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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

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A Cascade Collaborative Offloading Framework for Video analytics Based on Online Learning 13

A Cascade Collaborative Offloading Framework for Video Analy...

引用

2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on smart data, smartdata 2020

作者： Li, Yuanlin Luo, Bin Zhang, Yuzhe Sun, Zhenchuan Liu, Yunpeng Hubei Huazhong Electric Power Technology Development Co. Ltd. School of Automation School of Artificial Intelligence Hangzhou Dianzi University China School of Computer Science and Technology Huazhong University of Science and Technology China Wuhan Flyminer Science and Technology Co. Ltd China

ISBN: (纸本)9781728176475

Offloading has already become an essential component of the video analytics system due to its low latency and energy saving. The generic traffic offloading strategy allocates video tasks according to computation resources without considering video information, which leads to incorrect decisions that can not guarantee bandwidth reduction and accuracy in video analytics. Therefore, to alleviate the problem, we propose a Cascade Collaborative Offloading Framework (CCOF) in the paper. CCOF is a cross-edge framework, and it coordinates mobile devices, edge servers, and cloud servers to complete video analytics tasks. First, we model the traffic offloading problem in a video analytics scenario as an optimization problem and propose the CCOF as a solution. Then, we propose a periodic online learning algorithm that can update the model parameters with stream data. Afterwards, we adopt the online algorithm and the sharing-based classifier on the mobile devices and edge servers to filter out unnecessary frames and reduce the bandwidth cost. Furthermore, we feed the recognition results back to the edge and mobile devices for online learning. Evaluation of dataset Jackson shows that the proposed method reduces the bandwidth cost by an order of magnitude and achieves 10x speed-ups on video analysis tasks. © 2020 IEEE.

关键词： Bandwidth

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dataAutism: An Early Detection Framework of Autism in Infants using data Science 3rd

DataAutism: An Early Detection Framework of Autism in Infant...

引用

international conference on data Management, analytics and Innovation (ICDMAI)

作者： Shankar, Venkatesh Gauri Sisodia, Dilip Singh Chandrakar, Preeti Manipal Univ Jaipur Jaipur Rajasthan India Natl Inst Technol Raipur Chhattisgarh India

ISBN: (纸本)9789811393648;9789811393631

data Science with analytics and machine learning in the field of health care are the most prominent and emerging fields in today's scenario. Our research paper aims to the healthcare solution toward autism in infants. Autism is the neurodevelopment disorder categorized by diminished societal interaction, lingual and non-lingual communication, repetitive and antagonistic behavior. Autism neurodevelopment figures out in the infants nearly about one year of age. The overall process of autism detection is a very long and cost-oriented process that takes 6 months to 10 months in total. We are concentrating on two data set and developed a framework for early detection of autism in infants. Form the same above, we use the concept of data analytics with training of data model and inclusion of SVM classification. We have tested our model and novel algorithm "dataAutism" over large data set and figure out high precision, recall with accuracy approx. 89%.

关键词： data analytics Autism Classification data science Support vector machine

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Face Recognition Using Hybrid HOG-CNN Approach

Face Recognition Using Hybrid HOG-CNN Approach

引用

international conference on Research in intelligent and computing in Engineering, RICE 2020

作者： Hung, Bui Thanh Data Analytics and Artificial Intelligence Laboratory Engineering–Technology School Thu Dau Mot University 6 Tran van On Street Phu Hoa District Thu Dau MotBinh Duong Viet Nam

ISBN: (纸本)9789811575266

Face recognition is a biometric system used to identify a person from digital images;its application is primarily for security and monitoring purposes. Recently, there have been more research works on deep neural network for facial recognition achieving positive results. In this study, we propose an effective model to solve face recognition problem which could be applied to identify directly from camera. In particular, the study focuses on two phases: face detection and face identification. The face detection method that we propose uses HOG features and SVM linear classifier. The face recognition model is proposed based on CNN—convolution neural network. The efficiency of the model is evaluated on the FEI, LFW, UOF datasets, and the results show that the proposed model achieves high accuracy. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Vehicular Edge computing Based Driver Recommendation System Using Federated Learning 17

Vehicular Edge Computing Based Driver Recommendation System ...

引用

17th IEEE international conference on Mobile Ad Hoc and smart Systems (IEEE MASS)

作者： Vyas, Jayant Das, Debasis Das, Sajal K. Indian Inst Technol Comp Sci & Engn Jodhpur Rajasthan India Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9781728198668

Driver Stress and Behavior prediction is a significant feature of the Advanced Driver Assistance System. This system can improve driving safely by alerting the driver to the danger of unsafe or risky driving conditions. In this paper, we analyzed historical trip data to calculate the driving stress and its impact on different driving behavior. We used Long Short-Term Memory Fully Convolutional Network to predict the corresponding stress level of the driver. We further established a relationship between stress and driving behavior and developed an intelligent recommendation system for cab companies to recommend the driver for a subsequent trip. To meet the demand for Artificial Intelligence in the intelligent Transportation System, we leverage Federated Learning in Vehicular Edge computing in the proposed system architecture. It enables Road Side Units to do all computing of data on it. The model has been tested on the UMI-DriveSet dataset. We observed that the proposed model predicts the stress with an accuracy of 95% and assists in enhancing the driving quality and experience.

关键词： recommendation system driver stress and behavior vehicular edge computing federated learning

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Research on intelligent Security Protection of Privacy data in Government Cyberspace 5

Research on Intelligent Security Protection of Privacy Data ...

