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检索条件"任意字段=2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）"

共 1293 条记录，以下是111-120 订阅

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Measurement of Compulsory Education Resource Allocation under Population Factors in China 22

Measurement of Compulsory Education Resource Allocation unde...

引用

4th World Symposium on Software Engineering, WSSE 2022

作者： He, Xueqi Zhou, Jinchun Wang, Ying Wang, Xiaoning Department of Data Science and Big Data Technology Communication University of China No.1 Dingfuzhuang East Street Beijing China

ISBN: (纸本)9781450396950

The high-quality and balanced development of basic compulsory education is of great significance to the country, society and even individuals. The current situation of China's slowing down of social population growth and accelerated aging has brought new challenges to education. Measuring the rationality of education resource allocation from the new perspective of changing population is an indispensable part of optimizing compulsory education reform. From the perspective of time and space, this study collected the education statistics officially released by Chinese government websites in 31 regions in mainland China from 2013 to 2019 and concluded after cleaning, analysis, and modeling. Taking the population structure and quantity as the entry point, this paper firstly uses three-stage data envelopment analysis (DEA) to carry out a static analysis on the allocation efficiency of compulsory education resources. The results show that the internal technical management of 20 regions, including Beijing and Hebei, needs to be improved;5 regions including Inner Mongolia and Hainan can appropriately increase education investment;from the perspective of population, the increase in the number and proportion of students in compulsory education has an inhibitory effect on resource allocation efficiency. Then, the Malmquist index was used to perform a dynamic decomposition of total factor productivity, and it's concluded that the main reason for hindering the sustained and rapid growth of educational resource allocation is the lack of internal refined management. Finally, it is suggested that different regions should improve their internal allocation strategies and increase education investment and enhance science and technology reform respectively in conjunction with the number of in-migrating population. © 2022 ACM.

关键词： Resource allocation

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Using big data Analytics on Social Media to Analyze Tourism Service Encounters 1

引用

international conference on Artificial Intelligence and Sustainable Engineering, AISE 2020

作者： Kumar, Sunil Kar, Arpan Kumar Ilavarasan, P. Vigneswara Department of Management Studies Indian Institute of Technology New Delhi110016 India

ISBN: (数字)9789811685422

ISBN: (纸本)9789811685415

Indian tourism is the fastest-growing section of tourism;thus, the research of it is reasonable. This study examines the Indian tourism trends by examining the tweets on the social media Website Twitter. Twitter is a massive platform for sharing ideas all over the world. A total of 7, 91, 804 tweets were identified after removing the extra and non-related tweets from the downloaded tweets. When tourists visit any place, they often share their experience regarding service encounters at the destination on the social media platform. In this study, we endeavor to find out the sentiment and theme of discussions over social media by using sentiment analysis and topic modeling techniques. For sentiment analysis, we divided India into five zones (north, west, east, south, and north-east). Our findings show that overall tourists are enjoying their visits to the destination. However, east and north-east zones of India are facing some negativity. The Indian Government, service providers, and stakeholders can use these findings to do future planning for Indian tourism growth. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Neural Latent Factorization of Tensors for Dynamically Weighted Directed Networks analysis

Neural Latent Factorization of Tensors for Dynamically Weigh...

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IEEE international conference on Systems, Man, and Cybernetics (SMC)

作者： Wu, Hao Luo, Xin Zhou, MengChu Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Hengrui Chongqing Artificial Intelligence Res Ctr Dept Big Data Anal Tech Chongqing 401331 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781665442077

A big-data-related application like a Terminal Interaction Pattern analysis System (TIPAS) commonly involves massive entities interacting with each other dynamically. Such interactions can be represented by a Dynamically Weighted Directed Network (DWDN). A large number of involved entities results in a high-dimensional and incomplete (HDI) network, which can be represented by an HDI tensor with numerous missing entries. In spite of its HDI nature, this tensor contains much useful knowledge regarding various desired patterns like unobserved links in DWDN. However, due to its extremely high dimensionality and low data density, it is very challenging to build a learning model that can precisely represent an HDI tensor. To address this issue, this work proposes a Neural Latent Factorization of Tensors (NeuLFoT) model with three interesting ideas: a) adopting the principle of density-oriented modeling and Canonical Polyadic tensor factorization to build rank-one tensor series relying on three-dimensional latent factors for precisely representing an HDI tensor's known data;b) treating the obtained rank-one tensors as neurons to form a novel neural tensor network model;and c) proposing a novel Backward Propagation algorithm for Latent factorization of tensors (BPL) to ensure high training efficiency. Experimental results on two large-scale DWDNs generated by a real TIPAS demonstrate that compared with state-of-the-art models, the proposed model achieves significant gain in prediction accuracy for missing links of a DWDN and achieves highly competitive computational efficiency.

