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

1,560 篇 会议
1 篇 期刊文献

馆藏范围

1,561 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

884 篇 工学
- 764 篇 计算机科学与技术...
- 525 篇 软件工程
- 249 篇 信息与通信工程
- 192 篇 控制科学与工程
- 127 篇 电气工程
- 82 篇 网络空间安全
- 77 篇 生物医学工程（可授...
- 72 篇 安全科学与工程
- 63 篇 交通运输工程
- 61 篇 生物工程
- 55 篇 电子科学与技术（可...
- 44 篇 动力工程及工程热...
- 43 篇 仪器科学与技术
- 42 篇 石油与天然气工程
- 41 篇 光学工程
302 篇 理学
- 161 篇 数学
- 82 篇 生物学
- 71 篇 统计学（可授理学、...
- 60 篇 物理学
- 37 篇 系统科学
- 33 篇 化学
262 篇 管理学
- 199 篇 管理科学与工程(可...
- 104 篇 图书情报与档案管...
- 82 篇 工商管理
- 34 篇 公共管理
112 篇 医学
- 78 篇 临床医学
- 75 篇 公共卫生与预防医...
- 62 篇 基础医学(可授医学...
49 篇 法学
- 47 篇 社会学
37 篇 经济学
- 37 篇 应用经济学
18 篇 文学
15 篇 教育学
11 篇 农学
2 篇 军事学
1 篇 艺术学

主题

193 篇 data models
157 篇 accuracy
153 篇 computational mo...
92 篇 training
86 篇 feature extracti...
86 篇 machine learning
77 篇 predictive model...
69 篇 data mining
67 篇 deep learning
63 篇 real-time system...
57 篇 big data
49 篇 adaptation model...
45 篇 security
44 篇 robustness
41 篇 decision making
40 篇 convolutional ne...
39 篇 cloud computing
36 篇 analytical model...
35 篇 neural networks
34 篇 optimization

机构

14 篇 lovely professio...
7 篇 shandong provinc...
7 篇 key laboratory o...
6 篇 college of mecha...
6 篇 institute of aer...
5 篇 lloyd institute ...
5 篇 college of intel...
5 篇 national univers...
5 篇 department of co...
5 篇 shandong fundame...
5 篇 national univers...
5 篇 institute of aer...
4 篇 karnataka bangal...
4 篇 shanghai jiao to...
4 篇 school of comput...
4 篇 chitkara univers...
4 篇 new horizon coll...
4 篇 srm institute of...
4 篇 school of softwa...
4 篇 koneru lakshmaia...

作者

7 篇 nijhawan ginni
5 篇 jeanette miriam ...
5 篇 lu xiaobo
5 篇 singh navdeep
4 篇 nicola franco
4 篇 choudhry mani de...
4 篇 akhil karnam
4 篇 zhang rui
4 篇 yu zhang
4 篇 akshya j.
4 篇 lihui wang
4 篇 qu yuben
4 篇 wang lihui
4 篇 li lei
4 篇 dong chao
4 篇 dutt varun
4 篇 puri chetan
3 篇 yuangan wang
3 篇 junqi luo
3 篇 zhang lei

语言

1,452 篇 英文
108 篇 其他
3 篇 中文

检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是241-250 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：

complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks 2

Complex Event Processing in Web Streams with Ontology-Based ...

引用

2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishna, M Hari Manjunatha Kumar, Rakesh Singh, Navdeep Kumar, Ashwani Ftaiet, Adnan Allawi Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Smart cities embody the integration of information technology, urban infrastructure, and citizen engagement to optimize city functions and drive economic growth. A critical component in achieving this integration is the ability to process complex events in real-time, particularly from diverse web streams. This paper introduces a novel framework for complex Event Processing (CEP) in web streams by employing ontology-based abstraction layers, aiming to enhance the interpretability and interoperability of heterogeneous data sources within smart city frameworks. The proposed approach uses a layered architecture where the bottom layer deals with raw data streams from various sources, including IoT devices, social media, and sensor networks. The middle layer employs an ontology-based model to abstract and enrich the raw data, providing a unified and semantic representation. This representation facilitates the identification and correlation of complex events, which are pertinent to city management and planning. The top layer involves the application of advanced data mining techniques to predict, detect, and respond to urban events in a timely and efficient manner. Through this methodology, the framework addresses the challenges of scalability, semantic heterogeneity, and real-time processing needs inherent in smart city applications. Empirical evaluations demonstrate the efficacy of the approach in various urban scenarios, showing promising improvements over existing methods. This work lays a foundational architecture for future research and development in smart city technologies, aiming to make urban areas more livable, sustainable, and efficient. © 2024 ieee.

