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

3,080 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,244 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,110 篇 工学
- 1,926 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 412 篇 信息与通信工程
- 286 篇 电气工程
- 265 篇 生物工程
- 226 篇 控制科学与工程
- 169 篇 生物医学工程（可授...
- 81 篇 化学工程与技术
- 76 篇 机械工程
- 68 篇 光学工程
- 68 篇 仪器科学与技术
- 64 篇 网络空间安全
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 57 篇 动力工程及工程热...
767 篇 理学
- 435 篇 数学
- 290 篇 生物学
- 149 篇 统计学（可授理学、...
- 137 篇 物理学
- 128 篇 系统科学
- 90 篇 化学
502 篇 管理学
- 304 篇 图书情报与档案管...
- 228 篇 管理科学与工程(可...
- 93 篇 工商管理
222 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...
- 59 篇 公共卫生与预防医...
69 篇 法学
- 67 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
28 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

571 篇 data mining
546 篇 machine learning
241 篇 pattern recognit...
183 篇 deep learning
173 篇 feature extracti...
156 篇 artificial intel...
151 篇 support vector m...
114 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
82 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

3,100 篇 英文
135 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 3245 条记录，以下是111-120 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：

Efficient Temporal data mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

引用

3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM machine learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Classification Model Development for Enhancement the data Driven Based Human Falling Detection Performance 3rd

Hybrid Classification Model Development for Enhancement the ...

引用

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

作者： Ahmed, Saadaldeen Rashid Fadel, Hala Adnan. Ismael, Omar Ayad Sulaiman, Sazan Kamal Mahdi, Nidhal Raoof Ahmed, NoorUlhuda S. Abubaker, Brwa Abdulrahman Hussain, Abadal-Salam T. Artificial Intelligence Engineering Department College of Engineering Alayan University Nasiriyah Iraq Department of Laser Optoelectronic Engineering/Al-Ma’moon University College/Al-Washash Baghdad Iraq Department of Computer Science College of Sciences University of Diyala Diyala Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Medical Laboratories Techniques Al Hikma University College Baghdad Iraq College of Medicine University of Al-Ameed PO Box 198 Karbala Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq

ISBN: (纸本)9789819624676

It is thus important to identify the cause of a fall in order to be able to protect the lives of people, especially the elderly and hospitalized patients who are most likely to fall. Three fall detection models are compared in this paper: These are the auto-encoder (AEC), the feed forward neural network (FFNN), and the particle swarm optimization-feed forward neural network (PSO-FFNN). The idea is to assess how well these models identify falls, perform, and are suitable for deployment in real-life scenarios. This study was based on data from 17 candidates, and the four classes of falls that were considered here include: Based on this dataset, the models were trained and evaluated with the emphasis made on the correct classification of falls and their differentiation from other movements. From the results presented, all the three models show encouraging signs in fall detection. The PSO-FFNN model, with 98% of accurate optimal rate, however, gave the highest accuracy level. By this result, it is clear that the PSO-FFNN model is highly reliable and accurate in detecting falls. The objective of this research was to develop more precise and credible forecasts of various types of motion turbulence employing the autoencoder’s unsupervised learning and FFFNN’s partitioned classification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

ECG data mining Approach for Detection of Arrhythmia Using machine learning

ECG Data Mining Approach for Detection of Arrhythmia Using M...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Rahema Naaz Shafeeq Ahmad Department of Computer Science & Engineering Azad Institute of Engineering and Technology Lucknow India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The Electrocardiogram (ECG) is very important signal generated from human body that provides information for assessment of variation in cardiac activities. These ECG data are generally distorted due to high power line or magnetic field associated noise that results as a problem in error during diagnosis of heart related disorders on analysis through biomedical analysis software. This work contributes for very significant stage that is introduced to reduce the anomaly in the ECG signals to perform accurate automated diagnosis. This work follows a methodology of an automated machine learning algorithm is developed for accurate estimation of ECG data segment cycle using clustering approach integrated with Discrete Wavelet Transform (DWT). The key findings of this works cover the development and analysis of an approach to overcome the errors in detection of T-wave using Implantable Cardioverter Defibrillator (ICD). The work reports the solution of issues using fuzzy C-means clustering behind prediction of fatal Ventricular Arrhythmias (VA) for performing standard classifications.

