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BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networks 22

BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networ...

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22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Jiang, Kang Peng, Jia-Wang Yuan, Xin Xu, Wei Tong, He Chen, Sheng-Bing Xu, Li-Xiang College of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia College of Aeronautical Engineering Beijing Polytechnic Beijing100176 China College of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Artificial Intelligence Hefei Institute of Technology Hefei238076 China

ISBN: (纸本)9798331528072

Graph Neural Networks (GNNs) have achieved remarkable success in handling graph-related tasks. Most GNN frameworks are based on Message Passing Neural Networks (MPNNs). However, subgraph structures are often complex and multi-scale, and the performance of MPNN in graph isomorphism discrimination tasks may have certain limitations. To address this issue, this paper investigates the semantic associations between subgraphs of different scales and proposes a novel Bi-Kernel Multi-Scale Subgraph Neural Network (BK-MSSNN) to represent more finegrained subgraph semantic information. Specifically, we sample global subgraphs and local subgraphs of different scales, and integrate two graph kernel methods, namely the RW kernel and the Weisfeiler-Lehman kernel, to construct multiple associative relationships between subgraphs for updating node embeddings. Experimental results on five public datasets demonstrate that our proposed model outperforms other benchmark models in graph classification tasks. © 2024 IEEE.

关键词： Graph neural networks

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Development of Medical Internet of Things with big data using RF-BFA and DL in Healthcare System

Development of Medical Internet of Things with Big Data usin...

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2022 international conference on Edge computing and Applications, ICECAA 2022

作者： Anitha, Cuddapah Devi, K. Komala Jayasutha, D. Gomathi, B. Mahaveerakannan, R. Kaur, Chamandeep Department of Computer Science and Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Applications CMR Institute of Technology Bengaluru India Arjun College of Technology Thamaraikulam Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of CSE Saveetha School of Engineering Chennai India Department of Computer Science Jazan University Saudi Arabia

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Internet of Things (IoT) developments in biomedical and health care technology have opened up exciting new avenues for innovation. A wi de range of principles and fascinating examples are explored in this chapter, including theoretical, methodological, conceptual, and empirical aspects of the subject. This research study is initiated with a description on how IoT and big data are being used to analyze a massive image database created daily from diverse sources using big data, machine learning, and other kinds of artificial intelligence to produce structured data for remote diagnosis. Health care providers may rely on the heterogeneous IoT platform to manage their data reliably, thanks to dedicated computing equipment. It is critical to healthcare service reliability that varied data streams are effectively managed owing to variations and errors. To make sense of the gathered data, a Chi-square-based term feature extraction method was employed. Outliers in sensor data are filtered out and unwanted features are removed with the use of density-based spatial clustering (DBSCAN) and random forest (RF)backward feature elimination (BFE) as RF-BFE. The pre-trained model of Convolutional Neural Network (CNN) is used to make predictions based on these features. Finally, experiments are run to determine the effectiveness of the suggested model based on a number of different criteria. © 2022 IEEE.

关键词： Internet of things

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Counting Mobile Devices With Physical Layer Measurements in the 2.4GHz ISM Band 19

Counting Mobile Devices With Physical Layer Measurements in ...

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： Zhang, Zhaoyan Huang, Baoqi Zhang, Chaowei Jia, Bing Li, Wuyungerile Xu, Gang Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Power Marketing Service and Operation Management Center China

ISBN: (纸本)9798350346558

In general, the more mobile devices are operating in space, the more wireless communications are produced, leading to significant changes in the wireless spectrum, which can be utilized to infer the number of mobile devices conversely. Inspired by this idea, we pioneered present a novel mobile device counting method of measuring the wireless signals of the 2. 4GHz ISM band in which most WiFi-and Bluetooth-enabled mobile devices operate. Precisely, the in-phase (I) and quadrature (Q) components from the wireless signals are measured through a software-defined radio (SDR) platform and are then converted into spectrograms. On this ground, we propose a dual attention network based on EfficientNet-B4 (EANet) to estimate the number of mobile devices with the aforementioned spectrograms as inputs. In addition, a cross-domain estimation method is proposed to enhance the generalizability of our model. Experiments are conducted in real environments to validate our method. It is shown that the proposed regression model EANet outperforms the models based on popular convolutional neural networks (CNNs), and the proposed cross-domain method can effectively reduce the training time without sacrificing the estimation accuracy. This study preliminarily verifies the idea's feasibility and the validity of the proposed methods, paving the way for future research. © 2022 IEEE.

