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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101-110 订阅

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

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. © 2025 IEEE.

关键词： Federated learning

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Epidemic Amplifier Detection: Finding High-Risk Locations in COVID-19 Cases' Location Sequences via Multi-task Learning 23

Epidemic Amplifier Detection: Finding High-Risk Locations in...

引用

31st ACM SIGSPATIAL international conference on advances in Geographic Information systems (ACM SIGSPATIAL GIS)

作者： Hu, Yue Wang, Xiaoli Ren, Huimin He, Tianfu Tang, Tan He, Huajun Meng, Chuishi Han, Boyang Bao, Jie Sun, Ying Li, Gang Wang, Quanyi Yang, Peng Zheng, Yu Xidian Univ Xian Shaanxi Peoples R China JD Technol JD ICity Beijing Peoples R China JD Intelligent Cities Res Beijing Peoples R China Beijing Ctr Dis Prevent & Control Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China

ISBN: (纸本)9798400701689

To contain the transmission of respiratory diseases, such as COVID-19, it is vital to control the locations visited by the cases. However, not all locations pose the same risk, and quarantining all close contacts is costly. Therefore, precise identification of outbreak locations is essential for public health. Fortunately, public health data includes detailed epidemiological surveys, offering a data-driven approach. In this paper, we propose a novel epidemic amplifier detection model, namely EADetector, which extracts spatio-temporal features from candidate locations, and employs a multi-task learning-based method to fuse the infected location detection task along with the epidemic location inference task to acquire potential locations. We perform extensive experiments and present a set of case studies based on the real epidemiological surveys collected in Beijing. The proposed model is deployed as a part of the epidemiological survey system in Beijing, China.

关键词： Multi-task learning Urban computing Public Health

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MGCC: Multi-Granularity Cognitive computing 1

引用

2022 international Joint conference on Rough Sets

作者： Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

ISBN: (数字)9783031212444

ISBN: (纸本)9783031212437;9783031212444

Cognitive computing aims to develop a coherent, unified, universal mechanism with inspiration of mind's capabilities. Granular human thinking is a kind of cognition mechanism for human problem solving. Multi-Granularity cognitive computing (MGCC) is introduced to integrate the information transformation mechanism of traditional intelligent information processing systems and the multi-granularity cognitive law of human brain in this paper. The data-driven granular cognitive computing model (DGCC) developed in 2017 is a typical theoretical model for implementing MGCC. MGCC is a valuable model for developing highly intelligent systems consistent with human cognition. The theoretical research issues and some applications about MGCC are introduced.

关键词： Granular computing Cognitive computing data-driven granular cognitive computing Multi-granularity cognitive computing

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Power System Operation and Control: A data-driven Approach 1

Power System Operation and Control: A Data-Driven Approach

引用

1st international conference on intelligent Controller and computing for Smart Power, ICICCSP 2022

作者： Ahangar, Parvaiz Ahmad Lone, Shameem Ahmad Gupta, Neeraj Nit Dept. of Electrical Engineering Srinagar India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

Renewable energy is becoming more popular around the world, especially wind power and solar photovoltaic (SPV) systems with data interfaces and IoT sensors that generate significant volumes of data. In addition to serving as a monitoring device, the data provided by such devices can be used to improve system reliability and efficiency by providing real-time data. When compared to traditional model-based operation, data-driven based optimal renewable power operation is an emerging method for ensuring trouble-free power system operation. The data-driven method is effective for studying the impact of rapid distributed generation systems integration on utility power system functioning. In data-driven approach, Machine-learning (ML) is an emerging technology for addressing the optimal functioning power system networks. data-driven operated distributed energy resources (DER) provide real-time management of our dependable power supply through suitable forecasting methods and hence give rise to the smart grid idea. In this proposed work, our objective is to apply a data-driven based strategy to the smart grid in order to ensure the smooth operation and control of both utility and renewable-rich power system. © 2022 IEEE.

关键词： Support vector machines

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data Wrangling and Visualization for Credibility and Usability 6

Data Wrangling and Visualization for Credibility and Usabili...

