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

774 篇 会议
3 篇 期刊文献
2 册 图书

馆藏范围

779 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

596 篇 工学
- 560 篇 计算机科学与技术...
- 283 篇 电气工程
- 138 篇 软件工程
- 76 篇 信息与通信工程
- 32 篇 控制科学与工程
- 23 篇 生物工程
- 22 篇 生物医学工程（可授...
- 11 篇 机械工程
- 9 篇 交通运输工程
- 8 篇 安全科学与工程
- 7 篇 仪器科学与技术
- 6 篇 测绘科学与技术
- 6 篇 网络空间安全
- 5 篇 光学工程
- 5 篇 动力工程及工程热...
58 篇 管理学
- 35 篇 管理科学与工程(可...
- 29 篇 图书情报与档案管...
- 13 篇 工商管理
51 篇 理学
- 24 篇 生物学
- 22 篇 数学
- 12 篇 统计学（可授理学、...
- 11 篇 系统科学
- 9 篇 物理学
- 4 篇 化学
33 篇 医学
- 33 篇 临床医学
- 6 篇 基础医学(可授医学...
- 5 篇 药学(可授医学、理...
13 篇 教育学
- 13 篇 教育学
8 篇 法学
- 6 篇 社会学
5 篇 经济学
- 5 篇 应用经济学
2 篇 文学
2 篇 农学

主题

297 篇 machine learning
126 篇 training
87 篇 feature extracti...
66 篇 deep learning
45 篇 data models
43 篇 neural networks
39 篇 machine learning...
38 篇 computational mo...
37 篇 predictive model...
34 篇 support vector m...
30 篇 visualization
27 篇 training data
24 篇 data mining
23 篇 semantics
22 篇 three-dimensiona...
21 篇 cameras
20 篇 conferences
20 篇 computer vision
19 篇 image segmentati...
19 篇 optimization

机构

4 篇 ecole polytech f...
4 篇 univ edinburgh e...
4 篇 queen mary univ ...
4 篇 hunan univ coll ...
3 篇 carnegie mellon ...
3 篇 yunnan univ sch ...
3 篇 univ fed sao car...
3 篇 yonsei univ
3 篇 school of softwa...
3 篇 carnegie mellon ...
3 篇 sun yat sen univ...
3 篇 univ calgary hot...
3 篇 queens univ belf...
3 篇 idiap res inst m...
2 篇 johns hopkins un...
2 篇 school of comput...
2 篇 hong kong polyte...
2 篇 jsc russian rail...
2 篇 georgia inst tec...
2 篇 inesc tec porto

作者

5 篇 wang yingxu
4 篇 wang guojun
3 篇 li xin
3 篇 yang yun
3 篇 yun yang
3 篇 ghasemzadeh hass...
3 篇 gupta sandeep k....
3 篇 sandeep k. s. gu...
3 篇 banerjee ayan
3 篇 shayea ibraheem
3 篇 ibraheem shayea
3 篇 fleuret francois
3 篇 ayan banerjee
3 篇 nicoletti maria ...
3 篇 carvalho rommel ...
3 篇 ramin fallahzade...
3 篇 rokni seyed ali
3 篇 carvalho ricardo...
3 篇 hassan ghasemzad...
2 篇 pankaj dikshit

语言

747 篇 英文
32 篇 其他

检索条件"任意字段=16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017"

共 779 条记录，以下是101-110 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：

Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling 16

Self-supervised Dimensionality Reduction with Neural Network...

引用

16th international Joint conference on Computer Vision, Imaging and Computer Graphics theory and applications (VISIGRAPP) / 12th international conference on Information Visualization theory and applications (IVAPP)

作者： Espadoto, Mateus Hirata, Nina Telea, Alexandru Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Univ Groningen Dept Informat & Comp Sci Utrecht Netherlands Univ Groningen Johann Bernoulli Inst Groningen Netherlands

ISBN: (纸本)9789897584886

Dimensionality reduction (DR) is used to explore high-dimensional data in many applications. Deep learning techniques such as autoencoders have been used to provide fast, simple to use, and high-quality DR. However, such methods yield worse visual cluster separation than popular methods such as t-SNE and UMAP. We propose a deep learning DR method called Self-Supervised Network Projection (SSNP) which does DR based on pseudo-labels obtained from clustering. We show that SSNP produces better cluster separation than autoencoders, has out-of-sample, inverse mapping, and clustering capabilities, and is very fast and easy to use.

