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

1,091 篇 会议
15 篇 期刊文献
14 册 图书

馆藏范围

1,120 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

819 篇 工学
- 731 篇 计算机科学与技术...
- 613 篇 软件工程
- 197 篇 信息与通信工程
- 163 篇 生物工程
- 95 篇 生物医学工程（可授...
- 86 篇 控制科学与工程
- 63 篇 电气工程
- 62 篇 安全科学与工程
- 58 篇 动力工程及工程热...
- 51 篇 光学工程
- 46 篇 机械工程
- 40 篇 交通运输工程
- 38 篇 化学工程与技术
- 31 篇 仪器科学与技术
- 31 篇 电子科学与技术（可...
409 篇 理学
- 196 篇 数学
- 177 篇 生物学
- 83 篇 统计学（可授理学、...
- 71 篇 物理学
- 58 篇 系统科学
- 43 篇 化学
253 篇 管理学
- 151 篇 图书情报与档案管...
- 128 篇 管理科学与工程(可...
- 81 篇 工商管理
99 篇 医学
- 93 篇 临床医学
- 91 篇 基础医学(可授医学...
- 62 篇 药学(可授医学、理...
- 43 篇 公共卫生与预防医...
70 篇 法学
- 50 篇 社会学
33 篇 经济学
- 33 篇 应用经济学
30 篇 农学
23 篇 教育学
5 篇 军事学

主题

53 篇 computational mo...
45 篇 big data
44 篇 machine learning
38 篇 deep learning
38 篇 cloud computing
35 篇 predictive model...
31 篇 feature extracti...
28 篇 machine learning...
26 篇 internet of thin...
25 篇 neural networks
24 篇 data mining
23 篇 blockchain
23 篇 data models
22 篇 support vector m...
21 篇 forecasting
21 篇 training
20 篇 covid-19
20 篇 sentiment analys...
17 篇 technological in...
15 篇 object detection

机构

13 篇 teerthanker maha...
11 篇 jain university ...
9 篇 school of comput...
6 篇 galgotias univer...
4 篇 galgotias univer...
4 篇 department of in...
4 篇 kite group of in...
4 篇 department of co...
4 篇 chandigarh unive...
4 篇 department of ne...
4 篇 school of cyber ...
4 篇 department of co...
4 篇 department of co...
4 篇 college of compu...
3 篇 college of compu...
3 篇 university of en...
3 篇 department of co...
3 篇 china academy of...
3 篇 christ universit...
3 篇 institute of art...

作者

6 篇 jacob lija
6 篇 n. suresh kumar
6 篇 joshi kapil
6 篇 lija jacob
5 篇 vishal bharti
5 篇 aruna bhat
5 篇 bhat aruna
5 篇 naohiro ishii
4 篇 li yang
4 篇 annamalai suresh
4 篇 sharma vishnu
4 篇 ippei torii
4 篇 s. vijayalakshmi
4 篇 vishnu sharma
4 篇 wang tao
4 篇 bharti vishal
4 篇 malik aruna
4 篇 gupta gaurav
4 篇 vijayalakshmi s.
4 篇 li wei

语言

1,111 篇 英文
9 篇 其他
1 篇 中文

检索条件"任意字段=4th International Conference on Computing and Data Science, CONF-CDS 2022"

共 1120 条记录，以下是411-420 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：

A Fast 3D Multi-Object Tracking Method Based on Motion and Appearance Features 4

A Fast 3D Multi-Object Tracking Method Based on Motion and A...

