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

22,002 篇 会议
3,552 篇 期刊文献
208 册 图书

馆藏范围

25,762 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

15,810 篇 工学
- 11,148 篇 计算机科学与技术...
- 9,355 篇 软件工程
- 4,296 篇 信息与通信工程
- 2,504 篇 控制科学与工程
- 2,154 篇 电子科学与技术（可...
- 2,139 篇 电气工程
- 1,539 篇 生物工程
- 1,321 篇 生物医学工程（可授...
- 1,135 篇 光学工程
- 1,029 篇 机械工程
- 884 篇 仪器科学与技术
- 834 篇 动力工程及工程热...
- 744 篇 化学工程与技术
- 624 篇 安全科学与工程
- 524 篇 交通运输工程
- 467 篇 材料科学与工程（可...
- 445 篇 建筑学
8,214 篇 理学
- 4,524 篇 数学
- 2,415 篇 物理学
- 1,705 篇 生物学
- 1,374 篇 统计学（可授理学、...
- 916 篇 系统科学
- 760 篇 化学
3,990 篇 管理学
- 2,397 篇 管理科学与工程(可...
- 1,826 篇 图书情报与档案管...
- 1,258 篇 工商管理
1,071 篇 医学
- 948 篇 临床医学
- 882 篇 基础医学(可授医学...
- 574 篇 药学(可授医学、理...
713 篇 法学
- 561 篇 社会学
445 篇 经济学
346 篇 教育学
316 篇 农学
110 篇 文学
73 篇 军事学
26 篇 艺术学
4 篇 哲学
3 篇 历史学

主题

681 篇 accuracy
529 篇 feature extracti...
474 篇 deep learning
473 篇 computational mo...
376 篇 support vector m...
370 篇 training
365 篇 computer science
319 篇 internet of thin...
283 篇 machine learning
281 篇 real-time system...
278 篇 computer archite...
235 篇 predictive model...
221 篇 routing
220 篇 reliability
218 篇 optimization
212 篇 data mining
210 篇 neural networks
207 篇 convolutional ne...
199 篇 throughput
193 篇 image segmentati...

机构

85 篇 dept. of compute...
84 篇 institut univers...
83 篇 north-west unive...
83 篇 université de st...
82 篇 infn sezione di ...
82 篇 tno technical sc...
81 篇 national centre ...
79 篇 nikhef national ...
76 篇 infn sezione di ...
69 篇 university würzb...
66 篇 università di ca...
53 篇 leiden universit...
51 篇 dept. of compute...
50 篇 school of automa...
50 篇 university of jo...
48 篇 university moham...
44 篇 university moham...
43 篇 in2p3 lpc campus...
42 篇 infn lnf via enr...
42 篇 university of gr...

作者

95 篇 ardid m.
93 篇 ferrara g.
92 篇 kalekin o.
92 篇 díaz a.f.
91 篇 boumaaza j.
90 篇 coleiro a.
89 篇 calvo d.
83 篇 gal t.
82 篇 bozza c.
80 篇 chen a.
78 篇 gozzini s.r.
76 篇 huang f.
76 篇 dallier r.
72 篇 anguita m.
63 篇 enzenhöfer a.
60 篇 kulikovskiy v.
58 篇 boettcher m.
57 篇 celli s.
54 篇 aly z.
52 篇 dornic d.

语言

24,761 篇 英文
608 篇 其他
295 篇 中文
75 篇 日文
21 篇 朝鲜文
1 篇 阿拉伯文
1 篇 德文
1 篇 法文
1 篇 斯洛文尼亚文
1 篇 土耳其文

检索条件"机构=Dept. Computer Technology and Computer Graphics"

共 25762 条记录，以下是171-180 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：

Advanced Cryptocurrency and Stock Prices Forecasting via Bidirectional Long Short-Term Memory Model

Advanced Cryptocurrency and Stock Prices Forecasting via Bid...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ramsriprasaath D R. Bhavani S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Truthful prediction of the values of the cryptocurrencies and stocks is critical in the financial marketplace. We developed a Bi-Lstm model to forecast the next day prices of Bitcoin, Ethereum, and Microsoft stock. The model expands the temporal resolution to the 60-day sequence window, making predictions of financial time series more reliable due to higher temporal dependencies. The effectiveness of the model is evaluated using three error metrics: Hence, use of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error was employed to guarantee accurate evaluation. Anticipating prices for the year 2025, the proposed system provides information for overall long-term tendencies and possible gains from investments. The findings reveal that the application of Bi-Lstm model can avert the categories of fluctuation that characterises the financial market and is a powerful instrument for making future predictions. The future work might improve the accuracy of the predictions by incorporating other features like market sentiment and macroeconomic indicators.