关键词： Tensor Method Neural Network Dynamically Weighted Directed Network High-dimensional and Incomplete Tensor Link Prediction Latent Factorization of Tensors

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Relaxed Symmetric Non-negative Latent Factor analysis for Large-scale Undirected Weighted Networks

Relaxed Symmetric Non-negative Latent Factor Analysis for La...

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IEEE international conference on Systems, Man, and Cybernetics (SMC)

作者： Zhong, Yurong Luo, Xin Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Hengrui Chongqing Artificial Intelligence Res Ctr Dept Big Data Anal Tech Chongqing 401331 Peoples R China

ISBN: (纸本)9781665442077

A large-scale undirected weighted network (LUWN) is described by a symmetric high-dimensional and sparse (SHiDS) matrix. To correctly represent its symmetry, existing models adopt a strong symmetry assumption, i.e., reducing the solution space to adopt a unique latent factor matrix to describe an SHiDS matrix's symmetry. Yet it may impair a resultant model's representativeness to its numerical features. Aiming at addressing this issue, this work proposes a Relaxed Symmetric Non-negative latent factor analysis (RSN) model that adopts three-fold ideas: a) Introducing a triple equation constraint into its learning objective for relaxing the strong symmetry assumption, thereby greatly improving its representativeness to the numerical features of an SHiDS matrix;b) Adopting the framework of Alternating Direction Method of Multipliers to fast realize its learning objective subject to multiple constraints;and c) utilizing a data density-oriented principle during its modeling and optimization, thereby precisely representing an SHiDS matrix's imbalanced data. Empirical studies on four industrial SHiDS matrices describing real LUWNs demonstrate that RSN outperforms state-of-the-art models in representing an SHiDS matrix precisely, as well as achieves highly competitive computational efficiency. Hence, this work greatly advances the area of LUWN analysis.

关键词： Computational efficiency

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A Computational Model that Simulates the Human Immune System’s Response to Measles Vaccination

A Computational Model that Simulates the Human Immune System...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Maria Clara Campos Miquilito Maria Fernanda Ventura Dos Santos Leite Kauan Ferreira Rezende Gustavo Montes Novaes Carla Rezende Barbosa Bonin Marcelo Lobosco Department of Computing Centro Federal de Educação Tecnológica de Minas Gerais Leopoldina Brasil Department of Computing Instituto Federal do Sudeste de Minas Gerais Cataguases Brasil Department of Computer Science Universidade Federal de Juiz de Fora Juiz de Fora Brasil

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Measles is a highly contagious viral disease that poses a substantial public health risk. Vaccination is the primary strategy for measles prevention, requiring a 95% global coverage with two doses of the MMR (measles, mumps, and rubella) vaccine. Although the vaccine is 98% effective, the spread of anti-vaccine sentiment and misinformation about immunization mechanisms has significantly reduced vaccination rates, allowing the virus to persist. In 2016, there were 132,490 reported cases of measles globally; this number increased to 837,922 in 2019. This work presents a computational model, based on a previously established model from the literature, that simulates the immune response to measles vaccination. The objectives are to: (1) create an effective simulation tool that can model diverse vaccination scenarios, thereby reducing dependence on extensive clinical trials; (2) assess the immunological response to the measles vaccine through computational simulations, conserving both time and resources; and (3) conduct sensitivity analysis of the model’s parameters to identify key immune components and their interactions, leading to the most effective immune response.

关键词： Analytical models Immune response Sensitivity analysis Computational modeling Biological system modeling Measles data models Vaccines Viruses (medical) Immune system

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Regional research based on big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao 2

Regional research based on big data of hot spring industry i...

引用

2021 2nd international conference on Computer Information and big data Applications, CIBDA 2021

作者： Zhang, Bo Zhang, Zhiqin School of Computer Science Wuhan Donghu University Hubei Key Laboratory of Big Data in Science and Technology Wuhan Library of Chinese Academy of Science Wuhan430212 China

One belt, one road area, tourism research in the Tai Wan District of Guangdong, Hong Kong and Macao is mainly focused on the integration mechanism of culture, commerce and tourism in Guangdong, Hong Kong and Macau, and the leisure and recreational zone structure of the city group of Guangdong, Hong Kong and Macau. In this paper, combined with the actual needs of hot spring scenic spots in Guangdong, Hong Kong and Macao, we use the concept of global tourism to achieve information integration, marketing accuracy, intelligent management, and create humanized service, so as to create a new benchmark of smart hot spring scenic spots. We need a support platform and four kinds of application-oriented business systems, including technical service, technical management, technical marketing, and technical management. Using the methods of theoretical analysis, simulation test or implementation, this paper studies the regional scientific and technological research based on the scientific and technological big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao. © Published under licence by IOP Publishing Ltd.

