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

20,098 篇 会议
103 篇 期刊文献
49 册 图书

馆藏范围

20,250 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

14,204 篇 工学
- 11,476 篇 计算机科学与技术...
- 8,852 篇 软件工程
- 3,230 篇 信息与通信工程
- 2,497 篇 生物工程
- 2,262 篇 控制科学与工程
- 1,797 篇 电气工程
- 1,487 篇 生物医学工程（可授...
- 1,045 篇 机械工程
- 1,028 篇 光学工程
- 1,027 篇 电子科学与技术（可...
- 797 篇 安全科学与工程
- 778 篇 动力工程及工程热...
- 761 篇 交通运输工程
- 681 篇 仪器科学与技术
- 660 篇 化学工程与技术
- 484 篇 材料科学与工程（可...
6,786 篇 理学
- 3,489 篇 数学
- 2,659 篇 生物学
- 1,498 篇 统计学（可授理学、...
- 1,366 篇 物理学
- 844 篇 系统科学
- 738 篇 化学
4,237 篇 管理学
- 2,539 篇 管理科学与工程(可...
- 2,023 篇 图书情报与档案管...
- 1,281 篇 工商管理
1,434 篇 医学
- 1,287 篇 临床医学
- 1,240 篇 基础医学(可授医学...
- 764 篇 药学(可授医学、理...
- 631 篇 公共卫生与预防医...
693 篇 教育学
- 667 篇 教育学
560 篇 法学
488 篇 农学
450 篇 经济学
43 篇 军事学
27 篇 文学
8 篇 艺术学
1 篇 哲学

主题

1,326 篇 deep learning
1,122 篇 machine learning
832 篇 accuracy
558 篇 data mining
540 篇 support vector m...
526 篇 feature extracti...
430 篇 training
410 篇 data models
406 篇 convolutional ne...
398 篇 predictive model...
379 篇 internet of thin...
363 篇 real-time system...
328 篇 computational mo...
324 篇 neural networks
314 篇 big data
233 篇 reinforcement le...
200 篇 forecasting
180 篇 object detection
175 篇 machine learning...
170 篇 artificial intel...

机构

227 篇 chitkara univers...
64 篇 centre of interd...
54 篇 maharishi school...
43 篇 maharishi school...
34 篇 college of techn...
31 篇 pune
28 篇 school of comput...
28 篇 chitkara univers...
28 篇 the islamic univ...
28 篇 school of comput...
26 篇 karnataka bangal...
24 篇 computer science...
24 篇 computer science...
24 篇 uttaranchal inst...
23 篇 department of ar...
22 篇 graphic era deem...
22 篇 school of inform...
22 篇 department of in...
22 篇 national univers...
20 篇 department of co...

作者

23 篇 kukreja vinay
22 篇 gill kanwarparta...
21 篇 wang lei
20 篇 shiva mehta
19 篇 liu yang
18 篇 wang jing
16 篇 singh jagendra
15 篇 zhang tao
15 篇 malik bader alaz...
15 篇 joshi kapil
14 篇 yang yang
13 篇 zhang lei
13 篇 li yang
12 篇 meena belwal
12 篇 novais paulo
12 篇 li li
12 篇 liu ying
12 篇 zhang bo
11 篇 babu tina
11 篇 liu xin

语言

17,813 篇 英文
2,418 篇 其他
101 篇 中文
3 篇 土耳其文
2 篇 西班牙文
1 篇 俄文

检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20250 条记录，以下是301-310 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：

Academic Atlas: A Platform for Rich Resource Sharing for Academics 3

Academic Atlas: A Platform for Rich Resource Sharing for Aca...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Nazeer, Shaik Dadi, Jai Chiranjeeva Bharathi Mohan, G. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515911

Academic Atlas is a tool developed to facilitate access to academic materials such as previous year's question papers, capstone projects, and research papers. Of major concerns for the students would be that they cannot access the necessary materials and resources needed for academic development and to prepare for upcoming examinations. This application is designed and built upon the MERN stack: Mongo DB, Express JS, React JS, and Node JS, resulting in the total scalability and efficiency of the platform. Controlled contributions feature guaranteed data integrity and reliability since only valid holders of institutional email IDs are allowed to submit new materials. The security of this application and platform lies in the hashing of passwords stored in the database. Using pre-trained T5 (Text-to-Text Transfer Transformer) model. The arxiv dataset consists of large number of summarized articles, so it will use this dataset for both the model and testing purposes to verify how reasonable its obtained metric scores are as regards ROUGE. So, Academic Atlas seeks to revolutionize the academic experience with a well- organized, accessible, and even interactive learning environment. © 2025 IEEE.

