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

1,890 篇 会议
6 册 图书
1 篇 期刊文献

馆藏范围

1,897 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,395 篇 工学
- 1,275 篇 计算机科学与技术...
- 1,022 篇 软件工程
- 318 篇 生物工程
- 311 篇 信息与通信工程
- 190 篇 生物医学工程（可授...
- 162 篇 控制科学与工程
- 124 篇 电气工程
- 120 篇 光学工程
- 83 篇 机械工程
- 74 篇 化学工程与技术
- 65 篇 安全科学与工程
- 63 篇 网络空间安全
- 54 篇 电子科学与技术（可...
- 48 篇 仪器科学与技术
760 篇 理学
- 399 篇 数学
- 327 篇 生物学
- 173 篇 统计学（可授理学、...
- 152 篇 物理学
- 134 篇 系统科学
- 83 篇 化学
449 篇 管理学
- 253 篇 管理科学与工程(可...
- 232 篇 图书情报与档案管...
- 131 篇 工商管理
175 篇 医学
- 166 篇 临床医学
- 159 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
- 63 篇 公共卫生与预防医...
82 篇 教育学
- 82 篇 教育学
77 篇 法学
- 69 篇 社会学
52 篇 农学
- 51 篇 作物学
46 篇 经济学
4 篇 军事学

主题

325 篇 machine learning
135 篇 deep learning
78 篇 support vector m...
65 篇 machine learning...
55 篇 forecasting
46 篇 feature extracti...
37 篇 decision trees
35 篇 computational mo...
34 篇 accuracy
34 篇 computer vision
30 篇 predictive model...
29 篇 training
29 篇 convolutional ne...
28 篇 data mining
28 篇 students
26 篇 neural networks
22 篇 object detection
22 篇 artificial intel...
21 篇 sentiment analys...
20 篇 internet of thin...

机构

12 篇 chitkara univers...
7 篇 shenyang ligong ...
6 篇 uttaranchal inst...
5 篇 department of co...
5 篇 maharishi school...
5 篇 alibaba cloud bi...
4 篇 school of comput...
4 篇 college of compu...
4 篇 key laboratory o...
4 篇 lovely professio...
4 篇 jilin agricultur...
4 篇 uttaranchal univ...
4 篇 key laboratory o...
4 篇 department of co...
4 篇 master of comput...
4 篇 school of inform...
3 篇 school of comput...
3 篇 vivekananda glob...
3 篇 department of el...
3 篇 college of compu...

作者

4 篇 liu gang
4 篇 kumar rakesh
4 篇 waseemullah
4 篇 liu peng
3 篇 zhang tao
3 篇 kumar ajay
3 篇 rakesh kumar
3 篇 babu tina
3 篇 siddhant
3 篇 sharma arun
3 篇 faisal rehman
3 篇 wang xiaofei
3 篇 sharif hanan
3 篇 gupta rajeev kum...
3 篇 singh sunil kuma...
3 篇 rehman faisal
3 篇 tina babu
3 篇 yang wei
3 篇 sharma avinash
3 篇 prajwal

语言

1,776 篇 英文
119 篇 其他
18 篇 中文

检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是631-640 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：

Flower Leaf Image Classification using machine learning Techniques 3

Flower Leaf Image Classification using Machine Learning Tech...

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Aman, Bittu Kumar Kumar, Vipin Mahatma Gandhi Central University Dept. Computer Science & Information Technology Bihar India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

As per the report of Statista, more than 50,000 thousand categories of flower species exist worldwide;here, the problem arises identification of each type so that we can know the real advantages or the natural goodness of the flower plants. It is challenging to identify the flowers without prior knowledge/expertise. Therefore, it is crucial to make the effect and automated systems to classify the different flowers using their leaf images. This research collected 25 different categories of flowers and plants leaf images, which are 6619 total RGB images. Six classical machine learning algorithms have been utilized for the classification like K-Nearest Neighbours (KNN), Linear Regression (LR), Decision Tree (DT), Support Vector machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The comparative study of the classifier's performances has been done based on classification accuracy, precision, recall, and F1-score. This research aims to find an effective machine learning classification algorithm that can be utilized for automation. The analysis of the results shows that the MLP classifier has the highest classification accuracy, i.e., 89.61%. The confusion matrix of MLP performance has been analyzed and has identified that similar shaped and textured leaves are usually misclassified. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Defending ARP Spoofing-based MitM Attack using machine learning and Device Profiling 3

Defending ARP Spoofing-based MitM Attack using Machine Learn...

