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

88,685 篇 会议
235 篇 期刊文献
118 册 图书

馆藏范围

89,038 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

51,560 篇 工学
- 38,447 篇 计算机科学与技术...
- 31,335 篇 软件工程
- 13,107 篇 信息与通信工程
- 10,782 篇 控制科学与工程
- 8,468 篇 电气工程
- 5,597 篇 电子科学与技术（可...
- 5,031 篇 生物工程
- 4,272 篇 机械工程
- 4,058 篇 生物医学工程（可授...
- 3,094 篇 动力工程及工程热...
- 3,056 篇 仪器科学与技术
- 2,777 篇 光学工程
- 2,671 篇 安全科学与工程
- 2,487 篇 交通运输工程
- 1,678 篇 化学工程与技术
- 1,671 篇 网络空间安全
21,743 篇 理学
- 11,692 篇 数学
- 5,327 篇 生物学
- 5,005 篇 物理学
- 3,658 篇 统计学（可授理学、...
- 3,645 篇 系统科学
- 1,882 篇 化学
13,940 篇 管理学
- 9,382 篇 管理科学与工程(可...
- 5,824 篇 图书情报与档案管...
- 4,602 篇 工商管理
3,483 篇 医学
- 2,780 篇 临床医学
- 2,653 篇 基础医学(可授医学...
- 1,566 篇 药学(可授医学、理...
2,113 篇 法学
- 1,679 篇 社会学
1,690 篇 经济学
- 1,682 篇 应用经济学
1,085 篇 农学
1,061 篇 教育学
338 篇 文学
207 篇 军事学
132 篇 艺术学
1 篇 哲学

主题

3,165 篇 computer archite...
2,773 篇 computer science
2,492 篇 computational mo...
2,298 篇 feature extracti...
2,252 篇 control systems
1,968 篇 real-time system...
1,663 篇 training
1,618 篇 application soft...
1,604 篇 accuracy
1,537 篇 conferences
1,491 篇 hardware
1,250 篇 neural networks
1,228 篇 data mining
1,186 篇 costs
1,158 篇 robot sensing sy...
1,132 篇 monitoring
1,129 篇 testing
1,101 篇 protocols
1,100 篇 computer vision
1,099 篇 robustness

机构

148 篇 school of comput...
140 篇 school of comput...
120 篇 department of el...
118 篇 school of comput...
82 篇 school of comput...
67 篇 institute of inf...
64 篇 college of compu...
61 篇 school of automa...
60 篇 chitkara univers...
55 篇 department of sy...
54 篇 department of co...
52 篇 department of el...
52 篇 school of electr...
49 篇 ieee
48 篇 department of el...
46 篇 college of compu...
46 篇 department of el...
46 篇 department of el...
45 篇 department of el...
44 篇 school of comput...

作者

35 篇 lei wang
34 篇 zhang tao
34 篇 wang jing
33 篇 zhang lei
31 篇 li yang
28 篇 jun wang
26 篇 wang lei
25 篇 yu wang
24 篇 li xin
24 篇 ning xi
23 篇 rui wang
22 篇 sudeep tanwar
21 篇 weiming shen
21 篇 li peng
20 篇 dhabaleswar k. p...
20 篇 chengkuo lee
19 篇 hisao ishibuchi
19 篇 xiao liu
19 篇 hui wang
19 篇 yang bo

语言

86,470 篇 英文
2,130 篇 其他
461 篇 中文
14 篇 土耳其文
6 篇 西班牙文
5 篇 俄文
2 篇 法文
1 篇 葡萄牙文

检索条件"任意字段=IEEE International Conference and Workshops on Engineering of Computer Based Systems"

共 89038 条记录，以下是261-270 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：

IoT based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 ieee.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Novel Deep Learning Approach for Tooth Segmentation in Orthodontic Treatment Using STU-RestNet 14

Novel Deep Learning Approach for Tooth Segmentation in Ortho...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Kapoor, Saurabh Maheshwary, Priti Department of Computer Science and Engineering Rabindranath Tagore University M.P. Bhopal India

ISBN: (纸本)9798331531935

In this paper we introduce the novel approach in dentistry deep learning method for image segmentation which is helpful for orthodontics treatment. In oral diagnosis treatment the image segmentation plays an important role to determine caries for accurate diagnosis in dental treatment. The previous traditional method failed in the clinical segment in labeling the process of tooth i.e. both label and unlabel data. We address these types of issues using the Swin Transformer UNet and Rest Net method. It is a self-learning-based algorithm named as STU-RestNet-Swin Transformer U-Net RestNet method demonstrated the performance on the dataset which proving its performance and effectiveness, robustness, accuracy in tooth segmentation. Our architecture is based on the segmentation approach with novel mechanisms to decode the various regions and deep supervision technique using STU- RestNet. © 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

Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing 3

Improving Robotic Arms Through Natural Language Processing, ...