关键词： data integrity

来源：评论

学校读者我要写书评

暂无评论

Machine learning Approaches for Insect Species Recognition using Pyramid Vision Transformer 3

Machine Learning Approaches for Insect Species Recognition u...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Soundarya, M. Iswarya, G. Shree, P. Pooja Suganthi, Swarna Velammal College of Engineering and Technology Department of It Madurai India

ISBN: (纸本)9798331515911

Insect species recognition systems have significantly advanced with recent machine learning techniques, but many existing models still face challenges such as limited scalability, lower accuracy in diverse environments, and poor generalization across species variations. To address these limitations, we propose an insect species recognition system utilizing the Pyramid Vision Transformer (PVT) algorithm, which excels in handling hierarchical and multi-scale features. By utilizing a hybrid dataset that was produced by merging the Insect Village Synthetic dataset and the Agricultural Pest dataset, the proposed approach exceeds traditional models by a wide margin, achieving a recognition accuracy of 99.16%. The PVT-based system also demonstrates robustness in recognizing complex and diverse insect species, making it an effective solution for entomological studies. © 2025 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Timescale Scheduling in Cloudnative with Deep Reinforcement learning 3

Research on Multi-Timescale Scheduling in Cloudnative with D...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Qi, Difan Liu, Yong Jia, Yafei Deng, Wei Li, Gege Zhang, Aoqi Henan University of Science and Technology Luoyang China Huanghe University of Science and Technology Zhengzhou China

ISBN: (纸本)9798331506582

Cloud-native development is flexible and scalable, but resource scheduling still faces the challenge of improving resource utilization in the long and short term. traditional methods are based on static rule-based scheduling, which is not intelligent enough, leading to unbalanced resource utilization and affecting the system QoS. in this paper, we propose a multi-timescale resource balanced scheduling method based on intelligent decision making to coordinate resource scheduling requirements on different timescales of the long term, medium term and short term. By formalizing the scheduling process as a Markov Decision Process (MDP), a Deep Q-Network (DQN) model based on Successor Feature (SF) is constructed, which is able to make decisions on scheduling at different time scales. The model decouples the states and rewards of tasks, and combines the decision attention module with the generalized policy improvement algorithm to propose a composite policy improvement algorithm (DA-GPI), which improves the flexibility and accuracy of SF-DQN decision making. The experiments are based on the Alibaba computer cluster dataset v2018, and the results show that the method outperforms the existing methods in terms of resource utilization, balance and scheduling response time. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Decoding Voice Authenticity: An Exploration of Deep learning and Audio Feature Techniques 3

Decoding Voice Authenticity: An Exploration of Deep Learning...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yalla, Swathi Tejah Raju, Morampudi Gopi Prashanth Nagaraju, D. Ali, Mohammad Farhaan Department of AI & Data Science CBIT Hyderabad India

ISBN: (纸本)9798331527549

The increasing sophistication of spoofing, mimicry, and deepfake technologies exposes critical vulnerabilities in voice authentication systems, including the inability to generalize across diverse attack types, reliance on narrow feature sets, and a lack of interpretability, which seriously limits their applicability in the real world. This review will analyze the state-of-the-art techniques for Logical Access, Presentation Attacks, and Deepfake manipulations. It points out the limitations of the traditional models and progress based on modern frameworks. It explores using advanced audio features Mel-Spectrogram and MFCC along with LightGBM classifiers and incorporates SHAP (Shapley Additive Explanations) for model transparency and cosine similarity matrices for mimicry detection. The study identifies a dual-layered framework that attains high accuracy and robustness across datasets while ensuring adaptability to emerging attack strategies. It addresses the critical need for scalable, interpretable, and adaptive voice authentication solutions and provides a foundation for effectively mitigating evolving audio spoofing threats. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A federated approach towards fine-tuning LLMs for abstractive summarization 3

A federated approach towards fine-tuning LLMs for abstractiv...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Patil, Saraswati Jayappa, Atharva Joshi, Pranav Ingle, Akash Ketkar, Ojas Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331523893

A decentralized machine learning method known as federated learning (FL) for model-training has been discussed in this paper. FL is a subset of machine learning in which multiple clients(edge devices) train parts of a large dataset in a decentralized manner. This is a different approach to traditional machine learning in which data is centrally stored. The purpose of this study is to implement FL in a critical use case of summarizing legal papers. This study contrasts FL with centralized learning strategies by utilizing the Flower architecture. Flower is an open-source Python framework that simplifies FL tasks on a cluster of machines. The primary aim of this research is to achieve competitive summary quality metrics in legal document summarization. This work highlights the promise of FL as a scalable method to protect privacy in decentralized contexts during deep learning model training. The outcomes are on par with centralized procedures, and the least amount of money needs to be spent on model training. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Framework for Extracting Meteorological data with MapReduce and Predictive Modeling of Floods Using Machine learning 3

A Framework for Extracting Meteorological Data with MapReduc...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Ouedraogo, José Arthur Sere, Abdoulaye Kone, Souleymane Metouale Somda, Dekpeltakié Augustin Bobo Dioulasso Burkina Faso

