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

809 篇 会议
7 册 图书
1 篇 期刊文献

馆藏范围

817 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

491 篇 工学
- 443 篇 计算机科学与技术...
- 327 篇 软件工程
- 59 篇 信息与通信工程
- 58 篇 控制科学与工程
- 56 篇 生物医学工程（可授...
- 44 篇 生物工程
- 38 篇 电气工程
- 29 篇 安全科学与工程
- 28 篇 机械工程
- 21 篇 交通运输工程
- 17 篇 仪器科学与技术
- 16 篇 光学工程
- 16 篇 土木工程
- 16 篇 农业工程
177 篇 理学
- 101 篇 数学
- 55 篇 生物学
- 46 篇 统计学（可授理学、...
- 29 篇 系统科学
- 23 篇 物理学
96 篇 管理学
- 68 篇 管理科学与工程(可...
- 45 篇 工商管理
- 28 篇 图书情报与档案管...
71 篇 医学
- 56 篇 基础医学(可授医学...
- 52 篇 临床医学
- 26 篇 公共卫生与预防医...
- 23 篇 药学(可授医学、理...
24 篇 农学
- 24 篇 作物学
20 篇 法学
- 17 篇 社会学
19 篇 经济学
- 19 篇 应用经济学
15 篇 教育学
- 15 篇 教育学
3 篇 文学

主题

149 篇 machine learning
75 篇 accuracy
72 篇 machine learning...
62 篇 predictive model...
58 篇 optimization
53 篇 training
50 篇 data models
46 篇 deep learning
46 篇 adversarial mach...
42 篇 real-time system...
40 篇 computational mo...
35 篇 support vector m...
31 篇 feature extracti...
27 篇 random forests
26 篇 analytical model...
23 篇 prediction algor...
23 篇 federated learni...
21 篇 internet of thin...
21 篇 adaptation model...
19 篇 convolutional ne...

机构

9 篇 adhiparasakthi e...
9 篇 department of el...
6 篇 department of te...
6 篇 adhiparasakthi e...
5 篇 sri eshwar colle...
4 篇 department of el...
4 篇 school of comput...
4 篇 school of comput...
4 篇 univ granada dat...
4 篇 young researcher...
4 篇 department of co...
3 篇 vel tech multite...
3 篇 computer science...
3 篇 rmk college of e...
3 篇 department of el...
3 篇 st. joseph's col...
3 篇 laboratory for b...
3 篇 department of co...
3 篇 school of comput...
3 篇 easwari engineer...

作者

7 篇 gorriz juan m.
6 篇 teddy mantoro
4 篇 aghalya s.
4 篇 arunagiri bhuvan...
4 篇 bhuvaneswari aru...
4 篇 ramirez javier
4 篇 s. aghalya
3 篇 shoeibi afshin
3 篇 salcedo-sanz san...
3 篇 arco juan e.
3 篇 perez-aracil jor...
3 篇 ranganathan chit...
3 篇 chitra sabapathy...
3 篇 jehan c.
3 篇 ortiz a.
3 篇 granitzer michae...
3 篇 iskandar
3 篇 media anugerah a...
3 篇 c. jehan
3 篇 mittal usha

语言

774 篇 英文
42 篇 其他
10 篇 中文

检索条件"任意字段=10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024"

共 817 条记录，以下是1-10 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：

10th international conference on machine learning, optimization, and data science, lod 2024

10th International Conference on Machine Learning, Optimizat...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

