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

12,184 篇 会议
3,627 篇 期刊文献
236 册 图书

馆藏范围

16,047 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

9,101 篇 工学
- 6,084 篇 计算机科学与技术...
- 5,005 篇 软件工程
- 2,513 篇 信息与通信工程
- 1,481 篇 控制科学与工程
- 1,252 篇 电子科学与技术（可...
- 1,239 篇 电气工程
- 1,034 篇 生物工程
- 918 篇 生物医学工程（可授...
- 761 篇 光学工程
- 611 篇 仪器科学与技术
- 607 篇 机械工程
- 518 篇 动力工程及工程热...
- 450 篇 化学工程与技术
- 401 篇 安全科学与工程
- 361 篇 交通运输工程
- 332 篇 网络空间安全
5,280 篇 理学
- 2,414 篇 数学
- 1,969 篇 物理学
- 1,182 篇 生物学
- 800 篇 统计学（可授理学、...
- 524 篇 系统科学
- 500 篇 化学
2,373 篇 管理学
- 1,491 篇 管理科学与工程(可...
- 1,029 篇 图书情报与档案管...
- 748 篇 工商管理
886 篇 医学
- 731 篇 临床医学
- 688 篇 基础医学(可授医学...
- 418 篇 药学(可授医学、理...
- 393 篇 公共卫生与预防医...
456 篇 法学
- 369 篇 社会学
283 篇 经济学
209 篇 农学
172 篇 教育学
71 篇 文学
33 篇 军事学
13 篇 艺术学
1 篇 历史学

主题

415 篇 accuracy
400 篇 deep learning
308 篇 computational mo...
300 篇 feature extracti...
290 篇 real-time system...
242 篇 internet of thin...
227 篇 predictive model...
225 篇 support vector m...
224 篇 machine learning
179 篇 training
178 篇 informatics
175 篇 optimization
173 篇 computer archite...
160 篇 wireless sensor ...
157 篇 computer science
154 篇 data mining
149 篇 wireless communi...
148 篇 monitoring
145 篇 reliability
141 篇 image segmentati...

机构

416 篇 department for p...
412 篇 faculty of scien...
400 篇 department of ph...
395 篇 kirchhoff-instit...
386 篇 department of ph...
384 篇 department of ph...
355 篇 fakultät für phy...
341 篇 institute of phy...
321 篇 institute of phy...
307 篇 department of ph...
282 篇 instituto de fís...
281 篇 department of ph...
274 篇 department of ph...
273 篇 universidade fed...
270 篇 ictp trieste
269 篇 graduate school ...
257 篇 nikhef national ...
254 篇 yerevan physics ...
252 篇 department of ph...
249 篇 department of ph...

作者

338 篇 c. alexa
335 篇 j. m. izen
333 篇 g. bella
333 篇 d. calvet
333 篇 c. amelung
331 篇 j. strandberg
328 篇 g. spigo
328 篇 f. siegert
321 篇 r. ströhmer
321 篇 m. rijssenbeek
321 篇 s. jin
320 篇 g. gaudio
319 篇 s. mohapatra
302 篇 y. tayalati
301 篇 s. guindon
289 篇 a. kaczmarska
285 篇 m. klein
275 篇 j. schaarschmidt
263 篇 a. d. pilkington
260 篇 f. pastore

语言

13,225 篇 英文
2,644 篇 其他
139 篇 中文
42 篇 日文
9 篇 朝鲜文
6 篇 德文
3 篇 斯洛文尼亚文
2 篇 法文
1 篇 土耳其文

检索条件"机构=Computer Technology Institute and Computer Engineering and Informatics Dept"

共 16047 条记录，以下是91-100 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：

Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs 14

Cost-balanced Adaptive Replica Server Cache Management under...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

来源：评论

学校读者我要写书评

暂无评论

An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Fire and Smoke Detection Using Deep Learning 2

