ comprehensive knowledge and ability, while the experimental teaching, on the basis of basic experiments, designs functional experiments, emphasizes students’ independent choice, and further designs the innovative practical content of "small embedded system development". The flexible and diverse teaching methods are put forward, so that the teaching content and systematic view can really be conveyed to students. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： teaching

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Based Detection of Fungal Diseases in Apple Plants Using YOLOv8 Algorithm

Deep Learning-Based Detection of Fungal Diseases in Apple Pl...

引用

2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： BalaChandralekha, S. Thangakumar, J. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Over the last decade, the identification of plant illnesses by automatic means has become a challenging field. In recent times, computer vision has made it feasible to identify diseases quickly and accurately, which has enhanced yield by allowing for the necessary treatment. Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), Faster-CNN, Mask R-CNN, Siamese Networks, are the models built on deep learning have been applied to diagnose and cure crop disease. These models accomplish well when it comes to identifying plant diseases, but they have trouble with complex field images and frames with several leaves. The goal of this study is to enhance their application in real-world agriculture, where problems with intricate images and multiple leaves are frequent. This paper uses a YOLOv8 model to identify fungal diseases that affect apple leaves, including cedar rust, scab leaves, and black rot. The dataset used for the implementation was gathered from a Kaggle of 540 images, and the model obtained 86% mAP, outperforming the existing YOLOX model which has achieved 73%mAP. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Evaluating Quantum Machine learning Approaches for Histopathological Cancer Detection: Classical, Hybrid Simulation, and IBM Quantum computing 4

Evaluating Quantum Machine Learning Approaches for Histopath...

引用

4th IEEE international conference on Quantum computing and engineering, QCE 2023

作者： Baral, Biswaraj Majumdar, Reek Bhalgamiya, Bhavika Roy, Taposh Dutta Quantum Computing Group Qausal Ai San RamonCA United States Clemson University Civil Engineering Department ClemsonSC United States Mississippi State University Dept of Physics and Astronomy MS United States Kaiser Permanente San RamonCA United States

ISBN: (纸本)9798350343236

This study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on histopathological cancer detection. The research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers (Lima, Quito, Belem, and Jakarta), considering factors such as the number of qubits, Variational Quantum Classifier (VQC), and number of images. The objective was to aid users in selecting the optimal combination of classical and quantum units for accurate classification. The study found that the hybrid transfer learning model, when used with the Pennylane quantum simulator, yielded slightly higher accuracy than hybrid models implemented with IBM's quantum computers. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Explainable-AI for DoS Attacks Detection in 5G Network Using Deep learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

引用

5th international conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Adapting Deep learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer learning Approach 3

Adapting Deep Learning Transformer Model for Depression-Spec...

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Pawar, Dipti Phansalkar, Shraddha Department of Computer Science Engineering MIT School of Computing MIT Art DesignTechnology University Pune India

ISBN: (纸本)9798331532420

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

News Topic Categorization on Telugu Dataset Using Deep learning Models 15

News Topic Categorization on Telugu Dataset Using Deep Learn...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Deepika, M. Sree Subramanyam, N. Sri Lekhya Pranav, P. Shiva Reddy, Pulaputhuru Subramanyam Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The digital age has ushered in an unprecedented era of data proliferation across various domains, with the news sector being no exception, accumulating vast amounts of information, often measured in petabytes. Manual classification of such voluminous data is impractical, particularly considering the complexity of languages like Telugu, belonging to the Dravidian family, renowned for its intricate morphological characteristics. Natural Language Processing (NLP) offers a promising avenue for tackling textual data, leveraging diverse word embedding techniques to enrich model representations. Notably, prior research in this domain has predominantly relied on count-based algorithms for word embeddings. However, in this investigation, we explore alternative approaches, specifically custom-built Deep learning models integrated with word embeddings, to optimize the classification of newspaper articles. Through a series of methodical experiments, we endeavor to identify the most effective model architecture for this task, aiming to enhance the accuracy and efficiency of article categorization in Telugu. The proposed model has experimented with word embeddings such as FastText and GloVE with deep learning models like LSTM, Bi-LSTM, CNN. Among the models experimented, FastText in combination with CNN have reported the highest F1-Score of 0.93. © 2024 IEEE.

关键词： Categorization Deep learning News data Word Embedding

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Fracture Diagnosis: A Deep learning Approach for Bone Fracture Detection and Classification

Revolutionizing Fracture Diagnosis: A Deep Learning Approach...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mittal, Khushi Gill, Kanwarpartap Singh Aggarwal, Priyanshi Rawat, Ramesh Singh Sunil, G. Chitkara Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350373783

This study focuses on the crucial matter of identifying and categorising bone fractures via the use of sophisticated image analysis methods. Timely detection of bone fractures is crucial for appropriate treatment due to their prevalence and considerable impact on patient outcomes. Prompt identification is vital to minimise problems and optimise overall recuperation. The research uses a dataset of 4906 X-ray pictures that are categorised into two classes: fractured and not-fractured. It employs a Sequential Convolutional Neural Network (CNN) model. The CNN model achieves an amazing accuracy of 98% in fracture classification using a training set of 4099 pictures, a testing set of 401 photos, and an additional 406 images for validation. The technique highlights the capacity of deep learning in medical imaging to provide precise and efficient fracture diagnosis, demonstrating the potential of this approach to improve healthcare results. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Development of Deepfake Detection Techniques for Protecting Multimedia Information using Deep learning 3

Development of Deepfake Detection Techniques for Protecting ...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Siva Rama Lingham, N. Devakanth, J Jude Moses Anto Raj, Gowtham Gayathri, K. Janani, R. Dhanapal, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Predicting Mobile Prices with Machine learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

引用

2024 international conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Edge cloud cooperation environment driven multi-level edge computing method for delay optimization 4

Edge cloud cooperation environment driven multi-level edge c...

引用

4th international conference on Machine learning and Computer Application, ICMLCA 2023

作者： Jin, Honghan Ma, Shiwen Ding, Yi Deng, Xin Yao, Zhenxian Zhao, Jinyang Liaoning Electric Power Energy Development Group Co. Ltd. Shenyang110000 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the continuous and rapid development of the technology of the empowered Internet of Things (IoT), multi-service scenarios continue to appear, which poses a new challenge to the delay of large data services in IoT. Aiming at the cloud and edge cooperation for the IoT architecture, this work proposes a cloud and edge collaboration mechanism for the network delay optimization based on software-defined network, which tries to minimize the average delay by introducing a continuously variable cloud and edge task segmentation coefficient. Besides, we transform the delay optimization problem into a convex optimization one, based on which an optimized task segmentation strategy is obtained. The experimental results indicate that the service delay and network congestion can be reduced greatly by reasonable loading of edge node tasks to the cloud center. © 2024 SPIE.

关键词： Edge computing

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 << < 429 430 431 432 433 434 435 436 437 438 > >>

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：