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Application Research on Mining the Value of EMR data Based on Word Frequency Analysis 23

Application Research on Mining the Value of EMR Data Based o...

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4th international conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Weng, Suxiang Chen, Qinyin Li, Wei School of Artificial Intelligence Xiamen City University China School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9798400700705

Focusing on the discovery of the value of in-hospital electronic medical record data for the three "chronic diseases"of diabetes, liver disease and hypertension, it provides data support for improving the hospital's "patient-centered"service level. Through web crawler technology, word frequency analysis technology, WeChat applet development technology, etc., we complete the design and development of big data systems such as data collection, preprocessing, analysis, and visualization, and tap the potential value of ten-year electronic medical record data. The standardized data collation platform and the development of the "Community Online"WeChat applet were completed. The original html data was standardized and stored in a relational database;through data mining, the distribution rules of occupation, age, gender, etc. of regional chronic diseases were found;through word frequency analysis, three kinds of chronic disease admission symptoms, treatment medication and discharge life suggestions were found hot word. Taking the system as the carrier, and through the research on the value discovery of Electronic Medical Records (EMR) data, a systematic chronic disease service system from health warning to admission treatment to discharge tracking has been built for patients with diabetes, liver disease and hypertension. Provide decision-making support for "early warning, early detection, early diagnosis, and early treatment"of chronic diseases and regional improvement of comprehensive management of chronic diseases and scientific treatment. © 2023 ACM.

关键词： Hospitals

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Containerized Workflow Builder for Kubernetes 23

Containerized Workflow Builder for Kubernetes

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shan, Chenggang Wang, Guan Xia, Yuanqing Zhan, Yufeng Zhang, Jinhui School of Automation Beijing Institute of Technology Beijing China School of Artificial Intelligence Zaozhuang University Zaozhuang China School of Information Science and Engineering Zaozhuang University Zaozhuang China

ISBN: (纸本)9781665494571

Docker and Kubernetes have recently dominated the whole cloud-native technology ecosystem and speeded up the containerization process of workflows. By optimizing the task scheduling order, workflow scheduling algorithms signifi-cantly improve the execution efficiency of workflows. However, existing works do not support workflow scheduling following the optimized task scheduling order on Kubernetes. How to ensure the consistency of workflow scheduling algorithms and Kubernetes scheduler in task scheduling order is challenging. In this paper, we propose a Containerized Workflow Builder (CWB) for Kubernetes, a framework able to implement con-tainerization of workflows in a two-level scheduling scheme and connect workflow scheduling algorithms to Kubernetes, ensuring the consistency of the task scheduling order. It uses the Informer component to watch the underlying resource events of Kubernetes, providing the event trigger mechanism to respond to event callback in real-time and implement containerization of workflows by the Client-go package. Experimental results show that our proposed CWB ensures the consistency of the workflow scheduling algorithms and Kubernetes scheduler in the task scheduling order. Compared with the state-of-the-art, the CWB achieves better performance in terms of average task pod execution time, average workflow lifecycle, and resource usage rate. © 2021 IEEE.

关键词： Scheduling algorithms Smart cities Ecosystems Production Scheduling Real-time systems Task analysis

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Research on Worksite's Video Image Safety Helmet Detection Method Based on Cloud-edge Cooperation 3

Research on Worksite's Video Image Safety Helmet Detection M...

引用

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

作者： Lv, Jin Hao, Xiaolong Feng, Min Jia, Zheng Nanjing Nari Information and Communication Technology Co. Ltd. Nanjing China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

In order to ensure the safety of worksites staff, solve the problem that worksite monitoring video transmission occupies many network resources, has high time delay and cannot detect staff not wearing safety helmets in real time, a method for detecting safety helmets in video images of worksites based on cloud-edge cooperation is designed. The YOLOv3 algorithm is used for training the helmet detection model. Using the feature that the cloud-edge cooperation model can reduce processing latency, tasks such as model training and optimization, which are computationally intensive and have low real-time requirements, are handled by the cloud, and tasks such as target detection, which are relatively less computationally intensive and have high real-time requirements, are handled by the edge. Deploy the helmet detection model on the edge node to process the surveillance video images locally, and alarm in time when the behavior of not wearing a helmet is detected;the cloud server receives the surveillance video data and the processing results of the edge, and sends the information to the edge. The information and data are analyzed and calculated, the model is updated and optimized, and then the optimized model is pushed to the edge nodes, so as to realize the data interaction and automatic model optimization of the whole system. The experimental results show that this system can significantly reduce the surveillance video processing delay while maintaining a high recognition rate. © 2022 IEEE.

关键词： Edge computing

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EEG Emotion Recognition Based on a Novel 4D Feature Fusion and 3D Convolutional Networks 3

EEG Emotion Recognition Based on a Novel 4D Feature Fusion a...

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

作者： Gong, Xinhao Gu, Jin Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Emotion recognition based on electroencephalogram (EEG) signals has become an important research field in recent years due to its application in human-computer interaction. Feature fusion is the solution strategy in many pattern recognition works. However, there are few related works on EEG feature fusion. This research field has much room for development. This paper proposes a 4-dimensional representation method of multiple features across channels and frequency bands and 3D convolution neural network (3D-CNN) for EEG emotion recognition. Specifically, we perform two-dimensional electrode position relationship mapping on the extracted one-dimensional sequences of different features. The obtained 2D features are fused to the three-dimensional array to achieve complementarity between features. The 3D features of different frequency bands are then formed into the four-dimensional feature to capture cross-band information. We adopt the subject-independent approach to conduct experiments on the SEED dataset. Compared with the state-of-the-art emotion classification method, the classification performance is improved with an average accuracy of 98.64%. The 4-dimensional representation method combining different features proposed in this paper is proved to be significant in feature extraction and fusion. © 2022 IEEE.

