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

273,162 篇 会议
112,863 篇 期刊文献
2,781 册 图书

馆藏范围

388,805 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

225,103 篇 工学
- 149,617 篇 计算机科学与技术...
- 121,008 篇 软件工程
- 54,628 篇 信息与通信工程
- 35,815 篇 控制科学与工程
- 31,966 篇 电气工程
- 30,609 篇 电子科学与技术（可...
- 26,000 篇 生物工程
- 20,441 篇 生物医学工程（可授...
- 17,850 篇 光学工程
- 15,546 篇 机械工程
- 13,079 篇 化学工程与技术
- 12,209 篇 仪器科学与技术
- 12,154 篇 动力工程及工程热...
- 10,920 篇 材料科学与工程（可...
- 8,327 篇 安全科学与工程
- 7,877 篇 网络空间安全
127,332 篇 理学
- 63,220 篇 数学
- 41,829 篇 物理学
- 29,490 篇 生物学
- 20,402 篇 统计学（可授理学、...
- 14,882 篇 系统科学
- 14,048 篇 化学
55,978 篇 管理学
- 33,687 篇 管理科学与工程(可...
- 24,484 篇 图书情报与档案管...
- 16,673 篇 工商管理
19,407 篇 医学
- 16,215 篇 临床医学
- 14,576 篇 基础医学(可授医学...
- 9,158 篇 药学(可授医学、理...
- 7,764 篇 公共卫生与预防医...
10,518 篇 法学
- 8,605 篇 社会学
6,451 篇 经济学
5,099 篇 农学
4,167 篇 教育学
1,866 篇 文学
872 篇 军事学
511 篇 艺术学
75 篇 哲学
46 篇 历史学

主题

19,985 篇 computer science
8,799 篇 accuracy
8,606 篇 computational mo...
7,863 篇 feature extracti...
7,458 篇 deep learning
7,238 篇 machine learning
6,427 篇 training
5,257 篇 data mining
5,110 篇 computer archite...
5,016 篇 support vector m...
4,932 篇 wireless sensor ...
4,799 篇 predictive model...
4,649 篇 costs
4,531 篇 bandwidth
4,483 篇 optimization
4,478 篇 application soft...
4,369 篇 protocols
4,351 篇 algorithm design...
4,321 篇 testing
4,273 篇 neural networks

机构

1,696 篇 department of co...
1,364 篇 department of co...
1,346 篇 department of el...
1,158 篇 department of el...
1,063 篇 cavendish labora...
973 篇 department of ph...
879 篇 department of co...
856 篇 physikalisches i...
844 篇 oliver lodge lab...
818 篇 department of el...
745 篇 university of ch...
638 篇 department of co...
550 篇 infn sezione di ...
539 篇 department of co...
521 篇 infn laboratori ...
519 篇 infn sezione di ...
516 篇 chitkara univers...
502 篇 horia hulubei na...
502 篇 infn sezione di ...
486 篇 department of co...

作者

258 篇 s. chen
256 篇 j. wang
250 篇 barter w.
237 篇 a. gomes
225 篇 blake t.
223 篇 c. alexa
219 篇 bellee v.
219 篇 bowcock t.j.v.
218 篇 amato s.
217 篇 j. m. izen
217 篇 betancourt c.
216 篇 boettcher t.
215 篇 borsato m.
209 篇 brundu d.
207 篇 beiter a.
204 篇 bencivenni g.
195 篇 bay a.
193 篇 g. bella
193 篇 c. amelung
192 篇 j. strandberg

语言

372,465 篇 英文
12,661 篇 其他
3,698 篇 中文
220 篇 日文
47 篇 德文
34 篇 朝鲜文
33 篇 法文
17 篇 斯洛文尼亚文
8 篇 西班牙文
8 篇 荷兰文
5 篇 土耳其文
4 篇 俄文
3 篇 葡萄牙文
2 篇 阿拉伯文
2 篇 捷克文
1 篇 加泰罗尼亚文
1 篇 泰文

检索条件"机构=Department of Computer and Science and Engineering"

共 388806 条记录，以下是4921-4930 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：

Advancements in Deep Learning for Smart Surveillance: A Survey on Crime and Anomaly Detection

Advancements in Deep Learning for Smart Surveillance: A Surv...

