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

7,090 篇 会议
48 篇 期刊文献
39 册 图书

馆藏范围

7,177 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,913 篇 工学
- 4,098 篇 计算机科学与技术...
- 2,520 篇 软件工程
- 1,048 篇 信息与通信工程
- 789 篇 控制科学与工程
- 663 篇 生物工程
- 643 篇 电气工程
- 424 篇 生物医学工程（可授...
- 285 篇 交通运输工程
- 281 篇 机械工程
- 226 篇 仪器科学与技术
- 226 篇 安全科学与工程
- 206 篇 建筑学
- 197 篇 动力工程及工程热...
- 185 篇 光学工程
- 166 篇 化学工程与技术
- 154 篇 土木工程
1,899 篇 理学
- 1,065 篇 数学
- 717 篇 生物学
- 442 篇 统计学（可授理学、...
- 285 篇 物理学
- 252 篇 系统科学
- 140 篇 化学
1,151 篇 管理学
- 655 篇 管理科学与工程(可...
- 582 篇 图书情报与档案管...
- 315 篇 工商管理
407 篇 医学
- 353 篇 临床医学
- 297 篇 基础医学(可授医学...
- 188 篇 药学(可授医学、理...
244 篇 教育学
- 243 篇 教育学
211 篇 法学
- 197 篇 社会学
119 篇 农学
111 篇 经济学
50 篇 文学
7 篇 艺术学
5 篇 军事学

主题

558 篇 machine learning
456 篇 deep learning
267 篇 training
253 篇 data mining
245 篇 data models
220 篇 feature extracti...
211 篇 accuracy
184 篇 support vector m...
178 篇 predictive model...
138 篇 neural networks
133 篇 computational mo...
131 篇 reinforcement le...
129 篇 machine learning...
110 篇 convolutional ne...
103 篇 real-time system...
92 篇 artificial intel...
89 篇 learning systems
88 篇 transfer learnin...
87 篇 forecasting
86 篇 big data

机构

69 篇 chitkara univers...
17 篇 school of electr...
15 篇 computer science...
14 篇 harbin engineeri...
13 篇 graphic era hill...
12 篇 building civil a...
12 篇 uttaranchal inst...
12 篇 computer science...
11 篇 university of ch...
10 篇 school of electr...
10 篇 school of comput...
10 篇 graphic era deem...
10 篇 school of electr...
10 篇 yonsei univ dept...
9 篇 wuhan university...
9 篇 univ chinese aca...
9 篇 school of inform...
8 篇 school of remote...
7 篇 university of sa...
7 篇 microsoft resear...

作者

17 篇 kukreja vinay
15 篇 vinay kukreja
13 篇 yin hujun
12 篇 liu yang
12 篇 corchado emilio
12 篇 cho sung-bae
10 篇 shiva mehta
10 篇 hujun yin
10 篇 mehta shiva
9 篇 notsu akira
9 篇 silva catarina
9 篇 alhajj reda
7 篇 ksieniewicz pawe...
7 篇 hammer barbara
7 篇 zhang byoung-tak
7 篇 wozniak michal
7 篇 hussain abir
7 篇 huang de-shuang
6 篇 héctor quintián
6 篇 tang ke

语言

6,195 篇 英文
971 篇 其他
31 篇 中文
1 篇 西班牙文
1 篇 俄文

检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7177 条记录，以下是971-980 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：

Modified CNN Architecture for Paddy Leaf Disease Detection 11

Modified CNN Architecture for Paddy Leaf Disease Detection

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Indra, Zul Mahdiyah, Evfi Salambue, Roni Universitas Riau Departement of Computer Science Pekanbaru Indonesia

ISBN: (纸本)9798350355314

Paddy (rice) is the main food for the Indonesian people so that stable production is needed to meet domestic needs. However, data in 2023 showed a decline in rice production in Indonesia. the decline was due to pests and diseases that attacked rice plants. the lack of experience and knowledge of farmers in handling and detecting rice diseases is also a factor in the decline in rice productivity. this study aims to develop a computer-aided diagnosis (CAD) system for rice leaf diseases based on CNN algorithms. In creating CAD applications, this study proposes a new CNN architecture based on a modification of the MobileNetV3 architecture. the dataset used comes from direct data collection from rice fields in Indonesia which is then added with public data from Kaggle data totalling 6933 rice leaf images. Based on the results obtained, this study succeeded in surpassing the previous research benchmark with an average accuracy, precision, recall and F -1 Score value reaching 93%. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Quick Count Results of the 2024 Indonesian Presidential Election on Social Media 11