关键词： Network layers

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More is Less - Byte-quantized models are faster than bit-quantized models on the edge

More is Less - Byte-quantized models are faster than bit-qua...

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2022 IEEE international conference on big data, big data 2022

作者： Zhang, Pengfei Han, Chenxia Lo, Eric The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)9781665480451

Model quantization has been a popular approach to trade accuracy for speed during model serving, especially since new models are getting bigger and bigger. Traditionally, low-precision quantized models have better inference speed than their high-precision counterparts. However, the story may change with the advent of new machine learning instructions in modern processors. In this paper, we make a case for that using ARM processors. Our quantitative analysis shows that low-precision quantized models can be inferior in both accuracy and inference speed on modern processors. In response, we present, MiL, a new quantized neural network package. Operators in MiL are optimized for inferences based on the newly available machine learning instructions on the target platform. Experiments show that serving neural networks using PyTorch with MiL outperforms all state-of-the-art, including Riptide, TFLite with Ruy, and PyTorch with QNNPACK. © 2022 IEEE.

关键词： Edge computing

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Proceedings - 3rd international conference on Next Generation computing Applications, NextComp 2022

Proceedings - 3rd International Conference on Next Generatio...

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3rd international conference on Next Generation computing Applications, NextComp 2022

ISBN: (纸本)9781665469548

The proceedings contain 33 papers. The topics discussed include: an architecture for a smart fourth party in an online dispute resolution platform;optimized detection of Anredera Cordifolia (Madeira vine) using a mask-RCNN and Anredera Cordifolia’s prominent features as object classes;investigating data repair steps for EHR big data;user modelling to support behavioral modelling in smart environments;cyber threat intelligence enabled automated attack incident response;COVID-VIT: classification of COVID-19 from 3D CT chest images based on vision transformer model;using digital game-based learning to engage the next generation of students in higher education: Lecturers9 perceptions in South Africa;and a bibliometric analysis of privacy concerns.

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Application of blockchain technology in energy digital economy 2

Application of blockchain technology in energy digital econo...

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2nd IEEE international conference on Artificial intelligence in Everything, AIE 2022

作者： Zhou, Chunlei Li, Yang Yu, Han Dong, Xinwei Big Data Center of State Grid Corporation of China Beijing China AnHui Jiyuan Software Co Ltd AnHui China

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

The use of blockchain technology has increased dramatically in the past decade. Blockchain continues to develop, and new functions and capabilities continue to develop. Although the use of this technology began with cryptocurrency, it has been extended to other areas that can benefit from a shared and secure ledger. As an important technology of energy digitization, blockchain has the characteristics of distributed computing, storage, level data sharing, sharing, openness, transparency, tamperability and continuity, and can provide technical support for energy business digitization. Put forward the concept of blockchain breaking the Bureau of energy digital economy, and analyze the role of blockchain in building a new production relationship of energy digital economy from three aspects: ownership of means of production, new production relationship and product distribution form. © 2022 IEEE.

关键词： Blockchain

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Robust Classification of Traffic Signs using MRMR Feature Reduction Technique

Robust Classification of Traffic Signs using MRMR Feature Re...

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2022 international conference on Machine Learning, big data, Cloud and Parallel computing, COM-IT-CON 2022

作者： Vashisht, Manisha Kumar, Brijesh Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

With the advent of rising global population and vehicular traffic on the road, driver safety has become a great concern for mankind. There has been an increased interest of research in area of Traffic sign recognition (TSR) which happens to be one of the critical aspects of road safety and concept of autonomous cars. The technological advancements in artificial intelligence and related IOT technologies have provided much needed momentum to research. Cars have been fitted with advanced systems which can scan the traffic signs on the road and then can either recommend the best action to the driver or can be programmed to take the action by system itself. There have been excellent efforts put by researchers in past but still today, the accuracy prediction of traffic sign detection remains a challenge for most car producers and drivers, especially under tough weather situations. In this research paper, authors have proposed an innovative approach for traffic sign image recognition by using dimensionality reduction technique using maximum relevance and minimum redundancy (MRMR) algorithm coupled with ANN for classification. The results achieved have been compared with related work done in past and have been found to be encouraging. © 2022 IEEE.

关键词： MATLAB

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Modelling the IEC 61850 and DNP3 Protocol Using OPNET in an Electrical Substation Communication Network 5

Modelling the IEC 61850 and DNP3 Protocol Using OPNET in an ...