引用

5th IEEE international conference on Cloud computing and Big data analytics, ICCCBDA 2020

作者： Yang, Hui Huang, Liangqiang Luo, Chunfeng Yu, Qinyong CETC Big Data Research Institute Co. Ltd Chengdu China

ISBN: (纸本)9781728160245

Based on the analysis of the difficulties and pain points of privacy protection in the opening and sharing of government data, this paper proposes a new method for intelligent discovery and protection of structured and unstructured privacy data. Based on the improvement of the existing government data masking process, this method introduces the technologies of NLP and machine learning, studies the intelligent discovery of sensitive data, the automatic recommendation of masking algorithm and the full automatic execution following the improved masking process. In addition, the dynamic masking and static masking prototype with text and database as data source are designed and implemented with agent-based intelligent masking middleware. The results show that the recognition range and protection efficiency of government privacy data, especially government unstructured text have been significantly improved. © 2020 IEEE.

关键词： Sensitive data

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Integration of Fog computing and Internet of Things: An Useful Overview 2nd

Integration of Fog Computing and Internet of Things: An Usef...

引用

2nd international conference on Recent Innovations in computing (ICRIC)

作者： Rekha, G. Tyagi, Amit Kumar Anuradha, Nandula Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522502 Andhra Pradesh India Lingayas Vidyapeeth Dept Comp Sci & Engn Faridabad 121002 Haryana India

ISBN: (纸本)9783030294076;9783030294069

In the past decade, the evolution of computing has moved from distributed, parallel, grid, cloud, and now to fog computing. The massive amount of data generated by Internet of Things (IoT) devices is growing up exponentially. The flood of information (generated by those IoT/Internet-connected devices) becomes troublesome for data processing and analytical prediction functionality using cloud computation. Several problems have been investigated with cloud computing with respect to latency, limited bandwidth, low Internet connectivity, etc. Here, solution to such problems can be solved by introducing fog computing with powerful functionality of cloud framework, i.e., based on the deployment of fog nodes called microclouds at nearest edge of data sources. Fog computing for big data/IoT data analytics is in evolving phase and requires extensive research to produce more knowledge and smart decisions. This article discusses several basic facts related to fog commuting, challenges in fog computing and opportunities in the near future, in the context of fog big IoT data analytics. In addition, this work also emphasizes the key characteristics in some proposed research works, those make the fog computing as a suitable (useful) platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g. healthcare monitoring, smart cities, connected vehicles, and smart grid, etc.) will be discussed here to create a well-organized green and quantum computing paradigm to support the next generation of IoT applications.

关键词： Internet of Things Cloud computing Fog computing Big data data analytics

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Construction path of the national educational enrollment examination cadre database based on the big data analysis

Construction path of the national educational enrollment exa...

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Wang, Zhongxue Henan Provincial Admissions Office ZhengzhouHenan450064 China

ISBN: (纸本)9789811525674

Based on the computer system and the modern information technology, the big data mining and analysis technology extracts the most valuable information from the massive data, which provides an effective basis for the decision-making of the information demanders. The mining technology is a common technology in the modern data analysis. It is often used in combination with the genetic algorithm in the field of the computer research. The data mining is more scientific and reasonable, which provides the technical support for the construction of cadres in the national education enrollment examinations. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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An Architecture Based on Digital Twins for smart Power Distribution System 3

An Architecture Based on Digital Twins for Smart Power Distr...

引用

3rd international conference on Artificial Intelligence and Big data (ICAIBD)

作者： Zhang, Ganghong Huo, Chao Zheng, Libin Li, Xinjun Beijing Smart Chip Microelect Technol Co Ltd Beijing 102200 Peoples R China

ISBN: (纸本)9781728197418

Constructing an effective architecture based on digital twins using advanced artificial intelligent technologies remains a key challenge in smart power distribution system. Despite recent advances in important domains such as device health maintenance and manufacturing process, the conventional architecture does not offer a satisfactory solution for rapidly providing data models, a set of analytics or algorithms, and knowledge. In this work, we employ ideas from distributed network based on digital twins and from recent advances that augment artificial intelligent with big data. Our architecture provides a paradigm that uses a distributed network makes computer programming, software, and data spread out across more than one terminal, but communicate complex messages through terminal nodes, and are dependent upon each other, obviating the pressure from master station. We demonstrate the usefulness of digital twins that are used to track the performance of devices such as transformer during normal operation to detect possible defects and fix them before they result in failure. We also conclude that using digital twin technology will understand, predict, and optimize performance in order to achieve improved business outcomes, and apply machine learning and knowledge to predict the future.

关键词： smart power distribution system predictive maintenance digital twin machine learning big data

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DoS attack detection model of smart grid based on machine learning method

DoS attack detection model of smart grid based on machine le...

引用

2020 IEEE international conference on Power, intelligent computing and Systems, ICPICS 2020

作者： Wang, Zhe Cheng, Wei Li, Chunlin Software College Northeastern University Shenyang China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best. © 2020 IEEE.

关键词： smart power grids

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Implementation of CAPI and Traditional PCIe and Delay of Comparison 5

Implementation of CAPI and Traditional PCIe and Delay of Com...

引用

5th IEEE international conference on Cloud computing and Big data analytics, ICCCBDA 2020

作者： Wang, Fudong Zhang, Tao Zhao, Bingjie Yang, Fan Li, Qingxin Li, Ziyang Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systemsm Chinese Academy of Sciences Liaoning Shenyang China

ISBN: (纸本)9781728160245

With the development of artificial intelligence technology and heterogeneous computing, heterogeneous communication speed is becoming more and more important. The latency of transmitting data determines whether a heterogeneous computing architecture is available. Current acceleration cards usually use PCIe interface. IBM developed the CAPI communication architecture for heterogeneous computing. This paper compares the technical principles and delays of the Power CAPI architecture and a high speed PCIe communication method. © 2020 IEEE.

关键词： Computer architecture

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