Sentiment Analysis of Quick Count Results of the 2024 Indone...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Atmojo, Axell Prita Aurelie Arienaldi, Nadine Aulyaa Edbert, Ivan Sebastian Aulia, Alvina School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350355314

Sentiment analysis is a technique of analyzing text to determine the emotion of the message. the main objective of this sentiment analysis is to investigate whether Indonesian citizens on the platform X felt positive, negative, or neutral towards the Quick Count results of the recent 2024 Indonesian Presidential Election, in light of public distrust surrounding the legitimacy of the election organizers. the goal of this study is also to compare the performance of two classifier models;Naive Bayes and LSTM, based on standard metrics like accuracy, precision, recall, and f1-score. the data used were posts scraped from X taken after the election day on February 14th, along with datasets sourced from Kaggle and Github. the data preprocessing followed a sequence of data cleansing, case folding, and stop word removal. the final dataset is then split and vectorized using TF-IDF for Naive Bayes, and tokenized for LSTM. the result of this research shows 75.142% of accuracy for the Naive Bayes classifier and 80.03 % of accuracy for the LSTM-based classifier. From the 2,639 posts classified by the Naive Bayes model, 64.7% show positive sentiment towards the Quick Count results, whereas the LSTM model produced 58.2% positive sentiment of the same amount. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Delay-Aware Multi-Agent Reinforcement learning for Cooperative Adaptive Cruise Control with Model-Based Stability Enhancement 27

Delay-Aware Multi-Agent Reinforcement Learning for Cooperati...

引用

27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Liu, Jiaqi Wang, Ziran Hang, Peng Sun, Jian Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Department of Traffic Engineering Shanghai201804 China College of Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9798331505929

Cooperative Adaptive Cruise Control (CACC) represents a quintessential control strategy for orchestrating vehicular platoon movement within Connected and automated Vehicle (CAV) systems, significantly enhancing traffic efficiency and reducing energy consumption. In recent years, the data-driven methods, such as reinforcement learning (RL), have been employed to address this task due to their significant advantages in terms of efficiency and flexibility. However, the delay issue, which often arises in real-world CACC systems, is rarely taken into account by current RL-based approaches. To tackle this problem, we propose a Delay-Aware Multi-Agent Reinforcement learning (DAMARL) framework aimed at achieving safe and stable control for CACC. We model the entire decision-making process using a Multi-Agent Delay-Aware Markov Decision Process (MADA-MDP) and develop a centralized training with decentralized execution (CTDE) MARL framework for distributed control of CACC platoons. An attention mechanism-integrated policy network is introduced to enhance the performance of CAV communication and decision-making. Additionally, a velocity optimization model-based action filter is incorporated to further ensure the stability of the platoon. Experimental results across various delay conditions and platoon sizes demonstrate that our approach consistently outperforms baseline methods in terms of platoon safety, stability and overall performance. © 2024 IEEE.

关键词： Adaptive cruise control

来源：评论

学校读者我要写书评

暂无评论

Detecting Mental Stress Using K-NN Classifier and IoT Devices 11th

Detecting Mental Stress Using K-NN Classifier and IoT Device...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Prabu Shankar, K.C. Hema, M. Computer Science and Engineering SRM Institute of Science & Technology Kattankulathur Chennai India

