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

3,117 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,281 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,141 篇 工学
- 1,954 篇 计算机科学与技术...
- 1,095 篇 软件工程
- 405 篇 信息与通信工程
- 287 篇 电气工程
- 267 篇 生物工程
- 217 篇 控制科学与工程
- 172 篇 生物医学工程（可授...
- 79 篇 化学工程与技术
- 73 篇 光学工程
- 72 篇 机械工程
- 68 篇 仪器科学与技术
- 65 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 55 篇 动力工程及工程热...
- 54 篇 网络空间安全
777 篇 理学
- 430 篇 数学
- 296 篇 生物学
- 151 篇 统计学（可授理学、...
- 147 篇 物理学
- 127 篇 系统科学
- 90 篇 化学
505 篇 管理学
- 306 篇 图书情报与档案管...
- 231 篇 管理科学与工程(可...
- 95 篇 工商管理
218 篇 医学
- 193 篇 临床医学
- 125 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 55 篇 公共卫生与预防医...
72 篇 法学
- 70 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
31 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

568 篇 data mining
542 篇 machine learning
241 篇 pattern recognit...
195 篇 deep learning
174 篇 feature extracti...
154 篇 artificial intel...
153 篇 support vector m...
113 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
80 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 galgotias univer...
5 篇 centre of interd...
5 篇 pune
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,063 篇 英文
209 篇 其他
38 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 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：

Proceedings of 3rd international conference on Intelligent Engineering and Management, ICIEM 2022

Proceedings of 3rd International Conference on Intelligent E...

引用

3rd international conference on Intelligent Engineering and Management, ICIEM 2022

ISBN: (纸本)9781665467568

The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and deep learning methods;automobile insurance fraud detection: an overview;a survey on detection of lung cancer using different image processing techniques;deep learning methods for automatic number plate recognition system: a review;skin cancer detection using machine learning: a survey;transforming education system through artificial intelligence and machine learning;model for retailers with price dependent demand rates;neuromarketing: an emerging domain in the formal education system;design of dual slot microstrip antenna for wireless application;analysis of ancillary services provided by distributed energy resources in smart distribution grid;and grey scale image skeletonization of vein pattern using Hausdroff image binarization technique in biometric security system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based Sentiment Analysis of Movie Reviews 3

Deep Learning-based Sentiment Analysis of Movie Reviews

引用

3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Lou, Yuqi School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781510664814

Sentiment analysis, sometimes regarded as sentiment classification or opinion mining, can be utilized to assess public perceptions of certain products, occasions, people or ideas. There is a wide range of applications for sentiment analysis, including detecting the overall sentiment of text documents, identifying positive and negative opinions expressed in texts, and predicting the future stock market trend based on social media sentiment. In this study, the convolutional neural networks are employed to categorize the sentiment of English movie reviews, i.e., the CNNs are utilized to detect whether a review is positive or negative. Two text representation techniques, the bag-of-words model and TF-IDF, are applied to convert the movie reviews into numerical representation before transmitting the processed data to the CNN model. According to the model's prediction results on the testing set after training, the text processed with the bag-of-word model achieves a higher classification accuracy of 84.84% in the proposed CNN framework, while the TF-IDF for text processing has better precision of 88.96%. © 2023 SPIE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Improved Prediction of Electric Vehicle User Behavior with machine learning based Analysis 3

Improved Prediction of Electric Vehicle User Behavior with M...

引用

3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Prasad, Kavita Kiran Mali, Akshay Agarwal, Vivek Indian Institute of Technology Dept. of Electrical Engineering Bombay Mumbai India

ISBN: (纸本)9798350304732

With the rapid growth of Electric Vehicles (EVs), charging load demand is increasing significantly. Energy providers need to analyze the load requirements and meet them efficiently. Due to the highly nonlinear characteristics of EV user behavior, researchers are turning to machine learning (ML) models suited for nonlinear data. In the proposed study, the Adaptive Charging Network (ACN)'s public charging dataset is utilized to predict EV user behavior. New features like traffic, charging currents, and number of connection-disconnection events are integrated along with the weather data to improve prediction accuracy. Also, the features are included by observing the pattern in one-hour slots, which results in substantial improvements compared to previous work. While existing research mainly focuses on session duration and energy consumption, this work also predicts the charging duration, which is useful to get an idea of when the EV is ready to use. It is shown that ensemble model performs the best among various ML models, achieving a Mean Absolute Error (MAE) of 56 minutes for session duration and 43.2 minutes for charging duration. Also, the energy consumption is best predicted with Random Forest (RF) regression with MAE of 1.36 kWh. All the key results of this work are presented. © 2023 IEEE.

关键词： Electric Vehicles (EVs) machine learning (ML) Public Charging User Behavior Prediction

来源：评论

学校读者我要写书评

暂无评论

1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

引用

1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

machine learning Techniques For Software Component Reusability 3

Machine Learning Techniques For Software Component Reusabili...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Khan, Feroz Lingala, Gowtham Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India

ISBN: (纸本)9781665494991

Software reuse has considerably reduced the products time to market with increased reliability and productivity. Widespread use of software reuse, agile software development, User stories and component-based development methods can be observed nowadays. To reap the advantages of software reuse developers can access it from the repositories. The repositories are generated through different techniques used in data mining. In order to predict the reusability of a component machine learning techniques is used. This article provides a method of integration of agile, user stories and component-based developments methods using artificial neural network. The factors that determine the reusability of software are studied and corresponding software metrics are input to the proposed classifier. The metrics used for the study are Cyclomatic Complexity (CC), Design Structure Quality Index (DSQI), Cohesion, weighted method per class (WMP), Bugs, Coupling. Depth Inheritance Tree (DIT), Number of children (NOC). The classifier was tested with some standard classifiers namely Random Forest, Logit Boost, J48 and ESRT used in machine learning and the accuracy achieved is 91.8%. © 2022 IEEE.

