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2022 international conference on data Analytics, computing and Artificial intelligence, ICDACAI 2022

作者： Xie, Yang Jiang, Yulin Hu, Zenan Information Institute Liaoning University Liaoning Shenyang China Software Institute Tiangong University Tianjin China Shenzhen Stock Exchange Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

Railway construction can strengthen the connection between urban agglomerations in the region and promote economies. In this paper, we select the Yangtze River Delta as the empirical object, abstract the railway network in the study period into a directed adjacency matrix according to the social analysis method in the complex network theory, and design the index from the perspective of integration. Then, we use Python and ArcGIS to show the changing trend of railway integration degree in time and space in the Yangtze River Delta from 2008 to 2021 to explore the impact of railway integration on urban integration and try to analyze the reasons behind the changes by the meaning of the designed integration indicators and our previous investigation results. We found that railway integration strengthens the degree of urban integration qualitatively speaking. From 2008 to 2021, the railway density in the Yangtze River Delta increased significantly, the average eccentricity among urban agglomerations decreased, and there was an obvious small-world effect in the Yangtze River Delta. Besides, in space, the number of sub-hot spots of weighted centrality increased significantly in 2021 compared with 2008, and new agglomerated subgroups appeared in 2021, with some cities in Anhui merged into the region. © 2022 IEEE.

关键词： Complex networks

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Design and Implementation of Deep Learning Real-Time Streaming Video data Processing System 1

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Zhao, Qiming Wu, Jing Wu, Xiang Fan, Jie Wang, Linhao Wu, Fengling College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： big data

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A Grade Prediction Model Using Knowledge Graph and Time Series 15

A Grade Prediction Model Using Knowledge Graph and Time Seri...

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15th IEEE international conference on Advanced Infocomm Technology, ICAIT 2023

作者： Zhang, Yiwen Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University Big Data and Artificial Intelligence College Hefei China

ISBN: (纸本)9798350314120

In order to improve student achievement and reduce the failure rate, student achievement prediction is particularly important. At present, the methods of collaborative filtering and linear regression are mainly used to predict student achievements. However, only using academic performance to predict can not reflect the characteristics of students. Because according to the analysis of course content, there may be similarities between courses and time series *** the problems, this paper proposes a course prediction method based on knowledge graph to learn and extract user features and relationships, and forecast using a time series model. The experimental results show that the course grade prediction method based on knowledge graph can reduce the error and enhance the interpretability of the model. © 2023 IEEE.

关键词： Failure analysis

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Development Road Map and Planning Mode of Artificial intelligence Technology Under the Background of Internet Information 1

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5th EAI international conference on 6G for Future Wireless Networks, 6GN 2022

作者： Tang, Zheqing Sun, Xiqiang Luo, Yang School of Information Engineering Heilongjiang Vocational College Harbin150022 China

ISBN: (数字)9783031360145

ISBN: (纸本)9783031360138

With the progress of various software and hardware technologies, the data processing and computing power of the computer are further enhanced. Artificial intelligence technology has entered a period of rapid development. The current "Internet + " boom and the continued prosperity of the big data industry have further accelerated the integration of artificial intelligence technology with other related industries. This paper summarizes the Internet information, static information collection technology and artificial intelligence technology, analyzes the neural network, puts forward RRT path planning, and studies the pheromone increment dynamic update strategy. The results show that when LK is less than lbest, the increase of the difference will sharply increase the pheromone increment on the path, which makes the algorithm easy to fall into local optimization. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data handling

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Challenges and future directions for security and privacy in vehicular fog computing

Challenges and future directions for security and privacy in...

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2022 international conference on Innovation and intelligence for Informatics, computing, and Technologies, 3ICT 2022

作者： Nazih, Ossama Benamar, Nabil Lamaazi, Hanane Chaoui, Habiba Ibn Tofail University National School of Applied Sciences Kenitra Morocco Moulay Ismail University of Meknes School of Technology Meknes Morocco Uae University College of Information Technology Al Ain United Arab Emirates

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Cooperative Intelligent Transportation System (CITS) has been introduced recently to increase road safety, traffic efficiency, and to enable various infotainment and comfort applications and services. To this end, a bunch technologies have been deployed to maintain and promote ITS. In essence, ITS is composed of vehicles, roadside infrastructure, and the environment that includes pedestrians, and other entities. Recently, several solutions were suggested to handle with the challenges faced by the vehicular networks (VN) using future internet architectures. One of the promising solutions proposed recently is Vehicular Fog computing (VFC), an attractive solution that supports sensitive service requests considering factors such as latency, mobility, localization, and scalability. VFC also provides a virtual platform for real-time big data analytic using servers or vehicles as a fog infrastructure. This paper surveys the general fog computing (FC) concept, the VFC architectures, and the key characteristics of several intelligent computing applications. We mainly focus on trust and security challenges in VFC deployment and real-time BD analytic in vehicular environment. We identify the faced challenges and future research directions in VFC and we highlight the research gap that can be exploited by researchers and vehicular manufactures while designing a new secure VFC architecture. © 2022 IEEE.

