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Research on the Forecast of the Spread of COVID-19 21

Research on the Forecast of the Spread of COVID-19

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11th international conference on Biomedical engineering and Technology, ICBET 2021

作者： Guo, Lihao Yang, Yuxin James e Rogers College of Law The University of Arizona TucsonAZ85719 United States James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9781450387897

With the spreading of COVID-19, various existing machine learning frameworks can be adopted to effectively control the epidemic to help research and predict the spread of the virus before the large-scale application of vaccines. Based on the spatiotemporal graph neural network and mobility data, this paper attempts to offer a novel prediction by building a high-resolution graph with the characteristics such as willingness to wear masks, daily infection, and daily death. this model is different from the time series prediction model. the method learns from the multivariate spatiotemporal graph, the nodes represent the region with daily confirmed cases and death, and edges represent the inter-regional contacts based on mobility. Simultaneously, the transmission model is built by a time margin as the characteristic of the time change. this paper builds the COVID-19 model by using STGNNs and tries to predict and verify the virus's infection. Finally, the model has an absolute Pearson Correlation of 0.9735, far from the expected value of 0.998. the predicted value on the first and second day is close to the real situation, while the value gradually deviates from the actual situation after the second day. It still shows that the graph neural network uses much temporal and spatial information to enable the model to learn complex dynamics. In the future, the model can be improved by tuning hyper-parameter such as modulation numbers of convolution, or construction of graphs that suitable for smaller individuals such as institutions, buildings, and houses, as well as assigning more features to each node. this experiment demonstrates the powerful combination of deep learning and graph neural networks to study the spread and evolution of COVID-19. © 2021 ACM.

关键词： COVID-19

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Kathakali Mudras: An AI-Enhanced Predictive Approach 11

Kathakali Mudras: An AI-Enhanced Predictive Approach

引用

11th international conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

the goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. the study entailed a thorough analysis of how well-performing different deep learning techniques performed. the predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. the results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. the results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. this AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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three dimensional inversion of mise-a-la-masse-based electric data using U-Net convolutional neural network 11

Three dimensional inversion of mise-a-la-masse-based electri...

引用

11th international conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Meng, Yuhang Li, Jinghe He, Zhanxiang Zhang, Yujie College of Earth Science Guilin University of Technology Guilin541004 China Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology Southern University of Science and Technology Shenzhen518055 China School of Mathematics and Physics China University of Geosciences Wuhan430074 China

Nonlinear inversion algorithms are away ill-pose for the limited and special data measurement, such as mise-a-la-masse-based electric method and borehole to surface electromagnetic method. It is natural to develop new inversion algorithms without calculating the the Jacobian matrix and its inverse matrix, especially for three dimensional inversion. Basing on U-Net fully convolutional neural network, a three dimensional inversion of mise-a-la-masse-based electric data is proposed in this paper. the random rotated spheroids are used to synthesize diverse geological models. A typical U-Net learning network is constructed with incorporating the Dice coefficient and the input data feedback to the trained network. Synthetic datasets testing validates the effectiveness of this proposed algorithm for mise-a-la-masse-based electric data. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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JREP - A Job Runtime Ensemble Predictor for Improving Scheduling Performance on High Performance Computing Systems 11th

JREP - A Job Runtime Ensemble Predictor for Improving Sched...

引用

11th international conference on Future data and Security engineering, FDSE 2024

作者： Hai, thanh Hoang Le Nguyen, Manh thin Nguyen, Quang Hung thoai, Nam Vietnam National University Ho Chi Minh City Linh Trung Ward 268 Ly Thuong Kiet Street District 10 Thu Duc City Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604364

Efficient resource utilization in High Performance Computing (HPC) systems heavily relies on accurate job runtime prediction. this paper introduces JREP (Job Runtime Ensemble Predictor), a novel ensemble learning approach combining multiple prediction techniques to enhance HPC job runtime estimation accuracy. We evaluate JREP using real-world datasets from production HPC systems and integrate it with deviation backfilling, a prediction-aware scheduling method. Our results demonstrate that JREP not only outperforms individual prediction methods in accuracy but also significantly improves scheduling performance. this work contributes to optimizing HPC operations through advanced machine learning, offering a promising direction for enhancing overall system efficiency in diverse and dynamic HPC environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Vehicle detection based on the stereo concept of (axis, width, disparity) symmetry map

Vehicle detection based on the stereo concept of (axis, widt...