关键词： Dimensionality Reduction machine learning Neural Networks Autoencoders

来源：评论

学校读者我要写书评

暂无评论

Implementation of Collaborative Filtering in E-Commerce Recommender Systems: An Elementary Review

Implementation of Collaborative Filtering in E-Commerce Reco...

引用

international conference on Computational Intelligence and Communication Networks (CICN)

作者： Mamta B Savadatti Santhosh Krishna B V Department of ECE New Horizon College of Engineering (Affiliated to Visveswaraiah Technological University Belagavi) Bengaluru India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

this paper presents an elementary review of product recommendation systems which is growing rapidly with significant market value. this paper explores different types of recommendation systems (RS) focusing mainly on collaborative filtering (CF) and other filtering techniques like content-based filtering (CBF), demographic-based (DB), utility-based (UB), knowledge-based (KB), and hybrid systems (HS). It summarizes the algorithms supporting these systems and their applications across cross domains focusing on the challenges as well. this paper outlines the qualitative metrics for evaluation and assessing the efficiency of a recommendation system's performance and provides a stepby-step guide for constructing a machine learning based recommendation system. the key challenges in collaborative filtering like the cold start problem, problem of data sparsity, and scalability issues are also addressed. thus, the review aims to provide a detailed review of the current RS techniques, their implementation, and future growth in this field.

关键词： Measurement machine learning algorithms Reviews Filtering Collaborative filtering System performance Scalability Focusing machine learning Recommender systems

来源：评论

学校读者我要写书评

暂无评论

On the Evaluation of Sequential machine learning for Network Intrusion Detection 21

On the Evaluation of Sequential Machine Learning for Network...

引用

16th international conference on Availability, Reliability and Security (ARES)

作者： Corsini, Andrea Yang, Shanchieh Jay Apruzzese, Giovanni Univ Modena & Reggio Emilia Modena Italy Rochester Inst Technol Rochester NY 14623 USA Univ Liechtenstein Vaduz Liechtenstein

ISBN: (纸本)9781450390514

Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of network traffic flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios;moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a 'sequential' Long Short-Term Memory (LSTM) against a 'static' Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5% F1-score;while obtaining superior performance in the CTU13, with 95.7% F1-score against 91.5%. this paper thus paves the way to future applications of sequential learning models for NIDS.

关键词： Long Short Term Memory machine learning Network Intrusion Detection Cybersecurity Network Flows Deep learning

来源：评论

学校读者我要写书评

暂无评论

Using machine learning to Predict the Exact Resource Usage of Microservice Chains 23

Using Machine Learning to Predict the Exact Resource Usage o...

引用

Proceedings of the ieee/ACM 16th international conference on Utility and Cloud Computing

作者： Javid Taheri Arvid Gördén Auday Al-Dulaimy Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Mälardalen University Västerås Sweden

ISBN: (纸本)9798400702341

Cloud computing offers a wide range of services, but it comes with some challenges. One of these challenges is to predict the resource utilization of the nodes that run applications and services. this is especially relevant for container-based platforms such as Kubernetes. Predicting the resource utilization of a Kubernetes cluster can help optimize the performance, reliability, and cost-effectiveness of the platform. this paper focuses on how well different resources in a cluster can be predicted using machine learning techniques. the approach consists of three main steps: data collection and extraction, data pre-processing and analysis, and resource prediction. the data collection step involves stressing the system with a load-generator (called Locust) and collecting data from Locust and Kubernetes with the use of Prometheus. the data pre-processing and extraction step involves extracting relevant data and transforming it into a suitable format for the machine learning models. the final step involves applying different machine learning models to the data and evaluating their accuracy. the results illustrate that different machine learning techniques can predict resources accurately.

关键词： cloud computing resource management auto-scaling kubernetes microservice machine learning

来源：评论

学校读者我要写书评

暂无评论

Opening the Black Box: Explainable AI Insights into Obesity Prediction Using SMOTE-Balanced Stacking Ensembles

Opening the Black Box: Explainable AI Insights into Obesity ...

