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

2,938 篇 会议
174 册 图书
25 篇 期刊文献

馆藏范围

3,136 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,026 篇 工学
- 1,813 篇 计算机科学与技术...
- 948 篇 软件工程
- 329 篇 电气工程
- 312 篇 信息与通信工程
- 214 篇 控制科学与工程
- 207 篇 生物工程
- 149 篇 生物医学工程（可授...
- 70 篇 光学工程
- 64 篇 机械工程
- 61 篇 化学工程与技术
- 50 篇 电子科学与技术（可...
- 48 篇 仪器科学与技术
- 42 篇 安全科学与工程
- 35 篇 交通运输工程
644 篇 理学
- 350 篇 数学
- 244 篇 生物学
- 138 篇 物理学
- 134 篇 统计学（可授理学、...
- 95 篇 系统科学
- 62 篇 化学
399 篇 管理学
- 240 篇 图书情报与档案管...
- 176 篇 管理科学与工程(可...
- 76 篇 工商管理
261 篇 医学
- 240 篇 临床医学
- 122 篇 基础医学(可授医学...
- 74 篇 药学(可授医学、理...
- 39 篇 公共卫生与预防医...
43 篇 法学
- 40 篇 社会学
37 篇 教育学
- 31 篇 教育学
31 篇 经济学
- 29 篇 应用经济学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

572 篇 machine learning
563 篇 data mining
320 篇 pattern recognit...
202 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
128 篇 machine learning...
120 篇 training
116 篇 accuracy
90 篇 data models
89 篇 data mining and ...
71 篇 neural networks
71 篇 classification
67 篇 predictive model...
66 篇 face recognition
61 篇 learning systems
52 篇 computer imaging...
50 篇 convolutional ne...
48 篇 classification a...

机构

9 篇 school of comput...
9 篇 chinese academy ...
8 篇 zhejiang univers...
8 篇 chitkara univers...
7 篇 guangdong univer...
7 篇 school of inform...
6 篇 xi'an university...
6 篇 indian statistic...
6 篇 central south un...
6 篇 department of co...
6 篇 unsw sydney
6 篇 xi'an university...
6 篇 southern univers...
5 篇 study group aust...
5 篇 peking universit...
4 篇 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...
4 篇 sun yat sen univ...
4 篇 school of inform...

作者

11 篇 de-shuang huang
8 篇 petra perner
7 篇 krasotkina olga
7 篇 mottl vadim
7 篇 ying tan
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 phung dinh
6 篇 venkatesh svetha
6 篇 hongyi li
6 篇 jacek m. zurada
6 篇 chaojie li
6 篇 biao luo
6 篇 ilias maglogiann...
6 篇 lotfi a. zadeh
6 篇 leszek rutkowski
6 篇 lazaros iliadis
6 篇 long cheng
5 篇 vitoantonio bevi...

语言

3,044 篇 英文
79 篇 其他
19 篇 中文
1 篇 土耳其文

检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是161-170 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：

Research on Pedestrian recognition Based on Deep learning 5

Research on Pedestrian Recognition Based on Deep Learning

引用

5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Liqin, Liu Blancaflor, Eric Abisado, Mideth National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Mapua University School of Information Technology Manila Philippines

ISBN: (纸本)9798350359930

In the realm of computer vision, pedestrian recognition has gained popularity as a study topic in recent years, and has received widespread attention and research from scholars. How to extract more discriminative pedestrian information for pedestrian re recognition has always been a noteworthy issue. While deep learning models can extract features independently, they may face gradient vanishing as the number of network layers increases. Traditional pedestrian recognition techniques rely on the human construction of visual features, which have low recognition accuracy and are easily influenced by other external circumstances. An additional aspect influencing pedestrian recognition accuracy is partial occlusion of pedestrian images. this article proposes an enhanced neural network that combines random erasure and residual attention networks for pedestrian recognition to improve the accuracy of recognition. © 2023 IEEE.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Automated Emotion recognition from Facial Expressions using Convolutional Neural Network 5

Automated Emotion Recognition from Facial Expressions using ...