引用

4th international conference on Intelligent Information Processing, IIP 2022

作者： Xu, Jingyi Zhang, Zhian Nanjing University of Science and Technology College of Mechanical Engineering Nanjing China

ISBN: (纸本)9781665492225

3D-MOT(3D multi-target tracking) is an important direction in the research of autonomous driving and intelligent robots, which can provide a rich and reliable dynamic representation of environment for modules such as path planning. this paper proposes a 3D-MOT method that fuses 3D and 2D input data. this method takes into account the motion characteristics and appearance characteristics of the target during the data association process, and uses the Kalman filter to predict the target in 3D and 2D space respectively. State, and proposed a target motion similarity measurement method that integrates motion features in different spaces;at the same time, this method also designed a network for extracting the apparent features of the detected target, and based on this, proposed a similarity feature Finally, an integrated similarity calculation method integrating motion similarity and apparent similarity is proposed for data association. On the basis of ensuring various tracking indicators as much as possible, the purpose of 3D multitarget tracking is achieved with a small amount of calculation, which can meet the real-time algorithm requirements of service robots and other platforms with low detection accuracy and limited computing power. © 2022 IEEE.

关键词： Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-cds 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Impact of High Dimensionality Reduction in Financial datasets of SMEs with Feature Pre-processing in data Mining 4th

Impact of High Dimensionality Reduction in Financial Dataset...

引用

4th international conference on Communication, computing and Electronics Systems, ICCCES 2022

作者： Mahalingam, R. Jayanthi, K. Department of Computer and Information Science Annamalai University Tamil Nadu Chidambaram India Department of Computer Application Government Arts College Tamil Nadu Chidambaram India

ISBN: (纸本)9789811977527

High data Dimensionality Reduction (HDDR) removes the irrelevant features in a complex dataset and incorporates various techniques that could be used to foretell the research outcomes in a predictive model. the major objective of the paper is to analyse and survey the various models on the basis of HDDR and its feature pre-processing methods applied in financial dataset predictions. Numerous techniques of data mining and its strategies were discussed and assessed to ascertain the importance of augmenting the performance of the financial dataset with classifiers. the pre-processing techniques applied in various research works and their outcomes are highlighted. the HDDR methods used in financial prediction of Small Medium Enterprises (SMEs) are studied for existing frameworks and models by different authors. the paper encapsulates the gist of the models, frameworks and algorithms involved in effective elimination of irrelevant features and extraction of best features for best prediction of financial datasets. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Predicting and Analyzing College Students' Performance Based on Multifaceted data Using Machine Learning 4

Predicting and Analyzing College Students' Performance Based...

引用

4th international conference on Advances in Computer Technology, Information science and Communications, CTISC 2022

作者： Sun, Yuxia Tan, Ziyuan Li, Zhetao Long, Saiqin Jinan University College of Information Science and Technology Guangzhou China Xiangtan University College of Computer Science Xiangtan China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

During the teaching process of college courses, prediction of students' final performance at early stages can help teachers intervene students and improve the teaching effects. In recent years, there have been some researches on predicting students' performance based on machine learning techniques. However, many existing works lack comprehensive and sufficient student data, and there is still room for improvement in the effectiveness of predictions. Moreover, many existing methods require obtaining student data of the whole semester, which are unavailable until the end of the semester. In order to alleviate the above problems, 576 students' data are collected during five years teaching of our Software Testing course. the multifaceted data consists of 39 attributes covering students' demographic information, theoretical learning activities, practical training activities and contest learning activities. Several prediction models are created based on logical regression, random forest, and convolutional neural network. Experimental studies show that with the student data available in the middle or second half of the semester, our models can effectively predict students' final performance. Furthermore, the student features that most affect the prediction results are analyzed in the experiments. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Structured Ranking Method-based Feature Selection in data Mining 4

Structured Ranking Method-based Feature Selection in Data Mi...

