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

7,033 篇 会议
48 篇 期刊文献
13 册 图书

馆藏范围

7,094 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,888 篇 工学
- 4,081 篇 计算机科学与技术...
- 2,508 篇 软件工程
- 1,041 篇 信息与通信工程
- 787 篇 控制科学与工程
- 662 篇 生物工程
- 632 篇 电气工程
- 423 篇 生物医学工程（可授...
- 284 篇 交通运输工程
- 280 篇 机械工程
- 225 篇 仪器科学与技术
- 224 篇 安全科学与工程
- 205 篇 建筑学
- 195 篇 动力工程及工程热...
- 183 篇 光学工程
- 166 篇 化学工程与技术
- 153 篇 土木工程
1,886 篇 理学
- 1,057 篇 数学
- 717 篇 生物学
- 440 篇 统计学（可授理学、...
- 280 篇 物理学
- 252 篇 系统科学
- 140 篇 化学
1,142 篇 管理学
- 651 篇 管理科学与工程(可...
- 577 篇 图书情报与档案管...
- 313 篇 工商管理
405 篇 医学
- 351 篇 临床医学
- 295 篇 基础医学(可授医学...
- 188 篇 药学(可授医学、理...
242 篇 教育学
- 241 篇 教育学
209 篇 法学
- 195 篇 社会学
119 篇 农学
110 篇 经济学
50 篇 文学
7 篇 艺术学
5 篇 军事学

主题

553 篇 machine learning
452 篇 deep learning
264 篇 training
253 篇 data mining
234 篇 data models
219 篇 feature extracti...
214 篇 accuracy
183 篇 support vector m...
178 篇 predictive model...
138 篇 neural networks
136 篇 machine learning...
130 篇 computational mo...
120 篇 reinforcement le...
108 篇 convolutional ne...
102 篇 real-time system...
92 篇 forecasting
90 篇 learning systems
87 篇 transfer learnin...
86 篇 big data
78 篇 internet of thin...

机构

69 篇 chitkara univers...
17 篇 school of electr...
15 篇 computer science...
14 篇 harbin engineeri...
13 篇 graphic era hill...
12 篇 building civil a...
12 篇 uttaranchal inst...
12 篇 computer science...
11 篇 university of ch...
10 篇 school of electr...
10 篇 school of comput...
10 篇 graphic era deem...
10 篇 school of electr...
10 篇 yonsei univ dept...
9 篇 wuhan university...
9 篇 univ chinese aca...
8 篇 school of remote...
8 篇 school of inform...
7 篇 university of sa...
7 篇 microsoft resear...

作者

17 篇 kukreja vinay
15 篇 vinay kukreja
13 篇 yin hujun
12 篇 liu yang
12 篇 corchado emilio
11 篇 mehta shiva
10 篇 shiva mehta
10 篇 cho sung-bae
9 篇 notsu akira
9 篇 silva catarina
9 篇 alhajj reda
7 篇 ksieniewicz pawe...
7 篇 hammer barbara
7 篇 zhang byoung-tak
7 篇 wozniak michal
7 篇 hussain abir
7 篇 soubra hassan
7 篇 huang de-shuang
7 篇 camacho david
6 篇 tang ke

语言

6,912 篇 英文
171 篇 其他
15 篇 中文
1 篇 西班牙文
1 篇 俄文

检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7094 条记录，以下是161-170 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：

Hybrid Physics-Inspired Machine learning Framework for Predictive Maintenance of Forklift Chains: Leveraging Sensor data Characteristics 11

Hybrid Physics-Inspired Machine Learning Framework for Predi...

引用

11th international conference on Control, Mechatronics and Automation (ICCMA)

作者： Akcatepe, Osman Moeckel, Michael Univ Appl Sci Aschaffenburg Lab Hybrid Modelling Aschaffenburg Germany

ISBN: (纸本)9798350315684

Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. the discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.

关键词： Physics-inspired machine learning Feature engineering data-driven modeling Forklifts Predictive Maintenance Remaining Useful Life (RUL) Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

DiverSim: A Customizable Simulation Tool to Generate Diverse Vulnerable Road User datasets 11

DiverSim: A Customizable Simulation Tool to Generate Diverse...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Ander Iñiguez de Gordoa, Jon Hormaetxea, Martín Nieto, Marcos Vélez, Gorka Mujika, Andoni Mikeletegi 57 Donostia-San Sebastián20009 Spain Donostia-San Sebastián Spain