关键词： complex Event Processing data Mining Ontology-Based Abstraction Smart Cities Web Streams

来源：评论

学校读者我要写书评

暂无评论

An Extensive Analysis of ML Techniques for Predicting and Analysing Medical data 5

An Extensive Analysis of ML Techniques for Predicting and An...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajan, Akshay Manoj, Milan Santhosh, Gouri Sarath, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related datasets. Through an in-depth analysis of three major datasets including demographics, health indicators, and assessments of Parkinson's disease, this study reveals the subtle advantages and disadvantages of each algorithm. Whereas Random Forest has unmatched flexibility in figuring out complex correlations, Logistic Regression stands out as an example of simplicity and openness. SVM struggles with computing loads and parameter complexities at the same time. This thorough investigation avoids claiming a model is supe-rior, allowing practitioners to identify the complex interactions amongst machine learning giants when it comes to healthcare datasets. © 2024 ieee.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

LSAFE: a Lightweight Static Analysis Framework for binary Executables

LSAFE: a Lightweight Static Analysis Framework for binary Ex...

引用

2024 ieee international Performance, computing, and Communications conference, IPCCC 2024

作者： Yue, Xiao Qu, Guangzhi Oakland University Department of Computer Science and Engineering RochesterMI United States

ISBN: (纸本)9798350367942

Static analysis is a widely used technique for analyzing various aspects of programs. However, as programs become more complex, static analysis tools require larger resources, such as CPU time and memory, to perform the same tasks. Moreover, the source code of programs may not always be accessible, requiring static analysis to be performed on the binary executable code directly. To overcome these challenges, we propose a lightweight static analysis framework called LSAFE, which constructs control flow graphs (CFGs) and data dependency graphs (DDGs) of target programs with optimized performance in terms of CPU and memory usage. We evaluated the proposed framework using both Spec benchmark programs and real-world industrial applications, and found that it outperformed Angr, an existing state-of-the-art static analysis tool. Additionally, we demonstrate a case study that utilizes the CFG generated by LSAFE to detect memory leaks. © 2024 ieee.

关键词： data flow graphs

来源：评论

学校读者我要写书评

暂无评论

data Poisoning Attacks in cognitive computing 9

Data Poisoning Attacks in Cognitive Computing

引用

9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Koundinya, Anjan K. Patil, S.S. Chandu, B.R. B M S Institute of Technology And Management Department of Information Science And Engineering Karnataka Bengaluru India B M S Institute of Technology And Management Information Technology Solutions Karnataka Bengaluru India B M S Institute of Technology And Management Department of Electronics And Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350394474

data poisoning attacks pose a significant threat to the integrity of machine learning (ML) models, impacting their ability to generate accurate predictions. This research delves into the realm of adversarial ML [1], specifically focusing on poisoning attacks [4] during the training phase. A taxonomy of adversarial ML techniques is presented, classifying poisoning attacks into different categories. Real-world examples, including incidents involving Google's VirusTotal [8] scanning service, highlight the practical implications of data poisoning. The study explores defense mechanisms such as training data sanitization and robust optimization, acknowledging the nuanced balance required between model robustness and accuracy. The paper concludes with recommendations for safeguarding ML models against evolving adversarial threats, underscoring the ongoing importance of vigilance and education in the cybersecurity landscape. © 2024 ieee.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Multiple Machine Learning Models for Alzheimer's disease detection for Mixed data with Explainable AI 15

Multiple Machine Learning Models for Alzheimer's disease det...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rashmi, Uppin Beena, B.M. Department of CSE Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

Alzheimer‘s disease involves a gradual decline in mental function, characterized by the accumulation of abnormal protein deposits around brain cells, resulting in irreversible neural cells death. Unfortunately, existing medications and procedures cannot regenerate these neurons. As the disease progresses symptoms of forgetfulness worsens, significantly impacting daily routines and lifestyle. The progression of Alzheimer's disease is typically divided into three phases. The preclinical phase occurs when there are no noticeable symptoms, but early Alzheimer's biomarkers are present. The prodromal phase, or mild cognitive impairment (MCI), is characterized by mild cognitive decline that does not significantly affect daily activities. Lastly, dementia represents the final phase, where moderate to severe cognitive impairment prevents independent living. Early prediction of Alzheimer's onset is crucial to mitigate its progression. Machine learning (ML) and artificial intelligence (AI) models play a pivotal role in predicting the disease based on health and lifestyle parameters. These advanced technologies enable early detection, offering hope for interventions to delay or prevent further cognitive decline. The Machine learning model is built by developing 4 different models to get better base for disease prediction and after that the results are described using ExplainableAI tools such as SHAP and LIME as the healthcare domain needs explanation for all the results. Using this model we are getting an accuracy of around 94%-100%. ©2024 ieee.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Utilizing Deep Reinforcement Learning for Advanced Pattern Extraction in Big data Analytics and Ontology Systems 2

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

引用

2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Karthik, A. Asha, V. Bhardwaj, Garima Nijhawan, Ginni Khan, Irfan Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Amity University Amity Business School Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement Learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors. © 2024 ieee.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving Deep Learning for Autism Spectrum Disorder Classification 9

Privacy-preserving Deep Learning for Autism Spectrum Disorde...

