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

126 篇 会议
1 册 图书

馆藏范围

127 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

107 篇 工学
- 99 篇 计算机科学与技术...
- 45 篇 软件工程
- 16 篇 信息与通信工程
- 15 篇 生物工程
- 10 篇 控制科学与工程
- 6 篇 电气工程
- 5 篇 电子科学与技术（可...
- 4 篇 交通运输工程
- 4 篇 安全科学与工程
- 3 篇 动力工程及工程热...
- 3 篇 建筑学
- 3 篇 水利工程
- 3 篇 生物医学工程（可授...
- 2 篇 机械工程
- 2 篇 光学工程
- 2 篇 仪器科学与技术
- 2 篇 化学工程与技术
35 篇 理学
- 19 篇 数学
- 17 篇 生物学
- 11 篇 统计学（可授理学、...
- 5 篇 物理学
- 5 篇 系统科学
- 2 篇 大气科学
28 篇 教育学
- 28 篇 教育学
18 篇 管理学
- 14 篇 图书情报与档案管...
- 4 篇 管理科学与工程(可...
3 篇 医学
- 3 篇 基础医学(可授医学...
- 3 篇 临床医学
2 篇 经济学
- 2 篇 应用经济学
2 篇 法学
- 2 篇 社会学
1 篇 军事学

主题

14 篇 machine learning
7 篇 deep learning
6 篇 natural language...
3 篇 support vector m...
3 篇 decision trees
3 篇 accuracy
2 篇 object detection
2 篇 learning systems
2 篇 transformer
2 篇 deep neural netw...
2 篇 gamification
2 篇 data repository
2 篇 data symbion ei
2 篇 data mining
2 篇 multi-task learn...
2 篇 large language m...
2 篇 traffic congesti...
2 篇 image classifica...
2 篇 classification
2 篇 federated learni...

机构

2 篇 harvard grad sch...
2 篇 warsaw univ tech...
2 篇 laboratory of el...
2 篇 university claud...
2 篇 univ fed alagoas...
2 篇 fed rural univ s...
2 篇 department of ra...
2 篇 carnegie mellon ...
2 篇 mars research la...
2 篇 department of el...
2 篇 intelligent tran...
2 篇 yonsei univ dept...
2 篇 indian inst tech...
1 篇 univ poitiers f-...
1 篇 univ wroclaw ins...
1 篇 wiesbaden
1 篇 school of electr...
1 篇 department of co...
1 篇 school of inform...
1 篇 ceprei iptrkl of...

作者

2 篇 rajendran ramkum...
2 篇 hastings peter
2 篇 sotero roberto c...
2 篇 reis marcelo
2 篇 ding naikan
2 篇 sahin halil mert...
2 篇 isotani seiji
2 篇 grieve bruce
2 篇 santos matilde
2 篇 cohn clayton
2 篇 martín de diego ...
2 篇 bittencourt ig i...
2 篇 weitekamp daniel
2 篇 sanchez-bornot j...
2 篇 omri mohamed naz...
2 篇 camacho david
2 篇 omri asma
2 篇 yin hujun
2 篇 santos jario
2 篇 cho sung-bae

语言

127 篇 英文

检索条件"任意字段=24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023"

共 127 条记录，以下是31-40 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：

Misbehavior Detection in Cooperative intelligent Transportation Systems using Temporal Fusion Transformer 23

Misbehavior Detection in Cooperative Intelligent Transportat...

