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

941 篇 会议
259 篇 期刊文献

馆藏范围

1,200 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

637 篇 工学
- 493 篇 计算机科学与技术...
- 428 篇 软件工程
- 153 篇 信息与通信工程
- 107 篇 控制科学与工程
- 101 篇 生物工程
- 87 篇 生物医学工程（可授...
- 67 篇 光学工程
- 64 篇 电气工程
- 47 篇 电子科学与技术（可...
- 42 篇 动力工程及工程热...
- 40 篇 安全科学与工程
- 38 篇 交通运输工程
- 37 篇 机械工程
- 36 篇 网络空间安全
- 35 篇 化学工程与技术
357 篇 理学
- 160 篇 数学
- 110 篇 生物学
- 86 篇 物理学
- 73 篇 统计学（可授理学、...
- 31 篇 地球物理学
- 30 篇 化学
229 篇 管理学
- 142 篇 管理科学与工程(可...
- 101 篇 图书情报与档案管...
- 92 篇 工商管理
92 篇 医学
- 72 篇 临床医学
- 68 篇 基础医学(可授医学...
- 48 篇 公共卫生与预防医...
- 40 篇 药学(可授医学、理...
40 篇 经济学
- 40 篇 应用经济学
39 篇 法学
- 34 篇 社会学
22 篇 农学
9 篇 教育学
5 篇 文学
4 篇 军事学
1 篇 哲学

主题

118 篇 accuracy
61 篇 real-time system...
56 篇 computational mo...
50 篇 deep learning
49 篇 predictive model...
47 篇 machine learning
44 篇 training
41 篇 feature extracti...
38 篇 machine learning...
38 篇 convolutional ne...
32 篇 technological in...
31 篇 data models
25 篇 analytical model...
25 篇 adaptation model...
24 篇 prediction algor...
23 篇 artificial intel...
21 篇 reliability
20 篇 internet of thin...
20 篇 security
19 篇 adversarial mach...

机构

36 篇 lovely professio...
17 篇 institute of aer...
17 篇 institute of aer...
15 篇 lloyd institute ...
11 篇 computer science...
10 篇 dept. of compute...
10 篇 smart data & kno...
9 篇 st. martin's eng...
8 篇 knowledge park i...
7 篇 dept. of compute...
6 篇 college of healt...
6 篇 national univers...
6 篇 kalinga universi...
6 篇 data science and...
6 篇 dept. of informa...
6 篇 dept. of knowled...
6 篇 lloyd institute ...
5 篇 university of th...
5 篇 artificial intel...
5 篇 graphic era hill...

作者

12 篇 dengel andreas
12 篇 schröder markus
12 篇 jilek christian
11 篇 ashwini kodipall...
10 篇 kodipalli ashwin...
9 篇 nasraoui olfa
8 篇 watabe hirokazu
8 篇 tsuchiya seiji
8 篇 yuyun
7 篇 yoshimura eriko
7 篇 rao trupthi
6 篇 shoeibi afshin
6 篇 venu nookala
6 篇 praveen
6 篇 deepak upadhyay
6 篇 nookala venu
6 篇 alizadehsani roo...
6 篇 trupthi rao
5 篇 pradnya saval
5 篇 upadhyay abhay

语言

1,125 篇 英文
68 篇 其他
6 篇 朝鲜文
4 篇 日文

检索条件"机构=Dept. of Data Science and Knowledge Engineering"

共 1200 条记录，以下是161-170 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：

Big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures 9

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

引用

9th IEEE International Conference on Multimedia Big data, BigMM 2023

作者： Cuzzocrea, Alfredo University of Calabria The Big Data Engineering and Analytics Laboratory Rende Italy University of Paris City Dept. of Computer Science Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener... 详细信息

ISBN: (纸本)9798350360004

关键词： data Analytics

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Word2Vec, FastText, and Glove Embeddings for Synonym Identification in Bugis Language

Comparative Study of Word2Vec, FastText, and Glove Embedding...

引用

2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Arianto, Wahyu Ramadhan Yuyun Abrar, Ahmad Umar, Najirah Nasrullah Nurfaedah Gusnawaty Jarin, Asril Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia National Research and Innovation Agency Research center for data and information science Bandung Indonesia Handayani University of Makassar Dept. of informatics management Makassar Indonesia Hasanuddin University Dept. of Regional Literature Makassar Indonesia

ISBN: (纸本)9798331508579

This research aims to analyze the performance of word vectors generated by three pre-trained word embedding models, namely Word2Vec, Fast'Text, and Glove, to detect synonyms at the word level in the Bugis language. The word vectors from each model were integrated with a Bugis language dataset containing 12,060 monolingual texts, and then trained using the Long Short-Term Memory (LSTM) model. The results show that Word2Vec has the highest accuracy of 0.9412, followed by Glove with 0.9353 and FastText with 0.9262. Additional experiments with cosine similarity revealed that Glove performed best for predicting synonyms across all word occurrence frequency categories, while FastText and Word2Vec had inferior results. These findings indicate that although high training accuracy is achieved, it does not always imply that the model will excel in predicting synonyms. © 2024 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Inductive Graph Neural Network Models for Text Classification

Comparative Study of Inductive Graph Neural Network Models f...

