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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是31-40 订阅

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Analyzing Human Behavior in Digitized Society: Insights from Wordle data

Analyzing Human Behavior in Digitized Society: Insights from...

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Hu, Yitong Wang, Yizhe Zhu, Zixuan Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350326680

This paper focuses on analyzing the human behavior of Wordle players using available player data. While there have been many studies discussing the mechanism, strategy, and cultural influence of Wordle, research on the relationship between player data and human group behavior is still lacking. The authors conducted a time series analysis of the number of daily Wordle players and developed a MIMO XGBoost model to predict the distribution of player attempts. The model demonstrates high confidence through a test set, and the authors analyze the predicted data to provide insights for game designers and sociologists. © 2023 IEEE.

关键词： Time series analysis

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引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Fan, Yue Wang, Huiwen School of Economics and Management Beihang University Beijing China

ISBN: (纸本)9798350326680

With the rapid development of mobile internet and devices, a large amount of trajectory data of moving objects has been generated. The similarity measurement of trajectory data plays a crucial role in exploring valuable information, such as human mobile behavior and patterns. However, most existing work constructs similarity measurement offline, which cannot provide timely feedback for time sensitive applications. In light of this, we present a novel hierarchical framework based on incremental DBSCAN in this paper to measure the similarity between users in real-time. Our proposal includes a hierarchical directed graph framework (HDGF) to ensure the accuracy of trajectory similarity measurement and a stream processing algorithm based on incremental DBSCAN (IDBSCAN) to update the hierarchical framework of users in real-time. Experiment results on real trajectory dataset show that our proposed method is effective and offers competitive performance compared with related methods. © 2023 IEEE.

关键词： Trajectories

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A Novel Optimal Subsampling Algorithm for Big data Linear Regression Based on Cook Distance

A Novel Optimal Subsampling Algorithm for Big Data Linear Re...

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Jiang, Zhe Kunming University of Science and Technology Faculty of Science Kunming China

ISBN: (纸本)9798350326680

With the continuous surge of data volume, the re-search of big data has gradually become a hot issue in research. For massive data, the subsampling algorithm can effectively reduce the volume of data and improve the computational efficiency while ensuring the accuracy of model estimation. For the big data linear model, the optimal subsampling method based on leverage value, L optimal criterion and other methods has been proposed, and its effectiveness has been widely stud-ied. However, the method of constructing sampling probability based on statistical diagnosis has rarely been studied, so a novel optimal subsampling method based on cook distance has been proposed. The sampling probability design constructed by cook distance is based on a two-stage sampling algorithm weighted by inverse probability. Simulation studies and real data show the effectiveness of the proposed methodology. © 2023 IEEE.

关键词： Big data

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Curriculum Reform of Sampling Techniques Based on Machine Learning algorithms: A Panel data Analysis

Curriculum Reform of Sampling Techniques Based on Machine Le...

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Gao, Wang Zhang, Linlin Cao, Qian Hebei University of Economics and Business Research Center for Finance and Enterprise Innovation Shijiazhuang050062 China School of Finance Hebei University of Economics and Business Shijiazhuang China School of Information Technology Hebei University of Economics and Business Shijiazhuang China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing China

ISBN: (纸本)9798350326680

Integrating machine learning algorithms into sampling techniques and survey courses is a crucial step in educational reform in these fields. With the rapid growth of data-driven decision-making, the use of machine learning algorithms has become increasingly important in various industries and disciplines. By incorporating machine learning algorithms into sampling techniques and survey courses, students will be able to learn how to apply these algorithms to solve real-world sampling problems. This study validates the significant positive impact of machine learning algorithms on sampling technique courses through a panel data probit model. This will provide data support for educational instruction. © 2023 IEEE.

关键词： Decision making

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international conference on Big data Intelligence and computing, dataCom 2022

International Conference on Big Data Intelligence and Comput...

引用

international conference on Big data Intelligence and computing, dataCom 2022

ISBN: (纸本)9789819922321

The proceedings contain 40 papers. The special focus in this conference is on Big data Intelligence and computing. The topics include: A Novel Approach of Securing Medical Cyber Physical Systems (MCPS) from DDoS Attacks;LTCS: Lightweight Tree-Chain Scheme for ADS-B data Authentication;electronic and Remote Voting Systems: Solutions to Existing Problems – An Experimental Approach to Secure Voting Platforms on Endpoints;Research on the Mechanism of Cooperative Defense Against DDoS Attacks Based on Game Theory;Detection of Malicious Node in VANETs Using Digital Twin;the Storage and Sharing of Big data in Air Traffic Management System;automated Recognition and Classification of Cat Pain Through Deep Learning;perceiving Airline Passenger Booking Lifecycle with the Utilization of data Warehouse;utilizing an Airline data Warehouse for Website data Analytics: A Conceptual Design;adopting a Deep Learning Split-Protocol Based Predictive Maintenance Management System for Industrial Manufacturing Operations;Multi-platform Performance Analysis for CRUD Operations in Relational databases from Java Programs Using Hibernate;Enhancing data Warehouse Efficiency by Optimizing ETL processing in Near Real Time data Integration Environment;big data Visualization Tools, Challenges and Web Search Popularity - An Update till Today;the Australian Digital Observatory: Social Media Collection, Discovery and Analytics at Scale;a Web Application-Based Infant Health Monitoring System for Parents & Medical Professionals;Impact of Business Analytics and Decision Support Systems on E-commerce in SMEs;task Offloading and Resource Allocation in Satellite Terrestrial Networks: A Deep Deterministic Policy Gradient Approach;attendance Monitoring and Management Using Facial Recognition;reinforcement Learning-Based Task Scheduling Algorithm for On-Satellite data Analysis.

