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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1358 条记录，以下是261-270 订阅

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PABAU: Privacy Analysis of Biometric API Usage 19

PABAU: Privacy Analysis of Biometric API Usage

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Tang, Feiyang Norwegian Computing Center Oslo Norway

ISBN: (纸本)9798350346558

Biometric data privacy is becoming a major concern for many organizations in the age of big data, particularly in the ICT sector, because it may be easily exploited in apps. Most apps utilize biometrics by accessing common application programming interfaces (APIs);hence, we aim to categorize their usage. The categorization based on behavior may be closely correlated with the sensitive processing of a user's biometric data, hence highlighting crucial biometric data privacy assessment concerns. We propose PABAU, Privacy Analysis of Biometric API Usage. PABAU learns semantic features of methods in biometric APIs and uses them to detect and categorize the usage of biometric API implementation in the software according to their privacy-related behaviors. This technique bridges the communication and background knowledge gap between technical and non-technical individuals in organizations by providing an automated method for both parties to acquire a rapid understanding of the essential behaviors of biometric API in apps, as well as future support to data protection officers (DPO) with legal documentation, such as conducting a data Protection Impact Assessment (DPIA). © 2022 IEEE.

关键词： Biometrics

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Landslide Displacement Prediction Based on the Random Forest and Optimized Support Vector Regression 24

Landslide Displacement Prediction Based on the Random Forest...

引用

4th international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2024

作者： Li, Xiaojuan Liang, Xiao He, Yongqiang Wang, Huiqin Wei, Mingqiang Zhang, Guoming School of Civil Engineering Northwest Minzu University Lanzhou Gansu China School of Computing and Communications Lanzhou University of Technology Lanzhou Gansu China

ISBN: (纸本)9798400710247

In order to improve the accuracy of landslide prediction, this paper proposes a landslide displacement prediction model based on the random forests and optimized support vector regression. Firstly, Random Forests (RF) is used for preliminary prediction of landslide displacement, and Hunter-Prey Optimizer (HPO) is used to optimize the combination prediction model (HPO-SVR) of Support Vector Regression (SVR). The validation of the landslide displacement measured data shows that the HPO algorithm can lead to better prediction performance of SVR than particle swarm optimization and genetic algorithm. Compared with RF, SVR, and the combined prediction model of SVR with conventional particle swarm optimization algorithm, genetic algorithm and HPO, the combined RF-HPO-SVR model obtains more accurate landslide displacement prediction results. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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data Intelligent Reconstruction of the Practice Path of Media Information Ecology Based on Artificial intelligence 12th

Data Intelligent Reconstruction of the Practice Path of Medi...

引用

12th international conference on Frontier computing, FC 2022

作者： Liu, Xiao School of Advertising Communication University of China Beijing China

ISBN: (纸本)9789819914272

In the digital age, various media information is processed by digital technology, deconstructed into data, stored and circulated on computer networks. On this basis, the rapid development of big data and artificial intelligence has given birth to the concept of data intelligence and has had an impact on the media information ecology. Based on media practice driven by artificial intelligence technology, this paper analyzes the practice paths and problems of media information ecology. The paper argues that the media can achieve in-depth user and information understanding through data intelligence centered on algorithm logic. This kind of machine comprehension supports the media to realize intelligent information production, intelligent information distribution and intelligent information management, and reflects the practice path of data intelligent reconstructing media information ecology. With the wide application of data intelligence in specific media business scenarios, the issues of user privacy security and human-machine relationship have gradually emerged, requiring the media not to rely too much on the technical rationality brought by data intelligence, but to pay attention to the key role of human subjective consciousness in the media information ecology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information management

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Emerging Technologies of Industry 4.0: Challenges and Opportunities 5

Emerging Technologies of Industry 4.0: Challenges and Opport...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Okwu, Modestus O. Tartibu, Lagouge K. Maware, Catherine Enarevba, Dolor Roy Afenogho, Justice O. Essien, Aniekan Univ Johannesburg Dept Mech & Ind Engn Tech Johannesburg South Africa Univ Kentucky Inst Res Technol Dept IR4TD Ctr Mfg Bd 220D Lexington KY 40506 USA Oregon State Univ Coll Engn Corvallis OR USA Fed Univ Petr Resources Effurun Dept Mech Engn Delta Nigeria Univ Glasgow James Watt Sch Engn James Watt South Bldg Glasgow Lanark Scotland