关键词： Emotion Recognition

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Graph Neural Networks with Learnable and Optimal Polynomial Bases 40

Graph Neural Networks with Learnable and Optimal Polynomial ...

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40th international conference on Machine Learning

作者： Guo, Yuhe Wei, Zhewei Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Shenzhen Peoples R China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard's Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis fromWang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis.

关键词： Graph neural networks

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Secure and Privacy-preserving data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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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： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Sparse coding based on L2,1-norm and manifold regularization for remote sensing images 12

Sparse coding based on L2,1-norm and manifold regularization...

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

作者： Yuan Jianjun Li Shifeng Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China Chongqing Geomat & Remote Sensing Ctr Geomat & Remote Sensing Ctr Chongqing Peoples R China

ISBN: (纸本)9798350331547

Sparse coding is an important tool in image re-construction. In this paper, a novel sparse coding algorithm based on L2,1-norm and manifold regularization is developed to reconstruct remote sensing images. In the proposed algorithm, loss function based on L2,1-norm has the capability of reducing outliers. Dictionary and sparse coding based on L2,1-norm can select features with joint sparsity. Manifold regularization can retain more geometric information. An efficient algorithm for updating dictionary is developed, and the convergence is proved. To this end, we compare the proposed algorithm with other existing methods. Experiments on real remote sensing images demonstrate that the proposed algorithm has promising reconstruction results.

关键词： Sparse coding dictionary learning manifold regularization remote sensing image reconstruction

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RGB Color Model Aware Computational Color Naming and Its Application to data Augmentation

RGB Color Model Aware Computational Color Naming and Its App...

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2022 IEEE international conference on big data, big data 2022

作者： Yan, Zipei Xu, Linchuan Suzuki, Atsushi Wang, Jing Cao, Jiannong Huang, Jun The Hong Kong Polytechnic University Department of Computing Kowloon Hung Hom Hong Kong King's College London Department of Informatics London United Kingdom University of Greenwich Department of Computing and Mathematical Sciences London United Kingdom Anhui University of Technology School of Computer Science and Technology Ma'anshan China

ISBN: (纸本)9781665480451

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative *** expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images' diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://***/yanzipei/CCN_and_ItsApp. © 2022 IEEE.

关键词： Mapping

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Role-Oriented Dynamic Network Embedding

Role-Oriented Dynamic Network Embedding

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2022 IEEE international conference on big data, big data 2022

作者： Pan, Ting Wang, Wenjun Shao, Minglai Sun, Yueheng Jiao, Pengfei Tianjin University College of Intelligence and Computing Tianjin China Tianjin University School of New Media and Communication Tianjin China Hangzhou Dianzi University School of Cyberspace Hangzhou China

ISBN: (纸本)9781665480451

Exploring the differences and important patterns of nodes from the perspective of roles has gradually developed into an interesting and important topic in network analysis. However, existing role-oriented network embedding methods focus more on identifying underlying roles for static network, which leads to complex temporal behaviors being overlooked and degraded performance facing dynamic network. The few role analytics methods for dynamic networks either cannot learn general node representations or fail to discovery role transitions of nodes. In this work, we propose a unified framework RDNE (Role-oriented Dynamic Network Embedding) to tackle such challenges, which aim to learn multiple embeddings for individual nodes based on time-varying structural behaviors. Based on regular equivalence, RDNE propagates the structural features over the graph to derive the initial role-oriented representations. Then, it applies capsule network to further model the mapping between nodes and roles, which is the first time capsule network is used for role discovery. For the varying and temporal dependence within dynamic network, we utilize the Gated Recurrent Unit to compute historical information and use historical information to influence the generation of representations at the next snapshot. Comprehensive experiments on both synthetic and real-world networks validate the superiority of the proposed RDNE. © 2022 IEEE.

关键词： Network embeddings

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A study on CSD-based storage engine scheduling for high-speed processing of large-scale data 22

A study on CSD-based storage engine scheduling for high-spee...

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2022 conference on Research in Adaptive and Convergent Systems, RACS 2022

作者： Lee, Jusuk An, Jae-Hoon Kim, Younghwan Intelligent Idc Project Office Korea Electronics Technology Institute Seongnam Korea Republic of

ISBN: (纸本)9781450393980

As we enter the era of the 4th industrial revolution, artificial intelligence and big data technologies are being introduced in various industrial fields, and the computing architecture paradigm is shifting towards data. Accordingly, the importance of a database management system (DBMS) that manages a large amount of data is increasing, and a computing environment for processing memory-and storage-oriented data rather than process-oriented arithmetic processing is required. A computational storage device (CSD)-based DBMS performs query offloading in an environment in which the existing DBMS engine and computational storage are separated, enabling parallel computation and shortening query execution time. A snippet scheduling process considering the current situation is necessary. This study focuses on distributed parallel processing and high-speed query request processing by proposing the architecture of the CSD-based DBMS computational storage engine and the snippet scheduling technique that minimizes the movement of large-capacity data. © 2022 Owner/Author.

关键词： database systems

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