引用

5th international conference on data Analytics for Business and Industry, ICDABI 2024

作者： Hussain, Syed Aman Reddy, Nareddy Yashwanth Srivardhan, Junnuthula Sharma, Ajay Sharma, Shamneesh Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India upGrad Education Private Limited Delivery and Student Success Karnataka Bangalore India International Deemed University Constituent of Symbiosis Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350368710

In the era of digital communication, it is more about being able to detect emotions than anything else if we are to maintain this complicated and demanding human society. Emotion recognition research is pulling out all the stops, resulting in all manner of methods to recognize human feelings. Emotions are often transmitted through words and body language, and facial expressions play an important part in communicating them. In this paper, the use of a Convolutional Neural Network (CNN) is proposed as a real-time emotion recognition system based on machine learning algorithm, and the system is able to process video feeds. the classifier breaks emotions down into seven categories, with an accuracy of about 81 percent. In addition, the research extends to the use of various image datasets to improve emotion forecasting accuracy. © 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

Proceedings of 5th international conference on Computer Information and Big data Applications, CIBDA 2024

Proceedings of 5th International Conference on Computer Info...

引用

5th international conference on Computer Information and Big data Applications, CIBDA 2024

ISBN: (纸本)9798400718106

the proceedings contain 221 papers. the topics discussed include: UGENet: learning discriminative embeddings for unconstrained gaze estimation network via self-attention mechanism in human-computer interaction;research progress of deep learning in the diagnosis of retinopathy of prematurity;design and realization of high-quality development path of intelligent manufacturing based on artificial fish school algorithm and computer simulation;design and computer simulation of original doll trend brand communication path based on artificial bee colony algorithm;an adaptive hybrid intelligence model based on improved sparrow search algorithm and back propagation neural network;progress of machine learning potentials for material atomic simulation;research and implementation of bidirectional code association pattern based on requirement driven;and an improved neural network approach to end-to-end video compression.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Digitized Paper Evaluation for Universities Using machine learning Technique 5

Digitized Paper Evaluation for Universities Using Machine Le...

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Nair, Bipin B.J. Spoorthi, C. Narayanan, Gokul C. Koushik, K.S. Venkitesan, Pranav Dept.of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Zenloop GMBH Berlin Germany

ISBN: (纸本)9798350361155

Automating the assessment of handwritten university papers is a crucial advancement in today's digital era. this study focuses on leveraging Handwriting Text recognition (HTR) and machine learning (ML) techniques to streamline the evaluation process. By extracting key features and automating the grading process, this research aims to enhance efficiency and accuracy. the project emphasizes two primary objectives: feature extraction and automated calculation to replace manual evaluation methods. By employing a 70:30 split and training on 1000 images, the system undergoes rigorous filtering, preprocessing, region of interest (ROI) identification, segmentation, and validation based on ground truth data. Despite challenges like strike-throughs, overlapping letters, and inconsistent ink, this initiative ultimately aims to overcome the limitations of traditional paper evaluation methods. © 2024 IEEE.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Enhancing Acoustic-based Speech Emotion recognition Using CNN 5

Enhancing Acoustic-based Speech Emotion Recognition Using CN...

引用

5th international conference on Advanced Information Technologies, ICAIT 2024

作者： Hlaing, Hnin Oo thet Zin, thet Wai, thinn thinn Nway Oo, Aung University of Information Technology Yangon Myanmar University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Speech Emotion recognition (SER) is a crucial field aiming to enhance machine understanding of human emotions. Among all the variety of data sources, we choose the acoustic features of the speech to explore both positive and negative aspects in feature extraction, aggressive data augmentation, and complex model architecture, highlighting its potential to inflate false confidence (overfitting) and hallucinations if not wielded with caution. Furthermore, our investigation explores the complex link between accuracy and feature set size, highlighting the need to extract rich emotional characteristics from speech. An intriguing finding of our analysis is: downscaling the filter size and learning rate of the neural network works better with small training dataset and the model can differentiate diverse emotions more accurately. this finding paves the way for improving the classification model, potentially leading to more robust emotion recognition systems. this multifaceted analysis goes beyond the conventional focus on accuracy, challenging assumptions and pinpointing areas for future exploration in the ever-evolving field of speech-based emotion recognition. © 2024 IEEE.

