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

525 篇 会议
29 册 图书
4 篇 期刊文献

馆藏范围

558 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

383 篇 工学
- 353 篇 计算机科学与技术...
- 228 篇 软件工程
- 60 篇 信息与通信工程
- 46 篇 控制科学与工程
- 39 篇 电气工程
- 38 篇 生物工程
- 24 篇 生物医学工程（可授...
- 24 篇 安全科学与工程
- 20 篇 机械工程
- 19 篇 交通运输工程
- 17 篇 动力工程及工程热...
- 15 篇 化学工程与技术
- 14 篇 电子科学与技术（可...
- 12 篇 光学工程
137 篇 理学
- 71 篇 数学
- 43 篇 生物学
- 37 篇 统计学（可授理学、...
- 22 篇 系统科学
- 19 篇 化学
- 18 篇 物理学
108 篇 管理学
- 74 篇 管理科学与工程(可...
- 48 篇 工商管理
- 47 篇 图书情报与档案管...
27 篇 医学
- 26 篇 基础医学(可授医学...
- 26 篇 临床医学
- 17 篇 药学(可授医学、理...
24 篇 法学
- 22 篇 社会学
15 篇 教育学
- 15 篇 教育学
12 篇 经济学
- 12 篇 应用经济学
12 篇 农学
- 12 篇 作物学
1 篇 军事学

主题

36 篇 big data
27 篇 machine learning
20 篇 data mining
20 篇 artificial intel...
19 篇 internet of thin...
18 篇 cloud computing
10 篇 support vector m...
10 篇 deep learning
10 篇 computational in...
10 篇 predictive model...
10 篇 decision making
9 篇 computational mo...
9 篇 real-time system...
9 篇 data models
7 篇 blockchain
7 篇 industries
7 篇 machine learning...
7 篇 data analysis
6 篇 reinforcement le...
6 篇 data privacy

机构

6 篇 university of vi...
6 篇 university of mi...
5 篇 department of ma...
4 篇 electronics and ...
3 篇 tohoku univ ctr ...
3 篇 university of sa...
3 篇 center for data-...
3 篇 department of co...
3 篇 tongji universit...
2 篇 university of ai...
2 篇 school of comput...
2 篇 western universi...
2 篇 chongqing univer...
2 篇 new delhi
2 篇 uttaranchal scho...
2 篇 hindustan instit...
2 篇 university tun h...
2 篇 school of econom...
2 篇 department of el...
2 篇 cairo

作者

4 篇 swagatam das
4 篇 snehanshu saha
4 篇 carlos a. coello...
3 篇 yuda emi
3 篇 emi yuda
3 篇 altintas ilkay
3 篇 florentino fdez-...
3 篇 jagdish c. bansa...
2 篇 yuehui chen
2 篇 haijun zhang
2 篇 deepak gerard
2 篇 tianyong hao
2 篇 rozaida ghazali
2 篇 pan yongqiang
2 篇 neil yen
2 篇 nakamura shigena...
2 篇 m. rajmohan
2 篇 sridhar
2 篇 rajeswari d.
2 篇 miguel p. rocha

语言

445 篇 英文
109 篇 其他
6 篇 中文

检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是261-270 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：

Structured Process on FL for Big data Analysis

Structured Process on FL for Big Data Analysis

引用

Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Anitha Govindaram J. S. Prasath K. Jayasakthi N. Rajkumar G. Porkodi Jose Anand A Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Bengaluru Karnataka Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The integration of the Internet of Things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated Learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. This paves the way for a wide variety of intelligent IoT applications to be developed. This article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. These include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015–2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT.

关键词： Industries data privacy Federated learning Smart healthcare Memory Collaboration Internet of Things Informatics Mobile computing Convergence

来源：评论

学校读者我要写书评

暂无评论

Composite AI for Behavior Analysis in Social Interactions 25

Composite AI for Behavior Analysis in Social Interactions

引用

25th international conference on Multimodal Interaction, ICMI 2023 Companion

作者： Dos Santos Melicio, Bruno Carlos Xiang, Linyun Dillon, Emily Soorya, Latha Chetouani, Mohamed Sarkany, Andras Kun, Peter Fenech, Kristian Lorincz, Andras Department of Artificial Intelligence Eotvos Lorand University Budapest Hungary Department of Psychiatry Rush University Medical Center ChicagoIL United States Institute for Intelligent Systems and Robotics Cnrs Umr 7222 Sorbonne University Paris France Argus Cognitive Inc. HanoverNH United States