引用

3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Sikorski, Pascal Yu, Kaleb Billadeau, Lucy Esposito, Flavio Aliakbarpour, Hadi Babaias, Madi Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States

ISBN: (纸本)9798331544546

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. © 2025 ieee.

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

Solar Energy based Cogeneration System for Improved Solar Energy Utilization

Solar Energy Based Cogeneration System for Improved Solar En...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Arora, Ritika Kakkar, Sushma Sharda University Uttar Pradesh India

ISBN: (纸本)9798331513894

This paper presents the design and analysis of a solar energy based cogeneration system. The integration of photovoltaic (PV) cell units with thermal energy recovery units is analysed for improved energy harnessing schemes. In addition to the electrical energy produced by solar panels, solar heat energy can also be utilized by employing such types of hybrid systems. This effectively increases the total solar energy utilization prospect by the system. The solar cogeneration system is developed in MATLAB simulation. environment. The comprehensive analysis of energy utilization and performance improvement is presented in the paper. The simulation data utilises a full day 24 hour duration for the analysis. The system ratings, efficiencies for each type of energy harnessing method are related to the overall system performance by their fundamental relations. The major methods of heat energy tapping including active and passive heat exchangers are combined along with electrical energy by PV cells and thermoelectric generators in the system. The simulation results indicate that by combining the thermal energy harnessing model, total energy utilization can be increased by more than 100% as compared to the solar PV system only. This increases the overall efficiency of the system and helps in promoting solar energy units as a better renewable energy source for green energy generation and household applications. © 2025 ieee.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

DAMOT: A Novel Algorithm for High-Speed Target Sparse Flight Trajectory Interpolation 5

DAMOT: A Novel Algorithm for High-Speed Target Sparse Flight...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Fan, Xunchi Li, Qing Chu, Ruijuan Wu, Chaoyi College of Information Systems Engineering Information Engineering University Henan Zhengzhou China

ISBN: (纸本)9798331533694

High-speed target flight trajectories typically involve extensive temporal and spatial ranges, with data frequently missing in both discrete and continuous segments. Missing data lead to overall trajectory sparsity, and hindering the development of downstream applications. Most existing algorithms primarily address discrete missing data, lacking the capability to interpolate trajectories through continuous missing segments. To address this issue, we propose a Transformer-based decoupled adaptive learning multi-headed output algorithm (DAMOT) for sparse flight trajectory interpolation. DAMOT leverages the self-attention mechanism to learn inferential relationships among individual trajectory points and incorporates observed data to predict missing states. Using a multi-head attention framework with multi-head outputs, it decouples multi-dimensional attributes - such as latitude, longitude, altitude, and speed - to reduce systematic errors in attribute-specific interpolations. A boundary prediction layer further enhances spatial constraints for missing spans by leveraging boundary point information. Meanwhile, an adaptive loss function dynamically adjusts the model's focus during different training stages, improving convergence. Experimental results demonstrate that DAMOT's span-masking training method offers greater robustness to continuous missing data, and that its architecture and loss function achieve higher interpolation accuracy compared to existing approaches. © 2025 ieee.

关键词： Systematic errors

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumors from MRI scans using Deep Learning Model 14

Detection of Brain Tumors from MRI scans using Deep Learning...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Surendra, Yellanuri Ketan, S. Hupesh Naga Naaz, Mohebba Department of CSE G Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India Department of ECE Ravindra College of Engineering for Women Andhra Pradesh Kurnool India

ISBN: (纸本)9798331531935

The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatly aided by recent developments in computer-aided diagnosis (CAD) systems. Recent years have seen improvements in approaches used, for instance with CNN or tumor images being automated. In this work was proposed a model that is based on deep learning using Inception-V3 for the classification and localization of brain tumors. Our model was validated on a publicly available dataset and it was shown that the proposed model is accurate and robust in distinguishing among different kinds of brain tumors. The paper also addresses issues such as data imbalance and model interpretability and the feasibility of integrating the system to be used clinically for real time applications. © 2025 ieee.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Improve the Semantic Perception Ability of CNN-based Object Detection systems with Attention Model

Improve the Semantic Perception Ability of CNN-Based Object ...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Shi, TianYang Xu, XinYin Fu, Wei Huang University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Convolutional neural networks(CNNs) based object detection methods are prone to be interfered with by background noise and cannot make full use of semantic information in the positive sample-choosing phase. To overcome these issues, first, we proposed an external module called "Semantic Information Attention Module (SIAM)". The SIAM module can reduce the influence of features from non-target areas. Furthermore, we proposed a new positive sample selection strategy "Heat IOU", and the "Heat IOU" standard will give candidate samples with more semantic information more weight in the positive sample selection phase. Note that SIAM and Heat IOU can be easily combined with existing convolutional backbones, which provide a flexible plug-and-play way to spur the object detection system to focus on the zones in the image with abundant semantic information in forward propagation and positive sample choosing phase. Experimental results demonstrate that our SIAM and Hot IOU successfully improve the performance of the object detection task. The code is available at: https://***/OZERO98/SIAM. © 2025 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Smart Monitoring of High-Performance KY Converter in IoT-Enabled Electric Vehicle systems 14