引用

3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Prasad, Arvind Chandra, Shalini Babasaheb Bhimrao Ambedkar University Department of Computer Science Lucknow India

ISBN: (纸本)9781665462006

No matter how secure and encrypted a network communication is, if incoming and outgoing traffic is redirected through the intruder's land, there is no guarantee that those communications cannot be decrypted or manipulated. The highly sophisticated tools and techniques used by cybercriminals may enable them to interpret or manipulate highly secured https traffic or break TLS (Transport Layer Security) security. Safeguarding all the network traffics from intruders' reach is one of the best preventive measures. Through ARP (Address Resolution Protocol) spoofing, an intruder launches Man-in-the-middle (MitM) attack and gets access to all the incoming and outgoing network traffic from the victim device. This article proposes a dual technique based on machine learning and device profiling to detect ARP spoofing-based MitM attacks. The ML-based approach analyzes the network for any abnormality and finds the MitM attacks. When deployed live, the device profile generated from the device profiling module improves the detection accuracy. The device profiling-based technique proposes a client application that keeps track of the ARP cache table and can identify if the ARP cache is compromised. Once identified, it notifies to profiler running on a dedicated system. The profiler identifies the intruder and blocks the intruder device from further network access. It maintains a DEV-PROFILE that helps ML to detect MitM attacks with improved accuracy. © 2022 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Deep learning Based Event Detection Using Multilingual Tweets During Disaster 3

Deep Learning Based Event Detection Using Multilingual Tweet...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Valliyammai, C. Manikandan, D. Bharathkumaran, M. Gopi Agasthia, S. Logeswari, P.S. Madras Institute of Technology Campus Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350319125

Concerning the calamity, users typically provide data via social media in their native language, which is difficult to process and understand for others who are unfamiliar with that language. The proposed approach is intended to determine the location during a disaster using multilingual tweets. The study analysis with the tweets, extracted using # 2015 Chennai floods and # heavy rain in Tamil language. Multilingual tweets are trained using an auto-encoder model and then disaster-related tweets are grouped using the K-means clustering method. Integration of natural language processing and machine learning techniques helps to extract the location from the tweets. The trained model results in producing the encoded representation of tweets and the number of clusters is determined by the elbow method using K-mean clustering. IndicNER Fine-tuned model with 93% accuracy is used to identify the location entities of the Tamil Tweets extracted. DisMan application is designed to visualize the disaster location in the geographical map and it helps in quick response with the rescue team to identify the disaster-affected area. © 2023 IEEE.

关键词： Autoencoders Deep learning Multilingual Social Media Analysis Word2Vec

来源：评论

学校读者我要写书评

暂无评论

ICNSER 2022 - 3rd international conference on Industrial Control Network and System Engineering Research

ICNSER 2022 - 3rd International Conference on Industrial Con...

引用

3rd international conference on Industrial Control Network and System Engineering Research, ICNSER 2022

ISBN: (纸本)9781450384964

The proceedings contain 17 papers. The topics discussed include: on the equilibrium points for a discrete system of two fractional-type difference equations;predication of 400kA aluminum electrolytic cell output based on the generative adversarial networks;research on the correlation model between equipment status and carbon emissions of emission control enterprises based on convolutional neural network;comparison of support vector machine for apron allocation;landing runway assignment by airport traffic using machine learning;prediction of isobaric vapor-liquid equilibrium data for the ternary and quaternary acetonitrile + water mixture containing ionic liquid using artificial neural network: development of feedforward backpropagation artificial neural network as an alternative to conventional thermodynamic models for the estimation of vapor-liquid equilibrium data;relations learning between BMI and injuries by Bayesian matrix completion;and a discussion on computer security technology and security preventive measures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with machine learning 3rd

Student Engagement Detection Using Emotion Analysis, Eye Tra...

引用

3rd international conference on Technology and Innovation in learning, Teaching and Education (TECH-EDU)

作者： Sharma, Prabin Joshi, Shubham Gautam, Subash Maharjan, Sneha Khanal, Salik Ram Reis, Manuel Cabral Barroso, Joao de Jesus Filipe, Vitor Manuel Univ Massachusetts Boston MA 02125 USA Kathmandu Univ Dhulikhel Nepal Wentworth Inst Technol Boston MA USA Washington State Univ Ctr Precis & Automated Agr Syst Prosser WA 99164 USA Univ Tras Os Montes & Alto Douro Vila Real Vila Real Portugal Inst Elect & Informat Engn Aveiro Aveiro Portugal INESC TEC Porto Portugal

ISBN: (纸本)9783031229176;9783031229183

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policymakers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration indexwith three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.

关键词： E-learning Student engagement detection Facial emotion Eye-head movement machine learning

来源：评论

学校读者我要写书评

暂无评论

Design and application of College and University Entrepreneurship Platform Based on MVC Architecture 3

Design and Application of College and University Entrepreneu...

