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

22,774 篇 会议
3,747 篇 期刊文献
198 册 图书
4 篇 科技报告

馆藏范围

26,723 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

16,780 篇 工学
- 13,663 篇 计算机科学与技术...
- 11,872 篇 软件工程
- 4,382 篇 信息与通信工程
- 2,072 篇 控制科学与工程
- 1,859 篇 生物工程
- 1,507 篇 生物医学工程（可授...
- 1,283 篇 电气工程
- 1,171 篇 电子科学与技术（可...
- 960 篇 光学工程
- 766 篇 机械工程
- 766 篇 仪器科学与技术
- 710 篇 动力工程及工程热...
- 614 篇 安全科学与工程
- 575 篇 化学工程与技术
- 553 篇 交通运输工程
- 536 篇 建筑学
8,612 篇 理学
- 5,318 篇 数学
- 2,091 篇 生物学
- 1,999 篇 统计学（可授理学、...
- 1,622 篇 物理学
- 991 篇 系统科学
- 647 篇 化学
5,155 篇 管理学
- 2,997 篇 图书情报与档案管...
- 2,482 篇 管理科学与工程(可...
- 1,434 篇 工商管理
1,263 篇 医学
- 1,103 篇 临床医学
- 1,067 篇 基础医学(可授医学...
- 655 篇 药学(可授医学、理...
- 592 篇 公共卫生与预防医...
915 篇 法学
- 766 篇 社会学
534 篇 经济学
412 篇 农学
283 篇 教育学
107 篇 文学
58 篇 军事学
28 篇 艺术学
2 篇 哲学
1 篇 历史学

主题

846 篇 data mining
839 篇 accuracy
700 篇 data models
686 篇 computational mo...
652 篇 training
605 篇 feature extracti...
579 篇 computer science
554 篇 deep learning
483 篇 predictive model...
483 篇 machine learning
427 篇 support vector m...
342 篇 internet of thin...
313 篇 real-time system...
298 篇 computer archite...
251 篇 neural networks
242 篇 reliability
239 篇 big data
237 篇 convolutional ne...
202 篇 cloud computing
200 篇 scalability

机构

49 篇 institut univers...
49 篇 université de st...
48 篇 north-west unive...
48 篇 national centre ...
47 篇 infn sezione di ...
46 篇 tno technical sc...
44 篇 infn sezione di ...
43 篇 nikhef national ...
38 篇 university würzb...
37 篇 università di ca...
36 篇 dept. of compute...
31 篇 university of jo...
30 篇 leiden universit...
29 篇 school of automa...
29 篇 university moham...
28 篇 dept. of compute...
27 篇 dept. of compute...
27 篇 dept. of informa...
26 篇 dept. of informa...
26 篇 department of ph...

作者

56 篇 ferrara g.
56 篇 ardid m.
56 篇 kalekin o.
54 篇 boumaaza j.
53 篇 díaz a.f.
53 篇 coleiro a.
52 篇 calvo d.
49 篇 huang f.
48 篇 chen a.
47 篇 gal t.
47 篇 bozza c.
42 篇 gozzini s.r.
41 篇 dallier r.
41 篇 anguita m.
37 篇 kulikovskiy v.
37 篇 enzenhöfer a.
35 篇 celli s.
33 篇 boettcher m.
32 篇 ahmad jalal
31 篇 dornic d.

语言

25,867 篇 英文
646 篇 其他
155 篇 中文
31 篇 朝鲜文
24 篇 日文
2 篇 德文
1 篇 西班牙文
1 篇 葡萄牙文
1 篇 斯洛文尼亚文

检索条件"机构=Dept. of Statistics and Data Science Dept. of Computer Science"

共 26723 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：

Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 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

data Augmentation for Messenger Phishing Detection Using Large Language Models

Data Augmentation for Messenger Phishing Detection Using Lar...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Noh, Keonwoong Oh, Seokjin Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

