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

215 篇 会议
37 篇 期刊文献

馆藏范围

252 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

146 篇 工学
- 98 篇 计算机科学与技术...
- 92 篇 软件工程
- 40 篇 信息与通信工程
- 19 篇 电子科学与技术（可...
- 18 篇 控制科学与工程
- 18 篇 化学工程与技术
- 16 篇 电气工程
- 16 篇 生物工程
- 14 篇 安全科学与工程
- 11 篇 力学（可授工学、理...
- 11 篇 光学工程
- 11 篇 材料科学与工程（可...
- 10 篇 机械工程
- 9 篇 交通运输工程
- 8 篇 动力工程及工程热...
- 8 篇 建筑学
- 8 篇 生物医学工程（可授...
- 8 篇 网络空间安全
94 篇 理学
- 39 篇 数学
- 35 篇 物理学
- 18 篇 生物学
- 13 篇 化学
- 11 篇 系统科学
- 11 篇 统计学（可授理学、...
43 篇 管理学
- 30 篇 管理科学与工程(可...
- 21 篇 图书情报与档案管...
- 12 篇 工商管理
12 篇 医学
- 11 篇 公共卫生与预防医...
- 8 篇 基础医学(可授医学...
- 8 篇 临床医学
9 篇 法学
4 篇 农学
3 篇 文学
2 篇 教育学
2 篇 军事学
1 篇 经济学

主题

16 篇 accuracy
9 篇 support vector m...
9 篇 deep learning
9 篇 real-time system...
9 篇 feature extracti...
8 篇 random forests
8 篇 technological in...
7 篇 computational mo...
7 篇 machine learning...
6 篇 internet of thin...
6 篇 image segmentati...
6 篇 machine learning
6 篇 data models
6 篇 convolutional ne...
5 篇 recommender syst...
5 篇 medical services
5 篇 wireless sensor ...
5 篇 visualization
4 篇 scalability
4 篇 nearest neighbor...

机构

8 篇 dept. of compute...
7 篇 trento institute...
7 篇 i-50019
6 篇 dept. of compute...
6 篇 institute of mat...
5 篇 dept. of microel...
5 篇 r and d center f...
5 篇 dept. of mechani...
5 篇 dipartimento di ...
4 篇 dept. of compute...
4 篇 dept. of mech. a...
4 篇 school of comput...
4 篇 dept. of compute...
4 篇 school of comput...
4 篇 dept. of cse it ...
4 篇 dept. of compute...
3 篇 department of ph...
3 篇 infn e laborator...
3 篇 infn sezione di ...
3 篇 department of ph...

作者

7 篇 marin f.
7 篇 serra e.
7 篇 peng hsin-shu
7 篇 marino f.
7 篇 borrielli a.
7 篇 bonaldi m.
6 篇 priyanka das
6 篇 prodi g.a.
6 篇 vezio p.
6 篇 chen shia-chung
6 篇 sarro p.m.
6 篇 das priyanka
6 篇 morana b.
5 篇 kwon ki-ryong
5 篇 guru prasad m s
5 篇 chowdhury a.
4 篇 lee suk-hwan
4 篇 su pao-lin
4 篇 ki-ryong kwon
4 篇 cho hwan-gue

语言

234 篇 英文
16 篇 其他
1 篇 日文
1 篇 中文

检索条件"机构=Dept. of Computer Engineering and Application"

共 252 条记录，以下是1-10 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 Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

引用

1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Transformer Based Classification of Closely Related Dravidian Languages in Code-Mixed Data

Transformer Based Classification of Closely Related Dravidia...

