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

1,638 篇 会议
1 篇 期刊文献

馆藏范围

1,639 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

955 篇 工学
- 824 篇 计算机科学与技术...
- 565 篇 软件工程
- 290 篇 信息与通信工程
- 240 篇 控制科学与工程
- 154 篇 电气工程
- 106 篇 网络空间安全
- 92 篇 安全科学与工程
- 81 篇 交通运输工程
- 79 篇 生物医学工程（可授...
- 63 篇 生物工程
- 58 篇 电子科学与技术（可...
- 54 篇 动力工程及工程热...
- 52 篇 仪器科学与技术
- 52 篇 石油与天然气工程
- 43 篇 光学工程
329 篇 理学
- 177 篇 数学
- 81 篇 生物学
- 78 篇 统计学（可授理学、...
- 69 篇 物理学
- 46 篇 化学
- 37 篇 系统科学
306 篇 管理学
- 239 篇 管理科学与工程(可...
- 121 篇 图书情报与档案管...
- 89 篇 工商管理
- 42 篇 公共管理
125 篇 医学
- 85 篇 公共卫生与预防医...
- 84 篇 临床医学
- 64 篇 基础医学(可授医学...
58 篇 法学
- 55 篇 社会学
45 篇 经济学
- 45 篇 应用经济学
18 篇 教育学
18 篇 文学
11 篇 农学
2 篇 军事学
1 篇 艺术学

主题

193 篇 data models
158 篇 accuracy
153 篇 computational mo...
93 篇 machine learning
92 篇 training
87 篇 feature extracti...
77 篇 predictive model...
74 篇 deep learning
70 篇 data mining
64 篇 real-time system...
60 篇 big data
49 篇 adaptation model...
46 篇 security
44 篇 robustness
42 篇 decision making
40 篇 cloud computing
40 篇 convolutional ne...
36 篇 neural networks
36 篇 analytical model...
35 篇 artificial intel...

机构

14 篇 lovely professio...
7 篇 shandong provinc...
7 篇 key laboratory o...
6 篇 university of ch...
6 篇 college of mecha...
6 篇 institute of aer...
5 篇 lloyd institute ...
5 篇 srm institute of...
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 chitkara univers...
4 篇 karnataka bangal...
4 篇 srm institute of...
4 篇 school of comput...
4 篇 shenzhen institu...
4 篇 chitkara univers...

作者

7 篇 nijhawan ginni
5 篇 fu lijun
5 篇 choudhry mani de...
5 篇 jeanette miriam ...
5 篇 lu xiaobo
5 篇 singh navdeep
4 篇 nicola franco
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

语言

911 篇 英文
727 篇 其他
5 篇 中文

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

共 1639 条记录，以下是1151-1160 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：

Lightweight Spiking Neural Network Based Detector for Interweave cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

引用

ieee international conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

来源：评论

学校读者我要写书评

暂无评论

Design of data-Driven Learning Path Based on Knowledge Graph and Tracing Model

Design of Data-Driven Learning Path Based on Knowledge Graph...

引用

ieee international conference on High Performance computing and Communications (HPCC)

作者： Juli Zhang Ruheng Xia Quan Wang Academy of Advanced Interdisciplinary Research Xidian University Xian China College of Computer Science and Technology Xidian University Xian China

With the explosive growth of online courses, per-sonalized learning path recommendations have gained much attention in E-learning area. Studies aim to provide customized guidance that cater to the needs of each learner. However, generating an appropriate learning path remains a challenge due to the complex cognitive relationships among learning items and learners' different abilities. Existing methods often overlook the impacts of cognitive structure and mastery level on the outcomes of customized instructions, leading to cognitive confusion problem. To address this issue, we propose a model named data-Driven Learning Path based on Knowledge Graph and Tracing Model (LP-KAT), which combines knowledge graph (KG) and knowledge tracing model (KT) to store the relationships among learning items and reflect learners' abilities through the abundant data. The guidance is separated into concept and practice identification; besides, through reinforcement learning, our model can suggest a sequence of practices for different learners. Specifically, we first utilize a knowledge tracing model to track learners' evolving mastery levels at each learning step. Then we design a concept selector that employs knowledge graph navigation algorithm and reinforcement learning to determine which concept to learn next. Finally, we utilize a practice selector to identify practice question based on the relationship between mastery level and difficulty of practice. We evaluate the proposed model against KNN, GRU4Rec, MCS and DQN using a real-world dataset, and the results show an improvement of more than 30 % in the effectiveness of concept mastery level and nearly 20% better than GRU4Rec across various learning path lengths.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Early Dementia Detection Integrating Clinical data and MRI Imaging

Machine Learning for Early Dementia Detection Integrating Cl...