引用

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Pandian, Saran Chaurasia, Uttkarsh Ranjan, Shudhanshu Saxena, Shefali Daiict Data Science Gandhinagar India Nit Dept. of Computer Science and Engineering Hamirpur India Presidency University Dept. of Computer Science and Engineering Banglore India Nit Dept. of Electronics and Communication Hamirpur India

ISBN: (纸本)9781665461092

Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output. © 2022 IEEE.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

An Identification of Guava Fruit Disease Using ML 3

An Identification of Guava Fruit Disease Using ML

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Nandi, Subarna Hasan, Nazmul Rashid, Omor Faruk Al Miraz, Shafiun Reza, Md. Shafi Rahman, Farjana International University of Business Agriculture and Technology Uttara Model Town Miyan Research Institute Dhaka Bangladesh Kazla Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh London South Bank University Dept. of Data Science 103 Borough Rd LondonSE1 0AA United Kingdom Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jeonbuk National University Graduate School of Integrated Energy of AI North Jeolla Jeonju Korea Republic of

ISBN: (纸本)9798350388282

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers' socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framework Using Mask R-CNN and ResNet

Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framewo...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Jay Vithlani Kanchan Dabre Pratham Gala Satishkumar L. Varma Dept. of Computer Science and Engineering(Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering(Data Science) Mumbai India Pillai College of Engineering Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Pneumonia remains a significant factor in global mortality rates, highlighting the need for prompt and accurate diagnostic methods to improve patient outcomes. While radiographic examination is a standard diagnostic tool, accurate chest X-ray interpretation might be difficult because of the visual similarities between pneumonia and other respiratory issues. This research explores deep learning techniques for detecting and localizing pneumonia in chest X-ray images. This work has investigated Mask R-CNN’s performance with various backbone architectures. This approach includes comprehensive data preparation, including text removal, image resizing, and employing data augmentation techniques. The study examines different backbone architectures, loss functions, and optimization strategies to find the optimal configuration. The bounding boxes generated by the models identify potential pneumonia infections, and their performance is evaluated using various metrics at IoU thresholds between 0.5 and 0.95. The combined results of the Mask R-CNN model as base model and backbone model as ResNet-101 demonstrates superior performance, with an average IoU of 0.75, precision (88.21%), recall (86.03%), and an F1 score (87.11%). This proposed method not only performs competitively with current models but also provides precise localization through bounding boxes and segmentation masks, potentially enhancing the diagnostic process for radiologists. This research represents a significant advancement in utilizing deep learning for detecting and localizing pneumonia in CXR-images.

关键词： Deep learning Analytical models Visualization Pneumonia Accuracy Reliability X-ray imaging Standards Optimization Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

A Cloud-based Auto-Scaling System for Virtual Resources to Back Ubiquitous, Mobile, Real-Time Healthcare Applications 3

A Cloud-based Auto-Scaling System for Virtual Resources to B...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Khader Basha, Sk Purimetla, Nagamalleswara Rao Roja, D. Vullam, Nagagopiraju Dalavai, Lavanya Vellela, Sai Srinivas Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of CSE AP Guntur India Chalapathi Institute of Engineering and Technology Dept. of CSE AP Guntur India

ISBN: (纸本)9798350343632

The utilisation of virtualization technologies in conjunction with cloud computing has emerged as a significant and influential trend within the Information Technology (IT) sector. Cloud computing has significantly transformed the development landscape of mobile ubiquitous services by introducing notable attributes such as reliability and cost-effectiveness. Consequently, it has successfully penetrated the market, bringing about substantial changes in the industry. While it is possible to easily convert numerous types of best-effort mobile ubiquitous applications to cloud infrastructures, it is crucial to thoroughly analyse essential elements in order to enhance the effectiveness of cloud computing infrastructures for real-time healthcare applications. When programme clients observe an anomaly, there is a substantial increase in the data transfer rate. Nevertheless, there are inherent issues in the current allocation of computational resources by server-side processes to effectively manage the fluctuating and time-varying data quantities. This article introduces a novel auto-scaling technique for servers, which aims to dynamically allocate virtual resources. The suggested method's efficacy in scaling virtual resources up and down in response to the number of real-time tasks is evaluated through testing conducted on an Amazon EC2 platform. The obtained findings demonstrate the efficient performance of the mechanism. © 2023 IEEE.

