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

551 篇 会议
4 册 图书
4 篇 期刊文献

馆藏范围

559 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

330 篇 工学
- 302 篇 计算机科学与技术...
- 222 篇 软件工程
- 66 篇 信息与通信工程
- 55 篇 控制科学与工程
- 41 篇 生物工程
- 36 篇 生物医学工程（可授...
- 33 篇 电气工程
- 26 篇 电子科学与技术（可...
- 24 篇 动力工程及工程热...
- 21 篇 机械工程
- 20 篇 网络空间安全
- 19 篇 光学工程
- 12 篇 化学工程与技术
- 12 篇 安全科学与工程
- 10 篇 仪器科学与技术
- 8 篇 交通运输工程
115 篇 理学
- 47 篇 数学
- 45 篇 物理学
- 45 篇 生物学
- 25 篇 统计学（可授理学、...
- 12 篇 化学
95 篇 管理学
- 60 篇 管理科学与工程(可...
- 34 篇 图书情报与档案管...
- 19 篇 工商管理
29 篇 医学
- 28 篇 临床医学
- 23 篇 基础医学(可授医学...
- 12 篇 药学(可授医学、理...
- 9 篇 公共卫生与预防医...
16 篇 教育学
- 15 篇 教育学
13 篇 法学
- 12 篇 社会学
6 篇 农学
4 篇 经济学
2 篇 文学

主题

95 篇 machine learning
72 篇 human computer i...
70 篇 accuracy
57 篇 deep learning
35 篇 feature extracti...
31 篇 training
29 篇 convolutional ne...
28 篇 emotion recognit...
27 篇 predictive model...
27 篇 machine learning...
26 篇 computational mo...
21 篇 adversarial mach...
19 篇 real-time system...
18 篇 computer vision
18 篇 data models
17 篇 support vector m...
17 篇 artificial intel...
15 篇 reliability
14 篇 face recognition
14 篇 prediction algor...

机构

4 篇 school of materi...
3 篇 school of comput...
3 篇 school of inform...
3 篇 graphic era deem...
3 篇 new york univers...
2 篇 department of co...
2 篇 saveetha institu...
2 篇 school of comput...
2 篇 school of xiamen...
2 篇 singapore univer...
2 篇 dept. of compute...
2 篇 wuhan digital en...
2 篇 dept. of softwar...
2 篇 dept. of electro...
2 篇 chitkara univers...
2 篇 south china univ...
2 篇 school of comput...
2 篇 school of comput...
2 篇 department of in...
2 篇 dept. of compute...

作者

3 篇 surendran r.
3 篇 ggaliwango marvi...
2 篇 zhou jing
2 篇 r surendran
2 篇 julian anitha
2 篇 wenle bai
2 篇 qingle quan
2 篇 hanqing sun
2 篇 sharma ajay
2 篇 jac fredo agasti...
2 篇 jing zhou
2 篇 kulkarni hrishik...
2 篇 wu kai
2 篇 shahade makarand
2 篇 bharti
2 篇 sharma shamneesh
2 篇 bingyao liu
2 篇 nakayiza hellen
2 篇 vishalakshmi
2 篇 schiller dominik

语言

454 篇 英文
105 篇 其他
8 篇 中文

检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 559 条记录，以下是411-420 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：

Enhancing Emotion Recognition: machine learning with Phasic Spectrogram Texture Features

Enhancing Emotion Recognition: Machine Learning with Phasic ...

引用

Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE international conference on

作者： Sriram Kumar P Jac Fredo Agastinose Ronickom School of Bio-medical Engineering Indian Institute of Technology (BHU) Varanasi Uttar Pradesh India