ISBN: (纸本)9798400703218

Social interactions are fundamental to human life. Accurately identifying and interpreting verbal and non-verbal cues is essential for analyzing human behavior and human-machine interactions. The complexity of these interactions, along with the different communication signals, and their varying frequencies is a challenge that Deep Neural Networks cannot yet address. Composite AI, which combines Deep Learning (DL) and traditional AI methods, emerges as a potential approach. We propose a framework comprising three main modules: feature extraction, episode detection, and activity interpretation. The feature extraction module uses DL methods to extract relevant information about the environment, interacting partners, and context of the interaction. By combining these features with rule-based systems, we restrict the analysis to key episodic components, effectively reducing the false positive detection rate. Classifiers are then used to identify and recognize specific sub-events. We show our system's effectiveness in detecting communication cues on recorded sessions of a standardized autism diagnostic test (ADOS-2) involving autistic children, where detecting such cues can be highly challenging. In this scenario, we achieve 87% and 89% accuracy for verbal and non-verbal requests, respectively, and zero false positives. This comprehensive framework can significantly enhance social interaction analysis, enabling more effective automated event analysis. We also highlight the importance of sub-event segmentation and the components that could be improved with additional algorithmic combinations or data-driven improvements of the detection systems. © 2023 Owner/Author.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Interactive continual learning for robots: a neuromorphic approach 22

Interactive continual learning for robots: a neuromorphic ap...

引用

international conference on Neuromorphic systems (ICONS)

作者： Hajizada, Elvin Berggold, Patrick Iacono, Massimiliano Glover, Arren Sandamirskaya, Yulia Tech Univ Munich Neuromorph Comp Lab Intel Labs Chair Cognit Syst Munich Germany Tech Univ Munich Chair Computat Modeling & Simulat Munich Germany Ist Italiano Tecnol Event Driven Percept Robot Grp Genoa Italy Intel Labs Neuromorph Comp Lab Munich Germany

ISBN: (纸本)9781450397896

intelligent robots need to recognize objects in their environment. This task is conceptually different from the typical image classification task in computer vision. Robots need to recognize particular object instances, not classes of objects, which makes these tasks simpler. However, these instances need to be recognized under different viewing angles, poses, and lighting conditions reliably. Moreover, for many application, robots need the capability to learn new objects quickly, e.g., in an interactive session with the user, and adapt object representations if conditions change and mistakes are made. This scenario creates a demand for object learning that (1) is continual, i.e. new objects can be added at any time without causing forgetting, (2) requires a small amount of data that can be acquired in a short interactive session with the user, and (3) stays open to later adaptation. Deep neural networks trained with slow gradient-based backpropagation, despite of their excellent performance on image processing tasks, are not well-suited for interactive robotic learning tasks. We thus explore smaller neural architectures and increase autonomy of the learning process by a neuronal state machine (NSM). The NSM regulates learning in the network and enables continual adaptation of the learned object prototypes. We implement this model as a spiking neural network in Intel's neuromorphic research chip Loihi and test it on a custom event-based camera dataset generated in a simulated 3D environment. Our spiking neuronal network uses simple feature extraction layers and a single plastic layer that stores visual patterns using online, on-chip local learning rules. This network reaches 96.55 +/- 2.02% of testing accuracy on sets of 8 3D objects with 8 different views per object in interactive, on-demand learning experiments;it demonstrates up to x300 energy efficiency and better or on par latency compared to other online learning methods. This work contributes to neuronal-netw

关键词： Neuromorphic computing spiking neuronal networks event-based vision continual learning Hebbian-learning on-chip learning local learning online learning robot vision prototype-based approaches interactive learning

来源：评论

学校读者我要写书评

暂无评论

Cyber attack detection framework for cloud computing 8th

Cyber attack detection framework for cloud computing

引用

8th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2020

作者： Badde, Suryakant Kumar, Vikash Chatterjee, Kakali Sinha, Ditipriya Department of Computer Science and Engineering National Institute of Technology Patna PatnaBihar800005 India

ISBN: (纸本)9789811556784

To prevent cyber-attacks, cloud-based systems mainly depend upon different types of intrusion detection systems (IDS). Most of the approaches have high detection rate for known attacks. But in case of unknown attacks or new attacks, these intrusion detection system increases false alarm rate. Another problem is that the reduction of false alarm rate increases the computational complexities in case of genetic algorithm-based IDS and ANN-based IDS. For instance, to tackle challenges like zero-day attack, the only way is to rely upon a robust data-driven approach for security in cloud. Actually in cloud huge amount of data are processed for various activities. It is very difficult to correlate events over such huge amount of data. To improve the abilities of monitoring and fast decision-making, context management is used for correlating events and inferring contexts and evidences. In this paper, a new data-driven framework has been proposed which utilizes ontology and knowledge base to detect cyber-attack with intrusion detection system in cloud. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Predicting Models of Per Day Inbound/Outbound Baggage at Terminal 2 of Taoyuan international Airport During 2019–2023

Predicting Models of Per Day Inbound/Outbound Baggage at Ter...