关键词： Amazon Elastic Compute Cloud Auto-scaling Cloud computing Healthcare Mobile ubiquitous

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Based Approach for Frequency Response Prediction in Low Inertia Power System 11

A Machine Learning Based Approach for Frequency Response Pre...

引用

11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Panwar, Akhilesh Rather, Zakir Hussain Doolla, Suryanarayana Liebman, Ariel Dargaville, Roger Indian Institute of Technology-Bombay Dept. of Energy Science and Engineering Mumbai India Monash University & Monash Energy Institute Dept. of Data Science & AI Civil Engineering Dept. Melbourne Australia

ISBN: (纸本)9798350399660

The uncertainty in securely operating the power system has increased with large-scale integration of inverter based resources (IBRs). IBR-driven displacement of conventional sources results in diminishing inherent frequency support capability of such systems as, unlike conventional synchronous generators, IBRs do not inherently support the grid frequency. Therefore, with increasing vulnerability, there is a need for an accurate frequency prediction model that could help a grid operator better plan system resources and securely operate the power system. Against this backdrop, this paper initially provides a critical insight into the frequency response of IBR-dominated systems to highlight the limitation of using a linear prediction model. Following the critical analysis, the paper proposes a data extraction framework and XGBoost algorithm-based regression model to predict RoCoF, frequency nadir, and quassi steady-state frequency of the IBR-dominated power system. The proposed approach has been implemented on a modified IEEE-39 bus system, and its comparative analysis with other state-of-art algorithms supports the superiority of the proposed method. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

AIML for Road Accident Analysis 4

AIML for Road Accident Analysis

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rathore, Saurabh Pratap Singh Sanitha, P.C. Varshney, Manoj Verma, Anupama Vashisht, Manisha Saharan, Aaryan Icapsr Management & Research New Delhi India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Business Management Ranchi India Lingayas Vidyapeeth University Dept. of Cse Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Road traffic accidents pose a significant threat to both developed and developing nations, prompting global efforts to enhance road safety. With cars and drivers on the roads, pedestrians also face risks, often becoming innocent victims of accidents. Various factors contribute to road accidents, including driving under the influence, weather conditions, speed, road type, light conditions, road surface, and design. These accidents can cause severe, sometimes unseen injuries with lasting health implications. This study aims to explore road accidents in India using the random forest classifier algorithm and machine learning techniques. © 2024 IEEE.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking 38

WelQrate: Defining the Gold Standard in Small Molecule Drug ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Yunchao Dong, Ha Wang, Xin Moretti, Rocco Wang, Yu Su, Zhaoqian Gu, Jiawei Bodenheimer, Bobby Weaver, Charles David Meiler, Jens Derr, Tyler United States Neural Science Dept. Amherst College United States Chemistry Dept. VU United States Computer Science Dept. University of Oregon United States Data Science Institute VU United States MD Anderson Cancer Center United States Electrical and Computer Engineering Dept. VU United States Psychology Dept. VU United States Institute of Chemical Biology VU United States Center for Structural Biology VU United States Pharmacology Dept. VU United States Germany Computer Science Dept. LU Germany Chemistry Dept. LU Germany

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets;WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening;Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging data Mining for Cybersecurity Threat Detection 1

Leveraging Data Mining for Cybersecurity Threat Detection

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Chouhan, Ganga Singh Mishra, Atul Kumar Bandil, Deepika Kumari, Meenakshi Chandra Sekar, P. Chandigarh University Dept. of CSE Chandigarh India Madhav University Dept. of Commerce & Management Pindwara India Institute of Busi. Mang. & Commerce Dept. of Management Aligarh India Usha Martin University Dept. of Busi. Mang. & Commerce Jharkhand India Dhanalakshmi Srinivasan College of Engineering Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

In the face of increasingly sophisticated cyber threats, the application of data mining techniques has become essential for effective cybersecurity threat detection. This paper examines how data mining methodologies, including clustering, classification, and anomaly detection, can be leveraged to enhance cybersecurity defenses. By systematically analyzing large datasets from various sources such as network traffic, system logs, and user activities, data mining tools can identify patterns and anomalies that signify potential security threats. Will discuss the integration of these techniques with machine learning algorithms to improve the accuracy and efficiency of threat detection systems. The study explores several data mining approaches, evaluating their strengths and limitations in detecting both known and novel cyber threats. Additionally, it addresses the challenges associated with data privacy, the volume and complexity of data, and the need for real-time analysis. The results illustrate that data mining not only aids in early threat detection but also provides a foundation for developing adaptive and proactive cybersecurity strategies. The paper concludes by outlining future directions for research and advancements in data mining for cybersecurity, emphasizing the need for ongoing innovation to counter emerging threats. © 2024 IEEE.

关键词： Phishing

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120页 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：