ISBN: (纸本)9789819717231

Mental tension has become a constant in the fast-paced world of today. It is a problem that keeps getting worse and, if left unchecked, could have serious health effects. Consequently, it is of highest importance to detect stress in individuals to prevent any detrimental results. With its sophisticated algorithms and skills, machine learning can be essential in solving this problem. In this study, a theoretical framework for stress detection using wearable technology with machine learning algorithms and other cutting-edge features is proposed. Heart rate variability (HRV) and speech emotion recognition (SER), two crucial modules that help identify stress in people, are implemented in the proposed system. the SER module detects the emotions in speech, whereas the HRV module examines fluctuations in heart rate. It is anticipated that the suggested system will offer a precise and trustworthy stress detection method, opening the door to better health results. HRV for short is a method to help identifying stress. Many machine learning classification techniques, including logistic regression, k-nearest neighbors, random forest classifier, decision tree classifier, and neural network classifier (NNC), are used to create this particular module. the k-NN model outperformed the other algorithms, scoring a 99.7% accuracy rate. the Python programming language was chosen because of its abundance of libraries and header files to make implementation easier. Streamlit, an open-source app framework used in the domains of machine learning and data science for developing apps connected to ML modules, was used to deploy the HRV module locally. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimization Model for Reactive Voltage Control in Active Distribution Networks Based on Transformer Based Deep Reinforcement learning 11

Optimization Model for Reactive Voltage Control in Active Di...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Wenhai, Hao Ru, Duan Wei, Xue Xiaofei, Kang Yulong, Yang Co. Ltd Baotou China

ISBN: (纸本)9798350368345

Aiming at the challenges of insufficient data feature extraction, limited model generalization ability, and low computational efficiency in traditional deep learning methods for reactive voltage control (VRC) in active distribution networks (ADN), a Transformer based Deep Reinforcement learning (DRL) VRC for AND model is proposed. Firstly, the necessity of VRC in active distribution networks based on ADN coordinated control and its optimized operation mode was analyzed. then, by introducing a transformer into the traditional DRL model, a VRC optimization model based on transformer was designed to effectively improve ADN performance. Finally, a comparative analysis was conducted between the proposed method and three other methods under the same database conditions through numerical examples. the experimental results show that under the same experimental conditions, the proposed method has the smallest root mean square error and the highest correlation coefficient, with the highest degree of data fitting. the minimum value is 0.0289, and the maximum correlation coefficient is 0.996, which meets the requirements of VRC in ADN. Its performance is superior to other comparative methods. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

Rapid Identification of Lingnan Aromatic Traditional Chinese Medicinal Materials Using Deep learning-Based Headspace-Gas Chromatography-Ion Mobility 11

Rapid Identification of Lingnan Aromatic Traditional Chinese...

引用

11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Liu, Yaxiong Zhang, Xuan Luo, Zhuoya Li, Hua Yang, Zhiye Guandong Institute for Drug Control NMPA Key Laboratory of Rapid Drug Inspection Technology Guangzhou China

ISBN: (纸本)9798331531904

Aromatic herbs are an important part of Lingnan traditional Chinese medicine. However, the quality control of these herbs faces challenges in ensuring authenticity, compliance, and distinguishing traditional processing techniques, etc. the study introduces a novel approach for the rapid identification of aromatic traditional Chinese medicinal materials from the Lingnan region using a deep learning-enhanced headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). HS-GC-IMS technology, a method that merges the efficiency of gas chromatography with the high resolution of ion mobility spectrometry, was employed to gather intricate fingerprint data of volatile compounds in traditional Chinese medicinal materials. Further explore the deep meaning of the 'odor' fingerprint information of Chinese medicinal materials using deep learning models, aiming for the classification of aromatic Chinese medicinal materials. Experiment results demonstrate that EfficientNet-B0 has superior performance, and outperforming other models like ResNet18 and MobileNetV2. © 2024 IEEE.

关键词： Medicinal chemistry

来源：评论

学校读者我要写书评

暂无评论

learning from Future: Prediction-based data Augmentation to Enhance Power Grids Fault Detection 11

Learning from Future: Prediction-based Data Augmentation to ...

