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

3,079 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,243 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,107 篇 工学
- 1,924 篇 计算机科学与技术...
- 1,068 篇 软件工程
- 399 篇 信息与通信工程
- 285 篇 电气工程
- 265 篇 生物工程
- 215 篇 控制科学与工程
- 170 篇 生物医学工程（可授...
- 79 篇 化学工程与技术
- 72 篇 机械工程
- 69 篇 光学工程
- 68 篇 仪器科学与技术
- 63 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...
- 53 篇 网络空间安全
760 篇 理学
- 424 篇 数学
- 291 篇 生物学
- 148 篇 统计学（可授理学、...
- 140 篇 物理学
- 127 篇 系统科学
- 90 篇 化学
499 篇 管理学
- 301 篇 图书情报与档案管...
- 229 篇 管理科学与工程(可...
- 93 篇 工商管理
216 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 53 篇 公共卫生与预防医...
70 篇 法学
- 68 篇 社会学
51 篇 教育学
- 46 篇 教育学
35 篇 经济学
30 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...
184 篇 deep learning
173 篇 feature extracti...
154 篇 artificial intel...
152 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 centre of interd...
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 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
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

3,105 篇 英文
129 篇 其他
32 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 3244 条记录，以下是61-70 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 the Construction of Topic Model in the Field of Digital Humanities Based on Sentence-BERT and LDA 24

Research on the Construction of Topic Model in the Field of ...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Wang, Zhen Li, Honglei Liaoning Normal University Liaoning Dalian China

ISBN: (纸本)9798400712692

In order to solve the problems that traditional methods cannot fully capture text semantic information in text topic recognition and sentence representation is not suitable for unsupervised learning, we use the Sentence-BERT model for sentence embedding tasks combined with the LDA model to construct a topic mining model, which provides new methods and ideas in the field of topic model construction methods. Firstly, the relevant literature was reviewed, then the text vectors were obtained by using Sentence-BERT and LDA models, and then the vectors of the two models were connected, and then the optimal number of clusters was judged, the clustering was carried out and the clustering effect was evaluated, and finally conduct comparative experiments. Through experiments on relevant literature data in the field of digital humanities, the model has a good effect on topic extraction, clear topic division, short calculation time, and it can effectively capture the features in the text. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Ensemble learning and Time-Domain Feature mining based Appliance Power Consumption pattern recognition 22

Ensemble Learning and Time-Domain Feature Mining based Appli...

引用

22nd international learning and Technology conference, L and T 2025

作者： Qaisar, Saeed Mian Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73. © 2025 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Parallel Selective Meta-Heuristic Algorithms for Real-Time Non-Convex Optimization in Big data Analytics 3

Parallel Selective Meta-Heuristic Algorithms for Real-Time N...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ghosal, Shreya Shankar, Amar Agarwal, Trapty Suchithra, R. Raj, Nimesh Hansda, Prity Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

With big data in modern analytics becoming more common, a lot of it is real-time optimization, which is an essential problem. This is because big data requires efficient and accurate optimization methods, leading to developing parallel selective viable meta-heuristic algorithms for nonconvex optimization in large analytics. They can be used to find optimal solutions rapidly by merging the complementary advantages of meta-heuristics and parallel computing. Algorithms do this using an optimization process, but not always for the whole data set used, saving computational performance. As information is updated in real-time, its approach will constantly be improved, resulting in a better and more accurate solution. A parallel implementation of these algorithms makes them effective for real-time optimization using large datasets. However, the performance of big data analytics may be significantly improved by implementing these algorithms because they have achieved satisfactory results in a number of applications, including machine learning, data mining, and pattern recognition. © 2025 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Object Detection in Images using machine learning 3

Object Detection in Images using Machine Learning

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nagarajan, G. Piratla, Abhiram Golla, Karthikeya Department of CSE Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331527549

This study develops a scalable, effective, and user-friendly solution to tackle the problem of real-time object detection in photos. The suggested approach makes use of YOLOv5 (You Only Look Once version 5) for deep learning-based object detection and Flask for managing REST API queries. Users can submit base64-encoded photos to the system, which will analyze them using the YOLOv5 model to identify and categorize objects, label them with bounding boxes, and then return the base64-formatted images. High-speed performance is ensured by using PyTorch for model inference, while Non-Maximum Suppression (NMS) increases object localization accuracy by removing redundant detections. Because of its lightweight architecture and ease of integration with online applications, this system can be used for a variety of purposes, including surveillance and e-commerce. The study's output is a reliable, approachable object detection API that can effectively manage real-time image processing and deliver precise item recognition, showcasing the potential for implementing deep learning models in intuitive and scalable web-based settings. © 2025 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

ProductGuard: Empowering Informed Choices for Safe Living 3

ProductGuard: Empowering Informed Choices for Safe Living

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Shashikala, K.S. Patil, Suyash Ganesh Shetty, Rajath U. Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices. © 2025 IEEE.

