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

20,597 篇 会议
103 篇 期刊文献
50 册 图书

馆藏范围

20,750 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

14,634 篇 工学
- 11,797 篇 计算机科学与技术...
- 8,991 篇 软件工程
- 3,425 篇 信息与通信工程
- 2,499 篇 生物工程
- 2,428 篇 控制科学与工程
- 1,960 篇 电气工程
- 1,516 篇 生物医学工程（可授...
- 1,132 篇 电子科学与技术（可...
- 1,073 篇 光学工程
- 1,063 篇 机械工程
- 860 篇 动力工程及工程热...
- 813 篇 安全科学与工程
- 776 篇 交通运输工程
- 693 篇 仪器科学与技术
- 667 篇 化学工程与技术
- 553 篇 网络空间安全
6,922 篇 理学
- 3,586 篇 数学
- 2,668 篇 生物学
- 1,537 篇 统计学（可授理学、...
- 1,403 篇 物理学
- 844 篇 系统科学
- 759 篇 化学
4,379 篇 管理学
- 2,636 篇 管理科学与工程(可...
- 2,053 篇 图书情报与档案管...
- 1,309 篇 工商管理
1,518 篇 医学
- 1,337 篇 临床医学
- 1,274 篇 基础医学(可授医学...
- 762 篇 药学(可授医学、理...
- 701 篇 公共卫生与预防医...
711 篇 教育学
- 685 篇 教育学
580 篇 法学
500 篇 农学
456 篇 经济学
43 篇 军事学
36 篇 文学
8 篇 艺术学
1 篇 哲学

主题

1,409 篇 deep learning
1,169 篇 machine learning
888 篇 accuracy
560 篇 support vector m...
560 篇 data mining
550 篇 feature extracti...
455 篇 convolutional ne...
439 篇 training
436 篇 data models
410 篇 predictive model...
405 篇 real-time system...
403 篇 internet of thin...
331 篇 neural networks
330 篇 computational mo...
313 篇 big data
244 篇 reinforcement le...
202 篇 forecasting
181 篇 object detection
179 篇 machine learning...
175 篇 optimization

机构

251 篇 chitkara univers...
64 篇 centre of interd...
50 篇 maharishi school...
43 篇 maharishi school...
34 篇 college of techn...
31 篇 pune
29 篇 chitkara univers...
28 篇 school of comput...
28 篇 the islamic univ...
27 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 chitkara univers...
22 篇 graphic era deem...
22 篇 school of inform...
22 篇 department of in...
22 篇 national univers...

作者

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

语言

17,124 篇 英文
3,604 篇 其他
142 篇 中文
3 篇 土耳其文
2 篇 西班牙文
2 篇 罗马尼亚文
1 篇 俄文

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

共 20750 条记录，以下是201-210 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：

Real Time Violence Detection System using YOLOv7 and Deep learning Techniques 3

Real Time Violence Detection System using YOLOv7 and Deep Le...

引用

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

作者： Senthilkumar, Sudha Agarwal, Gracy Shirish, Arshia Kolte, Shruti Vellore Institute of TechnologyVellore Vellore India

ISBN: (纸本)9798331527549

In recent times, violence in public spaces such as schools, hospitals, and other community environments has increased a lot. Due to this alarming rise in violence CCTV cameras have been deployed everywhere to enhance safety and security. However, these systems depend on manual monitoring which is prone to human errors and cause delays in the identification of violent events and action which can lead to significant damage. To overcome these limitations, this study has proposed a violence detection and alarm system based on deep learning algorithms. The hybridization of YOLOv7 allows rapid recognition of possible threats by identifying human presence and movement, and the lightweight classifier provides a high accurate classification of the identified tasks while reducing the processing computational resources of the system. With this dual-model-based framework, it is guaranteed that both the speed and accuracy can be achieved, and that the system can be implemented on edge devices such as mobile devices. The function of the system is to identify violence while it is happening and to issue alerts to security or law enforcement authorities committed to rapid response. The system was tested in real-time scenarios and achieved 91% accuracy in detecting violent actions. The system incorporates a Telegram bot to send real-time alerts to the relevant authorities. By leveraging advanced deep learning techniques, this study seeks to close the gap between existing surveillance methods and the need for a more automated and accurate violence detection system. © 2025 IEEE.

关键词： Alarm systems

来源：评论

学校读者我要写书评

暂无评论

data Augmentation for Entity Resolution: A comparative evaluation 3

Data Augmentation for Entity Resolution: A comparative evalu...

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Rettenmeier, Tobias Jesser, Alexander Heilbronn Univ Appl Sci Inst Intelligent Cyber Phys Syst Kunzelsau Germany

ISBN: (纸本)9798350372977;9798350372984

data augmentation is an important technique to enhance the performance of models in different fields, such as computer vision and natural language processing. In entity resolution, few papers have applied general or task-specific data augmentation techniques, and systematic knowledge is sparse. This study evaluates eight rule-based, model-based, and sample interpolation by comparing them systematically across 10 datasets and two transformer-based models or entity resolution matching systems: BERT and Ditto, leveraging DistilBERT. The results show the potential of data augmentation to increase performance, although the influence of the technique used depends heavily on the model and dataset. Here, MixDA and random word insertion performed best, so they are most suitable for recommendation to practitioners dealing with entity resolution tasks.

关键词： data augmentation entity resolution product matching machine learning deep learning evaluation

来源：评论

学校读者我要写书评

暂无评论

Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

引用

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

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning for automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

引用

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

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis on Telugu Text Translated from English using NLP and ML 3

Sentiment Analysis on Telugu Text Translated from English us...