引用

6th international conference on intelligent computing and Control systems, ICICCS 2022

作者： Sahu, MadhuriPal Francis, Nankong Achana Sahu, Amit Ulhe, Pranjali Datta Meghe Institute of Medical Sciences School of Allied & Sciences Wardha India

ISBN: (纸本)9781665410359

In recent times of data driven world, where data is all around us, the difficulty is how to make meaning out of this huge and growing *** of improvements in technologies to work with data, data analysts still spend allot of time analyzing data quality related issues and how to make data credible and usable for effective decision making. Nonetheless the process of 'data wrangling' is often very cumbersome, time-consuming and requires a lot of resource. data Wrangling encompasses just modifying and cleaning data to provide user an interactive and efficient data, but is a method which has data identification, extracting, cleaning and integrating data for a dataset which would be analyzed as needed. This article, seeks to look at the challenges and opportunities related with how data credibility can be achieved through new interactive systems that integrate data verification, transformation and visualization with the aim of maximizing the quality of data. The under listed research questions have been captured, including how appropriate visualization can facilitate the apprehension of missing data, inconsistent data and uncertainty;how interactive visualizations might facilitate data transformed specification;finally, how documented and social interaction can enable wider reuse, verification, and modification of data transformations. © 2022 IEEE.

关键词： Python

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End-to-End Fault Tolerant Control of Discrete Event System Using Recurrent Neural Networks 2

End-to-End Fault Tolerant Control of Discrete Event System U...

引用

2nd international Mobile, intelligent, and Ubiquitous computing conference, MIUCC 2022

作者： El-Mahdy, Mostafa H. Maged, Shady A. Awad, Mohammed I. Ain Shams University Mechatronics Engineering Dept. Giza Egypt

ISBN: (纸本)9781665466776

Industry 4.0 is concerned with how the global production and supply network operates through the ongoing automation of traditional manufacturing systems which depends on closed-loop control system components. These components are prone to faults, especially in sensors. so, a fault-tolerant control framework is needed to deal with these kinds of faults. Many researchers work on developing more intelligence data driven fault-tolerant control techniques instead of traditional ones which need a complete sophisticated model. This paper proposes a learning fault tolerant control of discrete event systems subjected to sensors faults, based on recurrent neural networks. The proposed network has both previous and current sensors signals as inputs and current actuators signals as outputs. The new data driven fault tolerant control replaces the diagnosis and control reconfiguration block function in the traditional one. A case study that integrates automated pneumatic material handling is used to demonstrate the proposed approach. Factory I/O simulator is integrated with MATLAB based on the digital twin concept to simulate and verify the proposed approach. Results demonstrate that the proposed method is capable of handling sensors faults in faultless and various faulty scenarios. © 2022 IEEE.

关键词： Recurrent neural networks Fault tolerant systems Fault tolerant control Ubiquitous computing Sensor systems Production facilities Mathematical models

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Task-driven Semantic-Aware Image Compression 1

引用

16th international conference on Ubiquitous computing and Ambient Intelligence

作者： Zhou, Junwei Xiao, Haoyun Li, Dexin Wang, Liangliang Liang, Peidong Zhou, Jian Yang, Yanchao Wuhan Univ Technol Luoshi Rd 122 Wuhan 43007 Peoples R China Fujian Key Lab Intelligent Operat & Maintenance R Quanzhou Software Pk 9 Quanzhou 362008 Peoples R China Fujian Quanzhou Inst Adv Mfg Technol Quanzhou Software Pk 5 Quanzhou 362008 Peoples R China

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In the context of IoT/IoE, efficient image compression is crucial for reducing bandwidth consumption and enabling real-time data processing. Predominantly, existing compression techniques are designed with an emphasis on human visual perception, frequently overlooking the specific demands of tasks involving image analysis. This paper introduces a novel Task-driven Semantic-Aware Image Compression (TDSIC) approach with a feature enhancement network engineered to retrieve the latent semantic information typically lost in entropy-decoded results. TDSIC also leverages importance maps as a form of prior knowledge, which assists in guiding the bit allocation strategy of the compression technique. By refining the perceptual loss function, the network's emphasis is realigned to prioritize the retention of semantically significant information. Additionally, the training regimen incorporates insights from discriminative networks to harness feedback on human visual discernment, which is instrumental in meticulously calibrating the perceptual understanding of the image compression model. This calibration ensures that the model adeptly captures and correlates pertinent information. TDSIC achieves up to a 12% improvement in Top-1 image classification accuracy on the ILSVRC2012 dataset and up to a 0.3% enhancement in object detection on the COCO dataset over baseline methods. The Kodak dataset experiments underscore that images compressed with our method not only maintain visual appeal to human viewers but also enhance the performance of specific tasks. Supplementary investigations, including ablation studies on the feature enhancement module (FEM), indicate that the FEM is adept at recovering information that was previously lost during the encoding and quantization phases. The implementation code is made publicly accessible at https://***/lie209/TDSIC.