ISBN: (纸本)9783031824807

the proceedings contain 79 papers. the special focus in this conference is on machine learning, optimization, and data science. the topics include: Assessing the Impact of Government Policies on Covid-19 Spread: A machine learning Approach;optimal Design and Implementation of an Open-Source Emulation Platform for User-Centric Shared E-mobility Services;protein Sequence Generation Using Denoising Probabilistic Diffusion Model;individual Fairness in Generative Text Models;Refined Direct Preference optimization with Synthetic data for Behavioral Alignment of LLMs;artificial Intelligence and Cyber Security;exploring Digital Health Trends in the Headlines via Knowledge Graph Analysis;robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading;optimal Risk Scores for Continuous Predictors;post-Treatment Gait Prediction After Botulinum Toxin Injections Using Deep learning with an Attention Mechanism;leveraging Graph Networks and Generative Adversarial Networks for Controllable Trajectory Predictions;nearest Neighbors Counterfactuals;an Attention-Based Representation Distillation Baseline for Multi-label Continual learning;pattern Detection in Abnormal District Heating data;harnessing Graph Neural Networks for Pattern Classification in Heterogeneous Event Graphs;learn to Create Neighborhoods in Real-World Vehicle Routing Problem;PointerKex: A Pointer-Based SSH Key Extraction Method;addressing the Permutation Flowshop Scheduling with Grey Wolf Optimizer;MCGRAN: Multi-conditional Graph Generation for Neural Architecture Search;generative Reward machine for Reinforcement learning for Physical Internet Distribution Centre;Between Accurate Prediction and Poor Decision Making: the AI/ML Gap;cross-Metapath Based Hashing for Recommendation Systems;beyond Iterative Tuning: Zero-Shot Hyperparameter Optimisation for Decision Trees;Augmented Human-AI Forecasting for Ship Refit Project Scheduling: A Predict-then-Optimize Approach;evaluation of Document Dedupli

关键词：

来源：评论

学校读者我要写书评

暂无评论

10th international conference on machine learning, optimization, and data science, lod 2024

10th International Conference on Machine Learning, Optimizat...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

ISBN: (纸本)9783031824869

关键词：

来源：评论

学校读者我要写书评

暂无评论

10th international conference on machine learning, optimization, and data science, lod 2024

10th International Conference on Machine Learning, Optimizat...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

ISBN: (纸本)9783031824838

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting University Dropout Rates Using machine learning: UniCt Case 10th

Predicting University Dropout Rates Using Machine Learning: ...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Vincenzo, Miracula Francesco, Mazzeo Rinaldi Giovanni, Giuffrida Department of Physics and Astronomy University of Catania Catania Italy Department of Political and Social Science University of Catania Catania Italy

ISBN: (纸本)9783031824838

University dropout rates remain a significant concern for educational institutions, impacting both student outcomes and institutional performance. this study aims to develop a predictive model to identify students at risk of dropping out using various features extracted from historical data. the dataset used in this study includes data from 42,000 students enrolled in undergraduate programs at the University of Catania, covering a period of five academic years and spanning various university departments. We train and compare the performance of four different machine learning models (Logistic Regression: LR, Random Forest: RF, eXtreme Gradient Boosting: XGB, and Neural Network: NN). the XGBoost model achieved the highest accuracy at 97%, followed by the Random Forest with 94%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Inference of Abstraction for Human-Like Probabilistic Reasoning 10th

Inference of Abstraction for Human-Like Probabilistic Reas...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Kido, Hiroyuki Cardiff University CardiffCF10 3AT United Kingdom

ISBN: (纸本)9783031824869

We propose a conceptually new approach to unify symbolic reasoning and probabilistic reasoning, both core aspects of human higher-order cognitive function. the key underlying idea is inference of abstraction, or selective ignorance, in which abstract symbolic knowledge is derived directly from concrete data via models in formal logic. We compare this simple idea with the semantics of Bayesian networks and show that our idea does not need the assumption of independence or conditional independence of symbolic knowledge, an unrealistic but necessary assumption of Bayesian networks and their variants. We also show that, in the machine learning context, the idea can be seen as an all-neighbour method, which is a further generalisation of an all-nearest neighbour method, itself a generalisation of the k-nearest neighbour method. We use the MNIST dataset to empirically justify the theoretical advantage of the idea. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Government Policies on Covid-19 Spread: A machine learning Approach 10th

Assessing the Impact of Government Policies on Covid-19 S...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Khorashadizadeh, Hanieh Groppe, Jinghua Lückert, Jessica Tiwari, Sanju Groppe, Sven University of Lübeck Lübeck Germany Sharda University UP Greater Noida India

ISBN: (纸本)9783031824807

During the global Covid-19 pandemic, governments implemented various policies, including school closures, workplace closures, and gathering restrictions, to combat the virus and mitigate its severity. However, the effectiveness of these policies varies, and each policy can also negatively impact daily life and the economy. therefore, it is crucial to study the effectiveness of Covid-19 policies and identify those that are not so much efficient. In this study, we collected data on various policies, alongside daily new Covid-19 cases and deaths, from 183 countries worldwide. We then applied different machine learning approaches to analyze this data, aiming to determine the efficiency of individual policies and combinations of policies. Additionally, we clustered countries based on their Covid-19 policies. the results of this research can serve as a guideline for policymakers to choose optimal policies in future pandemics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Explainable machine learning for Enhanced Ship Performance Monitoring 10th