引用

international conference on intelligent Transportation

作者： Yaqian Li Gwenaelle Toulminet Abdelaziz Bensrhair LITIS INSA Rouen France CAOR Ecole des Mines de Paris France

In this paper, we introduced the concept of (axis,width,disparity) symmetry map. It is a stereo extension of the monocular concept of (axis,width) symmetry map. In its principle, this concept can be used for the detection of any symmetric obstacle provided that its approximate width and height are known. In this paper, we used it for vehicle detection. As the presence or absence of road features, and hilly or plan road do not perturb the process, the proposed method is an alternative to v-disparity approach.

关键词： Vehicle detection Testing intelligent transportation systems Layout intelligent vehicles Road safety Automotive engineering data mining Shape Image edge detection

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Automatic Vehicle Detection and Classification

Automatic Vehicle Detection and Classification

引用

international conference on intelligent Transportation

作者： Pedro M. Ferreira Goncalo Marques Pedro M. Jorge Arnaldo J. Abrantes Antonio Amador Department of Electronics Telecommunications and Computer Engineering Multimedia and Machine Learning Group ISEL Lisboa Portugal Brisa Auto Estradas de Portugal Lisboa Portugal

this paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. the proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. the AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose the use of Eigenprofiles, a technique based on Principal Components Analysis. the system should also support multi-lane free flow for future integration in this kind of environments.

关键词： Vehicle detection Vehicles Sensor systems Legislation Principal component analysis Sensor phenomena and characterization intelligent transportation systems intelligent sensors Optical pulses Pulse measurements

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Industrial Internet System and Working Method Based on Deep learning and Edge Computing 11th

Industrial Internet System and Working Method Based on Deep ...

引用

11th international conference on Frontier Computing, FC 2021

作者： Wang, Dahai Department of Business Administration Business School College of Humanities Northeast Normal University 1488 Boshuo Road Jilin Province Jingyue District Changchun City130117 China

ISBN: (纸本)9789811680519

With the advent of 5g era, its characteristics of low power consumption, high speed, low cost and low delay have brought positive and huge changes to the Internet of things industry. the increasing scale of the Internet of things brings challenges to the energy consumption, transmission bandwidth and processing delay of centralized cloud computing data center. the former is moving from the center node to the edge node with short delay, namely edge computing. Edge computing can share the work of the cloud, reduce the pressure, and meet the needs of users for real-time services. Migration decision and resource scheduling is one of the hot issues in the field of edge computing. this paper introduces the migration decision and resource scheduling of mobile edge computing and the theoretical basis of deep reinforcement learning, and summarizes and analyzes the existing migration decision and resource scheduling schemes and algorithms. On this basis, this paper focuses on how to use deep reinforcement learning algorithm to optimize the migration decision and resource scheduling in two actual scenarios: multi cell multi-user full migration and single cell multi-user partial migration. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Deep Reinforcement learning-Based Resource Allocation for 5G Machine-Type Communication in Active Distribution Networks 1

引用

11th European-Alliance-for-Innovation (EAI) international conference on Mobile Networks and Management (MONAMI)

作者： Li, Qiyue Cheng, Hong Yang, Yangzhao Tang, Haochen Liu, Zhi Cao, Yangjie Sun, Wei Hefei Univ Technol Sch Elect Engn & Automat Hefei Anhui Peoples R China Engn Technol Res Ctr Ind Automat Hefei Anhui Peoples R China Shenzhen Cyberaray Network Technol Co Ltd Shenzhen Peoples R China Univ Electrocommun Chofu Tokyo Japan Zhengzhou Univ Zhengzhou Peoples R China