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

作者： Njova, Dion Ogudo, Kingsley Umenne, Patrice Univ South Africa Dept Elect & Min Engn Johannesburg South Africa Univ Johannesburg Dept Elect & Elect Engn Fac Engn & Built Environm Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Communication protocols are a composite of supervisory control and data acquisition (SCADA) and they are used by the devices connected on the SCADA network. In this paper the distributed network protocol (DNP3) and international Electrotechnical Commission IEC 61850 communication protocols were modelled in OPNET. The simulation of DNP3 and MC 61850 communication protocol is done in different scenarios and the traffic behavior is analyzed. The DNP3 protocol is modelled as the medium protocol of communication during the maintenance of a 400kV Transformer at an Electrical Substation. Its network traffic behavior is then analyzed for this operation. The IEC 61850 protocol is then used as a medium of communication in the same Electrical Substation communication network (SCN) when a faulty backbone switch is present. In this scenario the network traffic behavior is again analyzed. The DNP3 simulation during the maintenance of the 400 kV Transformer shows that the model is working since the throughput is consistent without dropped packets at the Substation RTU end and the 400kV Transformer IED end. The IEC 61850 simulation when a faulty backbone switch is present shows that the model is working in this scenario since the throughput is again consistent. When the IEC 61850 protocol is modelled on the SCN, the time delay is 80 Its during normal operation and with a faulty switch the delay is 100 Its for this protocol. This shows that for the IEC 61850 model the time delay increases when there is a faulty backbone switch but not exceedingly since there is a backup switch in the structure. In the DNP3 model during the maintenance of the 400kV Transformer the time delay is approximately 160 Its. The IEC 61850 protocol performs approximately twice as fast as the DNP3 protocol during normal operation in an SCN.

关键词： Supervisory Control and data Acquisition (SCADA) Distributed Network Protocol (DNP3) international Electrotechnical Commission (IEC) 61850 Intelligent Electronic Devices (LED's) Remote Terminal Unit (RTU)

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Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks 1

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19th international conference on Advanced Intelligent computing Technology and Applications (ICIC)

作者： Zhong, Yurong Xie, Zhe Li, Weiling Luo, Xin Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

An undirected weighted network (UWN) is constantly encountered in various big data-related applications. A UWN's topological information can be expressed by a Symmetric, High-dimensional and Incomplete (SHDI) matrix, upon which the representation learning task is essential for knowledge acquisition. However, existing models mostly fail in modeling its intrinsic symmetry or low-data density, resulting in the model's weak representation learning ability to its numerical features. For addressing this vital issue, this study presents a Proximal Symmetric Non-negative Latent-factor-analysis (PSNL) model with three-fold ideas: a) building a proximal term-incorporated, symmetry-aware, and data density-oriented learning objective subjected to the non-negativity constraints for ensuring its high representation learning ability;b) designing an Alternating Direction Method of Multipliers (ADMM)-based learning scheme for solving the learning objective on the premise of fast convergence;c) implementing self-adaptation of the model's multiple hyper-parameters via the Tree-structured of Parzen Estimators (TPE) algorithm, thus enabling its high scalability. Empirical studies on four UWNs from real applications demonstrate that the proposed PSNL model achieves higher representation accuracy than state-of-the-art models do, as well as promising computational efficiency.

关键词： Undirected Weighted Network Representation Learning Symmetric Non-negative Latent Factor Analysis Alternating Direction Method of Multipliers Tree-structured of Parzen Estimators Missing data Estimation

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Design and Implementation of Monitoring System for New Energy data Call Based on Blockchain 23

Design and Implementation of Monitoring System for New Energ...

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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： Liu, Ziang Huang, Yuanyuan Zhou, Qiang Zhang, Yingli Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

With the energy revolution starting the third transformation to new energy, new energy has become essential for human energy. In order to better organize and utilize new energy, more and more research is committed to predicting the power generation capacity of new energy through the power generation data collected by intelligent systems to make better use of new energy. However, with the increase of new energy digital platforms and supporting infrastructure improvement, the distribution and management of relevant data also begin to face significant challenges. New energy data faces a substantial potential threat in distributing and using data to various digital platforms. Based on this, this paper designs and implements a new energy data call monitoring system based on blockchain to ensure the security of new energy data by tracking data operation information. © 2021 IEEE.

关键词： Smart cities Consensus algorithm Machine learning Blockchains Safety Recording Intelligent systems

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