关键词： Intelligent systems

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QT-STNet: A Spatial and Temporal Network Combined with QT Segment for MI Detection and Location 12

QT-STNet: A Spatial and Temporal Network Combined with QT Se...

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12th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： Yu, Jie Gao, Jian Wang, Ning Feng, Panpan Zhou, Bing Wang, Zongmin Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9798350331547

Myocardial infarction (MI) is a cardiovascular disease with high mortality, which can be diagnosed by electrocardiogram (ECG). Therefore, this paper proposes a spatial and temporal network combined with QT segment (QT-STNet) to detect and locate MI. First, QT segment is extracted by QT segment extraction module. Next, the QT segment is concatenated with the original signal to enhance the importance of the key information. Then, considering the spatial and temporal features of ECG, DenseNet is used to extract spatial features and Transformer is used to extract temporal features. Finally, the proposed method is test on PTB-XL dataset, and the precision, recall, F1 score and hamming loss are 0.881, 0.881, 0.876 and 0.051 respectively. The results show that the method is superior to other methods.

关键词： electrocardiogram myocardial infarction spatial and temporal network QT segment

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Livestock Recognition and Identification with Deep Convolutional Neural Networks: A Case Study of Pigs 23

Livestock Recognition and Identification with Deep Convoluti...

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9th international conference on computing and Artificial intelligence, ICCAI 2023

作者： Zhao, Feiyang Tian, Maohong Hu, Sheng Liang, Jian Zhou, Longfu Li, Hualin ChongQing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center ChongQing China ChongQing Institute of Engineering Shu Yi Xin Credit Management Co LTD ChongQing China ChongQing Institute of Engineering ChongQing China

ISBN: (纸本)9781450399029

Animal recognition with Object Detection (OD) method by using deep learning techniques has gained popularities in biodiversity preservation and "smart farm" program in recent years. Current research mainly focuses on model training and parameter optimization of different forms of livestock in a single scene. To meet the developmental needs of farms in modern agriculture, this paper proposes an image recognition method which preprocesses the morphological characteristics of livestock in different captivity scenarios, by combining Kennard Stone algorithm, K-means II algorithm and deep learning models for different scenes and live pigs of different behavior characteristics. Our results show that the F1-socre, mAP0.5, and mAP0.5-0.95 of the model were 98.48%, 99.27% and 73.03%, respectively. Our research shows significant improvements for promoting smart animal husbandry and saving labor costs for breeding enterprises, which can also speed up research of high accuracy livestock auto-weighing systems and subsequent applied in animal husbandry. © 2023 Copyright held by the owner/author(s).

关键词： Object detection

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Diagonal Region Division-Based Fly Neural Network on Omnidrectional Collision Detection 8

Diagonal Region Division-Based Fly Neural Network on Omnidre...

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8th IEEE international conference on Cloud computing and intelligence Systems, CCIS 2022

作者： Li, Lun Zhang, Zhuhong Wu, Xiyin Guizhou University College of Big Data and Information Engineering Guiyang550025 China

ISBN: (纸本)9781665477352

Camera-based road vehicle collision detection is a major challenge in the field of intelligent transportation, particularly it is still open to borrow motion sensitive neurons to construct computational models for multi-vehicle collision detection. To fill this gap, a bio-inspired fly visual collision detection neural network with presynaptic and postsynaptic neural networks is proposed to execute vehicle collision early warning in complex scenes. The former network includes four sub-neural networks which share four visual neural layers, each with a specific visual neuron;the latter network involves in one lobula plate layer and three spiking neurons. The experimental results have validated that the fly neural network can successfully execute collision detection when confronted with some approaching object(s) in real time. © 2022 IEEE.

关键词： Neurons

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Design and Application of the Intelligent Operation, Control and Maintenance of Distribution Networks Based on Wide-Area Distributed Sinking computing Technology

Design and Application of the Intelligent Operation, Control...

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2021 international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021

作者： Duan, Xiangjun Li, Jia Feng, Dezhi China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781643682549

The distribution network (DN) is characterized by wide coverage and highly scattered data distribution. To improve the business process and data calculation capability for DN management, a foundational computing technology, the wide-area distributed sinking calculation technology (W-A DSCT), is proposed based on the application of big data, artificial intelligence, block chain and other technologies. It is applicable to the OCMS (operation, control and maintenance) of distribution networks. The technology adopts the concept of 'moving computation, not moving to data' to realize fast processing of business applications. The algorithm and business logic are designed at the primary node level, and the algorithm compilation and data crawling and mining are performed at the sub-node level to realize the fast business response. This paper introduces the technical implementation methods and technologies of each link, and performs a comparative analysis for typical business scenarios between the sinking calculation scheme and the original framework calculation. It is verified that applying the sinking calculation scheme can significantly enhance the rapid processing ability and computational efficiency. © 2022 The authors and IOS Press.

关键词： big data

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals data

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data intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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