引用

4th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2022

作者： Bhuyan, Hemanta Kumar Brahma, Biswajit Nyamathulla, S. Mohapatra, Srikanta Kumar Vignan's Foundation for Science Technology and Research Deemed to Be University Department of It Andhra Pradesh Guntur India Mckesson Corporation San FranciscoCA United States Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665400732

Feature selection has been emphasized on an operative approach for dealing with large volume data. the majority of these approaches are skewed into high-ranking features to get well right features towards classification. this paper proposes a structured feature ranking (SFR) approach for large volume data to address this challenge. We present a subspace feature-based clustering approach to find out feature-based cluster as per class labels. the various feature clusters are created ranked for features independently using the SFR approach, based on the subspace weight provided by SFC. then, for ranking the features, we offer a structured feature weighting method in which the high-rank characteristics are utilized for class labels. SFC's approach has been tested in a variety of features. On a collection of large volume datasets, the proposed SFR approach is compared to six feature selection methods. the results demonstrate that SFR method outperformed than methods. © 2022 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Botnet Forensic Analysis in Software Defined Networks using Ensemble Based Classifier 4

Botnet Forensic Analysis in Software Defined Networks using ...

引用

4th international conference on Circuits, Control, Communication and computing, I4C 2022

作者： Sanjeetha, R. Mundada, Monica R. Vaibhavi, G.S. Sambhram Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350397475

Software Defined Networks (SDN), a developing technology is replacing the conventional networking solutions that are currently in use. It helps greatly with network programmability and disassociates data plane from control plane. Due to its centralized administration, SDN is susceptible to number of threats. Botnet attacks are the most predominant kind of attacks. Peer-to-peer (P2P) botnets are a new, sophisticated variety of botnets. It is a decentralized network of computers that lack C & C servers and have been infected with malware and cooperate for the benefit of an attacker without the owners' permission. To combat botnet attacks, there are networking system solutions that use a single machine learning (ML) classifier, like the Support Vector Machine (SVM), decision tree (DT), etc. To create a more dependable and precise detection and mitigation application, detection speeds must be improved. In order to provide a system that effectively detects, classifies botnet traffic as Peer-to-Peer (P2P) and mitigates it, this paper uses extensive forensic analysis by combining SDN with decision tree and bagging integrated as ensemble machine learning classifiers to satisfy the necessary needs. this suggested method has 97 percent accuracy rate for identifying and mitigating botnet attacks on SDN networks. © 2022 IEEE.

关键词： Botnet

来源：评论

学校读者我要写书评

暂无评论

Cloud data Storage Optimization by Using Novel De-Duplication Technique 4

Cloud Data Storage Optimization by Using Novel De-Duplicatio...

引用

4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kumar, P.M. Ashok Pugazhendhi, E. Lakshmi, K. Vara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522 502 India Dept. of Information Technology Madras Institute of Technology Anna University Tamil Nadu 600044 India

ISBN: (纸本)9781665401180

Cloud computing, an internet-based computing is composed of various applications and hardware deployed to the end-user. Existing system deals with block level de-duplication. But in case of block-level de-duplication maintenance of large number of blocks is highly difficult. It also requires high processing power when compared to other de-duplication techniques. this is the reason why File-level De-duplication comes into picture. File based de-duplication is considered due to faster access and easier storage and retrieval of files. this paper proposes to develop a Dynamic cloud storage system and improving the performance of cloud storage by minimizing the data replications using the De-duplication technology-De-duplication Based Cloud Storage (DBCS). In de-duplication, memory occupied by the replicates of the original file in the main server is reduced without removing them from the subsequent servers. For increasing the cloud storage efficiency, de-duplication and compression techniques are adapted. these techniques are to be implemented in the cloud environment via Amazon Web Services (AWS), which is an online web service provider. To develop a dynamic cloud storage system, the operations proposed are: 1. Inline de-duplication using MD5 (client side) 2. Dynamic data reconfiguration 3. Updating resources across servers. the main advantage of using cloud storage from the customers' point of view is that customers can reduce their expenditure in purchasing and maintaining storage infrastructure while only paying for the amount of storage requested, which can be scaled-up and down upon demand. the experiments are performed on different scenarios like 100% popular files, second combination of 30% unpopular files, 70% popular files and third combination of 70% unpopular files, 30% popular files. In all these cases, the proposed approach performs 95% better than traditional techniques in terms of space requirements. © 2022 IEEE

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Triphase Community Detection for Multimedia data 4th

Deep Learning-Based Triphase Community Detection for Multime...