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Exploring the Reform of Mathematical Finance Course Based on data Visualization: Evidence from Nonparametric Causality-in-Quantiles Tests

Exploring the Reform of Mathematical Finance Course Based on...

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

ISBN: (纸本)9798350326680

data visualization is the use of graphical or visual representations to convey complex information, such as financial data. By incorporating data visualization techniques into mathematical finance courses, educators can help students deepen their understanding of financial concepts and theories, and cultivate practical skills that are essential in the finance industry. This paper employs nonparametric causality-in-quantiles tests to verify that visualizing the entire process of mathematical finance can help improve the grades of the course, especially for average students. This can help students prepare for careers in finance and related fields. © 2023 IEEE.

关键词： data visualization

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international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2022

International Conference on Advances in Data-driven Computin...

引用

international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2022

ISBN: (纸本)9789819932498

The proceedings contain 66 papers. The special focus in this conference is on Advances in data-driven computing and Intelligent Systems. The topics include: Analysis of Online Health-Related Private data using Named Entity Recognition by Deep Correlation Techniques;Trust-Based DSR Protocol for Secure Communication in Mobile Ad-hoc Networks;The Modified Binary Sparrow Search Algorithm (mbSSA) and Its Implementation;stacked Ensemble Architecture to Predict the Metastasis in Breast Cancer Patients;A New Sensor Neuro-controller-based MPPT Technique for Solar PV Module;data-Driven Prediction of Effluent BOD5 from an Institutional Wastewater Treatment Plant;predictive Models for Equipment Fault Detection: Application in Semiconductor Industry;simulation Tools for Cloud computing: A Comparative Study;electrical Muscle Stimulation Models Identification Based on Hammerstein Structure and Gravitational Search Algorithm;identification of Ischemic Stroke Origin Using Machine Learning Techniques;comparative Study of Pre-trained Language Models for Text Classification in Smart Agriculture Domain;reinforcement Learning of Self-enhancing Camera Image and Signal processing;PBDPA: A Task Scheduling Algorithm in Containerized Cloud computing Environment;application of Fuzzy-Based Multi-criteria Decision-Making Technique for Highway Concrete Bridge Health Assessment;modality Direct Image Contrast Enhancement for Liver Tumour Detection;an Enhanced Deep Learning Technique for Crack Identification in Composite Materials;Vaccine-Block: A Blockchain-Based Prevention of COVID-19 Vaccine Misplacement;MRI-Based Early Diagnosis and Quantification of Trans-Ischemic Stroke Using Machine Learning—An Overview;a Federated Learning Approach to Converting Photos to Sketch;experimental Analysis of "A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm";corrosion Behaviour of Spray Pyrolysis Deposited Titanium Oxide Coating Over Aluminium Alloy;comparative Study of Pruning Technique

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international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2022

International Conference on Advances in Data-driven Computin...

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international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2022

ISBN: (纸本)9789819909803

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data Center KPI Prediction based on Contrastive Learning and Frequency Domain Enhancement

Data Center KPI Prediction based on Contrastive Learning and...

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Xue, Kun Feng, Xiangyang Zhu, Ming Luo, Qinglan Shang, Xia College of Computer Science and Technology Donghua University Shanghai China Board of Directors Moule Network Technology Co. Ltd Shanghai China Moule Network Technology Co. Ltd Product Department 2 Shanghai China

ISBN: (纸本)9798350326680

As the scale and complexity of data centers continue to increase, intelligent operation and maintenance (O&M) has become one of the important technologies to ensure high performance and availability. Among them, Key Performance Indicator (KPI) data reflects the health status of data center systems and plays a key role in intelligent O&M. Accurate KPI time series prediction is essential for detecting KPI anomalies and improving the reliability of data center systems. However, traditional time series prediction models have low accuracy when dealing with KPI data. Therefore, this paper proposes a time series prediction model based on contrastive learning and frequency domain attention mechanism to more fully capture the temporal features of KPI data, improve prediction accuracy and reliability. The experimental results show that the proposed model exhibits strong competitiveness in KPI time series prediction. © 2023 IEEE.

关键词： Forecasting

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DMM-WGAN: An Industrial Process data Augmentation Approach

DMM-WGAN: An Industrial Process Data Augmentation Approach

引用

2023 international conference on algorithms, computing and data processing, acdp 2023

作者： Gao, Shiwei Chen, Zhiwei Dang, Xiaochao Dong, Xiaohui Zhao, Wenfeng School of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9798350326680

How to apply effective data augmentation methods to supplement datasets in harsh industrial environments is an important problem in complex industrial process modeling. In response to this problem, this paper proposes a new industrial process data augmentation method, DMM-WGAN, based on WGAN. Firstly, a Deep Threshold Mixing Feature Extraction Module (DMM) is proposed in the generator, which adopts a dual-channel fusion strategy. one channel extracts deep features of industrial data, while the other channel extracts global features of industrial data to enhance the feature extraction ability of the generator. Then, the DMM module and the Wasserstein Generative Adversarial Network are combined to establish the DMM-WGAN generation model. Finally, the proposed model is optimized and extensively experimented on a thermal power plant dataset, and the results are evaluated based on MSE, RMSE, MAE, and R2. The results show that the proposed DMM-WGAN generation model is superior to traditional VAE, GAN and WGAN generation models. © 2023 IEEE.

关键词： Feature extraction

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