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

495 篇 会议
1 篇 期刊文献

馆藏范围

496 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

97 篇 工学
- 61 篇 计算机科学与技术...
- 43 篇 软件工程
- 29 篇 建筑学
- 28 篇 土木工程
- 28 篇 测绘科学与技术
- 10 篇 电气工程
- 8 篇 光学工程
- 7 篇 机械工程
- 7 篇 电子科学与技术（可...
- 7 篇 控制科学与工程
- 7 篇 安全科学与工程
- 6 篇 信息与通信工程
- 4 篇 材料科学与工程（可...
- 4 篇 石油与天然气工程
- 3 篇 仪器科学与技术
- 3 篇 动力工程及工程热...
- 3 篇 化学工程与技术
- 3 篇 环境科学与工程（可...
37 篇 管理学
- 24 篇 管理科学与工程(可...
- 24 篇 工商管理
- 15 篇 图书情报与档案管...
22 篇 理学
- 12 篇 数学
- 9 篇 物理学
- 4 篇 统计学（可授理学、...
9 篇 经济学
- 9 篇 应用经济学
9 篇 法学
- 7 篇 社会学
8 篇 医学
- 8 篇 基础医学(可授医学...
- 8 篇 临床医学
- 8 篇 公共卫生与预防医...
3 篇 教育学
- 3 篇 教育学
2 篇 农学
1 篇 文学

主题

180 篇 visualization
153 篇 visual analytics
121 篇 accuracy
106 篇 data models
85 篇 real-time system...
67 篇 feature extracti...
63 篇 training
56 篇 deep learning
56 篇 speech processin...
54 篇 data visualizati...
47 篇 signal processin...
43 篇 predictive model...
43 篇 convolutional ne...
42 篇 computational mo...
37 篇 adaptation model...
35 篇 transformers
35 篇 analytical model...
30 篇 scalability
29 篇 semantics
29 篇 robustness

机构

5 篇 department of el...
4 篇 symbiosis instit...
4 篇 department of bi...
3 篇 department of co...
3 篇 hindustan instit...
3 篇 rajalakshmi engi...
3 篇 school of comput...
3 篇 department of el...
3 篇 department of it...
3 篇 saveetha school ...
3 篇 department of el...
3 篇 saveetha school ...
2 篇 department of co...
2 篇 faculty of compu...
2 篇 shanghai jiao to...
2 篇 department of co...
2 篇 department of co...
2 篇 nagpur campus pu...
2 篇 department of in...
2 篇 department of co...

作者

7 篇 n. mohankumar
5 篇 r. sathya
5 篇 m. rajmohan
4 篇 asha sundaram
4 篇 p kumar
3 篇 l.n. jayanthi
3 篇 senthil pandi s.
3 篇 k. karthikeyan
3 篇 arpana prasad
3 篇 v asha
3 篇 rajmohan m.
3 篇 mohankumar n.
3 篇 t. r. ganeshbabu
2 篇 s. muthumarilaks...
2 篇 chuan-xian ren
2 篇 jose anand a.
2 篇 b. lavaraju
2 篇 jing zhang
2 篇 vamsilala purush...
2 篇 kumar p.

语言

411 篇 英文
85 篇 其他
10 篇 中文

检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 496 条记录，以下是51-60 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：

Lightweight Contextual LLMs for IoT data Interpretation in Smart Cities

Lightweight Contextual LLMs for IoT Data Interpretation in S...

引用

visual analytics and data visualization (icvadv), international conference on

作者： Tamilarasan Kannadasan

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

While the populations in cities are growing, the need for smart cities to optimize resources and life quality is increasingly a pressing matter. At the heart of this transformation lie the IoT devices creating massive volumes of data to be interpreted in real time. This paper introduces the Lightweight Contextual Large Language Models designed for efficient processing of IoT data in Smart Cities. In the approach proposed, advanced model pruning and knowledge distillation are used, keeping the solution computationally efficient with no sacrifice in interpretative power. We validate our model's effectiveness on the UrbanSensing dataset in anomaly detection, predictive maintenance, and analytics of engagement on tasks involving citizens. Our framework allows feasibility on edge devices with a 40% reduction in inference time and up to 50% decrease in computational overhead, thus guaranteeing scalability and sustainability in real-world smart city applications. The findings bridge the gap from computational efficiency to contextual understandings, thereby offering the premise for enhanced urban resilience and wiser decision-making.