引用

24th international conference on Distributed Computing and Networking (ICDCN)

作者： Goyal, Anish Bhatia, Archit Yadav, Arjun Sharma, Deepak Kumar Netaji Subhas Inst Technol Dept Informat Technol Delhi India Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India

ISBN: (纸本)9781450397964

the United States in the early 20th century became the first nation to introduce the concept of intelligence in the transportation systems followed by intensive research and development by the European Countries like (Sweden, Switzerland), Japan, South Korea in the similar area. Since 1991, intelligent Transport Systems (ITS) have been an emerging and promising area of research. this fact is also aided by the recent advancement and developments in the field of automated driving systems over the past few years. Cooperative intelligent Transportation Systems (C-ITS) is the latest advancement over the existing intelligent Transportation Systems (ITS) as now the systems are making use of communication and interaction with infrastructure using connected and automated vehicles (CAV's) in order to create helpful and exciting applications that concern road safety, environment, mobility. Trust and Node Cooperation are the most crucial factors on which C-ITS relies as the decisions that are taken by a vehicle are based on the data and information it gets from the network infrastructure that is installed on the roadsides which is basically a pool of information which has been gathered from other vehicles and smart infrastructures that are part of the VANET. Presence of any misbehaviour in the network or a compromised node in the transport network system will lead to atrocious results for both traffic and network safety. Detecting any misbehaviour in the network can save us a lot of resources that will go into creating a system that can defend the complete network against a threat. through the medium of our thesis project we will be detecting misbehaviour in the system using Deep learning techniques. A most Basic MLP Neuron Architecture based Artificial Neural Network (ANN) would serve as our baseline and proposed models include LSTM and Transformer Model which will be used for time series classification.

关键词： cits temporal fusion transformer Transportation misbehavior

来源：评论

学校读者我要写书评

暂无评论

Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced data Classification 24th

Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced...

引用

24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Miftahushudur, Tajul Sahin, Halil Mertkan Grieve, Bruce Yin, Hujun Bandung Indonesia Department of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9783031482311

the issue of imbalanced data in machine learning has gained significant attention in recent years. Imbalanced data, where one class has significantly fewer samples than others, can lead to poor performance for machine learning models, especially in detecting minority class samples. To address this problem, various resampling techniques have been proposed, including the popular SMOTE (Synthetic Minority Over-sampling TEchnique). However, SMOTE suffers from the overlapping problem and may misclassify samples near the separation boundaries. this paper presents a novel framework to optimise border-based-SMOTEs, including Borderline-SMOTE and SVM-SMOTE which were specifically developed to solve the problem of misclassifying border samples. the proposed method ensures that generated samples improve the decision boundaries and are free from overlapping issues. the proposed method is evaluated on synthetic and real-world datasets, and results demonstrate its effectiveness in enhancing the performance of machine learning models, particularly in classifying minority class samples. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

An Autonomous Self-learning and Self-adversarial Training Neural Architecture for intelligent and Resilient Cyber Security Systems 1

引用

24th international conference on engineering Applications of Neural Networks (EANN)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Sch Engn Fac Math Programming & Gen Courses Dept Civil Engn Xanthi Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. this research proposes an autonomous Self-learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. the CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. the proposed method in this research improves the CTRC's architecture by including a Conditional Tabular Generative Adversarial Network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model's performance and adapt to new and evolving threats without manual intervention. this, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

关键词： Reservoir Computing Continuous-Time Reservoir Computing Cyber Defense Time Series Analysis

来源：评论

学校读者我要写书评

暂无评论

Sample Size Issues in the Choice between the Best Classifier and Fusion by Trainable Combiners

引用

15th international conference on intelligent data engineering and automated learning (ideal)

作者： Raudys, Sarunas Fumera, Giorgio Raudys, Aistis Pillai, Ignazio Vilnius State Univ Fac Math & Informat Naugarduko St 24 LT-03225 Vilnius Lithuania Univ Cagliari Dept Elect & Elect Eng I-09123 Cagliari Italy

ISBN: (纸本)9783319108407;9783319108391

We consider an open issue in the design of pattern classifiers, i.e., choosing between the best classifier among a given ensemble, and combining all the available ones using a trainable fusion rule. While the latter choice can in principle outperform the former, their actual effectiveness is affected by small sample size problems. this raises the need of investigating under which conditions one choice is better than the other one. We provide a first contribution, by deriving an analytical expressions of the expected error probability of best classifier selection, and by comparing it with the one of a well known linear fusion rule, implemented with the Fisher linear discriminant.