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Industry 4.0 is beyond change and transformation but a comprehensive revolution that accelerates scientific and technological progress, which has led to great functionality of emerging technologies in which the artificial intelligence (AI), Internet of Things (IoT) and other supporting technologies serve as backbones for upgrading industrial chains. These have gained popularity over the years and known for far-reaching transformation, especially where complex systems`are involved. They are known for synchronization and speed in the new digitalization era and globalized milieu. However, developing countries are still besieged in terms of meeting up with the current technologies. This article is an exposition to the emerging technologies of the fourth industrial revolution. The first section of this study gives a brief overview of Industry 4.0. Secondly, the emerging technologies of Industry 4.0 are further discussed, which include the Internet of things, blockchain, Fifth Generation Wireless Communications Technologies (5G), 3D Printing, biometrics, Virtual and Augmented Reality, big data, Cloud computing, Biometrics, Nanotechnology, biotechnology, smart factory, and others are discussed. Finally, the challenges and opportunities of the industry 4.0 technologies are discussed. The findings of this review would be beneficial to strategic, and operational experts in industries, academia, stakeholders and government. Electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.

关键词： fourth industrial revolution (4IR) internet of things (IOT) smart machines globalization artificial intelligence

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MTEC: A Multi-tier Blockchain Storage Framework Using Erasure Coding for IoT Application 8th

MTEC: A Multi-tier Blockchain Storage Framework Using Erasur...

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8th international Joint conference on Web and big data and Web-Age Information Management (APWeb-WAIM)

作者： Xu, Xinwei Yu, Yongkang He, Jinchun Zhang, Qinnan Zheng, Hongwei Guo, Binghui Dong, Jin Qiu, Wangjie Beihang Univ Sch Artificial Intelligence Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Future Blockchain & Priva Beijing 100191 Peoples R China Zhongguancun Lab Beijing 100191 Peoples R China Beijing Acad Blockchain & Edge Comp Beijing 100190 Peoples R China

ISBN: (纸本)9789819772407;9789819772414

Blockchain technology is widely used in the Internet of Things (IoT) with its decentralization, traceability, and tamper-resistant features. Unfortunately, the massive data volumes in IoT systems, combined with the prevalent full-replication strategy employed by conventional blockchain, exert considerable storage demands on IoT edge computing devices and present a formidable challenge for resource-constrained nodes to join blockchain networks. Although some existing solutions improve storage scalability by reducing the number of redundant copies, they still pose issues of high storage complexity and significant cross-tier query costs. In this paper, we first propose a multi-tier storage framework using erasure coding for blockchain, called MTEC, tailored for IoT scenarios with an exponentially decaying data access pattern. Specifically, MTEC employs Reed-Solomon(RS) coding to build a tiered blockchain storage framework. Each tier is equipped with an encoding scheme and threshold time to minimize storage overhead. To enhance query efficiency, we design a block pre-loading strategy and node collaborative query strategy. Finally, we implement a prototype of MTEC and compare it with baseline blockchain systems. Extensive experiments demonstrate that MTEC achieves a substantial 86.3% decrease in storage overhead compared to conventional full-replication blockchain solutions. It further outperforms state-of-the-art MLDC scheme by an additional 15.8% storage optimisation and reduces the cost of cross-tier to 7.35%.

关键词： Blockchain Erasure coding Storage optimisation Internet of Things

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Analysis of supermarket member classification based on K-means algorithm 2

Analysis of supermarket member classification based on K-mea...

引用

2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Meng, Fanxing College of Cloud Computing Technology and Application Industry Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9783800760268

The loss of members and the decline of consumption are the problems that department stores are facing in recent years. How to improve customer loyalty with lower investment and improve the consumption amount and profit margin of each order is the problem that the company needs to solve. Based on the retail data of XX supermarket in recent one year, this paper constructs an RFM model, applies k-means clustering algorithm to identify and segment members, and divides members into key customers Focus on developing customers and general customers, and give corresponding service strategies. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Retail stores

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ADVANCED data PROCESSING METHODS IN AN INTELLIGENT HEALTH GATEWAY 16

ADVANCED DATA PROCESSING METHODS IN AN INTELLIGENT HEALTH GA...