Fire and Smoke Detection Using Deep Learning

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Divya, B. Kavitha, S. Muthu Pandeeswari, R. Sreenidhi, R. Ponvaishnavi, M.R. Loshine, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India India Rajalakshmi Engineering College Dept of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Of late, wildfires and commercial fires like fires in a shopping complex, firework factories, and industries, continue to cause extensive destruction throughout the world, frequently causing human fatalities. The solution is divided into two systems, where the first system focuses on the wildfire and the second system focuses on commercial fires. Identifying the fire and smoke correctly plays an important role. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to identify the smoke region in the image. This algorithm is used to assure the smoke or fire in the image. The algorithms like ResNet, CNN (Convolutional neural network), and VGG16 (Visual Geometry Group) algorithms are used in transfer learning. This aims to increase the accuracy of the detection of fire and smoke in forests to avoid mishaps. The GradCAM is an algorithm that finds out the positive score in the image. This confirms the smoke in the image using the heat map generation. The ReLU activation function is used to show the positive pixels. The saliency map is also used in finding out the difference between smoke and fog in case of wildfire. In the case of the building fire shopping complex, firework shops, factories, and industries would also require a better solution because the detection is easier but the rescue process is more difficult in such places. The same procedure is followed for detecting and finding the origin of the fire as the wildfire model. The YOLOv8 algorithm is used for real-time analysis of the building. The model constantly looks for the unusual behavior of the environment and this in turns induces the notification. The additional feature is added to the model for rescue purposes. Fire control and evacuation have become more complex and hence the solution to such a problem is not yet brought into consideration. Even after detecting the fire, most of the people die due to the poor rescue process. This is because the rescue team cannot find the people inside the buildi

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

Real-Time Stress Detection Using Facial Images and Convolutional Neural Networks

Real-Time Stress Detection Using Facial Images and Convoluti...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sindu, M. Ashwin, T. Shyam Sunder, V. Shivasankaran, S. Poongothai, P. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Mathematics Tamil Nadu India

ISBN: (纸本)9798331535476

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability. © 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

Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-based Predictive Framework for Road Accident Severity Classification and Real-Time Risk Assessment

Machine Learning-based Predictive Framework for Road Acciden...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Kumar, V.Aswin Radhan, GR.Habi Amshavalli, R.S. Rajalakshmi, R. Shafana, N. Jeenath Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798331521394

Road accidents are a major public safety issue and thus, appropriate predictive models for predicting severity of such accidents are of great interest. In this study, we propose a machine learning based predictive framework that classifies road accident severity into three classes, i.e., Low, Medium and High. The dataset contains important driver and environmental factors such as the age and gender of the driver, road surface condition, and light condition. A number of machine learning models (Random Forest, Gradient Boosting, Logistic Regression, Decision Tree, and Support Vector Machine (SVM)) are implemented and compared to determine which was the most effective classifier. Structured data preprocessing, encoding, and feature selection techniques are applied on the models, which are trained and being evaluated. % Accuracy of most models is 84%, with a small variation. A real time interactive widget interface is also developed for user input and for instant severity predictions. The proposed system can offer important information about accident prevention strategy and policy making to improve road safety by data driven analytics. © 2025 IEEE.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Credit Risk Assessment using Ensemble Models and Explainable AI 3

Credit Risk Assessment using Ensemble Models and Explainable...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srikanteswara, Ramya Naghera, Krinal Kukkaje, Shashank Balnad Kumar, Adarsh Shrey, P. Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331527549

New challenges are still there in the credit risk management in this context of the financial system, primarily because of the lack of good models for prognosis as well as low interpretability. To address this issue, this research brings together CatBoost, XGBoost, and LightGBM to improve the prospect of identifying high-risk borrowers. A minimal level of Explainable AI (XAI) is utilized to make confirmations that the model follows them and to build confidence in the outcome. Moreover, based on a difference to basic models, the obtained ROC-AUC score equals 0.9693, proving the feasibility of the proposed ensemble approach in questions of impact developing default risks. The end product is therefore a robust method with ensemble modeling and compliant with regulatory requirements in operational financial environments. © 2025 IEEE.

关键词： Credit Risk Credit Risk and Machine Learning Algorithms Ensemble Learning and Risk Interpretation in Financial Institutions Explainable AI and Credit Risk Management Risk Models

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 << < 6 7 8 9 10 11 12 13 14 15 > >>

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：