In this study, we introduce a new dataset specifically designed for detecting messenger phishing, an increasingly significant issue in cybercrime. To overcome the scarcity of labeled phishing data, we employ large language models (LLMs) to generate synthetic data, thereby expanding the dataset and improving detection capabilities. Our experimental results show that a model trained exclusively on synthetic data performs comparably to those trained with labeled data. Furthermore, combining synthetic data with labeled data achieves superior F1 and accuracy scores compared to using labeled data only while reducing misclassification errors. © 2025 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Improving Messenger Phishing Detection Using Heterogeneous Phishing data

Improving Messenger Phishing Detection Using Heterogeneous P...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Oh, Seokjin Noh, Keonwoong Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

Recently, as messenger phishing has been occurring more frequently, the need for its detection has increased;however, datasets for messenger phishing detection are publicly unavailable. In this paper, we address the data scarcity problem of the newly collected messenger phishing dataset by leveraging various types of pre-existing auxiliary phishing data. Experimental results demonstrate that the error rate decreased by up to 1.81 % and the F1 score improved by 7.35% when smishing and voice phishing data are used. These findings confirm that integrating heterogeneous phishing data can mitigate the data scarcity problem and enhance messenger phishing detection performance. © 2025 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis 6

PePR: Performance Per Resource Unit as a Metric to Promote S...

引用

6th Northern Lights Deep Learning Conference, NLDL 2025

作者： Selvan, Raghavendra Pepin, Bob Igel, Christian Samuel, Gabrielle Dam, Erik B. Dept. of Computer Science University of Copenhagen Copenhagen Denmark Dept. of Global Health King’s College London London United Kingdom

The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR1 score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using existing pretrained models that are fine-tuned on new data can significantly reduce the computational resources and data required compared to training models from scratch. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints. © NLDL *** rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

引用

1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Routing and Charge Planning Strategies for Ridesharing EV Fleets 11

Routing and Charge Planning Strategies for Ridesharing EV Fl...

引用

11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Singh, Ashutosh Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology KharagpurWB-721302 India

ISBN: (纸本)9789897587450

Ridesharing systems have become an important part of urban transportation. At the same time, electric vehicle (EV) adoption is also growing at a fast pace as an eco-friendly and sustainable transportation option. To operate a ridesharing system with EV fleets, scheduling an EV fleet to serve passenger requests requires consideration of both the requests, and the available charge and potential future charge requirements of the EVs. In this paper, we address the problem of scheduling EVs by a ridesharing operator, and propose four algorithms that schedule passenger requests while taking into consideration charging requirements of the EVs. Detailed simulation results are presented on a real world data set to show that the algorithms perform well. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Urban transportation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

引用

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

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

引用

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

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

来源：评论

学校读者我要写书评

暂无评论

Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing 18

Quality Assurance for LLM-RAG Systems: Empirical Insights fr...

引用

18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Ahmed, Bestoun S. Baader, Ludwig Otto Bayram, Firas Jagstedt, Siri Magnusson, Peter Karlstad University Sweden & American University of Bahrain Dept. Mathematics and Computer Science Bahrain Ludwig Maximilian University Munich Dept. Mathematics Informatics and Statistics Munich Germany Karlstad University Dept. Mathematics and Computer Science Karlstad Sweden Karlstad University CTF Service Research Center Karlstad Sweden

ISBN: (纸本)9798331534677

This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation (RAG) in tourism applications. Through systematic empirical evaluation of three different LLM variants across multiple parameter configurations, we demonstrate the effectiveness of our testing methodology in assessing both functional correctness and extra-functional properties. Our framework implements 17 distinct metrics that encompass syntactic analysis, semantic evaluation, and behavioral evaluation through LLM judges. The study reveals significant information about how different architectural choices and parameter configurations affect system performance, particularly highlighting the impact of temperature and top-p parameters on response quality. The tests were carried out on a tourism recommendation system for the Varmland region in Sweden, utilizing standard and RAG-enhanced configurations. The results indicate that the newer LLM versions show modest improvements in performance metrics, though the differences are more pronounced in response length and complexity rather than in semantic quality. The research contributes practical insights for implementing robust testing practices in LLM-RAG systems, providing valuable guidance to organizations deploying these architectures in production environments. © 2025 IEEE.

关键词： Software quality

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 << < 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：