引用

2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Saji, Anandhu Shamsudeen, Fousia M Centre for Artificial Intelligence Tkm College of Engineering Kerala Kollam India Tkm College of Engineering Dept. of Computer Application Kerala Kollam India

ISBN: (纸本)9798331507541

In recent years, code mixing, the blending of words and phrases from multiple languages within a single conversation has become increasingly common in multilingual contexts, particularly in India. This work investigates the effectiveness of transformer based models in identifying languages within code mixed data that includes English and three closely related Dravidian languages: Malayalam, Kannada, and Tamil, all written in English script. A comprehensive dataset was developed with five distinct labels: Tn-en (Tamil-English), Kn-en (Kannada-English), Ml-en (Malayalam-English), and En (English). Transformer models, including BERT, CamemBERT, mBERT, DistilBERT, and ELECTRA, were employed for classification, with CamemBERT achieving the highest validation accuracy of 97.62% due to its ability to handle morphologically rich languages effectively. The findings highlight the potential of transformer models in enhancing natural language processing (NLP) applications for code-mixed text, particularly in managing the complexities of closely related multilingual environments. © 2025 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain 2

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Shubh Pathak, Disha Mohini Jain, Dhyanendra Srivastava, Somya Vats, Prashant Upadhyay, Govind Murari Department of Computer Science ABES Engineering College U.P. Ghaziabad India Department of Computer Science Engineering-AIML ABES Engineering College U.P. Ghaziabad India Dept. of CSE School of CSE Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Application Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331530389

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Code-Mixed English-Indian Languages: Hate Speech Dataset Analysis

Code-Mixed English-Indian Languages: Hate Speech Dataset Ana...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Manoj Kumar Saini Rahul Pradhan Dilip Kumar Sharma Dept. of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In the era of Social Media Networks (SMN) and Online Forum (OF) such as Facebook, Instagram, Blogging Sites, Gaming Platforms etc., users tend to comment significantly in English and Indian Code Mixed (CM) languages. Code-mixing is a common manifestation in linguistically diverse social groups such as India. Many people do not know English, so thousands of people on these platforms write comments in code-mixed languages such as Hindi-English (Hinglish). Researchers have retrieved comments on the CM language from SMN and OF. Many people respond in the form of Hate Speech (HS) and Offensive Language (OL) to insult or hurt others, a person, or specific groups. SMN and OF encounter issues with pinpointing and eradicating HS and disagreeable information in diverse CM Indian languages. OL is straightforwardly or inadvertently used in hate speech posts to demean someone and that can cause psychological distress to persons. So, a specific and effective Code Mixed dataset and classification are required to detect HS and OL. Therefore, in this study, we carried out an in-dept. analysis on the retrieval methodology and classification of existing data sets for HS and OL. In this paper, we study and analyze 6 (SIX) datasets that were widely adopted in the literature.

关键词： Technological innovation Codes Social networking (online) Social groups Hate speech Semantics Psychology Syntactics Complexity theory Web sites

来源：评论

学校读者我要写书评

暂无评论

Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

来源：评论

学校读者我要写书评

暂无评论

Exploring the Capabilities and Limitations of Generative AI applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

引用

Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Spam Email Detection using K-Nearest Neighbors: An Enhanced Approach

Spam Email Detection using K-Nearest Neighbors: An Enhanced ...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Sumit Saklani Kalyan Thapa Devendra Singh Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Spam emails represent a large portion of global email traffic and pose several risks, such as phishing, fraud, and malware attacks. Traditional spam filters are ineffective in combating spam that is ever-evolving, which makes the problems more complex in nature. Therefore, advanced machine learning techniques need to be adopted. This study attempts to integrate an optimized K-Nearest Neighbors (KNN) model for spam detection that implements Term Frequency-Inverse Document Frequency (TF-IDF) for text representation, along with Principal Component Analysis (PCA) for feature selection. Classification is performed with relevance to the neighbors with more distance using an adaptive distance weighting algorithm. Enhanced KNN techniques improve the metrics on Naive Bayes and Support Vector Machines (SVM) with respect to accuracy, precision, recall, and F1 score. Experimental results demonstrated the classification accuracy to amount 96.4 %, exceeds the existing systems. This study develops efficient spam detection systems having the capability of adapting novel spam methods.

关键词： Support vector machines Adaptation models Accuracy Computational modeling Unsolicited e-mail Heuristic algorithms Nearest neighbor methods Feature extraction Bayes methods Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

引用

5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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26页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：