引用

International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Amrita Pal Promita Ghosh Shubhasri Roy Soma Das Shreejita Mukherjee Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The increasing prevalence of advanced surveillance technologies has spurred significant research in automated anomaly detection systems using deep learning. This survey synthesizes recent developments across various distinct studies addressing diverse security challenges. These include theft detection using Convolutional Neural Networks (CNN) with object tracking, robbery identification using convolutional LSTM models trained on the UNI-Crime dataset, anomaly detection leveraging weakly supervised learning on UCF-Crime data, and weapon recognition using YOLOv3. Several other approaches demonstrate significant accuracy and efficiency improvements, achieving accuracies. Despite varying datasets and architectures, the integration of CNNs, YOLO, and LSTM models reflects a shared emphasis on spatial-temporal feature extraction for real-time applications. This paper provides a comparative analysis of these methodologies, highlighting the role of dataset quality, transfer learning, and computational efficiency in advancing smart surveillance systems to mitigate crime and enhance public safety.

关键词： YOLO Deep learning Surveys Accuracy Weak supervision Surveillance Weapons Real-time systems Security Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Improved Welding Defects Recognition with Transfer Learning Based You Only Look Once Version 5 3

Improved Welding Defects Recognition with Transfer Learning ...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Patchamatla, Pavan Srikanth SubbaRaju Riadhusin, Raami Chandra, N. Subhash Pramodhini, R. Alagarraja, K. RKInfo-Tech Llc India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

In recent years, welding is a dynamic joining procedure for infrastructure development and energy production to ensure safety and reliability. However, weld defects often reduce the quality of welded parts. Existing TL-CNN models struggled with variations in defect appearance, leading to biased training and poor generalization. To overcome this issue, TL based You Only Look Once version 5 (TL-YOLOv5) is proposed. The pretrained YOLOv5 model is fine tuned for optimizing the welding data to generalize varied appearances. Initially, the input data is collected from asymmetric laser weld images and then preprocessed with median filter for image denoising and then enhanced with gray scale transformation. After that, Convolutional Neural Network (CNN) with improved activation function and adaptive pooling is incorporated to extract features from the preprocessed images. Finally, the input feature maps are further fed into the proposed TL-YOLOv5 for weld defect recognition with anchor boxes and fine-tuned to improve performance. From the results, the proposed TL-YOLOv5 attained better results in contrast to conventional TL-CNN including accuracy (99.12%) respectively. © 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

Evaluating the Tri-Script Writer Verification System Using a Handcrafted Features and Vision Transformer Learning Approach 1st

Evaluating the Tri-Script Writer Verification System Using ...

引用

1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Paul, Jaya Dutta, Kalpita Sarkar, Anasua Roy, Kaushik Das, Nibaran Department of Computer science and Engineering Jadavpur University Kolkata India Department of Computer science West Bengal State University Barasat India

ISBN: (纸本)9783031819346

A multi-script writer verification system poses a captivating research challenge in the fields of pattern recognition and computer vision. It entails the intricate task of authenticating the identities of writers who produce handwriting samples in various scripts. This scenario introduces complexities demanding robust techniques for accurate recognition across diverse scripts. In West Bengal, the native language is Bangla, with Hindi serving as the second language, while English finds use in official contexts. This study tackles the issue of block-level tri-script (Bangla, Hindi, and English) writer verification systems and presents promising results. The research demonstrates that a combination of handcrafted features outperforms automatically derived features, owing to limitations in the writers within the JUDVLP-TLWVdb dataset. Experimental results indicate that the SMO classifier outperforms other classifiers such as simple logistics and KNN. A novel dataset for writer verification systems using the tri-script approach is introduced, achieving a peak verification accuracy of 91.50% through a combination of Radon Transform, HOG, LBP, and LPQ features. The overall performance of the tri-script approach reaches 91.80%. Furthermore, this study employs the Vision Transformer (ViT) model for writer recognition, showcasing superior performance in comparison to ViT using tri-level block images of the page. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Verification

来源：评论

学校读者我要写书评

暂无评论

Supply chain tracking of drugs using Ethereum

Supply chain tracking of drugs using Ethereum

引用

Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Raghunandan K R Dhanya Avisha V Shetty Akshata D Bhat Devishree Bangera Nalini N Department of Computer Science and Engineering NMAM Institute of Technology (Deemed to be University) Udupi India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Counterfeiting, increased fake intermediate products, lack of transparency, unauthorized alteration of data, and other related problems throughout the supply chain have made it necessary for the modern pharmaceutical industry to ensure integrity and security. Thus, the above-mentioned problem can be solved by a decentralized blockchain-based system, which ensures security and traceability. In the proposed solution, there are seven stakeholders including manufacturers and consumers. Authorized manufacturers are allowed to store the drugs in the blockchain network, and corresponding QR and bar codes are generated. This generates a reliable record of drug movements by constructing an architecture that is decentralized to avoid alteration of the data by anyone. Consumers have the opportunity to scan QR codes to ensure that their products are genuine and trace their origin. By addressing the challenges of the conventional pharmaceutical supply chain that provides high transparency, minimal counterfeits, and assured data security, this system addresses the aforementioned disadvantages.