this study aims to develop an emotion detection system that involves the phasic decomposition of electrodermal activity (EDA) signals, segmentation, texture feature extraction, and machine learning algorithms. Initially, the EDA signals are obtained from the continuously annotated signals of emotions dataset and decomposed into tonic and phasic signals using convex optimization approach to electrodermal activity decomposition method. the phasic signals are divided into two halves, and each half is further divided into five non-overlapping segmented windows. Spectrograms are generated using the short-time Fourier transform for each segmented window of the phasic signal, and texture features are extracted using the gray-level co-occurrence matrix and gray-level run length matrix. We built machine learning models such as logistic regression, random forest (RF), and extreme gradient boosting using texture features extracted from the spectrogram of each segment of the first, and second half of phasic component of EDA signals. the results of our experiment using a process pipeline that included second-half phasic segments of EDA, spectrogram, texture features, and RF classifier yielded the highest classification results. We achieved an average accuracy, sensitivity, specificity, precision, and f1-score of 78.5%, 52%, 84.61%, 57.31%, and 53.36%, respectively. the results demonstrate the potential of this approach for emotion detection from EDA signals and can be useful in psychology, human-computer interaction, and health monitoring.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning Explainable Malware Characterization Using Knowledge Base Embedding

Learning Explainable Malware Characterization Using Knowledg...

引用

IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Daniel Trizna Peter Anthony Martin Homola Zekeri Adams Štefan Balogh Department of Applied Informatics Comenius University Bratislava Slovakia Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Malware detection is critical in cybersecurity, yet traditional signature-based methods often fail against emerging threats. Recent advancements suggest machine learning models can enhance malware classification accuracy; however, these models frequently lack explainability, limiting the insights they provide to cybersecurity experts. In this work, we introduce a novel approach using knowledge base embedding to classify malware while also delivering interpretable explanations. By learning vector representations for entities and relationships within a knowledge base, our method preserves semantic meaning, effectively distinguishing between malicious and benign software. Leveraging the RotatE model, our approach achieves 80% classification accuracy and generates meaningful, human-understandable explanations of model decisions, offering insights into malware behavior and improving transparency in detection and characterization.

关键词： Accuracy Knowledge based systems Semantics Knowledge graphs Syntactics Malware Vectors Logic computer security Testing

来源：评论

学校读者我要写书评

暂无评论

Optimizing LSTM, Bi-LSTM, and GRU Models with SSA for Daily Electricity Forecasting

Optimizing LSTM, Bi-LSTM, and GRU Models with SSA for Daily ...

引用

Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Xinxiong Tang School of Information and Electrical Engineering (Sussex Artificial Intelligence Institute) Zhejiang Gongshang University HangZhou China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Precise forecasting of electricity demand is vital for enhancing energy efficiency and ensuring grid stability. this research extends previous work by integrating Singular Spectrum Analysis (SSA) with enhanced deep learning architectures-specifically, dual-layered Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Longitudinal and Bidirectional LSTM networks have been enhanced through Dropout regularization and Multi-Head Attention mechanisms. the dataset utilized, sourced from the 2016 Electrical Engineering Cup, spans from January 2, 2012, to January 10, 2015, incorporating preprocessing like the exclusion of outliers and filling in missing values. the models show significant improvements in predictive accuracy: the SSA-enhanced BiLSTM model notably achieved an RMSE of 0.1050 and an R2 of 0.7085, a considerable improvement over the original BiLSTM model (RMSE 0.1395, R2 0.4898). Similarly, SSA-enhanced GRU and LSTM models demonstrated substantial performance gains over their original counterparts. modifications enhance the models' capability to effectively discern complex time series patterns, resulting in forecasts that are both more precise and dependable. the advancements underscore the potential of sophisticated model architectures in optimizing electricity demand management and advancing sustainable energy practices.

关键词： Deep learning Accuracy Electricity Time series analysis Bidirectional long short term memory Predictive models Data models Forecasting Spectral analysis Context modeling

来源：评论

学校读者我要写书评

暂无评论

Speech Emotion Recognition using machine learning 5

Speech Emotion Recognition using Machine Learning

引用

5th international conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Anusha, R. Subhashini, P. Jyothi, Darelli Harshitha, Potturi Sushma, Janumpally Mukesh, Namsamgari Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665415712

Speech emotion recognition is an act of predicting human's emotion through their speech along with the accuracy of prediction. It creates a better human computer interaction. though it is difficult to predict the emotion of a person as emotions are subjective and annotation audio is challenging, 'Speech Emotion Recognition(SER)' makes this possible [1]. this is the same theory which is used by animals like dogs, elephants and horses etc do to be able to understand human emotion [1]. there are various states to predict one's emotion, they are tone, pitch, expression, behavior etc. Among them, few states are considered to find the emotion through the speech. Few samples are used to train the classifiers to perform speech emotion recognition [2]. thhis research work considers the RAVDESS dataset (Ryerson Audio-Visual Database of Emotional Speech and Song dataset). Here, the three key features such as MFCC (Mel Frequency Cepstral Coefficients), Mel Spectrogram and chroma are extracted. © 2021 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of machine learning Algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier Models