关键词： Sentiment analysis Recurrent neural networks Accuracy Operating systems Time series analysis Bitcoin Predictive models Macroeconomics Blockchains Long short term memory

来源：评论

学校读者我要写书评

暂无评论

AI Academic Research Aggregator

AI Academic Research Aggregator

引用

Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Anupama T. A Chandragupta Kumar Ashay Amal Aman Kumar Dept. of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

In responding to the challenges of making the discovery and analysis of academic research papers efficient, APARA is an AI-based academic research aggregator. Classical search engines leave the researchers inundated with gigantic waves of unstructured information. Usually, they lack per-sonalized recommendations and less cumbersome summaries. By means of the arXiv API and natural language processing (NLP), APARA is formulated to automatically extract metadata, summarize research contents, and improve search efficiency. The platform is built on the Django framework which offers valuable user interface capabilities and gives space for content-based research paper recommendations. Because APARA streamlines the research discovery process, relevant studies can be found faster, and academic exploration becomes much more efficient. The work presents the core methodology, system architecture, and evaluation metrics of APARA in parenthesis with the prospect of generating idle works of academic research.

关键词： Soft sensors Systems architecture Metadata Data retrieval Data aggregation Search engines Smart systems Natural language processing User experience Data mining

来源：评论

学校读者我要写书评

暂无评论

Intrutuion Detection System Using Machine Learning Based IDS

Intrutuion Detection System Using Machine Learning Based IDS

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Bhavani R Veeramalai Sankardass Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The number of network attacks has increased in today's dynamic environment. Network assaults are unlawful actions taken against virtual assets within a corporate network. Community attacks are typically performed by malicious parties to change, destroy, or gets the private data. Attackers that want access to internal systems usually target network perimeters in their attacks on communities. Network attacks can be categorized into two major types: passive attack and active attack. In passive network attacks, unauthorized users get access to networks, display personal information, and steal it without making any changes. Active network attacks may include material that has been edited, encrypted, or is undesirable. Upon infiltration, malicious events may additionally leverage different hacking activities, inclusive of malware and endpoint assaults, to assault an organizational network. With greater organizations adopting far off operating, networks have end up greater liable to data theft and destruction. As a end result, our proposed device can monitor community visitors for suspicious pastime and come across the presence of any intrusion.

关键词： Passive networks Machine learning algorithms Intrusion detection Organizations Malware Decision trees Cryptography computer crime Random forests Monitoring

来源：评论

学校读者我要写书评

暂无评论

Alternative Pain Management and Control with VR - The Case for HCI with Gradual Calming Effects

Alternative Pain Management and Control with VR - The Case f...

引用

International Conference on New Trends in Computing Sciences (ICTCS)

作者： Raghad Awajneh Ammar Elhassan Dept. of Computer Science Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Current VR-based pain management solutions often do not address the habituation issue, which can reduce their effectiveness over time and limit their long-term efficacy. Furthermore, existing research has not explored adaptive VR that dynamically responds to pain levels or participant focus in real time. To address these limitations, we developed an adaptive VR system that integrates dynamic calming elements in gradual and real-time that adjust in response to participants' behavior. A pilot experiment was conducted with three groups, each experiencing a different condition: a static VR environment (control), gradual calming elements that unfold over time, and instant dynamic calming elements that adapt in real time based on the focus of the participants. Pain tolerance, heart rate, and blood pressure were measured before and after the VR session to evaluate effectiveness. Findings showed that the dynamic VR group exhibited the highest pain tolerance and a greater reduction in heart rate, suggesting enhanced relaxation and focus. Although blood pressure changes were minimal, heart rate differences indicate its potential as a more responsive physiological metric for pain management. Participant feedback also provided insights for refining the VR experience. Future research will focus on expanding the sample size and optimizing VR comfort to enhance immersion.