关键词： Drugs Data security Supply chains QR codes Data engineering Blockchains Stakeholders Reliability Pharmaceutical industry Counterfeiting

来源：评论

学校读者我要写书评

暂无评论

Optimizing SMS Spam Detection with Large Language Models and Transformer Architectures

Optimizing SMS Spam Detection with Large Language Models and...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Nazir Ahmed A. S. M. Shakil Ahamed Fahim Shakil Tamim Department of Computer Science & Engineering IUBAT—International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science & Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In recent years, the Short Message Service (SMS) has become omnipresent, with most people ignoring emails while nearly all check their daily text messages. These messages may contain spam, providing useless promotions or links that could lead to the installation of malicious applications into the system. Spam detection involves identifying minute linguistic signs and patterns in text messages, with detecting disguised spam presenting a significant challenge due to the continuous evolution of techniques. It aims to develop an intelligent system for classifying messages into two different classes, namely Spam or Ham from English SMS spam messages. In order to do so, this study explores different fine-tuned machine learning (Logistic Regression-LR, Multinomial Naive Bayes-MNB, Support Vector Machine-SVM) models, deep learn- ing(Convolutional Neural Network-CNN, Bidirectional Long Short-Term Memory-BiLSTM, CNN+BiLSTM) models, transformer(M-BERT, BERT base , XLM-R base , XLNet base ) techniques and two large language models(Phi-3 and H2O-Danube). Finally, we experimented with two large language models, Phi-3 and H2O-Danube. Out of all the models we tested, H2O-Danube outperformed all other models with a macro F1-score of 0.94, proving to be the best for SMS spam detection, surpassing traditional machine learning, transformer, and LLM models.

关键词： Large language models Transfer learning Text categorization Linguistics Message services Transformers Reliability engineering Vectors Intelligent systems Logistics

来源：评论

学校读者我要写书评

暂无评论

Bearing Signal Classification Using Dynamic Time Warping 5th

Bearing Signal Classification Using Dynamic Time Warping

引用

5th International Conference on Vibration and Energy Harvesting Applications, VEH 2024

作者： Choudhury, Madhurjya Dev Hong, Liu Dhupia, Jaspreet Singh School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China Department of Mechanical and Mechatronics Engineering University of Auckland Auckland1142 New Zealand

ISBN: (纸本)9789819611904

Bearing is a critical machine element whose fault-free health status is crucial to the reliable operation of the machine. In recent times, machine learning-based approaches of training models on huge amount of measured data are adopted to monitor the bearing health status. However, collecting a large number of complete fault samples in an industrial set-up is very expensive thereby fostering the need to introduce methods that have excellent classification and clustering performance without depending on an extensive amount of training data. To address this, a dynamic time warping (DTW)-based signal similarity measuring approach is proposed. In this paper, a reference signal corresponding to a particular bearing health class is generated, which is then warped by DTW on any test bearing signals to reveal the inherent similarity by calculating their cumulative distances. The distance measure is used to classify the bearing health. It is observed that DTW alone cannot obtain acceptable results when employed to handle complex bearing signals because of the presence of measurement noise and unwanted interfering components. An enhancement is proposed by integrating DTW-based similarity search with spectral kurtosis (SK)-based demodulation for enhanced classification. The proposed method is validated using the Case Western Reserve University (CWRU) bearing dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bearings (machine parts)

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Efficacy of Text Summarization Models: A Comparison of NLP Algorithms

Evaluating the Efficacy of Text Summarization Models: A Comp...

引用

International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Rounak Chakraborti Romit Banerjee Soma Das Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Text Summarization is a critical tool nowadays. People do not have enough time to read big articles which take a lot of time. A short summary is no doubt an enthralling option for this case. As this demand increases numerous algorithms and methods have come up which can serve this purpose. This survey paper encompasses the different algorithms and methods used by different researchers which includes methods like TF-IDF and LSA alongside neural networks like Seq2Seq, Pegasus, and Transformer-based models like BART and T5. We analyze these on metrics like ROUGE scores. This survey paper will provide a valuable resource for researchers in the future who want to study text summarization in the news domain.