A Comparative Study of Machine Learning Algorithms for Predi...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Ratnam Dodda C. Raghavendra Marpu Aashritha Hima Varshini Macherla Anusha Reddy Kuntla Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology CVR College of Engineering Hyderabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. this study investigates the effectiveness of three prominent machine learning algorithms Sequential, Random Forest, and Decision Tree Classifier in forecasting customer churn. Utilizing a dataset encompassing historical customer information such as demographics, transaction records, and interaction patterns, we preprocess the data by addressing missing values, encoding categorical variables, and scaling numerical features. Subsequently, each model is trained on the processed dataset, and their performance is assessed using accuracy as the evaluation metric. Our findings demonstrate promising predictive capabilities, with the Sequential model achieving an accuracy score of 94%, followed by Random Forest with 92%, and Decision Tree Classifier with 91%. Additionally, we conduct a comparative analysis of these models, focusing on interpretability, computational complexity, and robustness. While the Sequential model excels in predictive accuracy by capturing temporal dependencies in sequential data, Random Forest and Decision Tree Classifiers offer simplicity and interpretability, catering to scenarios where model transparency is paramount.

关键词： Analytical models Accuracy machine learning algorithms Computational modeling Predictive models Prediction algorithms Data models Numerical models Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

How are ML-Based Online Content Moderation Systems Actually Used? Studying Community Size, Local Activity, and Disparate Treatment 22

How are ML-Based Online Content Moderation Systems Actually ...

引用

5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Wang, Leijie Zhu, Haiyi Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450393522

machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging;2) predictive systems are used less in content areas where there are more local editing activities;3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms. © 2022 Owner/Author.

关键词： Online systems

来源：评论

学校读者我要写书评

暂无评论

Research on Style Transfer Method from human Facial Images to Peking Opera Facial Images

Research on Style Transfer Method from Human Facial Images t...

引用

Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Kaixin Zhang Lin Shi Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Face makeup transfer is designed to transfer the makeup features of one reference image to another face image to achieve the transformation of different makeup styles. Ordinary makeup has the characteristics of uniform color in a single area and simple color in the overall area, while opera facial makeup is mostly hand-painted images, rich in colors and textures. It is difficult for non-professional actors to draw. At present, there is no deep learning-based method to study makeup transfer between face and Peking Opera facial makeup images. In this paper, we proposes a facial makeup transfer method based on generative adversarial networks and affine transformation. We create a facial makeup dataset from Peking Opera facial makeup images, and obtains the corresponding mask dataset through a semantic segmentation feature extraction model for facial parsing. A local histogram matching strategy is used to calculate the pixel-level histogram loss, which can better preserve the color characteristics of the image. An auxiliary discriminant network is introduced to synthesize reference labels by performing affine transformation on Peking Opera facial makeup images and face images, which helps the generator network to better reduce the information loss caused by image transformation. through the research in this paper, it is expected to contribute to the research and application of this field, and provide new possibilities for the protection and inheritance of traditional culture and art.

关键词： learning systems Histograms Image transformation Image color analysis Semantic segmentation Generative adversarial networks Feature extraction Protection Faces Research and development

来源：评论

学校读者我要写书评

暂无评论

5th international conference on Financial Technology, ICFT 2024

5th International Conference on Financial Technology, ICFT 2...