关键词： machine learning algorithms Accuracy databases Arrhythmia Clustering algorithms Transforms Electrocardiography Discrete wavelet transforms Classification algorithms Standards

来源：评论

学校读者我要写书评

暂无评论

2025 international conference on Automation and Computation, AUTOCOM 2025

2025 International Conference on Automation and Computation,...

引用

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

ISBN: (纸本)9798331542375

The proceedings contain 296 papers. The topics discussed include: integrating automation and sustainability: Arduino-based solar panel cleaning system for renewable energy optimization;utilizing transforming portfolio management through automation using advanced deep reinforcement learning algorithms for optimized investment strategies;leveraging quantum computing for multi-criteria decision analysis in strategic business planning;secure multi-party computation for privacy preservation in collaborative networks;integration of continuous improvement approach in sustainable e-government services towards citizen satisfaction in health sector development of conceptual model;unlocking scholarly insights using deep learning-based evaluation method for research impact assessment in academic institutions;balancing technology with pedagogy in smart classrooms for English language;empirical study to select a place to stay in NCR area using data mining and machine learning;and the impact of ultra-wideband sensors in industrial automation and robotics applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design and Application of English Pronunciation Correction Algorithm Based on SVM+LDA

Design and Application of English Pronunciation Correction A...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Huizhong Yu Ting Zhou Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

For the common pronunciation errors of English learners in the pronunciation process, an English pronunciation error correction algorithm based on SVM (Support Vector machine) and LDA (Latent Dirichlet Allocation) is introduced. The algorithm aims to accurately identify and correct the problems in learners' pronunciation, thereby improving their pronunciation accuracy and fluency. By combining the feature extraction of speech signals with machine learning methods, different types of pronunciation errors can be effectively distinguished and corrected. First, the pronunciation data of English learners are collected and preprocessed to extract speech features, including MFCCs (Mel-Frequency Cepstral Coefficients), formants, pitch, etc. Secondly, LDA is used to reduce the dimension of the extracted features to reduce the complexity of the feature space and enhance the ability to distinguish pronunciation errors. Then, SVM is used for classification training to train a pronunciation error model. Finally, the trained model is used to predict the learner's pronunciation, and corresponding pronunciation correction suggestions are made according to the classification results. This method can effectively identify different types of pronunciation errors and provide personalized correction feedback according to the nature of the error. When the pronunciation error correction algorithm based on SVM+LDA is used for testing, the learners' error rate is 31.9% on average before error correction, and drops to 26.3% after error correction. The English pronunciation error correction algorithm based on SVM+LDA introduced in this study can effectively improve the pronunciation accuracy of English learners. This method has strong practicality and scalability, and can provide strong support for English pronunciation teaching and speech recognition technology.

关键词： Support vector machines Training Accuracy machine learning algorithms Systematics Speech recognition Feature extraction Error correction Classification algorithms data mining

来源：评论

学校读者我要写书评

暂无评论

Design of a Real-Time Crime Monitoring System using machine learning

Design of a Real-Time Crime Monitoring System using Machine ...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： A. Krishnaveni C. Manikandan Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system's ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches.

关键词： Accuracy Recurrent neural networks Law enforcement Face recognition Surveillance Prevention and mitigation Speech recognition Real-time systems Public security data models

来源：评论

学校读者我要写书评

暂无评论

Predictive Models for Career Guidance, Based on Academic and Behavioral data with data mining and machine learning Technique

Predictive Models for Career Guidance, Based on Academic and...

引用

Disruptive Technologies (ICDT), international conference on

作者： Brijesh Kumar Verma Nidhi Srivastava Ajay Kumar Bharti Amity Institute of Information Technology (AIIT) Amity University Lucknow U.P INDIA Ambalika Institute of Management and Technology (AIMT) Lucknow U.P INDIA

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A person's future is greatly influenced by career counseling, especially for students making decisions about their education and careers. This project focuses on using data mining and machine learning approaches to leverage behavioral and academic data to create prediction models for career advising. To produce individualized career suggestions, the suggested system combines data gathering, preprocessing, feature engineering, and predictive analytics. Academic performance indicators (e.g., grades, test scores), behavioral characteristics (e.g., learning styles, personality tests), and demographic information are important sources of data. While clustering techniques like K-Means make it easier to group students with similar profiles, machine learning algorithms like Random Forest, Support Vector machines, and Neural Networks are used for classification. Furthermore, hybrid models that combine clustering and classification improve the accuracy and customization of the system. Metrics including precision, recall, F1 score, and clustering validation indices are used to evaluate the system, guaranteeing solid and trustworthy results. By assigning students to appropriate career groupings and recommending certain positions, the prediction algorithms offer useful insights. This study fills in the gaps in conventional career counseling approaches and opens the door for scalable, effective, and customized career advice systems by showing how cutting-edge data-driven approaches can enable students to make well-informed career decisions.

关键词： Support vector machines Employee welfare Accuracy Engineering profession Stability criteria Neural networks Predictive models data models data mining Random forests

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Monitoring of Industrial Processes Using IoT Sensors and Predictive Algorithms

Energy-Efficient Monitoring of Industrial Processes Using Io...