关键词： Surveys Measurement Analytical models Machine learning algorithms Semantics Text summarization Bidirectional long short term memory Transformers Faces Testing

来源：评论

学校读者我要写书评

暂无评论

Development of a Comprehensive Deep Learning Framework for Enhanced Detection and Accurate Classification of Renal Cancer

Development of a Comprehensive Deep Learning Framework for E...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： J Bino C. Preethi M. Renukadevi T.K.S Rathish babu S. Kanageswari Leema Nelson Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Department of Computer Science Engineering Sethu institute of technology Department of Computer Science Engineering SRM Institute of Science and technology Ramapuram Chennai Department of computer science and Application Loyola College Chennai Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Accurate and early diagnosis of renal cancer is important for the improvement of outcomes in patients; hence, timely intervention has much to do with the effectiveness of treatments and survival rates. While there has been an increase in medical imaging modalities, early detection of renal cancer has become quite plausible, though the complexity introduced in the characteristics of tumors calls for advanced methods for their reliable classification. In this work, a new LSTM+CNN-based model is developed for renal cancer disease detection by integrating sequential learning capability from LSTM networks together with the powerful feature extraction abilities of CNN. The system is designed to improve both the accuracy and efficiency in renal cancer diagnosis based on the medical imaging data using spatial and temporal features. Among these, the proposed LSTM+CNN-based model has turned in better accuracy with quicker processing time and better overall classification performance compared to the state-of-the-art models. The proposed model also allows for the non-invasive, high-precision differentiation of renal tumors into low- and high-grade ones, with a view to early diagnosis and prediction. These results provide a proof of the enormous potentials of deep learning models, especially the LSTM+CNN architecture, toward making renal cancer detection an efficient and practical clinical solution.

关键词： Deep learning Support vector machines Accuracy Computational modeling Predictive models Reliability Medical diagnosis Long short term memory Cancer Tumors

来源：评论

学校读者我要写书评

暂无评论

SSE: A Speaking Style Extractor Based on Fine-Grained Contrastive Learning between Speech and Descriptive Text

SSE: A Speaking Style Extractor Based on Fine-Grained Contra...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Zixing Wu, Yimeng Dong, Zhongren Xiang, Wulong Shen, Shengfan Schuller, Björn W. College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China GLAM Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9798350368741

Effective extraction of paralinguistic features from speech, such as emotion, accent, and age, remains a challenging task in speech processing. Traditional methods typically address each type of paralinguistic information with separate classification or regression tasks-e. g., emotion recognition, accent detection, and age estimation. These approaches can result in fragmented and incomplete descriptions of speech style, failing to capture the full range of paralinguistic attributes in an integrated way. To address these limitations, we propose the Speech Style Extractor (SSE), a novel approach that aims to provide a more comprehensive extraction of speech style features. SSE leverages an optimized fine-grained contrastive learning scheme, enhancing the extraction of diverse paralinguistic features. The optimization approach outperforms the baseline on 11 out of 13 datasets, with an average improvement of 2.1% in terms of the R@1 evaluation metric. We also propose a complete data generation framework using large language models to create 199k text samples across 9 paralinguistic categories, such as emotion, deception, stuttering, and accent, to fill the gap in speaking style descriptions in public datasets. This extensive dataset not only facilitates our research but also serves as a valuable resource for the speech processing community. © 2025 IEEE.

关键词： Contrastive Learning Fine-Grained Loss Prompt engineering Speaking Styles

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Techniques for Early Diagnosis of Diabetic Macular Edema

Advanced Machine Learning Techniques for Early Diagnosis of ...

引用

International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Muthuselvi M Suneel Kumar Duvvuri Geetha Rani K Surendran R K S. Balamurugan Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science and Applications Government College (Autonomous) Rajahmundry Andhra Pradesh Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

One consequence of diabetic retinopathy is diabetic macular edema (DME), a condition that effects the eyes of individuals with diabetes. More than 28 million people been affecting by diabetic macular edema due to diabetes, late diagnose of diabetic macular edema leads to vision loss. Available method with deep learning algorithms and artificial intelligence (AI) for prediction of diabetic macular edema is not effective way for diagnosing in early. Proposed a method with XGBoost algorithm for the classification, pre-processing, extraction and detection of a disease by fundus samples. This method resulted in 0.992, 0.989, 0.991 and 0.990 for accuracy, Recall, precision and F1-score respectively.

关键词： Training Diabetic retinopathy Machine learning algorithms Accuracy Medical treatment Transforms Prediction algorithms Diabetes Classification algorithms Medical diagnostic imaging

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 << < 489 490 491 492 493 494 495 496 497 498 > >>

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：