引用

international conference on Computational Intelligence and Communication Networks (CICN)

作者： Raj Gaurang Tiwari Vrince Vimal Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Obesity, caused by an energy imbalance, is a major public health issue. machine learning algorithms can be hampered by class imbalance in obesity dataset analysis. this research makes an attempt tobalance an unbalanced obesity dataset using the Synthetic Minority Over-sampling Technique (SMOTE) and uses this upgraded dataset for training Decision Trees, Support Vector machines, Logistic Regression, and sophisticated boosting techniques like XGBoost and AdaBoost. through thorough experimentation, the best three machine learning approaches are determined and a stacking ensemble model is created using these models. the proposed model achieves 92.5% accuracy on the test dataset. Explainable machine learning methods like SHAP is also used to improve forecasts' interpretability. these approaches reveal the feature contributions affecting the model's predictions, helping us understand obesity's causes. Findings show that SMOTE oversampling can alleviate class imbalance and that stacking ensemble models can improve predictive performance. Explainable AI also makes obesity research machine learning applications more transparent, enabling better public health interventions and policy decisions.

关键词： Training Support vector machines Obesity machine learning algorithms Accuracy Explainable AI Stacking Predictive models Boosting Public healthcare

来源：评论

学校读者我要写书评

暂无评论

Federated learning on Raspberry Pi 4: A Comprehensive Power Consumption Analysis 23

Federated Learning on Raspberry Pi 4: A Comprehensive Power ...

引用

Proceedings of the ieee/ACM 16th international conference on Utility and Cloud Computing

作者： Serena Sebbio Gabriele Morabito Alessio Catalfamo Lorenzo Carnevale Maria Fazio University of Messina Messina Italy Università degli Studi di Messina Messina Italy

ISBN: (纸本)9798400702341

Edge Computing, a rapidly evolving sector within information technology, redefines data processing and analysis by shifting it closer to the data source, away from centralized cloud servers. this paradigm promises substantial benefits for diverse applications. In the realm of Artificial Intelligence and machine learning, Federated learning emerges as a pioneering technique that harnesses Edge Computing for statistical model training. Federated learning presents numerous advantages over traditional centralized machine learning, including reduced latency, heightened privacy, and real-time data processing. Nonetheless, it introduces concerns regarding energy consumption, particularly for battery-powered Edge devices designed for remote or harsh environments. this study provides a comprehensive assessment of power consumption within the context of Federated learning operations. To achieve this, a Raspberry Pi 4 and an INA 219 current sensor are employed. Results show that, during communication operations, the power consumption of the target device increases from a minimum of 8% to a maximum of 32% with respect to its idle state. During the local training operations it increases respectively by up to 32% for a CNN model and by up to 40% for a RNN model.

关键词： edge computing

来源：评论

学校读者我要写书评

暂无评论

Outlier Detection in Wireless Sensor Networks Based on machine learning: Review

Outlier Detection in Wireless Sensor Networks Based on Machi...

引用

international conference on Computational Intelligence and Communication Networks (CICN)

作者： Burak Baykal Bilal Saoud Ibraheem Shayea Rzayeva Leila dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria dept. Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Due to their deployment in harsh situations with hundreds to thousands of nodes, wireless sensor networks, or WSNs, have proven vital in a variety of applications, from military monitoring to healthcare. But these networks have a lot of security issues, such outlier identification, which makes it difficult to find nodes that behave abnormally. With an emphasis on the benefits of Bayesian Networks in precisely identifying outlier nodes and calculating missing data values, this study explores machine learning-based outlier identification techniques. Furthermore, a viable answer in face of security issues is the proposal of the Bayesian classification method for assessing the conditional reliance of nodes in WSNs. this survey article also examines several machine learning algorithms used for outlier detection in WSN data, highlighting requirement for accurate anomaly identification without sacrificing data quality in the face of issues like energy efficiency and data integrity. the goal of the study is to give an overview of methods that provide better performance while using less network resources, with a focus on offline and online detection modes. Using expertise obtained from the development of Internet of things (IoT) systems, this paper underlines significance of anomaly detection in WSNs for industries such as industry, healthcare, and agriculture. It explores MLA-based methods, showing how well they work to precisely detect abnormalities and improve data integrity in Internet of things applications.

关键词： Wireless sensor networks machine learning algorithms Reviews Data integrity Medical services Energy efficiency Security Internet of things Anomaly detection Faces

来源：评论

学校读者我要写书评

暂无评论

Fox- Based Feature Selection Algorithm for Detection of Parkinson's Disease

Fox- Based Feature Selection Algorithm for Detection of Park...