引用

9th ieee international conference on Smart Cloud, SmartCloud 2024

作者： Gao, Guangmao Zhang, Hanlin Lin, Jie Xu, Hansong Kong, Fanyu Yu, Leyun Qingdao University College of Computer Science and Technology Qingdao266071 China Xian Jiaotong University School of Computer Science and Technology Xian710049 China Shanghai Jiaotong University School of Computer Science and Technology Shanghai200240 China Shandong University School of Computer Science and Technology Jinan250100 China JIC IOT CO. LTD Nanchang330000 China

ISBN: (纸本)9798350389500

Autism Spectrum Disorder (ASD) encompasses a range of complex neurodevelopmental conditions typically identified in early childhood. ASD is characterized by challenges in social interaction, communication, and by repetitive behaviors with restricted interests. The variability in symptoms' severity and expression among individuals presents significant diagnostic challenges to physicians. Advancements in computer technology have led various fields to adopt deep learning for constructing classification models. However, given the private nature of patient data, its leakage could have grave consequences. To mitigate this risk, we employ secure multiparty computing techniques and introduce a deep learning framework that ensures data interoperability without compromising privacy. Our framework facilitates deep learning training and inference via a lightweight, replicated secret-sharing technique. Experimentally, the scheme has been proven to exhibit high security, accuracy, and efficiency. © 2024 ieee.

关键词： Hospital data processing

来源：评论

学校读者我要写书评

暂无评论

A Novel Feature Fusion-Based and complex Contextual Model for Smoking Detection 6

A Novel Feature Fusion-Based and Complex Contextual Model fo...

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Ren, Zheng College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350353174

Smoking target detection plays a central role in contributing to the improvement of public health and fire prevention. Given the limitations of existing techniques in multi-scale fusion, feature fusion efficiency, and accurate extraction of key targets in complex data environments, this paper proposes an advanced and efficient innovative algorithmic model-BEC-YOLO, which focuses on solving these challenges with a view to improving the overall detection performance. In this paper, BiFPN is firstly adopted to realize multi-level feature fusion in order to make up for the shortcomings of existing target detection algorithms in feature extraction and recognition, which effectively improves the feature fusion effect. Then, ECA is introduced to significantly improve the efficiency and accuracy of key target information extraction in complex data environments. Finally, the feature representation and fusion efficiency are further enhanced by introducing CBAM. The experimental findings demonstrate a notable 5.3% enhancement in mAP when comparing the approach in this paper to the original model. This highlights the accuracy of the BEC-YOLO model for detecting smoking in intricate settings. © 2024 ieee.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

Study of Machine Learning Tactics for Missing data Imputation and Classification

Study of Machine Learning Tactics for Missing Data Imputatio...

引用

2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Kapare, Suresh Anu, Maria Sathyabama Institute of Science and Technology CSE Department Chennai India MIT ADT Univerisity Pune India Vellore Institute of Technology CSE Departement Vellore India

ISBN: (纸本)9798350362879

Different data analysis techniques are used to perform data analysis on big data. Results of the analysis are affected by missing data values generated due to data generation sources. The general approach is to discard missing data values or use complex algorithms which leads to standard errors. In many applications like health, space, defense, etc., errors beyond the desirable threshold are not acceptable. This paper studies the different traditional and advanced data imputation methods available as per specific applications. The qualitative and optimized error analysis of these methods is discussed. Furthermore, a comparison of several machine learning approaches used to the task of imputation of missing data demonstrates the significance of the Iterative Robust Semi-Supervised based Imputation Algorithm (IRSSI) method in conjunction with the Credal Transfer Learning (CTL) classification method. © 2024 ieee.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Machine learning model for predictive maintenance of modern manufacturing assets 29

Machine learning model for predictive maintenance of modern ...

引用

29th international conference on Automation and computing (ICAC)

作者： Shaalan, Abdu Baglee, David Dixon, Derek Univ Sunderland Fac Technol Sunderland England

ISBN: (纸本)9798350360882;9798350360899

Predictive maintenance is considered a powerful practice for manufacturing assets health assessment, facilitating the identification of potential failure occurrences. By proactively addressing such failures, manufacturers can avoid unplanned downtime and allocate necessary resources for required maintenance activities. Machine Learning (ML) methods have emerged as a promising tool for preventing equipment failures in Predictive Maintenance applications. However, the effectiveness of Predictive Maintenance applications is largely determined by the Machine Learning techniques utilized and the quality of the data utilised. In this research, we adapted the cross-industry standard process for data mining to develop a predictive maintenance model for a unique, large, and complex manufacturing asset, utilizing various machine learning techniques. Specifically, the research incorporate Random Forest, Support Vector Machines, K-Nearest Neighbors, eXtreme Gradient Boost, and Logistic Regression algorithms to the asset failure records. Following the fitting of all models, Random Forest emerged as the best-performing model based on the recall parameter. However, the algorithm performance was not satisfactory due to the poor data quality. In addition, an exploratory data analysis process was conducted on the data to derive insights into the failure pattern of the machine.

关键词： Machine Learning data analysis Predictive Maintenance

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157页 << < 21 22 23 24 25 26 27 28 29 30 > >>

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：