关键词： data dimensionality sample size complexity collective decision expert fusion accuracy classification

来源：评论

学校读者我要写书评

暂无评论

Group-Personalized Federated learning for Human Activity Recognition through Cluster Eccentricity Analysis 1

引用

24th international conference on engineering Applications of Neural Networks (EANN)

作者： Al-Saedi, Ahmed A. Boeva, Veselka Blekinge Inst Technol Karlskrona Sweden

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. the proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. the performance of the proposed FL scheme is studied and evaluated on real-world HAR data. the evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison.

关键词： Federated learning Clustering Eccentricity Analysis Non-IID data HAR

来源：评论

学校读者我要写书评

暂无评论

FETCH: A Memory-Efficient Replay Approach for Continual learning in Image Classification 24th

FETCH: A Memory-Efficient Replay Approach for Continual Lea...

引用

24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Weißflog, Markus Protzel, Peter Neubert, Peer Faculty of Electrical Engineering and Automation Technology Chemnitz University of Technology Chemnitz Germany Institute for Computational Visualistics University of Koblenz Koblenz Germany

ISBN: (纸本)9783031482311

Class-incremental continual learning is an important area of research, as static deep learning methods fail to adapt to changing tasks and data distributions. In previous works, promising results were achieved using replay and compressed replay techniques. In the field of regular replay, GDumb [23] achieved outstanding results but requires a large amount of memory. this problem can be addressed by compressed replay techniques. the goal of this work is to evaluate compressed replay in the pipeline of GDumb. We propose FETCH, a two-stage compression approach. First, the samples from the continual datastream are encoded by the early layers of a pre-trained neural network. Second, the samples are compressed before being stored in the episodic memory. Following GDumb, the remaining classification head is trained from scratch using only the decompressed samples from the reply memory. We evaluate FETCH in different scenarios and show that this approach can increase accuracy on CIFAR10 and CIFAR100. In our experiments, simple compression methods (e.g., quantization of tensors) outperform deep autoencoders. In the future, FETCH could serve as a baseline for benchmarking compressed replay learning in constrained memory scenarios. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Hebbian learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks 24th

Hebbian Learning-Guided Random Walks for Enhanced Community ...

引用

24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Sotero, Roberto C. Sanchez-Bornot, Jose M. Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada Intelligent Systems Research Centre Ulster University Derry Londonderry United Kingdom

ISBN: (纸本)9783031482311

Community detection in complex signed networks is a significant challenge, traditionally addressed using the Louvain method directly applied to the correlation matrix. this study introduces a two-tier approach that integrates a Hebbian learning rule within an adaptive signed random walk (ASRW) framework, then applies the Louvain method to the final weight matrix. this approach refines the network analysis process, providing a new tool for exploring community structure. Tested extensively on synthetic signed networks with defined community structures, our methodology consistently outperformed the traditional Louvain approach, particularly when communities were less clearly demarcated. Further application to resting-state functional MRI data from the ABIDE Preprocessed Initiative highlighted functional connectivity differences between neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). Our approach found key areas of significant difference, including several cerebellum regions, consistent with existing ASD literature. Our findings underscore the potential of the proposed technique to advance community detection in correlation-based networks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Population dynamics

来源：评论

学校读者我要写书评

暂无评论

24th international conference on Artificial Intelligence in Education, AIED 2023

24th International Conference on Artificial Intelligence in ...