关键词： Computer software reusability

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Different machine learning Methods for Hate Speech recognition in Twitter Text data 3

Comparative Analysis of Different Machine Learning Methods f...

引用

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

作者： Tiwari, Abhay Agrawal, Anupam Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The purpose of this work is to solve the challenges faced by us in the field of hate speech recognition on various social media platforms, that is to get a better machine learning model that can detect hate speech with greater accuracy. As the reach of the internet and mobile phones has extended abruptly in the past few years, everyone has the power to share their opinions, but some use it as an opportunity to spread hate among one another. In this paper, we used the Davidson [10] dataset which is the most popular Twitter dataset for hate speech detection, and further we implemented various machine learning-based algorithms and compared them on the basis of various parameters such as accuracy, precision score, recall and F1 scores. After the study, we found out that XGBoost when used with TF-IDF transformer embedding gave us an accuracy of 94.43%, which is the maximum among these three models for the given benchmark dataset. © 2022 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Dynamic Hand Gesture Classification Based on Attention Mechanism and Transformer 3

Dynamic Hand Gesture Classification Based on Attention Mecha...

引用

3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Ren, Zhen He, Haonan Huang, Zhengyi School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Mechanical & Automotive Engineering South China University of Technology Guangdong Guangzhou510641 China School of Electromechanical Engineering Guangdong University of Technology Guangdong Guangzhou510006 China

ISBN: (纸本)9781510667563

In the era of rapid development of deep learning, the popular transformer in recent years has played an outstanding role in many fields, but most of it focuses on the development of language models, and is still in the development stage in computer vision. Gesture recognition is a relatively popular module in computer vision, but the mainstream method still uses CNN for gesture recognition, and there are few articles combining gesture recognition with attention mechanism and transformer. In this paper, we propose a dynamic gesture recognition model based on attention mechanism and Transformer. In order to extract the valid information in each frame, we add the attention mechanism to the feature extraction network, followed by passing it into the transformer to predict the hand gesture through the self-attention mechanism. The model has been tested on the IsoGD dataset, achiving good experimental results. Not only do we confirm that the attention mechanism can improve the recognition accuracy through ablation study, but also prove that transformer is feasible to process and identify the temporal information among frames in dynamic gestures and perform recognition. © 2023 SPIE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

A Review of Multi-Label Feature Selection 3

A Review of Multi-Label Feature Selection

引用

3rd international conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2022

作者： Wang, Xiujuan Zhou, Yuchen Beijing University of Technology Faculty of Information and Technology Beijing100020 China Beijing University of Technology Beijing-Dublin International Technology Beijing100020 China

Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized. © Published under licence by IOP Publishing Ltd.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Phishing Attack Detection Using machine learning 2nd

Phishing Attack Detection Using Machine Learning

引用

2nd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： NaliniPriya, G. Damoddaram, K. Gopi, G. Nitish Kumar, R. Department of Information Technology Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819919451

Another kind of online crime is phishing, in which the perpetrator attempts to steal sensitive information from an unsuspecting victim by luring them to a phony website that looks like the real thing. This is a major worry in many sectors, including e-commerce and financial management. There are a lot of moving parts and criteria involved in detecting phishing sites, making it a highly unpredictable and elemental problem. Some astute proactive strategies can be helpful, and powerful tools, such as fuzzy, neural system, and data mining methods, can be a successful mechanism in distinguishing phishing sites. This is due to the aforementioned, as well as the ambiguities in arranging sites due to the intelligent procedures programmers are using. One of the many machine learning-based techniques used to identify Phishing websites was Random Forest (RF), which we employed. Finally, we evaluated the classifier's accuracy and compared its results to others (Chatterjee and Namin in IEEE 43rd annual computer software and applications conference (COMPSAC), [4], Blum et al. in 3rd ACM workshop on Artificial intelligence and security, Chicago, Illinois, USA, pp 54–60, [6]). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Speckle pattern and AI 3

Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Spe...

引用

3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Bello, Valentina Bassi, Irene Fiocchi, Matteo Nuzzi, Cristina Bellassi, Paolo Cattaneo, Luca Pasinetti, Simone University of Pavia Diii Pavia Italy University of Brescia Dimi Brescia Italy Università Cattolica del Sacro Cuore DiSTAS Cremona Italy Universita Cattolica del Sacro Cuore Diana Piacenza Italy

ISBN: (纸本)9798350378009

This paper introduces a novel method for raw cow milk classification combining speckle pattern (SP) imaging and AI-based processing of statistical parameters. Raw cow milk has been classified considering (i) 6 statistical features extracted from raw SP images, and (ii) 4 features extracted from the Gray-Level Co-occurrence Matrix (GLCM) computed on each SP image. We conducted 4 experimental campaigns, resulting in 24, 000 SP images retrieved from 20 milks produced by 20 cows. We then considered two different datasets: (i) a Complete dataset (made of all 24,000 frames), and (ii) a Reduced dataset, built by excluding data from one acquisition campaign. The two datasets were then split into training and testing sub-datasets. We trained three Wide Neural Networks (WNN) using different strategies. WNN1 and WNN2 have been trained and tested using the Complete and the Reduced datasets respectively, while WNN3 has been trained on the Reduced tested on the Complete dataset. WNN3 only attained 72% accuracy (revealing sensitivity to environmental conditions), while WNN1 and WNN2 achieved a test accuracy higher than 90%, demonstrating effectiveness without relying on computationally expensive models such as CNNs. This approach could be considered as a promising initial step for cow milk classification using SP imaging. © 2024 IEEE.

关键词： data analysis machine learning measurement science milk analysis speckle pattern

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329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：