检索结果-内蒙古大学图书馆

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago United States University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States University of Oslo Department of Informatics Oslo Norway Illinois Institute of Technology Department of Civil Engineering Chicago United States

ISBN: (纸本)9798350351255

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms - Federated Vehicular Network, NOMA © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Distribution Grid Compatible Renewable Energy Through Optimal Exploitation of Bottom Up Flexibility 14

Distribution Grid Compatible Renewable Energy Through Optima...

引用

14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Seklos, Konstantinos Efthymiopoulos, Nikolaos Michalas, Angelos Dimitriou, Theodoros Vergados, Dimitrios J. Vergados, Dimitrios D. University of Western Macedonia Department of Informatics Kastoria Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Piraeus Department of Informatics Piraeus Greece

ISBN: (纸本)9798350318067

The critical challenge that modern smart grids face in term of their management is the maximum possible exploitation of the energy that Renewable Energy Sources (RES) produce. The non-controllable nature of the rate that RES produce energy depends on the dynamic and non-accurately predictable weather conditions. In this context, flexibility noted as the modification of the energy demand, harmonizes (balances dynamically) energy production and energy consumption. Its advanced modeling and the optimization of its use highly affects energy cost in modern smart grids. In this paper we present a novel Distribution-Level Flexibility Market (DLFM) architecture which is noted Reactive DLFM (R-DLFM). R-DLFM operates after the clearing of the well-known day ahead energy market and/or balance market and ensures: i) the minimization of the energy cost and ii) the compliance of the dispatch with the constraints of the distribution system. The evaluation of R-DFLM depicts that its flexibility exploitation has high impact in the energy cost and compliance with distribution network constraints. © 2023 IEEE.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

引用

computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Enhancing Restaurant Customer Review Analysis: Multi-Class Text Classification with BERT 6

Enhancing Restaurant Customer Review Analysis: Multi-Class T...

引用

6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Sunarko, Budi Hasanah, Uswatun Hidayat, Syahroni Universitas Negeri Semarang Department of Informatics and Computer Engineering Education Semarang Indonesia Universitas Negeri Semarang Department of Computer Engineering Semarang Indonesia Universitas Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350358346

Online customer reviews can be valuable information when processed with the right techniques. Businesses aim to enhance their strategies in managing operations by leveraging sentiment analysis from their clientele. In a business context, the primary focus of SA is to capture individual perspectives on products. Presently, sentiment analysis not only deals with emotional polarity but also classifies sentiments towards various product-related aspects. This research aims to classify restaurant customer review data on Google Maps into categories such as 'menu,' 'taste,' 'indoor atmosphere,' and 'outdoor atmosphere'. We utilize 450 entries for this dataset. The model used is Bidirectional Encoder Representation from Transformers (BERT), a technique based on transformer-based neural networks, capable of learning grammatical structures, interpreting semantics, and deriving relationships among contextual entities. The results show a less satisfying accuracy score of 41.110/0. This is due to several inaccuracies in the label annotations of the utilized data. Further analysis is discussed in the Discussion and Conclusion section. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Selected Combinatorial Problems Through the Prism of Random Intersection Graphs Models 1

引用

13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Spirakis, Paul G. Nikoletseas, Sotiris Raptopoulos, Christoforos Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9783031304484

ISBN: (纸本)9783031304477

We discuss a simple, yet general family of models, namely Random Intersection Graphs (RIGs), initially introduced by Karoński et al. [4] and Singer-Cohen [10]. In such models there is a universe of labels and each one of n vertices selects a random subset of. Two vertices are connected if and only if their corresponding subsets of labels intersect. A formal definition is given below: © 2023, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

引用

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Abdulla, Nawaf Demirci, Mehmet Ozdemir, Suat Gazi University Graduate School of Informatics Department of Information Systems Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey Hacettepe University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350306590

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models. © 2023 IEEE.