引用

24th international conference on Artificial Intelligence in Education, AIED 2023

ISBN: (纸本)9783031362712

the proceedings contain 79 papers. the special focus in this conference is on Artificial Intelligence in Education. the topics include: the Road Not Taken: Preempting Dropout in MOOCs;does Informativeness Matter? Active learning for Educational Dialogue Act Classification;can Virtual Agents Scale Up Mentoring?: Insights from College Students’ Experiences Using the *** Platform at an American Hispanic-Serving Institution;Real-Time AI-Driven Assessment and Scaffolding that Improves Students’ Mathematical Modeling during Science Investigations;Improving automated Evaluation of Student Text Responses Using GPT-3.5 for Text data Augmentation;the automated Model of Comprehension Version 3.0: Paying Attention to Context;smartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues;analysing Verbal Communication in Embodied Team learning Using Multimodal data and Ordered Network Analysis;improving Adaptive learning Models Using Prosodic Speech Features;neural automated Essay Scoring Considering Logical Structure;"Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure Analysis;leveraging Deep Reinforcement learning for Metacognitive Interventions Across intelligent Tutoring Systems;enhancing Stealth Assessment in Collaborative Game-Based learning with Multi-task learning;how Peers Communicate Without Words-An Exploratory Study of Hand Movements in Collaborative learning Using Computer-Vision-Based Body Recognition Techniques;scalable Educational Question Generation with Pre-trained Language Models;involving Teachers in the data-Driven Improvement of intelligent Tutors: A Prototyping Study;reflexive Expressions: Towards the Analysis of Reflexive Capability from Reflective Text;Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned;algebra Error Classification with Large Language Models;exploration of Annotation Strategies for Automati

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Model for Probabilistic Web Resources Under Uncertainty 24th

Efficient Model for Probabilistic Web Resources Under Uncer...

引用

24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Omri, Asma Benslimane, Djamal Omri, Mohamed Nazih MARS Research Laboratory University of Sousse Sousse Tunisia University Claude Bernard Lyon 1 LIRIS Research Laboratory Villeurbanne France

ISBN: (纸本)9783031482311

Individuals, organizations, and connected objects produce and publish vast data on the web. these data are then combined into mashups to produce new valuable data. However, combining data from different sources may lead to data uncertainty because these sources contain heterogeneous, contradictory, or incomplete information. In expert and intelligent systems, the word "uncertainty" is related to working with inaccurate data, imprecise and incomplete information, and unreliability of results that can lead to irrational decisions. An expert and intelligent system can consider all the uncertain information, and manage web services and web resources based on the best possible answer rather than on the quality of the exact answer to manage such problems. this paper proposes a new model to define, compute, and interpret uncertain web resources in the context of classical hypertext navigation and evaluation of data queries. the experimental study as well as the analysis of the results, which we carried out on different corpora, confirmed the usefulness of our approach. It provides the effect of the treatment of uncertainty on the execution time and the importance of calculating the confidence level of the response returned to the user. the analysis of the IT execution time shows that the time required for the composition of the services by our approach is negligible, as compared to that of the other approach studied. the impact of the variation in the number of nodes of type "mux" and type "ind" were also evaluated. Our algorithm checks all possibilities in polynomial time and can adapt to many possibilities of multiplexing values. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： intelligent systems

来源：评论

学校读者我要写书评

暂无评论

the M-OLAP Cube Selection Problem: A Hyper-polymorphic Algorithm Approach

The M-OLAP Cube Selection Problem: A Hyper-polymorphic Algor...

引用

11th international conference on intelligent data engineering and automated learning

作者： Loureiro, Jorge Belo, Orlando Escola Super Tecnol & Gestao Inst Politecn Viseu Dept Informat Campus Politecn Repeses P-3505510 Viseu Portugal Univ Minho Escola Engn Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783642153808

OLAP systems depend heavily on the materialization of multidimensional structures to speed-up queries, whose appropriate selection constitutes the cube selection problem. However, the recently proposed distribution of OLAP structures emerges to answer new globalization's requirements, capturing the known advantages of distributed databases. But this hardens the search for solutions, especially due to the inherent heterogeneity, imposing an extra characteristic of the algorithm that must be used: adaptability. Here the emerging concept known as hyper-heuristic can be a solution. In fact, having an algorithm where several (meta-)heuristics may be selected under the control of a heuristic has an intrinsic adaptive behavior. this paper presents a hyper-heuristic polymorphic algorithm used to solve the extended cube selection and allocation problem generated in M-OLAP architectures.

关键词： Hyper-Heuristics Distributed data Cube Selection Multi-Node OLAP Systems 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13页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：