ISBN: (纸本)9789897587450

this work presents DiverSim, a highly customizable simulation tool designed for the generation of diverse synthetic datasets of vulnerable road users to address key challenges in pedestrian detection for Advanced Driver Assistance Systems (ADAS). Although recent Deep learning models have advanced pedestrian detection, their performance still depends on the diversity and inclusivity of training data. DiverSim, developed on Unreal Engine 5, allows users to control various environmental conditions and pedestrian characteristics, including age, gender, ethnicity and mobility aids. the tool features a highly customizable virtual fisheye camera and a Python API for easy configuration and automated data annotation in the ASAM OpenLABEL format. Our experiments demonstrate DiverSim’s capability to evaluate pedestrian detection models across diverse user profiles, revealing potential biases in current state-of-the-art models. By making both the simulator and Python API open source, DiverSim aims to contribute to fairer and more effective AI solutions in the field of transportation safety. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Self-supervised Task-agnostic Embedding for EEG Signals 11

A Self-supervised Task-agnostic Embedding for EEG Signals

引用

11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Partovi, Andi Burkitt, Anthony N. Grayden, David Univ Melbourne Dept Biomed Engn Melbourne Vic Australia

ISBN: (纸本)9781665462921

Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may be limited by the amount of labelled training data available for supervised machine learning algorithms. the success of deep learning algorithms in other computer science areas has not reached the field of BCI decoding due to this lack of abundant labelled data. We use a novel deep learning architecture trained in a selfsupervised manner to learn a common vector representation (embedding) of EEG signals that can be used in different BCI tasks. the vector representation is trained using EEG recordings without using any task labels. We validate our embedder using two separate BCI tasks: seizure detection and motor imagery, and assess its usefulness through distance similarity metrics in a clustering approach. the derived embeddings were successful in distinguishing binary classes in both tasks.

关键词： Self-supervised learning Embedding Transfer learning Representation learning

来源：评论

学校读者我要写书评

暂无评论

Pedestrian Detection Using Hybrid DWT and HOG in Multiple Scenarios 11

Pedestrian Detection Using Hybrid DWT and HOG in Multiple Sc...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chhabra, Rishu Khullar, Vikas Singh, Saravjeet Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

With the advent of smart transportation, technology plays a key role in improving the safety of people on roads. Pedestrian detection techniques have various applications in driving assistance systems, intelligent vehicles, surveillance systems, etc. the driving assistance systems and intelligent vehicle technology aim to improve the safety of pedestrians by preventing them from collisions with vehicles on the road. Different pedestrian detection machine learning and deep learning techniques have been proposed in the literature. the key to the accuracy of these techniques is the image preprocessing feature extraction phase. In this paper, we have considered three different datasets representing three different scenarios and applied Discrete Wavelet Transform (DWT), Histogram of Oriented Gradients (HOG) to extract features and hybrid DWT -HOG that combines the features of DWT and HOG for pedestrian detection. An experimental analysis has been done using these feature extraction techniques on the three datasets and it is evident that the maximum accuracy is generated by using Logistic Regression and MLP with HOG features and hybrid DWT-HOG features with input images in extracted/ pixelated form. For most of the machine learning algorithms, the hybrid technique with DWT-HOG features performs better than HOG and DWT feature extraction techniques. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Cloud Based Predictive Maintenance System 11

Cloud Based Predictive Maintenance System

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Saini, Nikesh Yadav, Anup Lal Rahman, Ataur Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350350357

Maintenance is a critical aspect of modern industrial operations, as it ensures the reliability and longevity of equipment while minimising unplanned downtime. Traditional, schedule-based maintenance approaches are often inefficient and costly and fail to harness the full potential of data-driven insights. In response, cloud-based predictive maintenance systems have emerged as a transformative solution, leveraging the power of the cloud, data analytics, and machine learning to enable proactive maintenance strategies. this research paper explores the architecture, advantages, challenges, and potential applications of cloud-based predictive maintenance systems. We delve into the underlying technology, the role of the cloud in data storage and processing, and the use of machine learning algorithms for predictive analytics. We discuss how real-time monitoring and analysis of sensor data can help anticipate equipment failures, reduce operational costs, and increase overall efficiency. © 2024 IEEE.

关键词： data Analytics

来源：评论

学校读者我要写书评

暂无评论

Integrating Photonics and Fiber Bragg Grating Sensors with Deep Reinforcement learning for Advanced Robotic Systems 18

Integrating Photonics and Fiber Bragg Grating Sensors with D...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jaideep, R. Manjula, C. Seema, V. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Mechatronics Engineering Bangalore India