关键词： Analytical models Smart cities Computational modeling Scalability Decision making Real-time systems Computational efficiency Internet of Things Sustainable development Resilience

来源：评论

学校读者我要写书评

暂无评论

Optimizing Air Pollution Prediction in Urban Environments using a Hybrid RNN-PBO Model with IoT data

Optimizing Air Pollution Prediction in Urban Environments us...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Mohandas, Prajul Subramanian, P. Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331521394

This research study proposes a novel hybrid model based on Recurrent Neural Network (RNN) combinatorial with Polar Bear Optimization (PBO) to improve air pollution prediction and control measures over cities. The model aims at learning the temporal relationships and interdependence among air-quality data. To implement the model styled with real-time Internet of Things (IoT) sensor data, tracking pollutants and other environmental factors in the urban region of study, the sequential nature of the RNNs allows it to build model patterns yet has a slow convergence and local minima when learning. To address this challenge, the PBO algorithm is introduced to optimize the parameter weight of the RNN, thus enhancing learning capacity and prediction accuracy. The exploration and exploitation of the PBO allows an effective mechanism of saving time with fast convergence and better process of complex data. The hybrid method was validated on an actual urban data set, giving improved prediction accuracy and stability over traditional RNNsand other optimization schemes. The proposed model aspires to deliver an efficient and trustworthy methodology for real-time air quality forecasting smart city management and a healthier city. © 2025 IEEE.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Based Synergistic Swarm Drone System to Eradicate Locust Swarms: Project Mynah

A Machine Learning Based Synergistic Swarm Drone System to E...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Surve, Ishaan S Dhirubhai Ambani International School Mumbai India

ISBN: (纸本)9798331521394

The United Nations Sustainable Development Goal (UNSDG) 2 - calls for creating a world with zero hunger by 2030. However, locust swarms threaten over 10% of the global food supply. Locusts normally live in a harmless solitarious state. However, on overcrowding multiple morphological transformations of the solitarious locusts turn them into a gregarious swarm forming state. Due to climate change locust swarms are predicted to become more frequent, intense and long-lasting. The current detection, monitoring and control systems for locusts is lacking. The developed Project Mynah ensures reliable regular locust hotspot monitoring, early gregarization detection and an effective eco-friendly control which induces the solitarization of the locusts, preventing swarm formation. The solution consists of an AI system including a Yolov8 nano object detection model and a Mobile Net V2 CNN which accurately detects and differentiates between solitarious locusts and gregarious locusts based on morphology. To facilitate reliable and regular monitoring of remote areas, the system includes a synergistic autonomous swarm of drones governed by bioinspired decentralized algorithms like vulture frontier radial exploration and A*. The overall system has been engineered to optimize resource usage and ensure global scalability. © 2025 IEEE.

关键词： Robot learning

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis in Financial Markets: Using NLP to Predict Stock Price from News and Social Media data

Sentiment Analysis in Financial Markets: Using NLP to Predic...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Surulivel, S.T. Ashfaq Sultan, M. Ajithkumar, K. Selvabaskar, S. School of Management Sastra Deemed University Tamil Nadu Thanjavur India

ISBN: (纸本)9798331521394

This research explores the application of NLP techniques to forecast changes in stock prices by applying sentiment analysis based on views expressed by the public in 51 news stories and 28 social media posts. Investor sentiment plays a crucial role in influencing market volatility and price trends. This study specifically focuses on Tesla Inc. (TSLA) as a case study, collecting data for a period of one month (October 1 to November 2). Sentiment data was sourced from Alpha Vantage for news articles and Reddit for social media posts. Sentiment scoring was conducted using VADER and TextBlob, with polarity values between -1 (negative) to +1 (positive). The correlation analysis between sentiment scores and Tesla's daily closing prices actually presents a weak positive correlation, with the VADER social media sentiment scoring the highest correlation value of +0.2332. The results indicate that, although social media sentiments present a moderate association with stock price movements, news data is neutral. This study highlights real-time public sentiment as a supplementary tool for financial forecasting and, thus, draws significant implications for investment strategies and market trend analysis. © 2025 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

AI Powered Ayurvedic Leaf Prediction and Disease Classification: A Comprehensive Analysis

AI Powered Ayurvedic Leaf Prediction and Disease Classificat...

引用

visual analytics and data visualization (icvadv), international conference on

作者： R. Sathya M. Suganthi M. Trisha E. Janani S. Dhivya R.V. Viswanathan Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Ayurvedic medicine, a fundamental component of India's traditional healthcare framework, is heavily dependent on the quality and efficacy of medicinal plants. Nevertheless, the medicinal properties of these plants can be adversely affected by diseases stemming from pathogens and environmental factors. This research explores the utilization of artificial intelligence (AI) methodologies, such as deep learning (DL), machine learning (ML), and technologies, for the prompt identification and diagnosis of plant diseases. The review emphasizes recent developments in this field, evaluates their success in disease detection, and pinpoints areas where further research is needed. By merging AI technologies with traditional practices, the integrity of medicinal plants can be preserved, promoting sustainable agricultural practices and improved disease management

关键词： Medicinal plants Deep learning Plant diseases Explainable AI visual analytics Environmental factors data models Robustness Sustainable development Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Co-Sketch: Drawing Together and Taking Notes Forever