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

Developing a Green AI Platform for SMEs to Analyze Open-source Information with the Approach of Society 5.0

引用

SN computer Science 2024年第7期5卷 923页

作者： Akarslan, Hüseyin Sağiroğlu, Şeref Management Information Systems Graduate School of Informatics Gazi University Türkiye Ankara Turkey Computer Engineering Department Engineering Faculty Gazi University Türkiye Ankara Turkey

Management and information systems are considered two major disciplines that have to be combined to benefit from knowledge for rational decision-making which means strict procedures utilizing objective knowledge and logic for many years. Effective and efficient use of already invested organizational resources is a key subject to gain a competitive advantage by making decisions with the help of AI techniques which benefit from both internal and external knowledge. This paper introduces a new model to get benefits from open-source intelligence (OSINT) in the context of external knowledge with a green computing approach for solving global organizational challenges in the scope of Society 5.0. This research has a dual background, theoretical and practical. Problem defining and hypothesis formulation was carried out by literature review. Then, the application developed according to the proposed model was put into action in some organizations for testing, and feedback from experts who participated in the proof of concept (POC) activities was evaluated and discussed empirically. Finally, the findings of our research, the advantages of our model, and both academic and business future work were summarized. It has been practically determined that the model and application we have developed are largely successful in line with our hypotheses. However, the weaknesses and advantages of our study were also revealed. Our research is the first study that combines Management Information Systems and Open-Source Intelligence disciplines with the approach of Society 5.0 to support SMEs in analyzing open-source information to build better decision-making mechanisms. The developed application in this study can also be applied to solving other green distributed computing needs in different domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed computing External knowledge Green computing Management information systems (MIS) Open-source intelligence (OSINT) Society 5.0

来源：评论

学校读者我要写书评

暂无评论

IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

引用

Digital Communications and Networks 2023年第4期9卷 906-919页

作者： Yizhou Shen Shigen Shen Qi Li Haiping Zhou Zongda Wu Youyang Qu Department of Computer Science and Engineering Shaoxing UniversityShaoxing312000China School of Computer Science and Informatics Cardiff UniversityCardiffCF243AAUnited Kingdom School of Information Engineering Huzhou UniversityHuzhou 313000ZhejiangChina School of Information Technology Deakin UniversityBurwoodVIC3125Australia

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data *** generated data is collected and shared using edge-based IoT structures at a considerably high ***,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge *** address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing *** particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the *** devices may make malicious requests to achieve their goals of stealing ***,edge nodes should deny malicious IoT device requests to prevent IoT data from being *** dynamically adjust their own strategies according to the opponent's strategy and finally maximize the *** upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning ***,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation ***,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

关键词： Privacy preservation Internet of things Evolutionary game Data sharing Edge computing

来源：评论

学校读者我要写书评

暂无评论

A Machine-Learning-Based Sentiment Classification Approach for Gojek Application 7

A Machine-Learning-Based Sentiment Classification Approach f...

引用

7th International Conference of computer and informatics engineering, IC2IE 2024

作者： Putra, I Putu Edy Suardiyana Hermawan, Indra Kusuma, I Komang Arya Adi Puridiasta, I Gede Deindra Dwija Pramudya, Dewa Gede Bhaskara Kowaas, Clerencia Isabell Institut Pariwisata Dan Bisnis Internasional Digital Business Department Denpasar Indonesia Politeknik Negeri Jakarta Informatics and Computer Engineering Department Depok Indonesia Ganesha University of Education Faculty of Engineering and Vocational Computer Science Department Singaraja Indonesia

ISBN: (纸本)9798331505530

This study proposes a machine-learning-based sentiment classification approach for Bahasa Indonesia, where Gojek App's reviews are used for a case study. The sentiment classification is important since users often give 5-star ratings but leave negative reviews. A total of 19,772 reviews from The Google Play Store are used in this study. We use five machine learning algorithms to train the model, such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Stochastic Gradient Descent (SGD). The best results of our approach are 66.6% precision, 88.2% recall, and 69.2% F-score, where an SVM-based classifier is used. These results show a promising future for using machine-learning algorithms for sentiment classification. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：