引用

ieee Punecon

作者： Vaidehi Sarag Chhaya Gosavi Jahnavi Patil Department of Computer Engineering Cummins College of Engineering for Women Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Dementia, particularly Alzheimer's disease, is a leading cause of cognitive decline, affecting millions globally. Early detection is vital for effective management, yet it remains challenging due to the complex nature of the disease. This paper explores the application of machine learning techniques to predict dementia, focusing on integrating clinical data and MRI images. We propose a hybrid model that combines Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to enhance prediction accuracy. The study addresses challenges such as data heterogeneity and model interpretability, highlighting the potential of multimodal approaches in dementia research. The results demonstrate the effectiveness of combining clinical and imaging data, offering insights into the future of personalized treatment for dementia.

关键词： Support vector machines Accuracy Magnetic resonance imaging Machine learning Transforms Predictive models data models Vectors Convolutional neural networks Dementia

来源：评论

学校读者我要写书评

暂无评论

DynaCEP a framework for Dynamic Event Streaming in IoT

DynaCEP a framework for Dynamic Event Streaming in IoT

引用

ieee/ACM international Symposium on Big data computing (BDC)

作者： Vincenza Carchiolo Michele Malgeri Giulio Samperi Salvatore Sorbello Dip. Ingegneria Elettrica Elettronica Informatica Università di Catania Via Santa Sofia 64 Catania Italy Bax Energy Italia s.r.l Via Sclafani Italy

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This paper presents a system designed to handle real-time streaming and the prioritization of events in dynamic, high-volume data environments. The system efficiently processes incoming events and organizes them according to customizable prioritization rules, ensuring that high-priority events are addressed promptly. A standout feature of the system is its ability to support multiple prioritization rules simultaneously, offering the flexibility to modify or create new rules on the fly without disrupting the event processing pipeline. This adaptability makes the system particularly well-suited for real-time, data-driven environments such as IoT ecosystems, smart cities, industrial processes, and renewable energy systems, where the ability to prioritize and view events through different lenses is essential for operational efficiency and responsiveness. By dynamically adjusting to new rules and outputting rule-specific timelines, the system offers a powerful solution for managing and analyzing event streams in complex, rapidly evolving contexts.

关键词： Renewable energy sources Smart cities Pipelines Ecosystems Big data Real-time systems Streams Lenses

来源：评论

学校读者我要写书评

暂无评论

A Hybrid RRNS-CRC Based data Coding Technique for Internet of Things 10

A Hybrid RRNS-CRC Based Data Coding Technique for Internet o...

引用

10th international conference on computing, Engineering and Design, ICCED 2024

作者： Mohamed Firdhous, Mohamed Fazil Elbreiki, Walid Sudantha, B.H. Abdullahi, Ibrahim Budiarto, Rahmat University of Moratuwa Dept. of Information Technology Moratuwa10400 Sri Lanka College of Computer Technology Dept. of Computer Science Benghazi Libya Ibrahim Badamasi Babangida University Dept. of Computer Science Niger State Lapai Nigeria College of Computing and Information Al-Baha University Alaqiq65779-7738 Saudi Arabia

ISBN: (纸本)9798331529376

Internet of Things (IoT) is a revolutionary technology that transforms the environment into a smarter one by making ordinary objects into smarter ones. The IoT objects with limited resources communicate with each other using wireless technology through free space. Free space is a hostile environment as it is filled with many natural and man-made noise sources including the earth's magnetic field. These noise sources affect the transmitted information inducing bit errors in them. Hence, data needs to be encoded using special algorithms for detecting and correcting errors. Since, IoT objects are resource constrained devices, it is not possible to implement complex algorithms in them. In this paper, the authors propose a hybrid technique leveraging Redundant Residue Number System and Cyclic Redundancy Check for error correction. The simulation results show that the proposed technique adds less overhead as well as has fixed processing delay irrespective of data block size compared to Hamming code. © 2024 ieee.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

A Multi-Class Classification Framework with SMOTE Based data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

引用

Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), international conference on

作者： Kaivaram Sivarama Krishna Bommala Deepak Kumar Morumpalli Deekshith Reddy CH Saketh Gayatri Ramasamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders.

关键词： Deep learning Computational modeling Magnetic resonance imaging Computer architecture data augmentation Resonance Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

DUDPA-TATD: A Lightweight Privacy-Preserving Anomaly Traffic Detection Method for Edge computing Scenarios

DUDPA-TATD: A Lightweight Privacy-Preserving Anomaly Traffic...

引用

ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Guanghan Li Wenzhong Yang Xiaodan Tian Jiaren Peng College of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the field of network security, anomaly traffic detection is a key technology for identifying network attacks. It detects abnormal behavior and counteracts it by monitoring changes in traffic patterns. Deep learning-based methods significantly improve detection performance due to their ability to efficiently learn and extract complex feature representations. However, these methods require large amounts of data for training, which exacerbates the risk of privacy breaches and increases training time consumption. This paper proposes an anomaly traffic detection method based on differential privacy and temporal convolutional networks, named DUDPA-TATD. DUDPA-TATD consists of two components: the Dynamic Update Strategy with Differential Privacy Protection Laplace Mechanism (DUDPA) and the Temporal Aggregation Traffic Detection network based on Temporal Convolutional Networks (TATD). DUDPA gradually adjusts the privacy protection strategy according to the inference rules of TATD, while TATD enhances the learning of macro and micro differences in time-series data by aggregating key features, further improving detection performance. We conducted experiments on two public datasets. Compared with baseline methods, extensive experiments on the DAPT2020 and SMAP datasets demonstrate that our DUDPA-TATD not only exhibits superior detection performance but also requires less average training time than most baseline methods, qualifying it as a lightweight detection model.