引用

international Symposium on Soft computing and intelligent systems (SCIS)

作者： Sui-Ling Li Dept. of Shipping and Transportation Management National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study aims to develop a data-driven predictive model for daily inbound and outbound baggage volumes at Terminal 2 of Taoyuan international Airport. The research spans 2019 to 2023, encompassing data from the COVID-19 pandemic period. The study considers multiple influencing factors by establishing regression models, including daily passenger numbers, COVID-19 impact, seasonal variations, and policy factors. The models demonstrate high accuracy with R 2 values exceeding 0.98. Key findings reveal that the average daily outbound baggage (5,388 pieces) significantly exceeds inbound (3,239 pieces), with outbound passenger numbers having a more significant influence on baggage volume (coefficient 1.045) compared to inbound (coefficient 0.918). Sensitivity analysis shows that changes in passenger numbers have the most significant impact on baggage volume, followed by COVID-19, the 2022 effect, and the January effect. Notably, COVID-19 impacted outbound baggage (-4,680 pieces) more than inbound (-2,517 pieces). The study also established high and low thresholds for baggage handling, with 90% and 68% thresholds for outbound baggage at 36,844 and 25,573 pieces, respectively, and 31,123 and 20,812 pieces for inbound. The model developed in this study accurately predicts daily baggage volumes and serves for long-term planning, such as forecasting inbound baggage for January 2024. These findings give airport managers valuable decision-support tools to optimize resource allocation, improve service quality, and prepare for potential disruptions, enhancing operational efficiency and passenger satisfaction.

关键词： COVID-19 Accuracy Sensitivity analysis Pandemics Atmospheric modeling Predictive models Airports Planning Resource management Forecasting

来源：评论

学校读者我要写书评

暂无评论

intelligent gateway for data-centric communication in internet of things 3rd

Intelligent gateway for data-centric communication in intern...

引用

3rd international conference on Innovative computing and Communication, ICICC 2020

作者： Raj, Rohit Sinha, Akash Kumar, Prabhat Singh, M.P. Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9789811551475

Extending a wireless sensor network to the Internet of Things poses a plethora of challenges. One of the major causes is that these networks have been designed as a resource constrained network while Internet of Things require delivering a variety of services to the end users irrespective of their geolocation. This paper proposes an intelligent gateway to facilitate data-driven communication in a resource constraint networks so as to enable the extension of resource constraint sensor/actuator networks to the Internet of Things. The proposed solution allows for multiple levels of Quality of Service in low end devices without extra infrastructural changes. Results obtained show that the proposed solution is feasible in real-time scenarios and is capable of handling multiple types of data simultaneously. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

intelligent Obstacle Avoidance Controller for QBot2 7th

Intelligent Obstacle Avoidance Controller for QBot2

引用

7th international conference on advances in Control and Optimization of Dynamical systems (ACODS)

作者： Asad, Muhammad Usman Gu, Jason Farooq, Umar Dey, Rajeeb Balas, Valentina E. Abbas, Ghulam Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3H 4R2 Canada Natl Inst Technol Silchar Elect Engn Silchar Assam India Aurel Vlaicu Univ Arad Dept Automat & Appl Software Arad Romania Univ Lahore Dept Elect Engn Lahore Pakistan

This paper describes the design process and the steps involved in the implementation of a fuzzy logic-based controller for autonomous navigation of an educational robot called Qbot2. The differential-driven robot is equipped with three ultrasonic sensors (SRF-04) mounted on the front bumper and DC motors with built-in encoders and current sensors. The controller takes the inputs from three ultrasonic sensors and generates the speed commands to avoid any obstacle in its path. PCB is designed to process all the data from the sensors and direct the signal to the motors via motor driver circuits. The low-level implementation of the hurdle avoidance controller is implemented using an inexpensive Arduino UNO in real-time. The controller performance is validated through an experimental run. The designed platform can be used to implement various fuzzy inference systems in real-time and hence can be utilized as laboratory work for testing soft computing algorithms on mobile robotics. Copyright (C) 2022 The Authors.

关键词： Fuzzy Logic Robot sensing system Mobile robot Trajectory Microcontroller

来源：评论

学校读者我要写书评

暂无评论

Enabling Software Defined Networking for Industry 4.0 Using OpenStack 17th

Enabling Software Defined Networking for Industry 4.0 Using ...