关键词： Image Compression Semantic-Aware Machine Vision

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OASIS: An Ontology-driven Autonomic Management System for intelligent Substations 24

OASIS: An Ontology-Driven Autonomic Management System for In...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xie, Xiaodong Yang, Jianxu Hua, Xiong Zhou, Qianru State Grid Corporation of China Beijing100031 China Nari Group Corporation State Grid Electric Power Research Institute Hefei230088 China School of Compute Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350319934

With the soar of the demand for electricity all over the so-ciety, an intelligent autonomic management system for substations is in urgent need. Ontology driven knowledge based system plays a vital role in achieving a truly autonomic, zero-touch substation management system. This paper proposes OASIS, an ontology driven autonomic management system for intelligent substations, which is able to organize first-nary devices and verify human operations to prevent mis-operation in real-time. The ontology used and develop procedures are presented in detail. OASIS has been tested in a real-life substation, and the test results are presented in detail. It is evident that OASIS is competent for autonomic substation management, including device power status calculation, main device running status retrieval, interval organization, general faulty check and single operation mis-operation verification, in a efficient way, with the responding time less than two seconds in general. At last, several lessons learnt during the developing procedure are discussed as a conclusion. © 2022 IEEE.

关键词： Ontology

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A Cooperative Flow Simulator for Distributed computing Based on Full-Dimensional Definable Network 4

A Cooperative Flow Simulator for Distributed Computing Based...

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4th international conference on data-driven Optimization of Complex systems, DOCS 2022

作者： Liang, Yuan Xiao, Geyang Yao, Shaofeng Wang, Hongsheng Yi, Xiaoyu Tu, Yonggang Intelligent Network Research Institute Zhejiang Lab Hangzhou China Intelligent Computing Institute Zhejiang Lab Hangzhou China Jiaxing Big Data Center Jiaxing Municipal Peoples Government Jiaxing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

To improve the efficiency of cluster computing, the research of cooperative flow scheduling algorithms provides another idea for further optimization. Cluster computing frameworks such as Spark and Map-Reduce carry running cluster computing tasks and have distinct stage division and dependency characteristics. With limited network resources, the overall completion time of the job can be reduced by minimizing the wait time of the computing phase due to the bottleneck of transmission capacity. The introduction of an artificial intelligence algorithm can provide a solution to this NP-hard problem. The simulator designed in this paper can realize the overall process of cooperative traffic generation, scheduling policy configuration, and scheduling effect feedback closed-loop through packet sender, packet receiver, scheduler, and programmable switch components according to the dependency between cluster computing stages. © 2022 IEEE.

关键词： Cluster computing

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Improving Real-Time intelligent Transportation systems in Predicting Road Accident 5th

Improving Real-Time Intelligent Transportation Systems in Pr...

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5th international conference on Recent Innovations in computing , ICRIC 2022

作者： Ademola, Omolola Faith Misra, Sanjay Agrawal, Akshat Covenant University Ogun State Ota Nigeria Ostfold University College Halden Norway Amity University Haryana India

ISBN: (纸本)9789819906000

advances in the use of an intelligent transportation system (ITS) have been deployed in most of the world, which presents new opportunities for developing sustainable transportation system. This paper focuses on improving intelligent transportation systems using Big data tools in predicting road accidents in Nigeria, based on real-time data gotten from Twitter. The work gives a review of common problems associated with the intelligent transportation system, and how this can be improved by utilizing Apache Spark Big data. The revolution in intelligent transportation systems can be impacted by the availability of large data that can be used to generate new functions and services in intelligent transportation systems. The framework for utilizing Big data Apache Spark will be discussed. The Big data Apache Spark applications will be used to collect a large amount of data from various sources in intelligent transportation system;in return, this will help in predicting road accidents before it happens, and also, a feedback system for alerting can be projected. The use of machine learning algorithms is being used to make necessary predictions for the intelligent transportation system. The result obtained shows that for the classified data relating to road accidents, KNN gave a 94% accuracy when compared to other classification algorithms such as the Naïve Bayes, support vector machine, and the decision tree. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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