引用

international conference on Computational Intelligence and Communication Networks (CICN)

作者： Neha Singh Sapna Sinha Laxman Singh Amity Institute of Information Technology Amity University Noida Uttar Pradesh India Dept. of Computer Science (AI- ML) KIET Group of Institutions Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

this research presents a FOX based feature selection method for detecting Parkinson's disease (PD). PD is a progressive neurological ailment that requires early detection for optimal treatment. the proposed method improves diagnostic accuracy by picking a small subset of discriminative features from high-dimensional data, resulting in better model interpretability and computational economy. Extensive research on several PD datasets reveals that the approach outperforms in both feature selection and disease detection accuracy. Comparative analyses using existing machine learning approaches demonstrate the efficacy of the FOX method. Furthermore, the selected features provide important insights into the underlying biomarkers associated with PD pathogenesis. the proposed FOX-based algorithm provides a viable option for early PD detection, with practical applications for improving patient outcomes through timely intervention and individualized treatment regimens.

关键词： machine learning algorithms Accuracy Parkinson's disease High dimensional data Computational modeling Soft sensors Pathogenesis Feature extraction Optimization Computational intelligence

来源：评论

学校读者我要写书评

暂无评论

Exploring SSL Enhancements through Triplet-Based Representation learning

Exploring SSL Enhancements Through Triplet-Based Representat...

引用

16th ieee international conference on Advanced Infocomm Technology, ICAIT 2024

作者： Hang, Ruan Maul, Tomas Chen, Yifan Chandesa, Tissa Liao, Iman Yi Chen, Zhiyuan School of Computer Science University of Nottingham Malaysia Selangor Semenyih43500 Malaysia

ISBN: (纸本)9798350391497

In the research area of machine learning, self-supervised learning (SSL) strategies have demonstrated substantial promise in harnessing the vast pools of unlabeled data. this study presents the Barlow Triplets method, an SSL strategy that builds on the Barlow Twins framework to address its limitations in complex and diverse datasets. Our method enhances representation learning by introducing an additional augmented view through a triplet-based mechanism, which refines feature interactions and balances redundancy reduction with feature invariance. We employed an adapted loss function and validated our approach through experiments on the CIFAR10 dataset using a KNN classifier. Results show that Barlow Triplets surpasses traditional SSL methods, including Barlow Twins predecessor, across accuracy, precision, recall, and F1-scores, demonstrating its potential for practical applications and scalability. We compare two key formulations of Barlow Triplets, one of which is reported here for the first time. Our method requires fewer computational resources while achieving commendable performance. this research aims to contribute to SSL by introducing a method that seeks to optimize performance while potentially maintaining computational efficiency. © 2024 ieee.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Approach to MBTI Personality Classification: Insights from machine learning Models

A Data-Driven Approach to MBTI Personality Classification: I...

引用

international conference on Computational Intelligence and Communication Networks (CICN)

作者： Fares A. Dael Symbat Maidanova Ibraheem Shayea Gulnara Abitova Aigul Seraly Management Information Systems İzmir Bakırçay University İzmir Turkey Computational and Data Science Astana IT University Astana Kazakhstan Electronic and Communication Engineering Istanbul Technical University Istanbul Turkey Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

the Myers-Briggs Type Indicator (MBTI) is one of the most widely recognized psychological tools for categorizing personality types, often used in various professional and personal development contexts. this study presents a data-driven approach to MBTI personality classification using a range of machine learning models. By leveraging a dataset comprising user responses and linguistic patterns, we aim to predict the MBTI personality types with greater accuracy and reliability. Various models, including Support Vector machines (SVM), Random Forests, and Gradient Boosting machines, were evaluated to determine their effectiveness in classifying the 16 MBTI types. Our findings reveal that machine learning models can significantly enhance the predictive accuracy of MBTI classification compared to traditional methods. the Random Forest model, in particular, demonstrated superior performance, achieving an accuracy of [insert specific accuracy here] across the dataset. We also explore the importance of feature selection and data preprocessing in improving model outcomes, highlighting key features that contribute to personality type prediction. the results of this study suggest that a data-driven approach, combined with machine learning techniques, provides a promising avenue for more nuanced and accurate MBTI personality assessments. this approach not only enhances our understanding of personality prediction but also offers practical implications for applications in psychology, human resources, and personal development.

关键词： Support vector machines Accuracy Computational modeling Semantics Psychology Predictive models Linguistics Natural language processing Reliability Random forests

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78页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：