ISBN: (数字)9783030947637

ISBN: (纸本)9783030947637;9783030947620

With the development of smart grids and active distribution networks (ADNs), reliable and low-latency communication is the key to advanced applications such as energy management and situation awareness (SA). However, with the increasing amount of data and location information to be collected, ensuring the real-time transmission of sampling data has become a challenge. In addition, the operating environment of ADNs is complex, and external interference will affect the reliability of transmission. In particular, the occurrence of power emergencies is random, and the high reliability of emergency data transmission caused by emergencies has attracted much attention. Although repeated data transmission in 5G machine-type communication (MTC) can improve the reliability, how to dynamically allocate communication resources according to the transmitted data and external interference remains a problem. To this end, we propose a scheme of repeated data transmission to eliminate the influence of external interference on the outage probability of emergency data transmission. Our scheme is modeled as a dynamic programming problem to maximize the energy efficiency. First, external interference is considered in the calculation of the transmission outage probability of smart meters (SMs), and the number of repeated transmissions of emergency data is placed in the position of the index, which is determined by reaching the target outage probability. then, to allocate dynamic resource in real time in a changing environment, we propose a deep reinforcement learning method, which has fast computing speed, can more quickly allocate resources and reduce the delay of data transmission. Simulation results have verified the superiority of the proposed scheme.

关键词： Situation awareness Reliable and low-delay data communication Resource allocation Deep reinforcement learning

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FedIOD: A Federated learning Framework for Aerial Insulators Orientation Detection

FedIOD: A Federated Learning Framework for Aerial Insulators...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Xiao Zhang Xunjun Zhang Fenghe Zhang Xiaowen Sun Bo Zhao School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China State Grid Gansu Electric Power Company Lanzhou China

Aiming at the limitation of aerial insulators detection by Unmanned Aerial Vehicle(UAV) combined with deep learning technology, we propose a federated learning framework for aerial insulators orientation detection, dubbed FedIOD. It addresses the challenge of utilizing original insulator image data, which cannot be involved in centralized deep learning model training due to the reasons of policy and confidentiality, as well as horizontal anchor boxes select insulators and their defective regions inaccurately. In FedIOD, we improve the head structure and loss function of the YOLOv5 algorithm, so that it can detect insulators and their defective parts directionally, and add an attention mechanism module to its backbone network to enhance the feature extraction capability. In addition, FedIOD trains the global model collaboratively based on federated learning, and the parameters are aggregated by using the weighted average method. the experimental results show that the precision and recall of FedIOD can reach above 79.16% and 80.06%, respectively. FedIOD has a great practical significance for building an intelligent grid equipment detection system.

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A New Methodology for Processing Time Varying Traffic data in Multiple States

A New Methodology for Processing Time Varying Traffic Data i...

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international conference on intelligent Transportation

作者： Weihua Zhu Kanok Boriboonsomsin Matthew Barth Electrical Engineering Department and CE-CERT University of California Riverside CA USA CE-CERT University of California Riverside CA USA

Freeway management systems are becoming increasingly important, serving as a core of many intelligent transportation system (ITS) applications. Advancement in various areas of traffic monitoring system technology has provided planners and engineers with rich sets of traffic data, both macroscopically and microscopically, that can be used to achieve even better freeway management. In this paper, we propose a new methodology specifically designed to process traffic data that vary in time according to their state. this methodology consists of two algorithms, i.e. a scale space smoothing algorithm and a time segmentation algorithm, which can be used either separately or sequentially to process the data. the scale space algorithm smoothes time series traffic data to eliminate the embedded random effect while preserving the sharp transition state of the data. the time series segmentation algorithm divides the time series data into different "semantically" meaningful segments belonging to different states. this paper also discusses potential applications of this methodology to a variety of ITS implementations. Several experiments have been conducted on selected applications and their results verify the effectiveness of the proposed methodology.

关键词： Traffic control intelligent transportation systems Monitoring data engineering Microscopy Technology management engineering management Design methodology Process design Smoothing methods

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