引用

3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zhang, Huilian Jilin Agricultural Science and Technology University Jilin China

In order to better cultivate students' comprehensive practical ability and entrepreneurial ability and autonomous learning ability, provide more perfect entrepreneurial platform support for college and university students, a platform for talents in colleges and universities based on the needs of system is built from the perspective of the MVC architecture. At the same time, with the help of a mix of multivariate information personalized services recommendation algorithm, a more perfect business information support is provided for college and university students. The key technologies of entrepreneurial platform, MVC design pattern, platform network technology, service architecture and other aspects are introduced in detail. Through the system test, it is found that the response time of the system will slow down with the increase of the number of online people. When the number of online people is 3000, the response time is 2.2s, which also meets the requirements of system design. © 2023 The Authors. Published by Elsevier B.V.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation 1

引用

3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： Shi, Yaying Zhang, Xiaodong Yan, Yonghong University of North Carolina at Charlotte CharlotteNC United States The University of Texas MD Anderson Cancer Center HoustonTX United States

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

machine learning, especially deep learning, has achieved state-of-the-art performance on various computer vision tasks. For computer vision tasks in the medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tomography/computed tomography (PET/CT) images which includes useful metabolic and anatomical information to sufficiently make an accurate tumor segmentation. In this paper, we proposed a stacked-multi-scaled medical image segmentation framework to automatically segment the Head and Neck tumor using PET/CT images. The main idea of our network was to generate various low-resolution feature maps of PET/CT images to make a better contour of Head and Neck tumors. We used multi-scaled PET/CT images as inputs, and stacked different intermediate feature maps by resolution for a better inference result. In addition, we evaluated our model on the HECKTOR challenge test dataset. Overall, we achieved a 0.69786, 0.66730 mean Dice score on GTVp and GTVn respectively. Our team’s name is HPCAS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Stock price analysis and forecasting based on machine learning 3

Stock price analysis and forecasting based on machine learni...

引用

3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Wenqing Gu, Yuting Chang, Sanfei Li, Jing Zhao, Qingbo Ge, Fangli Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450052 China PLA Strategic Support Force Information Engineering University Zhengzhou450052 China

ISBN: (纸本)9781510661240

With the rapid development of the global economy and the stock market, stock investment has become one of the most commonly used financial management methods, and more and more investors have begun to enter the stock market. At the same time, the high risk of stocks has also become a problem faced by many investors who are new to the market. Traditional stock price research methods mainly use technical indicator analysis or time series analysis, which has the problem of not being able to fit nonlinear data well, while machine learning methods can handle the randomness, chaos and nonlinearity of stock prices well. Therefore, this paper chooses machine learning algorithms as methods for stock price prediction, namely neural networks and support vector machines. After feature engineering processing of historical stock data, relevant machine learning algorithm models are established to predict stock prices, and the analysis and prediction results are presented in a visual way. © 2022 SPIE.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

Network Security with Network Intrusion Detection System using machine learning Deployed in a Cloud Infrastructure 3

Network Security with Network Intrusion Detection System usi...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Kaushik, C. Ram, T. Ritvik, C. Lakshman, T. Vnrvjiet Ece Dept. Hyderabad India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

A software/device which monitors the network or the internet for any cyber-attack or malicious activity is called Network Intrusion Detection System (NIDS). The conventional detection systems are limited to slow detection and have to be monitored by a network engineer. This research study has attempted to improve the conventional detection systems by including powerful machine learning algorithm to the software thereby increasing its accuracy and preventing the need of a network engineer. The proposed study has used NSLKDD dataset, which has many independent features for different cyber-attacks. The data is pre-processed in order to make the data fit for the algorithm. The most important features are selected using feature selection. The model is trained using different machine learning algorithms. The accuracies of these algorithms are evaluated, and the validation metrics are calculated for the models. The trained model with best accuracy is containerized using Docker to make it machine/operating-system independent. This containerized model is stored in Docker repository. It is deployed into a cloud service provider which has many machine learning dependencies assisting the detection system. In the cloud, a virtual machine is created with required memory and processing unit. The detection system which is in Docker repository as a container is deployed into the cloud. After successful deployment, the user is provided with an application Programming Interface (API). The user can connect to the internet and use the Network Intrusion Detection System via API to detect any attacks present in the network and secure the devices/software in the network. © 2022 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Quantization Method Integrated with Progressive Quantization and Distillation learning 3

Quantization Method Integrated with Progressive Quantization...

引用

3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Huang, Heyan Pan, Bocheng Wang, Liwei Jiang, Cheng CSG EHV Electric Power Research Institute Guangzhou China Institute of Microelectronics Chinese Academy of Sciences Beijing China

This paper proposed a quantization method based on the integration of progressive quantization and distillation learning, aiming to address the shortcomings of traditional quantization methods in maintaining model accuracy while reducing model size. This method converts the weight from floating point number to smaller integer number through progressive quantization, thus reducing the storage space required by the model. At the same time, distillation learning technology is used to integrate the gradually quantified model with the original floating-point model to improve the accuracy of the model. The experimental results show that the proposed method can maintain high accuracy of the model while reducing its size, and has better performance compared to traditional quantization methods. This method has a broad application prospect in model compression, and can be widely used in scenarios such as Edge device and cloud servers. © 2023 The Authors. Published by Elsevier B.V.

关键词： Distillation

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190页 << < 60 61 62 63 64 65 66 67 68 69 > >>

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：