引用

17th Annual conference of the international-Association-of-Online-Engineering (IAOE) and the Global-Online-Laboratory-Consortium (GOLC) on Remote Engineering and Virtual Instrumentation (REV) - Cross Reality and data Science in Engineering

作者： Gowripeddi, Venkata Vivek Ganesan, Nithya Shailendra, Samar Thangaraju, B. Bapat, Jyotsna Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9783030525750;9783030525743

Industry 4.0 refers to the fourth generation of advancements in Manufacturing that enables data driven processes, Predictive maintenance, Lights-Out Manufacturing among others. Industry 4.0 comprises of elements from Cyber Physical systems, Industrial IoT, Big data, Cloud computing, AI and ML. However, Networking at the which is at the heart of these technologies has not had a major improvement, we wish to address this in this paper. The goal of this paper is to highlight the challenges in the current networking infrastructure for Industry 4.0 and propose a new standard for networking in Industry 4.0. Software Defined Networking is a emerging technology in the field of networking which provides for a centralised Control Plane and enables Network Function Virtualisation for Industry 4.0. OpenFlow Stack is used for building the solution. The outcomes of the paper include: Provide an SDN Architecture for the above system. Different zones have to be integrated to provide the ability for Centralized Control plane without compromising confidentiality. The system should be scalable for widespread deployment across multiple plants and manufacturers. This project deals with design and development of a Software Defined Networking (SDN) architecture for Industry 4.0, which involves integration of various components of OpenStack and openFlow. In this project, we identify the limitations and gaps in the current networking architecture for Industry 4.0 and propose a solution for the same. The openstack implementation of the project is done, Different Results of the implementation and its advantages are discussed along with scope for future implementation. This addresses a key challenge in Networking for Industry 4.0.

关键词： Industry 4.0 Networking data science SDN OpenStack

来源：评论

学校读者我要写书评

暂无评论

Model Construction, Problems and Countermeasures of intelligent Logistics Ecosystem driven by Blockchain Technology

Model Construction, Problems and Countermeasures of Intellig...

引用

computing, Robotics and System Sciences (ICRSS), international conference on

作者： Xing Huang Guangzhou Sontan Polytechnic College Guangzhou China

In the process of the development of the digital economy increasingly, block chain and the Internet of things, cloud computing, big data analysis, a new generation of digital technologies such as artificial intelligence fusion application drives the intelligent logistics constantly change, build a logistics business system, the logistics system of supply chain and logistics chain system as the core of ecological intelligent logistics system, but due to the block chain technology is still in its infancy, Therefore, there are some problems such as rough technology and imperfect three core systems. Countermeasures and suggestions for the development of intelligent logistics ecological system are put forward.

关键词： Cloud computing Computational modeling Biological system modeling Supply chains Ecosystems Blockchains Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction

Comparative Analysis of Machine Learning Techniques for Hear...

引用

Ubiquitous computing and intelligent Information systems (ICUIS), international conference on

作者： N. Susitha H. Selvi N.B. Mahesh Kumar VR. Nagarajan Syed Sumera Ali L. Sathish Kumar Vinayaka Mission's Research Foundation (DU) Paiyanoor OMR Chennai TamilNadu India Saveetha College of Liberal Arts and Sciences Chennai TamilNadu India School of Computing Science and Hindusthan Institute of Technology Coimbatore Tamil Nadu India Faculty of Arts Science Commerce and Management Karpagam Academy of Higher Education Coimbatore Tamil Nadu India CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India SCSE VIT Bhopal University Kothrikalan Madhya Pradesh India

Due to its substantial effects on society, efforts to enhance heart disease diagnosis and treatment have increased. Through data mining and the archiving of medical records, improved patient management is now possible thanks to the fusion of technology and medical diagnoses. Understanding the interaction between risk factors in the histories of patients and their impact on their prospects for heart disease is of utmost importance. This study aims to accurately predict cardiac disease by analyzing various data elements from patients. The selection and feature extraction methods are the most efficient choices for prediction system for heart disease. The following variables, such as age, sex, occupation, smoking, obesity, diet, exercise, mental stress levels, type of chest pain, history of chest pain, pressure, ECG, and results, have all been identified as significant factors in diagnosing heart disease. Various machine learning methods, such as Multilayer Perceptron (MLP), Naive Bayes (NB), K-nearest Neighbor (K-NN) and Support Vector Machine (SVM) were utilized to analyze the heart disease dataset. These datasets consisted of one with all features and another with only selected features. The objective was to compare the performance of these methods and determine which ones yielded accurate predictions. The results revealed that random forest, using the chosen features, outperformed other artificial intelligence algorithms, including those using all input features, achieving a maximum accuracy rate of 90%. As a support framework for predicting cardiac disease in its early stages, the suggested approach shows promise. This study advances the prognosis of cardiac disease, enables prompt therapies, and enhances patient outcomes by fusing the strength of data-driven AI algorithms with thorough feature selection.

关键词：

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56页 << < 23 24 25 26 27 28 29 30 31 32 > >>

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：