引用

4th international conference on Communication, computing and Electronics Systems, ICCCES 2022

作者： Sowmyadevi, D. Srividhya, S. Department of Computer Science PSG College of Arts and Science Coimbatore India Department of Computer Science KPR College of Arts Science and Research Coimbatore India

ISBN: (纸本)9789811977527

Network analysis plays a significant role in business which is achieved through community detection. the relationship between the nodes is mined by community detection, which facilitates the analysis of complicated networks. In the current era, social media is the commonest mode of communication that leads to complex online social networks, from which useful information can be retrieved. Not only textual data is shared in social media, in addition multimedia data plays a significant role in content sharing in social network. this research work proposes a community detection method based on deep learning algorithm to detect communities for both textual content and multimedia content. the proposed method triphase–deep learning community detection (TriDL-CD) method illustrates the relationship of data in a graphical way in the first step. Second step converts the graph into user relationship table using similarity weightage analysis from which the communities are formed using convolution neural network. the proposed method proved to be efficient in detecting high quality communities for multimedia content compared with Louvain community detection algorithm, Leiden community detection, and surprise community detection algorithms. Experimental results show the efficacy of deep learning in the concept of community detection. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Research on Face Detection and Recognition with Face Mask Based on FaceNet 4

Research on Face Detection and Recognition with Face Mask Ba...

引用

4th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Wang, Hongxia Gu, Yuting Li, Hui School of Big Data and Artificial Intelligence Anhui Xinhua University Heifei China

ISBN: (纸本)9781450398343

According to the requirements of face recognition with masks in epidemic environment, this paper analyzes the difficulties of face recognition with masks, and puts forward a scheme combining face detection, data enhancement and face recognition. Firstly, face detection is performed by MtCNN algorithm;then, data enhancement technology is used to enhance the complexity of sample data sets and further optimize FaceNet model. Finally, the face feature vector is extracted by FaceNet model to realize the correct recognition of mask face. Experiments show that this scheme can improve the accuracy of face recognition with masks to a certain extent, and at the same time improve the traffic speed. © 2022 ACM.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

An Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced database 4th

An Ensemble MultiLabel Classifier for Intra-Cranial Haemorrh...

引用

4th international conference on Soft computing and its Engineering Applications (icSoftComp)

作者： Choudhary, Bharat Babu, Akhitha Talukdar, Upasana Cent Univ Rajasthan Dept Data Sci & Analyt Ajmer Rajasthan India Indian Inst Informat Technol Dept Comp Sci & Engn Gauhati Assam India

ISBN: (纸本)9783031276088;9783031276095

Fast and accurate detection of traumatic brain injuries such as intracranial Haemorrhage (ICH), is substantial as it may lead to death or disability. In this paper, a novel deep learning-based approach is proposed for automated detection of ICH from non-contrast CT scans. the proposed model is a hybrid methodology that amalgamates Convolution Neural Network(CNN) with Support Vector Machine (SVM) and Extreme Gradient Boost (XG-Boost) for detection of ICH. Methodologies for dealing with multi-labeled and imbalanced data are very rarely employed in the literature. this paper put forwards an ensemble multilabel classifier for automatic ICH detection on a huge, heterogeneous, clinical, and imbalanced database of brain CT studies. In the proposed method, CNN is trained and then the extracted feature output received from CNNs is fed to the CNN, XG-Boost, and SVM for classification. Experimental results presents a high performance by the proposed model in the detection of ICH when compared with CNN, CNN with SVM, and CNN with XG-Boost.

关键词： Intracranial Haemorrhage (ICH). MultiLabel classification. Imbalance pre-processing. Deep Neural Network

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112页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：