引用

16th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on big data Analytics, data Mining and Computational intelligence, bigDaCI 2022, and 11th international conference on Theory and Practice in Modern computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Lysmos, Mezui Eya'a Guy Hanoune, Mostafa Dept. of Computer Science University Hassan II of Casablanca Faculty of the Sciences Ben M'Sik Av Driss El Harti B.P 7955 Sidi Othmane Casablanca Morocco

ISBN: (纸本)9789898704429

In this article, we introduce advanced data processing methods in an intelligent gateway. These methods are techniques for optimizing, in a first stage, the storage space of the data of the system coming from the sensors in real time and, in a second stage, the bandwidth of the network for the transfer of the data from the local to the cloud or the remote server. Among these methods, we have first, centralized data processing that uses cyclical supervision at each detected data stream. This supervision is carried out thanks to a controller agent, which filters and segments the data according to their nature, their size and their sensitivity over regular time intervals. Second, random data processing, which consists of randomly processing the data sent by the sensors when the behavior of the compression node is stable. Third, dynamic targeted data processing that allows you to dynamically choose which node to query. During this approach, we used global variables to determine the limits of the volumes of data stored during a given period, to control the stopping and the resuming of data transfer. Finally, a comparative study of these methods has been highlighted to highlight the performance and efficiency of each. © MCCSIS *** rights reserved.

关键词： Internet of things

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A Review of State-of-Art Methods for Passive Driver Fatigue Detection: Progress and Prospect 3

A Review of State-of-Art Methods for Passive Driver Fatigue ...

引用

3rd international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Wang, Dong Wu, Pianhui Guangdong Baiyun University Big Data and Computing Institute Guangzhou China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangzhou China

ISBN: (纸本)9798350376173

In recent years, there has been a noticeable rise in traffic accidents attributed to driver fatigue, sparking increased interest among researchers. Current research on fatigue driving is broadly categorized into two approaches: active detection and passive detection. Passive detection involves the use of technological tools to unobtrusively monitor physical signs of fatigue. This approach is favored over active detection for several reasons, including its higher precision, enhanced dependability, and independence from the driver's personal judgment. Passive detection has become the predominant focus in the study of driver fatigue monitoring. This paper presents a comprehensive review of the latest advancements in passive driver fatigue detection technologies over the last few years. It highlights key passive detection techniques, thoughtfully assesses the merits and drawbacks of various fatigue monitoring strategies, and concludes by suggesting potential areas for future research in fatigue detection. © 2024 IEEE.

关键词： Fatigue of materials

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Mining of Microsatellites in Genomic data Using a Hybrid Approach with Range Tree Applications 7

Mining of Microsatellites in Genomic Data Using a Hybrid App...

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7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on big data and Computational intelligence, ICBDCI 2022

作者： Umang Bharti, P.K. Husain, Akhtar Shri Venkateshwara University School of Computer Science UP Gajraula India MJP Rohilkhand University Department of CS and IT U P Bareilly India

ISBN: (纸本)9798350309904

Microsatellites are molecular markers with ubiquitous repeat patterns found in genome sequences. They are the primary source of studying interspecies variations, gene discovery, disease identification, and hypervariability in genome sequences. However, in vivo analysis of sequence data is costly and time-consuming. In addition, many next-generation sequencing tools have been developed to analyze sequence data having several new features and objectives. Therefore, researchers always need a wide range of in silico microsatellite exploring tools to examine research data apart from extracting microsatellites. This study aims to identify additional features of simple sequence repeats, such as their location in CDS, t- RNA, and rRNA regions. Also, to mark them as coding, the noncoding and coding-non-coding areas of upper bounds and lower bounds, the flanking sequences, statistics information of repeats, and other genomic features using pattern matching and computational geometry applications using range tree search algorithms. This study may help researchers retrieve and analyze the enhanced simple sequence repeats features and fill the gap for future studies and applications. © 2022 IEEE.

关键词： data mining

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Research on task offload for edge computing in high-speed railway vehicle ground communication network 3

Research on task offload for edge computing in high-speed ra...

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3rd international conference on big data, Artificial intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Shi, Yi Liu, Bohong Lanzhou Jiaotong University School of Automation and Electrical Engineering Lanzhou China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

As the key technology of Intelligent Railway 5g network, edge computing sinks the data caching capacity, traffic forwarding capacity and application service capacity to the edge of the network, effectively meeting the requirements of Intelligent Railway for low delay, large bandwidth and massive connection, so as to support the application of intelligent rail transit. However, due to the complexity and high dynamics of the high-speed railway operation environment, its train ground communication network has some problems, such as poor transmission stability, low throughput, limited wireless resources between trains and ground, and low utilization efficiency. In order to further optimize scheduling resources and improve system performance, this paper studies the task unloading problem of mobile edge computing (MEC) in the high-speed railway train ground communication network, A task unloading scheme is designed, and the effectiveness of the scheme is verified by simulation. © 2022 IEEE.

关键词： Railroads

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