关键词： Hot springs

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modeling, simulation, and Analytical Comparison of PID, ANN, and ANFIS Controllers in Two-Area AGC Systems using MATLAB Simulink

Modeling, Simulation, and Analytical Comparison of PID, ANN,...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Saiful Islam Tuhin Md. Sahadat Hossain Sani Md. Arif Hasan Masum Md. Wahidur Rahman Easin Md. Nure Alam Siddiki Md. Shamsul Arefin Department of Electrical and Electronic Engineering (EEE) Bangladesh University of Business and Technology (BUBT) Dhaka Bangladesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

A two-area Automatic Generation Control (AGC) model is utilized to evaluate the performance of PID, ANN, and ANFIS controllers, with a novel approach of combining all three controllers into a single model. MATLAB/Simulink version 2019(a) has been used for model development. Within the same AGC model, PID, ANN and ANFIS controllers were implemented separately for comparison. The primary data for the ANN and ANFIS training were derived from the input and output of the PID controller. The findings of this study indicate that the ANFIS controller outperforms both ANN and traditional PID controllers in terms of Settling-time, Risetime, Overshoot, and other performance metrics. The ranking of the controller’s performance is ANFIS, ANN, PID respectively. However, the performance of the combined controller’s model was found to be the least effective.

关键词： Training Measurement Analytical models Software packages Automatic generation control Mathematical models MATLAB Time-domain analysis Informatics

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data analysis for Risk Prediction of Cervical Cancer Metastasis and Recurrence Based on DCNN-RF

Data Analysis for Risk Prediction of Cervical Cancer Metasta...

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2020 international conference on modeling, big data Analytics and simulation, MBDAS 2020

作者： Zhou, Xiaohong Li, Weihong Wen, Zhicheng Jiangxi University of Engineering Xinyu Jiangxi Province China

In allusion to the problem of the low survival rate of cervical cancer metastasis and recurrence, combining the advantages of deep learning, a hybrid DCNN-RF method based on patched pathological image was proposed to predict the risk of metastasis and recurrence in cervical cancer patients. In order to improve the generalization ability of the model, according to the features, predicting result could be obtained from random forest, and the integration result was invoked as the result of haematoxylin and eosin pathological whole-slide images (WSI). The experimental results show that the model yielded an accuracy of 90.32% for prediction based on sliding window in cross-validation, and 0.83 AUC in the WSI. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Research on High-Voltage Transmission System Operation on the Same Tower 3

Research on High-Voltage Transmission System Operation on th...

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3rd international conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2021

作者： Maalla, Allam Jia, Qiwei Ye, Lizhu Research Institute of Open Data Commercial Application School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China

The high-voltage DC adopts the simulation idea of a two-circuit system on the same convertor. The current DC system availability indicators were insufficient and proposed two reliability indicators, operating utilization and operating availability. At the same time, the active planned outage mode and the activities planned energy unavailability rate index was proposed, and the reliability evaluation program algorithm analysis based on programming developed to verify the feasibility of the power outage maintenance optimization mode, and at the same time provide certain guidance and reference significance for the subsequent design work of the same type of research. A calculation algorithm for evaluating the impact of the outage DC on the reliability of the system is proposed to calculate the value of the transmission system. © Published under licence by IOP Publishing Ltd.

关键词： Outages

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Research on grid load model based on SPACK architecture of power big data

Research on grid load model based on SPACK architecture of p...

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2021 international conference on Computer, Remote Sensing and Aerospace, CRSA 2021

作者： Xin, Rui Jiang, Dan Liu, Hong Chen, Xi Information and Communication Branch of State Grid Hebei Electric Power Co. Ltd. Hebei No. 10 Fuqiang Street Yuhua District Shijiazhuang050013 China

big data service is the key to the construction of smart grid, and the method of analyzing electricity consumption behavior based on improved AP clustering and the method of forecasting electricity load based on random forest is proposed. To address the problem of high complexity of AP clustering analysis, the entropy weight method is used to establish the index weights and improve the similarity calculation method to realize the fast and accurate analysis of customers' electricity consumption behavior. For the problem of power load prediction, the fuzzy C-means method is used to construct the index weights. The fuzzy C-means is used to construct the historical similarity daily sample set, and the random forest is used to predict the electric load. The SPACK multi-level data processing framework is proposed to meet the needs of parallel processing speed for large amounts of data. The simulation results verify the effectiveness of the method proposed in this article and the important guiding price for the Power grid. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Decision trees

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