关键词： Convolutional Neural Feature Extraction Network (CNN)data Augmentation Speech Emotion recognition (SER)

来源：评论

学校读者我要写书评

暂无评论

Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing IT Security with LLM-Powered Predictive threat Intelligence 5

Enhancing IT Security with LLM-Powered Predictive Threat Int...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bokkena, Bhargava Clarivate New York United States

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504403

the primary objective of this research is to explore the integration of Large Language Models (LLMs) into IT security frameworks for enhancing predictive threat intelligence capabilities. Traditional threat detection systems often struggle with the dynamic nature of modern cyber threats. LLMs, with their advanced natural language processing abilities, present a novel approach by predicting and identifying potential security threats through pattern recognition and anomaly detection in vast datasets. this research employed a systematic methodology involving extensive data collection from diverse IT security logs and public threat databases. the selected LLM was trained on these datasets, focusing on recognizing linguistic and non-linguistic patterns indicative of potential security threats. Validation was conducted through a series of controlled tests comparing the LLM's performance against traditional rule-based and machine learning models in simulated and real-world environments. the results demonstrate that the LLM significantly outperformed existing models in terms of detection speed, accuracy, and the ability to identify zero-day exploits. Notably, the LLM achieved an accuracy rate exceeding 95%, with substantial improvements in false positive reductions. the study concludes that leveraging LLMs in threat intelligence systems can profoundly enhance predictive capabilities and dynamically adapt to evolving cyber threats. this advancement not only bolsters security postures but also supports proactive security management, underscoring the critical role of LLMs in future IT security strategies. © 2024 IEEE.

关键词： Analytical models Accuracy Large language models Transforms Predictive models threat assessment pattern recognition Security Anomaly detection Testing

来源：评论

学校读者我要写书评

暂无评论

data Anti-jamming Method for Ad Hoc Networks Based on machine learning Algorithm 5th

Data Anti-jamming Method for Ad Hoc Networks Based on Machin...

引用

5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Zhang, Yanning Ma, Lei Beijing Polytechnic Beijing100016 China

ISBN: (纸本)9783031505768

the current data anti-jamming methods lack the feature classification process, which leads to poor anti-jamming effect. In order to solve this problem, this paper proposes a data anti-jamming method based on machine learning algorithm for ad hoc networks. First of all, based on machine learning algorithm, the data transmitted in the ad hoc network is processed by feature mining and classification, and the ad hoc network information transmission management platform is constructed. then optimize the steps of extracting the anti-jamming information features of the ad hoc network data, and combine machine learning algorithm to optimize the anti-jamming evaluation algorithm of the communication data of the internet of things, so as to achieve the identification and protection of the ad hoc network interference data. the experimental results show that this method has high practicability and can meet the research requirements. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Research on machine learning Based Fault Diagnosis Methods for Power Transformers 5

Research on Machine Learning Based Fault Diagnosis Methods f...

引用

5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Sun, Xiaoyu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350310603

With the rapid development of science and technology, the power system has become an indispensable part of people's daily life. Transformers play an irreplaceable role in the power system, and their operation status directly affects the reliability of the power supply of the grid. In order to improve the accuracy of oil-immersed dissolved gas analysis to diagnose transformer faults, a transformer fault diagnosis method based on the hybrid grey wolf optimization algorithm (HGWO-SVM) is proposed from the perspective of improving the classification and recognition performance of the support vector machine (SVM) and mining the deep feature information embedded within the data of the dissolved gas in the oil. Elite backward learning, nonlinear control parameters and Levy flight strategy are adopted to improve the grey wolf optimization algorithm, and the penalty factors and kernel parameters of SVM are efficiently optimized to ensure the best classification performance of SVM. the results of constructing the HGWO-SVM fault diagnosis model show that the feature extraction of data can effectively improve the diagnosis accuracy and computational efficiency, which provides theoretical support for the subsequent research. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Research on Object Detection Algorithm Based on Deep learning 5

Research on Object Detection Algorithm Based on Deep Learnin...

引用

5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Zheng, Dingchao Chen, Caiwei School of Artifical Intelligence Zhejiang Dongfang Polytechnic WeiZhou China

ISBN: (纸本)9798350386943

the YOLO-CM detection algorithm proposed in this paper is designed for this challenge. By introducing a channel adaptive mechanism, the algorithm enables the network to dynamically learn and adjust the channel weights of the feature map, thereby enhancing the attention to important features and improving the object recognition performance of the network in complex scenes. At the same time, the introduction of multi-scale feature fusion technology effectively integrates feature maps of different scales, helps the network better capture object information of different scales, and further improves the accuracy and robustness of recognition. the experimental results on VOC2007 data set show that the mAP value of the YOLO-CM model combined with channel adaptation mechanism and multi-scale feature fusion technology is 0.0145 higher than that of the benchmark model, which fully proves the significant improvement of object detection performance. these innovations not only provide new insights and strategies for deep learning algorithms in the field of object detection, but also have far-reaching theoretical significance and practical value, and inject new vitality into the development of related fields. © 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

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314页 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：