引用

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

作者： Karthik, A.H.N. Aneesh, Ch. Saumik, Gembali Varun, K.V.V. Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research study mainly uses Natural Language Processing (NLP) and Machine learning. The general approach of this tool is to apply sentiment analysis in Telugu Text translated from English. The Technique contains many preprocessing steps, for example, tokenization of texts, normalization and noise removal for translating English text into Telugu which then will use machine translation services. It is followed by sentiment analysis of the statement using NLP models and machine learning approaches and classified them as;positive, negative, or neutral. The main objective of this study is to enhance the accuracy of sentiment identification with non-English language. Environments and also to develop rigorous sentiment analysis tools optimized for Telugu, which are designed as to the language and context-related difficulties to be taken into account. Additionally, the research examines analyses the impact of bilingual datasets on increasing translation accuracy and sentiment classification, while also tackling the obstacles given by idiomatic idioms and context-specific interpretations in Telugu Language. © 2025 IEEE.

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

Predicting Public Sector Energy Efficiency with Cloud data Centre using a Consolidation of Machine learning 3

Predicting Public Sector Energy Efficiency with Cloud Data C...

引用

3rd international conference on Advancement in Computation and Computer Technologies, InCACCT 2025

作者： Pandey, Vikas Kumar Chopra, Shivali Arora, Mohit School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331543891

The next generation of data centres along with the ever-increasing demand for power efficiencies in the infrastructure of cloud computing is creating buzz over innovative solutions. Among various techniques towards the optimization of energy costs along with environmentally friendly options, VM (Virtual Machine) consolidation is seen as one of the most viable solutions. intelligent learning approaches such as machine learning and deep learning have emerged as powerful management tools towards optimization of the resources of a cloud data centre. Since most of these VM migration approaches between the host failed to consider the migration costs with their consideration of total energy consumptions;some include the migration distance and time. We have reviewed more than 40 research paper and discusses different techniques for improving energy efficiency-from SVM (Support Vactor Machine) to Decision trees, Deep learning, and RL (Reinforcement learning) while comparing and contrasting the approaches used. We focus specifically on RL methodologies and analyse how well they can reduce energy consumption, towards understanding which one has a promising potential in minimizing energy consumption in cloud data centres. © 2025 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Neural Contextual Adaptation (NCA): For Unsupervised learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

引用

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

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Harnessing Medical Imaging Applications: Survey on 3D Deep learning Models for Image Segmentation 3

Harnessing Medical Imaging Applications: Survey on 3D Deep L...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Nambiar, Rajashree Nanjundegowda, Raghu Dept of Electronics and Communication Bengaluru India Dept of Robotics and Ai Engineering Nitte India Dept of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331528201

The domain of 3D medical image segmentation has progressed markedly with the incorporation of deep learning methodologies, offering critical instruments for accurate and quick examination of intricate medical pictures. Various research has created 3D convolutional neural networks (CNNs) designed to segment diverse anatomical and pathological features, tackling specific issues such as high dimensionality, class imbalance, and computing requirements. Innovative models such as DeepMedic, which implemented a multiresolution strategy for brain lesion identification, together with other U-Net adaptations, have attained remarkable outcomes in tumor and organ segmentation through the integration of contextual and spatial data. Recent advancements encompass models employing large-kernel (LK) attention modules, transformer-based architectures, and hybrid approaches such as UNETR++ with Efficient Paired Attention, all enhancing accuracy while optimizing computing efficiency. These developments highlight the potential of 3D deep learning for clinical applications;yet, challenges such as restricted data availability, substantial annotation expenses, and generalizability across varied datasets persist as avenues for additional investigation. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

2024 IEEE 3rd international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

2024 IEEE 3rd International Conference on Electrical Enginee...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

ISBN: (纸本)9798350380989

The proceedings contain 323 papers. The topics discussed include: design of atmospheric environment monitoring system based on Internet of Things;prediction of automatic parking accuracy based on PSO optimized BP neural network;predictive modeling in employee satisfaction: an approach with RA-SSA-SVM-based machine learning optimization;research on multibeam line planning based on affine transformation and Softmax improved K-means clustering analysis;a prediction model for electricity consumption in the Beijing-Tianjin-Tangshan region based on informer;flow field analysis of UAV power unit;surveillance information processing strategy of distribution network under power supply reliability;research on anti-telecom fraud based on artificial intelligence and edge computing;and research on the optimization of supermarket staff scheduling based on intelligent algorithm combined with binary coding.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning based Smart Energy Management System for Indoor Lighting 3

Reinforcement Learning based Smart Energy Management System ...

引用

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

作者： Ladhiya, Himanshu Malik, Rajib Indian Institute of Information Technology Guwahati Department of Electronics and Communication Engineering Assam Guwahati781015 India

ISBN: (纸本)9798331523893

The design of energy-efficient indoor lighting systems is a very interesting research challenge. Although this field has experienced significant developments, Reinforcement learning (RL) remains relatively under explored. With reward-based optimization, RL seems to be the promising approach toward achieving sustainable and adaptive lighting solutions. The proposed approach introduces an RL-driven lighting control system that dynamically adjusts the brightness and orientation of LED lighting to optimize energy consumption while ensuring comfort for the users. The approach employs the Soft Actor-Critic (SAC) algorithm for processing of sensor data, related to light intensity, glare, and user response, to come up with lighting strategies at any point in time. The prototype system developed in simulation tests reveal that, compared to existing systems, energy usage can be reduced up to 25% without compromising the level of user comport. The results demonstrate the next generation of energy-efficient interior lighting solutions by providing a scalable, intelligent solution to smart building applications. © 2025 IEEE.

关键词： Energy Intensity

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 << < 17 18 19 20 21 22 23 24 25 26 > >>

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：