Smart Monitoring of High-Performance KY Converter in IoT-Ena...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Rathika, N. Ramya, R.R. Ganga, P. Patwa, Sharda Balaji, V. Sivarajan, S. Department of Electrical and Electronics Engineering 6116-Mahendra Institute of Technology Mallasamuthiram Namakkal India Department of Information Technology University College Of Engineering Nagercoil India Department of Electrical and Electronics Engineering Annapoorana Engineering College Tamilnadu India Department of Electrical Engineering Medi-caps University Madhyapradesh Indore India Department of Electrical and Computer Engineering MAI-NEFHI College of Engineering and Technology Eritrea Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798331531935

The smart Electric Vehicle (EV)-Improved KY Converter monitoring system, which is Internet of Things (IoT)based, provides an advanced method for improving the dependability and efficiency of EV power systems. By utilizing IoT technology, this system keeps a close eye on the enhanced KY converter's efficiency, which is essential for controlling power conversion in electric vehicles. The Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) effectively operates the system at maximum power level with fast response and low oscillations. It effectively optimizes energy conversion from Photo Voltaic (PV) by dynamically modifying to the operating point under varying environmental conditions. The deployment of this intelligent monitoring system is expected to yield notable progress in the administration and functioning of electric vehicle power systems, hence augmenting the general sustainability of electric-powered transportation. The proposed system is implemented in MATLAB, employing its sophisticated simulation capabilities to analyse the PV system. This simulation framework enables for the testing and validation of key performance metrics of 98% efficiency exhibiting in boosting power extraction and grid stability. © 2025 ieee.

关键词： Fuzzy neural networks

来源：评论

学校读者我要写书评

暂无评论

Novel Real-Time EEG Signal Decoding Algorithm based on Deep Learning 3

Novel Real-Time EEG Signal Decoding Algorithm based on Deep ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk Li, Kun Anhui University School of Computer Science and Technology Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of Anhui University School of Electronic and Information Engineering Hefei230031 China

ISBN: (纸本)9798331509675

This study proposes the novel real-time electroencephalogram (EEG) signal decoding algorithm based on deep learning from the theoretical modeling level. The original intention of the design of this algorithm is to optimize the decoding accuracy and timeliness of EEG signals in the brain-computer interface (BCI) system. Considering the low efficiency of feature extraction and data processing in traditional EEG signal decoding methods, this study adopted a multi-core collaborative signal transmission architecture and deep learning model for overall optimization. First, the study used multi-core parallel computing mode to increase data processing speed. This operation can reduce system latency and achieve efficient decoding of real-time EEG signals. In terms of core architecture, convolutional neural network (CNN) is used to extract and classify EEG signals. This operation can further improve the accuracy of signal decoding. In addition, in order to solve the problem of artifacts in EEG signals, independent component analysis (ICA) is used to pre-process the original signal to remove the interference of noise actions such as eye movement, electromyography and the other noise on data. The experimental results show that the proposed deep learning algorithm can accurately analyze the characteristics of EEG signals in the different frequency bands, thereby significantly improving the decoding accuracy. © 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

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sundararamaiah, Munikrishnaiah Rao Katru, Chandrasekhar Kolli, Chandra Sekhar Sutaria, Kruti Rao, K.B. Venkata Brahma Maranan, Ramya Indian Land SC29707 United States Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

People are making daily purchases and sales through the internet, and most of them require them to use their credit cards. Besides making things easier for the customer, this heightens the probability of online credit card frauds. Credit card fraud detection has increased in several nations in the recent past due to the increased incidence of fraud that requires it to become even more of a security issue than before. In this paper, a new deep-learning-based approach for credit card fraud detection is proposed. It is based on the preprocessing stage, hybrid feature selection, and classification stage. In the first instance, the proposed method is used on the Credit Card Fraud Detection (CCF) data set. A prescreening step that uses Z-score Min-Max normalization is applied to ensure that the ranges of each data set are the same. The Crayfish Optimization Algorithm (COA) and the Emperor Penguin Optimizer (EPO) are integrated and used to identify the better attributes. Next, a similarity-navigated graph neural network (SNGNN) is utilized for classification so that the fraudulent transactions and the transactions that are not fraudulent can be differentiated easily. The overall system is also enhanced with Greylag Goose Optimization (GGO) that maximizes the chances of detection. The proposed method achieves a high accuracy in detecting fraudulent transactions, which means the proposed process has virtually limitless potential to speed up the entire process. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 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

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 << < 23 24 25 26 27 28 29 30 31 32 > >>

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：