ISBN: (纸本)9789380544519

this research introduces the Photonics-Enhanced Embedded Robotic Intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DRL) for advanced robotics applications. Addressing the limitations of current AI models in dynamic and complex environments, PEERIM leverages the rapid data transmission capabilities of photonics to process high-frequency sensor data, enabling real-time decision-making and enhanced automation. the methodology encompasses the development and integration of a variant of the Proximal Policy Optimization (PPO) algorithm, tailored to manage the continuous data stream and execute precise, adaptive control. the proposed model has been empirically evaluated, demonstrating a significant improvement in handling real-time sensor data with an average reward of 99.02 and a low average loss of 0.099, indicating robust performance and learning stability. these findings suggest that PEERIM provides a substantial advantage over existing AI-driven robotic systems, offering a scalable solution for a variety of challenging applications. the study's contributions lay the groundwork for future advancements in autonomous systems, promising a new era of precision and reliability in robotics. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Autonomous Systems Deep Reinforcement learning Fiber Bragg Grating Sensors Photonics-Enhanced Robotics Proximal Policy Optimization Real-Time Decision Making

来源：评论

学校读者我要写书评

暂无评论

Accuracy Prediction for Music therapy data using Kappa Model 18

Accuracy Prediction for Music Therapy Data using Kappa Model

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. University of Mumbai Terna Engineering College Department of Computer Engineering Mumbai India Vidyavardhini's College of Engg. & Tech Mumbai India

ISBN: (纸本)9789380544519

this research explores the therapeutic potential of music, specifically Indian classical ragas, for individuals suffering from diabetes, hypertension, and thyroid disorders. the proposed system uses a LGBM classifier model which analyses a dataset comprising 417 data entries of patients. Kappa model is used to check the performance of a classification model. Findings gave good results that classical ragas have measurable effects on sugar levels, hypertension, and thyroid function. Machine learning model plays an important role to show the improvements in health. Finally, Kappa score of 0.7319 indicates that model is substantially moderate which is a good level of agreement. And Machine learning model also gives accuracy of 86.90%. thus, Music therapy acts an alternative medicine which is a unique and novel approach. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cohen's Kappa Kappa Coefficient Kappa Statistic Machine learning Music therapy Predictive Analytics

来源：评论

学校读者我要写书评

暂无评论

Advanced Neural Network Approaches for Distinguishing Real from Synthetic in GAN-generated data Authenticity Challenges 18

Advanced Neural Network Approaches for Distinguishing Real f...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jayaraj, N. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9789380544519

Amid the transformative advancements of Generative Adversarial Networks (GANs) in machine learning, a pertinent challenge arises: discerning real instances from synthetic ones. this research introduces a novel neural network model meticulously tailored to differentiate between genuine tasks and those artfully crafted by GANs. the paper elaborates on the unique architectural design and optimization techniques employed, offering a comprehensive insight into the model's development and testing phases. Empirical evaluations reveal an unparalleled accuracy rate, underscoring the model's practicality and efficacy. Notwithstanding its high precision and recall balance, the study identifies potential areas of refinement, ensuring its adaptability to future GAN sophistications. As the realm of artificial data generation continues to evolve, this research stands as a beacon, advancing the understanding and tools essential for maintaining authenticity and integrity in data-driven domains. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： data Authenticity Generative Adversarial Networks Neural Network Precision Recall Synthetic data Detection

来源：评论

学校读者我要写书评

暂无评论

Machine learning in Particle Physics 11th

Machine Learning in Particle Physics

引用

11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Purohit, Milind, V Univ South Carolina Dept Phys & Astron Columbia SC 29208 USA

ISBN: (纸本)9783031585012;9783031585029

this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.

关键词： particle physics machine learning statistics simulation optimal transport physics

来源：评论

学校读者我要写书评

暂无评论

the Performance Analysis of Federated learning Methods for IoT with Big data 18

The Performance Analysis of Federated Learning Methods for I...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Govindaram, Anitha Jegatheesan, A. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamilnadu India

ISBN: (纸本)9789380544519

ML that creates a global framework by gathering knowledge from a number of different dispersed edge clients. FL allows on-device training, keeps client information in private, and updates the frameworks. FL approaches presume computational capabilities at each edge-device/client, which may not always be the case. Due to its inherent distributed architecture, FL has the features that make it a good fit for IoT networks. though, there are a few obstacles that are specific to FL, the maximum significant is training across significantly heterogeneous data sets on IoT devices. Numerous new studies contended to minimize consequences of diversity but obviously, the efficacy of potential resolutions has concluded researched or enumerated. this is because the homogeneity of devices and designs in the complicated Internet of things (IoT) networks ultimately affects the FL learning process and produces conventional FL inappropriate to be straightforwardly organized. this research aimed to provide an introduction to FL, as well as a thorough examination of the problem statements and developing issues, especially those that arise when attempting to implement FL in heterogeneous IoT contexts using ML and DL techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Aggregation Deep learning Federated learning heterogeneity 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500页 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：