ISBN: (纸本)9798331529222

This article introduces an innovative approach that integrates machine learning with the MapReduce algorithm to efficiently extract and analyze meteorological data. The proposed methodology combines these elements to improve the prediction of extreme weather events while reducing system execution time. Evaluations conducted through simulations reveal a significant enhancement in prediction accuracy compared to traditional methods. The findings of this research present new opportunities for application in fields such as agriculture and urban planning. However, certain limitations have been identified, including the narrow geographical scope of the analysis and the quality of the meteorological data used. To strengthen this approach, future research should focus on extending its application to other geographic areas and improving the quality of the meteorological datasets used. ©2024 IEEE.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Visual Analysis of Time Series data for Multi-Agent Systems Driven by Large Language Models 24

Visual Analysis of Time Series Data for Multi-Agent Systems ...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Xu, Chao Zhang, Qi Li, Baiyan Wang, Anmin Bao, Jingsong College of Computer Science and Technology Donghua University Shanghai China College of Mechanical Engineering Donghua University Shanghai China

ISBN: (纸本)9798400710834

The application of digitalization in manufacturing involves using sensors to collect and transmit large amounts of data in real-time. These complex, timestamped sequence data require effective analytical support to drive the generation of analysis summaries. Visual analysis helps researchers identify hidden patterns by intuitively displaying complex data, but its implementation relies on specialized knowledge and a deep understanding of data analysis techniques. Moreover, the analysis process often requires users to recall and process large amounts of information, which increases the complexity of the analysis. Therefore, there is an increasing demand for intelligent visual analysis technology. Large language models, with their powerful reasoning, summarization, and code generation capabilities, provide the possibility of visual analysis for general users. We propose a large language model-based multi-agent framework that integrates domain knowledge to achieve an automated data analysis workflow, from data acquisition to analysis summarization, helping users extract valuable analytical results from complex data and improving both efficiency and experience. © 2024 Copyright held by the owner/author(s).

关键词： Interactive data Analysis

来源：评论

学校读者我要写书评

暂无评论

Air Pollution Forecasting using Machine learning with Temporal Fusion Transformer and Graph Neural Networks 3

Air Pollution Forecasting using Machine Learning with Tempor...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rath, Subhashree Maneesha, P. Dept.of.Computer Science and Engineering New Horizon College Of Engineering Autonomous College Affiliated to VTU Bengaluru560103 India

ISBN: (纸本)9798331527549

Air pollution is a significant obstacle to achieving urban sustainability and public health, particularly in rapidly developing regions. Accurate air quality prediction is essential for proactive pollution management and policy formulation. In this study, we employ sophisticated machine learning frameworks, such as Temporal Fusion Transformer (TFT) and Graph Neural Networks (GNNs), to predict air quality levels. TFT is designed to capture temporal dependencies, making it ideal for time-series forecasting, while GNNs model spatial relationships, enabling the analysis of pollutant dispersion across monitoring stations .A synthetic dataset comprising 50 rows was generated, simulating pollutant concentrations (PM2.5, PM10, CO, NO2, SO2) and meteorological variables (temperature, humidity, wind speed). The models were assessed using criteria like R2, RMSE, and MAE. TFT achieved an R2 of 0.97 and an RMSE of 4.2 µg/m3, significantly outperforming traditional models like Random Forest and XGBoost. GNN provided spatial insights, identifying pollutant hotspots and their dispersion patterns. This research highlights the superior accuracy and scalability of TFT and GNN for air quality prediction. The results underline their potential for real-time monitoring, smart city integration, and proactive environmental management, offering a robust solution for addressing urban air pollution challenges. © 2025 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Research and Application of an Improved TD3 Algorithm in Mobile Robot Environment Perception and Autonomous Navigation 3

Research and Application of an Improved TD3 Algorithm in Mob...

引用

3rd international conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Fu, Bo Yao, Xulin Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Mobile robots demonstrate broad application potential in industrial, medical, and agricultural fields, with their core autonomous behavior capabilities relying on environmental perception technology. Environmental perception involves multiple aspects such as map construction and target recognition, requiring high real-time performance and accuracy. However, traditional methods are limited by predefined rules and models, making it difficult to adapt to complex and dynamic environments. This paper proposes an improved TD3 algorithm based on the PReLU activation function. The algorithm combines policy gradient and deep learning, optimizing algorithm training through the PReLU activation function to enhance convergence speed and model robustness. By integrating GCNV2 with the improved TD3 algorithm, the approach leverages the feature extraction capabilities of graph-structured data and the decision-making abilities of reinforcement learning, enhancing mobile robot performance in environmental perception and decision-making. This combined method exhibits significant advantages in handling complex graph-structured data and continuous control tasks. In a simulated environment, the dynamic scenario created in this study validates the effectiveness of the improved algorithm, achieving a high task completion rate for exploration. The algorithm was also deployed on a real vehicle, further verifying its robustness.

关键词： TD3 Reinforcement learning Mobile Robots GCNV2 environmental perception

来源：评论

学校读者我要写书评

暂无评论

Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

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500页 << < 27 28 29 30 31 32 33 34 35 36 > >>

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：