Co-Sketch: Drawing Together and Taking Notes Forever

引用

visual analytics and data visualization (icvadv), international conference on

作者： Siddharth Bhorge Mayur Talewar Sidhesh Goud Department Of Electronics and Telecommunication Engineering Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In today's digital era, effective collaboration tools are essential for fostering creativity and communication. Co-Sketch is a cutting-edge web application that addresses this need by providing a platform for real-time collaborative drawing and note-taking. Authors developed an innovative web application designed to facilitate real-time collaborative drawing and note-taking along with a video meet feature that enhances. Built with a robust technology stack that includes React, HTML5 Canvas API, WebSocket's, and ***, WebRTC Co-Sketch provides a seamless and interactive platform for users to sketch, annotate, and share ideas in a dynamic online environment. Co-Sketch is designed with user collaboration in mind, offering features such as live chat, and session management to enhance the interactive experience. Whether for educational purposes like online lectures, brainstorming sessions or official meetings, Co-Sketch stands out as a reliable and engaging platform for collaborative digital creation and note-taking as well as teaching. Security and privacy is prioritized, using robust mechanisms to protect user data and manage access to collaborative sessions.

关键词： visual analytics Heuristic algorithms Education Collaboration data visualization Streaming media Security Reliability Particle swarm optimization WebRTC

来源：评论

学校读者我要写书评

暂无评论

Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality

Integration of Chemometric Analysis and Machine Learning for...

引用

visual analytics and data visualization (icvadv), international conference on

作者： Gokul Padmanaban Princy Suganthi Bai S Department of Computer Application Hindustan Institute of Technology & Science Chennai India Department of Computer Science Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The integration of chemometric analysis and machine learning techniques to enhance the predictive classification of raisin quality, a vital task for agricultural and food processing industries. The study employs chemometrics to analyze physical and chemical characteristics, selecting critical features such as color, texture, and chemical composition to reduce data redundancy and optimize model accuracy. Various machine learning algorithms, including Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are applied and compared to predict raisin quality. By merging chemometric approaches with advanced machine learning methods, the proposed framework demonstrates improved classification accuracy over traditional techniques, showcasing its potential for real-world applications in quality control and automated sorting systems. Future efforts will aim to expand the dataset, refine model performance, and explore new feature selection techniques to further boost classification accuracy.

关键词： Support vector machines Accuracy visual analytics Machine learning Quality control Artificial neural networks Real-time systems data models Chemicals Sorting

来源：评论

学校读者我要写书评

暂无评论

A Simple Framework for Predicting Student Dropout Analysis Using data Mining Algorithms

A Simple Framework for Predicting Student Dropout Analysis U...

引用

visual analytics and data visualization (icvadv), international conference on

作者： E. Balraj P. Manikandan P. Sakthivel School of Computer Science and Engineering Vellore Institute of Technology Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Dropout is an essential concern in the educational sector with implications for students as well as the institutions. This paper proposed a predictive model that uses machine learning techniques to identify at-risk students for dropout. Four years of data were used for 270 students on an online LMS by using different models such as Logistic Regression, Decision Tree, Naive Bayes, SVM, Random Forest, and ANN. The key features like LMS access, grades, and performance of assignments were evaluated after the data preprocessing involved cleaning and one-hot encoding. AUC measures were used for evaluating the performance of the model. Naive Bayes turned out to be the most effective model and followed by SVM. The results demonstrate the utility of machine learning for early dropout prediction, equipping educators with an important resource for intervention to help atrisk students.

关键词： Support vector machines Training Logistic regression visual analytics Artificial neural networks Prediction algorithms Bayes methods Classification algorithms Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud data Management

A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for S...

引用

visual analytics and data visualization (icvadv), international conference on

作者： Thiruneelakandan. S Sherin Eliyas Sathish Kumar M Department Of Computer Application Hindustan Institute of Technology and Science Chennai Tamilnadu India Department of computer applications Hindustan institute of technology and science Chennai Tamilnadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems.

关键词： Measurement Cloud computing visual analytics Scalability Memory Encryption Reliability Proposals Optimization Testing

来源：评论

学校读者我要写书评

暂无评论

Czech Salivary Gland database

Czech Salivary Gland Database

引用

18th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Jelínek, Vojtĕch Brůha, Petr Kalfĕrt, David Nový, Pavel Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitní 8 Pilsen Czech Republic Department of Otorhinolaryngology and Head and Neck Surgery 1st Faculty of Medicine University Hospital Motol Charles University V Uvalu 84 Prague150 06 Czech Republic

Salivary gland tumors require comprehensive data collection and analysis to support clinical decision-making, yet existing databases need more focus on specific tumor-related data and visualization tools. This absence hinders oncologists’ ability to track patient outcomes effectively and identify potential prognostic indicators. To address this, we developed the Czech Salivary Gland database (CSGDB), a specialized clinical application designed to manage patient data and provide visual analytics. The database includes secure and anonymized data handling alongside Kaplan-Meier survival analysis for outcome visualization. Deployed at the University Hospital in Motol, CSGDB empowers healthcare professionals with enhanced tools for tracking and analyzing patient progress, ultimately contributing valuable data and insights to the field of head and neck oncology. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： visual analytics

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50页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：