关键词： Heart rate Hands Visualization Pain Refining Virtual reality Real-time systems Blood pressure Reliability Testing

来源：评论

学校读者我要写书评

暂无评论

Towards Touchless Interaction: Implementing Hand Gesture Recognition for Presentation and Media Control

Towards Touchless Interaction: Implementing Hand Gesture Rec...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Shailaja Uke Atifa Shaikh Harshada Rayate Anjali Kamble Sanavi Rahane Dept. Computer Science and Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The integration of hand gesture recognition with modern multimedia systems offers a novel and intuitive interface for users to interact with. This research implements a reliable framework for gesture-based control of slide presentations and media functionalities, using real-time hand detection and tracking through computer vision technologies such as OpenCV and cvzone. Recognizing individual hand gestures, the system allows for smooth navigation through slides in a presentation, annotating on slides, changing the brightness, managing windows like window minimization and maximization, and controlling media playback. The solution uses machine learning-based hand pose estimation along with optimal gesture mappings to make the recognition highly accurate and responsive. This research is meant to represent the possibility of hands-free interaction in professional and academic settings. The approach also studies applications in remote work scenarios, thereby increasing the incorporation of gesture recognition technology into everyday computing work. Moreover, the system is flexible and scalable and has the potential to integrate with virtual reality systems, remote learning platforms, and interactive kiosks. The proposed system, under controlled conditions, achieves an average accuracy of about 85 percent to 90 percent. It recognizes predefined hand gestures for navigation and interaction. When multiple gestures are shown at the same time, the system might experience temporary lags, but it recovers functionality within a few seconds. Thus, this research underlines the role of gesture recognition technology in transforming the way a user experience is provided.

关键词： Hands Productivity computer vision Technological innovation Accuracy Navigation Gesture recognition Media Real-time systems Windows

来源：评论

学校读者我要写书评

暂无评论

IoT-Enabled Real Time Intravenous Monitoring and Alert System for Healthcare Management

IoT-Enabled Real Time Intravenous Monitoring and Alert Syste...

引用

Advanced Computing Technologies (ICoACT), International Conference on

作者： P Pabitha J Selvakumar Dept. of Computer Technology Anna University MIT Campus Chennai India Dept. of Computer and Science Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

This paper presents the design and implementation of a Smart-drip Infusion Monitoring System incorporating various sensors and IoT technologies. The system aims to enhance intravenous infusion monitoring by providing precise control and timely alerts during the infusion process. The load cell sensor measures fluid volume, while the air bubble detection sensor detects air bubble status in the intravenous line, ensuring accurate and safe medication delivery. The Arduino Uno coordinates sensor data acquisition, processing, and infusion control, dynamically adjusting the solenoid valve to maintain the prescribed infusion rate. Additionally, the buzzer alerts healthcare providers to critical events such as dose completion or system malfunctions. This integrated approach offers a comprehensive solution for optimizing intravenous therapy, enhancing patient safety, and improving healthcare outcomes.

关键词： Volume measurement Process control Valves Real-time systems Software Sensor systems Sensors Safety Solenoids Monitoring

来源：评论

学校读者我要写书评

暂无评论

Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

引用

29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Activity Prediction for Localizing the Events in Imagined Speech EEG Signals

Activity Prediction for Localizing the Events in Imagined Sp...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Arun Balasubramanian Kartik Pandey Gautam Veer Debasis Samanta Dept. of Computer Science and Enginnering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech electroencephalogram (EEG) signals are often collected for longer durations than necessary, leading to a difficulty in understanding the generation of EEG during the task as it is likely that most of the data collected is that of resting state. Developing a filter to identify the segments of the trials with actual information and remove those that contain the EEG from resting states could significantly advance our understanding of EEG signals in neuroscience and biomedical engineering. This work uses derivatives-based features to form the feature vectors used to train the classifiers. Based on feature importance analysis, it has been found that the derivative-based features contributed more to the classification than the traditionally preferred feature, band power in the alpha frequency band. Using the proposed features, the classification performance experienced a significant enhancement, surpassing the results reported in previous studies. The greater accuracy of the classifiers with the proposed features implies that they are effective at filtering out the resting state segments from imagined speech EEG signals.

关键词： Location awareness Image segmentation Accuracy Neuroscience Signal processing Information filters Feature extraction Electroencephalography Vectors Speech processing

来源：评论

学校读者我要写书评

暂无评论

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Supritha Kulkarni Sanchit Sharma Manavi J Vachana U M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments.

关键词： Deep learning Adaptation models Ultrasonic imaging Accuracy Surgery Imaging Medical services Robustness Vectors Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Supritha Kulkarni Suhaas Rao S Tushar Kumar Chopra Yeshwanth S P Bhanushree K J Suneetha K R Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs)—specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet—to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present" or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model’s decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption.

关键词： computer vision Visualization Accuracy Computed tomography Transfer learning Medical services Transformers Data models Reliability Convolutional 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 << < 14 15 16 17 18 19 20 21 22 23 > >>

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：