Exploring Explainable Machine Learning for Enhanced Ship Pe...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Barhrhouj, Ayah Ananou, Bouchra Ouladsine, Mustapha Marseille France

ISBN: (纸本)9783031824838

Ship performance monitoring is integral to maritime operations, encompassing propulsion, safety, and environmental considerations. Accurate prediction of ship fuel consumption is vital for optimizing efficiency, reducing costs, and achieving sustainability objectives. In this context, machine learning (ML) techniques have emerged as powerful tools for analyzing vast amounts of data collected from ships onboard sensors. However, the black-box nature of conventional ML models presents challenges in understanding the rationale behind their decisions and predictions, hindering their adoption in critical maritime applications. this paper explores the integration of explainable machine learning (XAI) techniques into ship performance monitoring systems to enhance transparency, interpretability, and trustworthiness. We conduct a comprehensive review of XAI methods suitable for maritime applications, considering their ability to provide insights into ship operational data while maintaining computational efficiency. Furthermore, we present a case study demonstrating the application of XAI techniques to analyze historical ship performance data and identify critical factors influencing fuel efficiency. Our findings illustrate the effectiveness of XAI in uncovering actionable insights, enabling ship operators to make informed decisions to optimize performance, reduce operational costs, and mitigate risks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Deep learning Models for EEG Neural Decoding 10th

Exploring Deep Learning Models for EEG Neural Decoding

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Dixen, Laurits Heinrich, Stefan Burelli, Paolo IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031824869

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. the thINGS initiative [11] provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models’ performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmark Deep learning EEG Neural Decoding thINGS

来源：评论

学校读者我要写书评

暂无评论

Pattern Detection in Abnormal District Heating data 10th

Pattern Detection in Abnormal District Heating Data

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Mbiydzenyuy, Gideon Sundell, Håkan University of Boras Boras501 90 Sweden

ISBN: (纸本)9783031824807

data generated by industrial systems such as District Heating (DH) often lack meaningful labels for supervised machine learning (ML) methods in anomaly detection. Consequently, unsupervised and semi-supervised ML methods are widely used. these methods frequently uncover numerous anomalies, necessitating labor-intensive post-processing. this paper proposes an algorithm to detect topK anomaly instances with similar patterns (energy signatures) to known anomalies, and to identify clusters of similar anomalies using hierarchical clustering. Similarities between anomaly instances are computed using Dynamic Time Warping and Matrix Profiles. Generative Adversarial Networks (GANs) are employed to augment small anomaly datasets. Results demonstrate the effectiveness of the proposed algorithm in reducing the manual effort required for post-processing anomalies in a DH dataset. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Solving Two-Stage Stochastic Programming Problems via machine learning 10th

Solving Two-Stage Stochastic Programming Problems via Machi...

引用

10th international conference on machine learning, optimization, and data science, lod 2024

作者： Chou, Xiaochen Messina, Enza W. Wallace, Stein Department of Informatics Systems and Communication University of Milano-Bicocca Viale Sarca 336 Milan20125 Italy Department of Business and Management Science NHH Norwegian School of Economics Helleveien 30 Bergen5045 Norway

ISBN: (纸本)9783031824807

Decision-making under uncertainty addresses real-world problems that are often hard to solve, particularly when involving both discrete and binary decision variables. In two-stage stochastic programming problems, decisions are made before uncertain data is revealed, with adjustments optimized in a recourse problem after the revelation. the ideal solution is robust and resilient across all possible future scenarios. Classic solving techniques rely on selected sets of scenarios to approximate the problem. Accuracy improves with the inclusion of more scenarios and concurrently, the complexity of the problem increases, frequently resulting in instances that are computationally intractable. With the rapid advancement in machine learning technologies, our study aims to train neural networks as surrogate models for the recourse problem. these networks aim to predict solution quality across diverse scenarios, thereby reducing the complexity of the original optimization model. through empirical analysis, we present preliminary findings demonstrating the application of this predictive capacity and examine the practical challenges encountered. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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82页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：