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

1,577 篇 会议
1 篇 期刊文献

馆藏范围

1,578 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

900 篇 工学
- 775 篇 计算机科学与技术...
- 533 篇 软件工程
- 254 篇 信息与通信工程
- 195 篇 控制科学与工程
- 131 篇 电气工程
- 85 篇 网络空间安全
- 78 篇 生物医学工程（可授...
- 75 篇 安全科学与工程
- 64 篇 交通运输工程
- 62 篇 生物工程
- 55 篇 电子科学与技术（可...
- 46 篇 动力工程及工程热...
- 44 篇 仪器科学与技术
- 43 篇 石油与天然气工程
- 42 篇 光学工程
307 篇 理学
- 164 篇 数学
- 83 篇 生物学
- 73 篇 统计学（可授理学、...
- 62 篇 物理学
- 37 篇 系统科学
- 34 篇 化学
267 篇 管理学
- 203 篇 管理科学与工程(可...
- 106 篇 图书情报与档案管...
- 84 篇 工商管理
- 34 篇 公共管理
114 篇 医学
- 80 篇 临床医学
- 77 篇 公共卫生与预防医...
- 64 篇 基础医学(可授医学...
51 篇 法学
- 48 篇 社会学
39 篇 经济学
- 39 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
154 篇 computational mo...
92 篇 training
86 篇 feature extracti...
86 篇 machine learning
78 篇 predictive model...
69 篇 data mining
68 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 university of ch...
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...

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

1,274 篇 英文
303 篇 其他
4 篇 中文

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

共 1578 条记录，以下是1421-1430 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：

Novel Strategies for Road Network Disruption Analysis 23

Novel Strategies for Road Network Disruption Analysis

引用

Proceedings of the ieee/ACM 16th international conference on Utility and Cloud computing

作者： Annamaria Ficara Giacomo Fiumara Maria Fazio Antonio Celesti Massimo Villari University of Messina Messina Italy

ISBN: (纸本)9798400702341

Road (or street) networks refer to interconnected systems of streets, roads, and pathways which constitute the transportation infrastructure within a city or urban area. These networks play a crucial role in urban planning and design, as they influence various aspects of city life. There are several key elements to be considered in the study of street networks: hierarchy, grid and patterns, connectivity, walkability, transit integration, traffic flow, land use and zoning, and smart mobility solutions. In this study, we use social network analysis tools to identify the most effective strategies to simulate and assess the impact of road network disruption. Our scenario has been constructed by building a road network based on a portion of the geographic map data of Messina (Italy) and implementing random and targeted attacks based on edge centrality measures and street types. The impact of our strategies on the road network structure is evaluated in terms of connectivity.

关键词： network science graph theory complex network road network street network network disruption

来源：评论

学校读者我要写书评

暂无评论

Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： Image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

引用

ieee international conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction complexity theory data mining Cybernetics Convergence

来源：评论

学校读者我要写书评

暂无评论

Sickle Cell Disease Epidemiology and Management in Africa: Current Trends and Future Directions in Digital Health Technologies

Sickle Cell Disease Epidemiology and Management in Africa: C...

引用

ieee international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olamide O. Afolalu Oluwaseyi A. Akpor Sunday A. Afolalu Oladele F. Afolalu Helen Bosede Oyewole Ajibade O. Oke Faculty of Nursing Sciences Afe Babalola University Ado Ekiti Nigeria Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti Nigeria Department of Mechanical Engineering Science University of Johannesburg Johannesburg South Africa Centre of Excellence for Broadband Networks University of Cape Town Cape Town South Africa Department of Electrical and Electronic Engineering Federal Polytechnic Ede Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Sickle Cell Disease (SCD) is a genetic blood disorder of global significance, with a particularly high prevalence in Africa. This comprehensive review aims to provide a holistic understanding of the epidemiological trends, and the most recent developments in digital health technology for SCD management, highlighting how these developments progress in the future to overcome current obstacles and enhance patient outcomes.A range of scholarly databases, including PubMed, Scopus, and Google Scholar, were used in a careful and comprehensive search. Search phrases such as "sickle cell disease," "current trend," " digital health technology," and "future innovations" were used to locate relevant studies. Epidemiological data reveal a high prevalence of SCD in Africa, particularly of the SS than the SC variants. The results of this review indicate the disease's complex effects on socioeconomic, psychological, and physical health. Various digital health initiatives have demonstrated efficacy in improving health outcomes and hold promise for managing SCD. These resources can help with inadequate monitoring, underuse of clinical best practices, and a lack of illness education. However, there are significant obstacles to its comprehensive management, particularly regarding access to appropriate *** study is necessary to assess the efficacy, effectiveness, and cost-effectiveness of these innovations, as the current evidence is still limited in Africa. Future studies should investigate individualistic technological features that address patients' distinct requirements and intellectual capacities to overcome present barriers and improve patient outcomes.

关键词： Technological innovation Reviews Telemedicine Training data Africa Market research Electronic healthcare Epidemiology Standards Diseases

来源：评论

学校读者我要写书评

暂无评论

Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods

Leveraging Machine Learning for Gait Phase Classification wi...