引用

Disruptive Technologies (ICDT), international conference on

作者： Gunjan Agarwal Arnav Kaushik Manoj Kumar Singh Vaishali Bhargava Saloni Bansal Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science & Engineering Galgotia University Greater Noida UP India Computer Science & Engineering IMS Engineering College Ghaziabad UP India Department of Electrical Engineering GLA University Mathura UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Industrial processes are becoming increasingly complex and larger in scale, which has resulted in a constantly growing demand for robust, scalable monitoring systems. Conventional monitoring strategies entail a huge quantity of sensors and steep infrastructure charges, main to excessive electricity intake and operational costs. Burkhard Theen, vice president at weather analytics company Iteris describes a smart monitoring device using IoT sensors and predictive algorithms for accurately tracking industrial processes on this track. The system is made using a low-voltage IoT sensor community at essential points within the process, which constantly collects and transmits statistics in real time. This data is then fed into the predictive algorithms that use machine learning techniques to analyze and determine any anomalies in the system. The system helps to improve process performance and reduce power consumption by identifying & addressing potential issues at their early stages.

关键词： machine learning algorithms Power demand Electricity Companies Prediction algorithms Real-time systems Sensor systems pattern recognition Monitoring Intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on Computing, Communication and learning, CoCoLe 2024

3rd International Conference on Computing, Communication and...

引用

3rd international conference on Computing, Communication and learning, CoCoLe 2024

ISBN: (纸本)9783031790409

The proceedings contain 34 papers. The special focus in this conference is on Computing, Communication and learning. The topics include: Unveiling the Future of Agriculture: Transformative Impact of Advanced Deep learning with Mobile App Technology for Plant Leaf Disease Detection;air Quality Prediction Using Ensemble learning;real-Time Highway Accident Detection and Response with Deep learning and Edge Caching;Detection of Yoga Poses Using CNN and LSTM Models;Enhancing Rhetorical Role Identification in Legal Documents Using Large Language Models and IN_place data Augmentation;persona-Driven Dialog Generation: Enhancing User Engagement Through Linguistic Proficiency and Personalization;design and Development of a Working Tool for Visual Speech and Speaker recognition for Marathi and Gujarati Languages;analysis of Tracking Algorithms for Multi-person Tracking;personalized Human Activity recognition Using Smartphone Technology;a Comprehensive Comparative Study of Breast Cancer Detection Using machine learning Techniques to Improve Diagnosis;performance Improvement of machine learning Algorithms Through Information-Theoretic Class Based Feature Multicorrelation Enabled Feature Selection for Cervical Cancer Prediction;Towards Robust Skin Cancer Diagnosis: Deep Fusion of VGG16 and MobileNet Features;deep Transfer Model Based Accurate Brain Tumor Classification in Magnetic Resonance Images;pneumonia Detection from X-Ray Images Using Deep Transfer learning;crop Identification by Using machine learning Classification Algorithm;biotic Stress Classification of Pear Leaves Diseases Using Stacking Ensemble Approaches;unveiling Emotions from Audio: A Multi-model Exploration Leveraging Diverse datasets;a Contrastive Meta-learning Approach with Isotropic Sparse Decomposition for Scalable Audio-Visual learning;audio-Based Video Segmentation for Long Duration Videos Using Triplet-Loss Based Sentence Transformers and Acoustic Characteristics;video Content Moderation in Instagram;an E

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis on Telugu Text Translated from English Using NLP and ML

Sentiment Analysis on Telugu Text Translated from English Us...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： A.H.N Karthik Ch. Aneesh Gembali Saumik K.V.V Varun Kavitha C.R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study mainly uses Natural Language Processing (NLP) and machine learning. The general approach of this tool is to apply sentiment analysis in Telugu Text translated from English. The Technique contains many preprocessing steps, for example, tokenization of texts, normalization and noise removal for translating English text into Telugu which then will use machine translation services. It is followed by sentiment analysis of the statement using NLP models and machine learning approaches and classified them as; positive, negative, or neutral. The main objective of this study is to enhance the accuracy of sentiment identification with non-English language. Environments and also to develop rigorous sentiment analysis tools optimized for Telugu, which are designed as to the language and context-related difficulties to be taken into account. Additionally, the research examines analyses the impact of bilingual datasets on increasing translation accuracy and sentiment classification, while also tackling the obstacles given by idiomatic idioms and context-specific interpretations in Telugu Language.

关键词： Sentiment analysis Translation Accuracy Noise machine learning Tokenization Object recognition machine translation Internet of Things data communication

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 << < 8 9 10 11 12 13 14 15 16 17 > >>

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：