关键词： Bottles

来源：评论

学校读者我要写书评

暂无评论

data mining for Fraud Detection in Telecommunications: Detecting Anomalous Behaviors in Real-Time 3

Data Mining for Fraud Detection in Telecommunications: Detec...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Nidhi Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In the recent past, telecom experts have been confronted with so many problems emanating from fraudulent deeds that apart from causing loss of money discredits organizations. To mitigate these problems, real-time fraud detection systems are very essential in detected those unusual and unauthorized activities. This study aims at examining how data mining can be used in the identification of the actual fraud in the telecommunication area through users' interactions in the course of which a huge amount of data is produced. In this study, the case uses different machine learning algorithms including clustering, decision trees, and anomaly detection for mapping out unusual call pattern, unauthorized access and Identity theft. To capture anomalous behavior, the research uses both time-domain and frequency domain data from different telecom networks for training and testing models. The research also intends to extend existing theories and practices of Naive Bayes, Support Vector machines (SVM), and deep learning networks to improve accuracy and effectiveness of the sort of systems used in the detection of fraud. The performance of these models is measured in terms of accuracy in classification and prediction of fraudulent actions thereby reducing potentials false negative and false positive cases. Further, the paper outlines how these models can be integrated into existing network security architecture to assess the prospects for scalability of these solutions within a maturing fraud environment. The findings also affirm that data mining is effective in real-time monitoring and prevention of frauds as such highlighting the importance of frequent updates of the fraud detection solutions within the telecommunication sector. © 2025 IEEE.

关键词： Anomalous data mining Fraud Detection SVM Telecommunication

来源：评论

学校读者我要写书评

暂无评论

Improving Emotion recognition in Audio Signals: Leveraging Novel Features and Deep learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Power data Security Defense Technology Based on Behavior Profiling and Its Application in Cloud Environment 3rd

Power Data Security Defense Technology Based on Behavior Pro...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ren, Qingqing Li, Haosheng Tian, Haomiao Feng, Haonan Cao, Shu Xinjiang Uygur Autonomous Region State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830000 China

ISBN: (纸本)9789819616978

This article focuses on the security challenges faced by power data in cloud environments, particularly the risk of data leakage caused by abnormal user behavior. The study proposes an innovative power data security defense technology based on behavioral profiling. This technology constructs a user behavior model through fuzzy C-means clustering algorithm, analyzes real-time access and operation characteristics of power data, forms accurate behavior portraits, and uses machine learning algorithms to identify abnormal behavior patterns. The experimental results show that this technology has extremely high accuracy in detecting normal and abnormal electricity usage behavior, with most data points having an accuracy rate of over 90%, and some even reaching 100%, effectively distinguishing between normal and abnormal behavior. The research conclusion shows that the defense strategy based on behavioral profiling provides a new perspective and efficient means for the security protection of power data, significantly improving the security protection level of power data in cloud environments, and building a solid defense line for the digital transformation of the power industry and the safe operation of smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Research on Wordle’s Popularity, Pass Rate, and Difficulty Based on Deep learning 3rd

Research on Wordle’s Popularity, Pass Rate, and Difficulty ...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Jian, Shuoqu Gu, Nan Huo, Sihan Zhao, Runhao Xu, Dan National University of Defense Technology Changsha China Sun-Yat-Sen University Guangzhou China

ISBN: (纸本)9789819624676

Wordle is a very popular word game, which is not only attracted many players, but researchers’ interests. However, the prediction, pass rate, and the distinction of difficulty are rare be studied. In this paper, we study the game popularity of Wordle by using LSTM to predict the popularity of the game based on previous scenarios. We use BP-neural network to predict the pass rate of Wordle. Moreover, we evaluate the game difficulty by TOP-SIS model, and train a classifier that can make classification on the Wordle puzzles. In experiment part, we use the data from to predict the popularity of the game on March 1, 2023, and analyze the difficulty of four game levels, predict the difficulty of some levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

SERTUS dataset Collection from Spontaneous Environments 3rd

SERTUS Dataset Collection from Spontaneous Environments

引用

3rd international conference on Language Processing and Knowledge Management-LPKM

作者： Nasr, Latifa Iben Masmoudi, Abir Belguith, Lamia Hadrich Sfax Univ MIRACL Lab ANLP RG FSEGS Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

This paper introduces SERTUS (Speech Emotion recognition TUnisian Spontaneous), an extensive dataset collection intended to propel research in Speech Emotion recognition (SER), particularly within the realm of Tunisian Dialect (TD). SERTUS encompasses both registers of the Tunisian Dialect: the Popular (familiar) register and the intellectual register, capturing a diverse range of emotions in spontaneous environments and natural interactions across different regions of Tunisia. This work delineates the methodology utilized in crafting SERTUS, highlighting the challenges and strategies involved in capturing spontaneous interactions. Moreover, we underscore the importance of including TD and the multidomain nature of the dataset, illustrating its potential applications in various domains such as sports, politics, and culture. It's imperative to note that this corpus collection adhered to a rigorous protocol to ensure corpus quality, as it will undergo annotation in future research endeavors.

关键词： data collection Spontaneous emotion speech Tunisian Dialect

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325页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：