关键词： Training Measurement Differential privacy Computational modeling Traffic control Feature extraction data models Convolutional neural networks Protection Edge computing

来源：评论

学校读者我要写书评

暂无评论

Predicting Alzheimer’s Disease and Mild cognitive Impairment with Off-line and On-line House Drawing Tests

Predicting Alzheimer’s Disease and Mild Cognitive Impairmen...

引用

ieee international conference on e-Science and Grid computing

作者： Nina Hosseini-Kivanani Elena Salobrar-García Lorena Elvira-Hurtado Mario Salas Christoph Schommer Luis A. Leiva University of Luxembourg Luxembourg Ramon Castroviejo Institute of Ophthalmologic Research Universidad Complutense of Madrid Spain Memory Unit Geriatrics Service Hospital Clínico San Carlos Madrid Spain

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

There is growing interest in developing reliable, non-invasive, and cost-effective methods for early diagnosis of neurodegenerative diseases such as Mild cognitive Impairment (MCI) and Alzheimer’s Disease (AD). In this regard, handwriting-based tasks have shown potential in differentiating MCI and AD patients from healthy controls (HCs). However, previous work has reported mixed results when using different symbols and data representations. We address this research gap by developing computational models (convolutional and recurrent neural networks) to differentiate MCI and AD from HCs with off-line (scanned images) and on-line (discrete time series) house drawings. Notably, we observed that augmenting on-line data and then converting it to off-line format, a method we refer to as "OnOff-line", yielded the best performance results in binary classification tasks. These findings highlight the effectiveness of on-line representations in capturing handwriting dynamics more accurately. Ultimately, our work opens new avenues for future research to enhance automated diagnostic of MCI and AD from handwriting analysis.

关键词： Recurrent neural networks Computational modeling Time series analysis Symbols Medical services Reliability Alzheimer's disease

来源：评论

学校读者我要写书评

暂无评论

A framework for hyperparameter optimization of genomic cloud pipelines: improving the performance of GATK VETS variant filtration

A framework for hyperparameter optimization of genomic cloud...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Wanying Dou Samuel K. Lee Kiran V. Garimella Northwestern University Feinberg School of Medicine Chicago IL USA Broad Institute of MIT and Harvard Data Sciences Platform Cambridge MA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Running complex machine-learning pipelines in the cloud is standard practice in genomics. However, optimizing these pipelines with respect to their hyperparameters can be challenging, which may limit their effectiveness. To address this gap, we developed an Optuna-based hyperparameter optimization framework and integrated it within the Terra cloud computing platform. To demonstrate our framework, we optimized the Genome Analysis Toolkit Variant Extract-Train-Score (GATK VETS) pipeline for genomic variant filtration. We evaluated the performance of our framework using both human and malaria genomic callsets, demonstrating improved accuracy in detecting sequencing artifacts. Our work underscores the importance of a flexible framework for tuning genomic cloud pipelines and highlights its potential for broader applicability in other genomic machine-learning analyses.

关键词： Cloud computing Sequential analysis Accuracy Filtration Malaria Pipelines Genomics Hyperparameter optimization Bioinformatics Tuning

来源：评论

学校读者我要写书评

暂无评论

Research on Natural Language Generation System of BERT Model Computer Big data Technology

Research on Natural Language Generation System of BERT Model...

引用

ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Jianqin Zhang Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper focuses on the application and research of pre-trained model BERT in natural language generation system to meet the needs of natural language processing tasks in the era of big data. With the emergence of massive data, it is of great significance to use big data technology to understand and generate natural language efficiently. This study aims to build and optimize a natural language generation system based on BERT model, which can fully tap and utilize large-scale unannotated text data and capture complex context dependencies through deep two-way Transformer architecture to achieve accurate and smooth natural language generation. First, this paper discusses the core mechanism of BERT model and its superiority in natural language understanding, and analyzes its ability to effectively learn massive text resources in big data environment. Then, we design a BERT model extension scheme that ADAPTS to big data environment. By combining distributed computing framework and targeted model optimization strategy, the training efficiency and generalization performance of the model can be improved when processing massive data. In the experimental part, we conduct a detailed evaluation of the constructed natural language generation system using large-scale real-world data sets, compare multiple benchmark models, and demonstrate significant effects on specific tasks such as news summary generation and conversation response generation. Experiments show that the natural language generation system based on BERT model effectively utilizes the advantages of big data while maintaining high language quality, and improves the relevance and accuracy of generated content.

关键词： Training Adaptation models Accuracy Computational modeling Natural language generation Distributed databases Big data Transformers data models Optimization

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164页 << < 112 113 114 115 116 117 118 119 120 121 > >>

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：