引用

5th international conference on Financial Technology, ICFT 2024

ISBN: (纸本)9789819638109

the proceedings contain 17 papers. the special focus in this conference is on Financial Technology. the topics include: Improved machine learning Algorithms for Fraud Detection in Fintech Companies;A Cognitive Analysis of CEO Speeches and their Effects on Stock Markets;does E-Money Mediate the Effect of Fundamental Factors on the Stock Price Index?;Multi-Attribute Decision Making (MADM) Model Based on Bayesian Neural Network Classification with Comparative Scoring Quantification Method Can Solve Intangible Value Valuation Issues in Benefit-Cost Analysis (BCA);Exploring Vulnerabilities in Near Field Communication (NFC) Devices: A Comprehensive Investigation;a Novel Electronic Payment System Based on Zero-Knowledge Proof and Blockchain;A Comprehensive AI and Blockchain Framework for Detecting and Preventing Money Laundering in Bangladesh Financial Systems;detecting Persuasion in Financial Short Texts: A Computational Approach;the Impact of Colored Noise on the CIR Model;A Secure NFC Cardless Cash Withdrawal System;FinTech Digital Transformation: Generative AI, humanoid Robots, Metaverse, human-AI Collaboration, and Industry 5.0;Artificial Intelligence (AI) and Virtual Reality Convergence in Financial Services: the Power of Digital Twin Robo-Advisers;CITRONN: A Convolutional Neural Network for Crypto Image-Based Trading;can the Government’s Distribution of Consumption Vouchers Stimulate Adoption of Digital Payment Channels? Insights from a Social learning Perspective;financial Time Series Simulation with Transformer-Based Generative Models Under Continuous Conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TYJL-III computer Interlocking Equipment Training System Key Technology

TYJL-III Computer Interlocking Equipment Training System Key...

引用

5th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Liu, Yiming Xu, Ming Zhang, Junzheng Zhang, Fang China Academy of Railway Sciences Co. Ltd. Beijing100081 China

this paper carries out key technology research on TYJL-III computer interlocking equipment training system, aiming to improve the training efficiency and quality of railway signal maintenance personnel through augmented reality, natural language modelling and other new technologies. Firstly, the overall scheme of the system based on augmented reality is proposed, which solves the problems of unclear recognition of the equipment, lack of understanding of the internal structure, and incomplete mastery of the fault situation in the traditional practical training. Secondly, the 3D structure recovery and reconstruction technology of the equipment was studied to achieve high-precision modelling of the equipment through multi-view images and depth information, and to provide data support for position tracking. Further, a real-time tracking and registration technique for virtual and real environments is developed to optimise the multimodal sensor data fusion by combining deep learning and attention mechanism, which significantly improves the accuracy and stability of position estimation. In addition, the human-computer intelligent interaction technology based on natural language model is introduced to enhance the naturalness and real-time interaction in the practical training process. the experimental results show that the optimised system performs well in the position estimation accuracy, real-time performance and fault simulation test, and significantly improves the operation efficiency, accuracy and safety compared with the traditional method, while reducing the training cost. Despite the achievements, the system still needs to be further improved in terms of the comprehensiveness of fault simulation scenarios, compatibility with other railway signalling systems and user experience. © 2025 the Authors. Published by Elsevier B.V.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Model of Multiple CNNs with Attention Mechanism for Optimized Facial Expression Recognition

A Hybrid Model of Multiple CNNs with Attention Mechanism for...

引用

Artificial Intelligence and computer Engineering (ICAICE), international conference on

作者： Wanqiu Chen Yuting Sun Yihao Yang Jiaxu Yu Multimedia Mobile and Web Development Fuzhou University Fuzhou China Data Science and Big Data Technology Fuzhou University Fuzhou China Software Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Facial expression recognition is integral to enhancing human-machine interactions, applicable across security, advertising, and social media. Traditionally, these systems relied on handcrafted features or basic algorithms, but advancements in computational power and neural network architecture like Convolutional Neural Networks (CNNs) have shifted this trend. this study presents a hybrid model combining multiple CNNs-MobileNetV1, MobileNetV2, and ResNet50-with an attention mechanism to improve the robustness and accuracy of facial expression recognition. Utilizing the strengths of these networks, the model utilizes an ensemble approach where feature extractions are integrated and optimized using a random forest to determine the most effective weightings for final predictions. this method not only harnesses the power of deep learning to enhance feature detection across diverse conditions but also implements transfer learning (i.e. ImageNet weights) to address challenges posed by small or non-varied datasets, significantly advancing the abilities of facial expression recognition systems.

关键词： Training Attention mechanisms Accuracy Social networking (online) Face recognition Computational modeling Transfer learning Robustness Ensemble learning Convolutional neural networks

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56页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：