引用

11th IEEE international conference on Smart Energy Grid engineering, SEGE 2023

作者： Rogers, Jennifer Danilczyk, William Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350340716

As power grids modernize to include wide area monitoring and advanced metering infrastructures, the applications for data-driven methods based on artificial intelligence (AI) for situational awareness, reliability, and security continue to grow. However, obtaining effective training data that captures normal and anomalous operations in power systems remains a great challenge. this paper proposes a data-augmentation method that can efficiently build effective training data sets, benefiting various data-driven applications used in power grids without increasing the size of training data set. By leveraging load prediction in smart grids, we obtain the knowledge of future operating conditions and potential anomalies, which are integrated with the historical data in the training data set. By utilizing this augmented training data set, we significantly increase the accuracy of data-driven fault detection, e.g., 8.6% on average, compared to the results trained based on historical data only. © 2023 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

引用

7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437885

the proceedings contain 71 papers. the special focus in this conference is on international Scientific conference on intelligent Information Technologies for Industry. the topics include: About Explainable Machine learning Models for Anomaly Detection in Cyber-Physical Systems;intelligent Graph-Based Correlation of Security Events in Cyber-Physical Systems;classifier-Based Combined Measure of Syllable Pronunciation Similarity in Speech Rehabilitation;application of Computer Vision Technologies to Reduce Injuries in the Athletes’ Training;architecture of a Hybrid Clinical Decision Support System;Synthesis of PLL in Capture Mode with a Fuzzy Controller of Quasi-Optimal Structure Based on the Maximum Condition of the Generalized Power Function;automated Discovery of Direct Speech to Increase Quality and Battery Saving in Telephony;temporal Prediction Models for Technological Processes Based on Predictive Analytics;distributed Control System of Protection of Autonomous Object, Based on Hybrid Technology of Artificial Intelligence;algebraic Bayesian Networks: the Generation of the Network Canonical Representation;using Semantic Annotation of Tabular data for Domain Knowledge Graph Population;robust Filtering of Nonlinear Stochastic Processes in Machine learning Systems;selecting a Machine learning Model to Optimize the Burner Digital Twin;modeling of Intuition in Human-Machine Decision-Making Complexes in the Management of Transport Systems;intelligent Maintenance and Repair on Railway Transport;traction Induction Motor State Observer Based on an Luenberger Filter;intelligent Monitoring of Transportation Processes Based on Dynamic Fuzzy PCA Analysis;method for Determining and Fixing the Information Need of Users in the Low-Code Development;intelligent Design of Images of engineering Networks for Spatial Analysis;method of Dynamic Multi-Criteria Quality Assessment and Optimization for Proactive Complex Objects Functioning Schedules.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Application of a Nine-Variate Prediction Ellipsoid for Normalized data and Machine learning Algorithms for Keystroke Dynamics Recognition 11

Application of a Nine-Variate Prediction Ellipsoid for Norma...

引用

11th international Research conference Information Technology and Implementation - Workshop: intelligent Systems and Security, IT and I-WS 2024: ISS

作者： Prykhodko, Sergiy Trukhov, Artem Admiral Makarov National University of Shipbuilding Heroes of Ukraine Ave. 9 Mykolaiv54007 Ukraine Odesa Polytechnic National University Shevchenko Ave. 1 Odesa65044 Ukraine

Keystroke dynamics recognition is a crucial element in enhancing security, enabling personalized user authentication, and supporting various identity verification systems. this study offers a comparative analysis of a nine-variate prediction ellipsoid for normalized data and machine learning algorithms specifically, autoencoder, isolation forest, and one-class support vector machine for keystroke dynamics recognition. Traditional methods often assume a multivariate normal distribution. However, real-world keystroke data typically deviate from this assumption, negatively impacting model performance. To address this, the dataset was normalized using the multivariate Box-Cox transformation, allowing the construction of a decision rule based on a nine-variate prediction ellipsoid for normalized data. the study also includes outlier removal during preprocessing, further improving the model's performance. the results revealed that the application of the Box-Cox transformation significantly enhanced both the accuracy and robustness of the prediction ellipsoid. Although all models demonstrated strong performance, the nine-variate prediction ellipsoid for normalized data consistently outperformed the machine learning algorithms. the study highlights the importance of careful feature selection and multivariate normalizing transformations in keystroke dynamics recognition. Future studies could benefit from broader datasets that include a wider range of user behaviors, such as variations in environmental factors and longer key sequences. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

引用

7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437915

关键词：

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 << < 94 95 96 97 98 99 100 101 102 103 > >>

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：