引用

international conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunis Tunisia Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Accurate gait phase recognition is crucial for real-time analysis and intervention in rehabilitation, biomechanics, and prosthetics. However, achieving this is challenging due to the diverse machine learning (ML) training methods. This study employs ML algorithms to classify gait phases, focusing on stance and swing phases, utilizing open-source data from $\mathbf{1 0 0}$ participants ($41.91 \pm 5.3$ yrs). The classification algorithms considered are k-Nearest Neighbor (k-NN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB) algorithms. The study evaluates these algorithm performances using two training methods with five and ten lower body movements: one randomly selects $80 \%$ of stance and swing phase data for training, while the other divides data by participants, allocating $80 \%$ for training and $20 \%$ for testing. When assessing accuracy with five movements, RF achieved $99.8 \%$ for both training methods. With ten movements, RF achieved a high accuracy of $99.9 \%$ using the second method. Notably, the performance of all ML algorithms exhibited improvement when considering data from ten movements versus five. Additionally, it was observed that the second training method proved more effective with five-movement data compared to data involving ten movements. This comprehensive evaluation highlights the potential of machine learning algorithms for accurate gait phase recognition in diverse applications with varied training methods.

关键词： Training Radio frequency Support vector machines Machine learning algorithms Accuracy Real-time systems Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power Systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

This paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Textionnaire: An NLP-Based Questionnaire Analysis Method for complex and Ambiguous Task Decision Support

Textionnaire: An NLP-Based Questionnaire Analysis Method for...

引用

ieee international conference on cognitive Informatics

作者： Sajjad Rashidiani Thomas E. Doyle Reza Samavi Laura Duncan Paulo Pires Roberto Sassi Department of Electrical and Computer Engineering McMaster University Hamilton Canada School of Biomedical Engineering McMaster University Hamilton Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton Canada Department of Psychiatry University of British Columbia Vancouver Canada

Questionnaires are one of the most popular means of gathering information in many domains. Traditional health surveys, particularly those focusing on specific populations of patients or certain conditions tend to have sample sizes that are too small to apply advanced artificial intelligence methods. In this study, we introduce a method that enables researchers to apply deep learning algorithms on small questionnaire datasets. We introduce a method to represent the information of fixed response questions of questionnaires in text format analogous to a word presentation for a number stored in expanded form. Then we use a pre-trained, domain-relevant deep learning model for questionnaire classification. We applied this method to two databases: i) a hospital-maintained database of mental health questionnaires linked with hospital administrative resource uti-lization data, and ii) a similarly structured publicly available dataset, the National Survey on Drug Usage and Health. In all experiments, the deep pre-trained model had a better Area Under Receiver Operating Characteristic Curve (AUROC) than a 3-layer artificial neural network (ANN). The proposed method enhanced classification performance by 22.95 % on Mental Health private data and 16.679% on small sub-sample of NSDUH data compared to a baseline ANN machine learning algorithm. Also, our technique improved the performance by up to 15.38% when tested for generalizability of classification.

关键词： Deep learning Machine learning algorithms databases Soft sensors Sociology Artificial neural networks Mental health

来源：评论

学校读者我要写书评

暂无评论

Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit

Vehicle Type Detection and Classification Using Improved Rel...

引用

data Science and Information System (ICDSIS), ieee international conference on

作者： K M Monica Mohammed Al-Farouni G K Madhura K. Ghamya M. Geethalakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic complexity database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database.

关键词： Wavelet transforms Visualization Accuracy data integrity Bidirectional control Logic gates Feature extraction

来源：评论

学校读者我要写书评

暂无评论

scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

scMonica: Single-cell Mosaic Omics Nonlinear Integration and...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Li Rui Zhang Saba Aslam Huijun Li Yuxi Chen Zequn Zhang Ruey-Song Huang Hongyan Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Centre for Cognitive and Brain Sciences University of Macau Macau SAR China Faculty of Science and Technology University of Macau Macau SAR China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China Faculty of Health Sciences University of Macau Macau SAR China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica’s potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains.

关键词： Biological system modeling Microprocessors Large language models Noise Computer architecture Transformers Oncology Robustness Gene expression Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Solution Blueprints for Use Cases Surveyed in Austrian Industries

Federated Learning Solution Blueprints for Use Cases Surveye...

引用

ieee conference on Commerce and Enterprise computing, CEC

作者： Thomas Blumauer-Hiessl Angela Fessl Gert Breitfuss Daniel Schall Stefan Schulte TU Wien and Siemens Technology Vienna Austria Know-Center GmbH Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria Know-Center GmbH Graz Austria Siemens Technology Vienna Austria Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things Institute for Data Engineering TU Hamburg Hamburg Germany

ISBN: (数字)9798331529093

ISBN: (纸本)9798331529109

Federated Learning (FL) holds immense potential for transforming the industrial landscape by leveraging distributed data to solve Artificial Intelligence (AI) use cases with collectively trained models in a privacy-preserving way. In this regard, Industrial FL (IFL) arose as a collaborative approach for training AI models between multiple industry partners and devices without the need to share the actual training data. However, despite its promising prospects, the transition and successful implementation of FL in practice is currently lagging behind, posing challenges for industrial companies. To address this, it is of crucial relevance to analyze different business types and involved stakeholders to be able to design FL-based solutions tailored to the industries needs. This paper presents the results of 13 semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains. We identify AI applications, pain points, and attitudes towards AI and FL. Based on the interviews, three industry personas are derived, namely, service business, production optimization, and complex product and project business. To address the needs of these personas, three collaborative FL solution blueprints are proposed. The blueprints include system architectures, implementation steps, and collaboration modes for the involved parties. The blueprints are discussed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges.

关键词： Industries Pain Collaboration Systems architecture Production Companies Artificial intelligence Interviews Optimization Business

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158页 << < 139 140 141 142 143 144 145 146 147 148 > >>

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：