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Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023).

Bibliographic Details
Main Author: Hemachandran, K.
Other Authors: Boddu, Raja Sarath Kumar., Alhasan, Waseem.
Format: eBook
Language:English
Published: Dordrecht : Atlantis Press (Zeger Karssen), 2023.
Edition:1st ed.
Series:Atlantis Highlights in Intelligent Systems Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Peer-Review Statements
  • 1 Review Procedure
  • 2 Quality Criteria
  • 3 Key Metrics
  • Digital Economy Enabling High-Quality Manufacturing Development: Evidence from Chinese Provincial Data
  • 1 Introduction
  • 2 Theoretical Mechanism and Research Hypothesis
  • 2.1 Digital Economy Enables the Direct Mechanism of Action of High-Quality Manufacturing Development
  • 2.2 Digital Economy, Innovation Effect and High-Quality Development of Manufacturing
  • 2.3 Digital Economy, Efficient Resource Allocation and High-Quality Development of Manufacturing
  • 3 Study Design
  • 3.1 Model Building
  • 3.2 Variable Selection and Data Sources
  • 4 Empirical Analysis
  • 4.1 Baseline Regression Analysis
  • 4.2 Endogeneity Analysis
  • 4.3 Subregional Inspection
  • 4.4 Mechanism of Action Analysis
  • 5 Conclusions
  • References
  • The Lexicon Construction and Quantitative Research of Digital Economy Policy Texts
  • 1 Introduction
  • 2 Research Status
  • 3 Lexicon Construction of Digital Economic Policy
  • 3.1 Data Sources
  • 3.2 Neologism Discovery
  • 3.3 Comparison of Segmentation Effect
  • 4 Classification of Policy Text
  • 4.1 Experimental Data
  • 4.2 Model and Parameter
  • 4.3 Experimental Result
  • 5 Analysis of Policy Hotspots
  • 5.1 Policy Keywords
  • 5.2 Policy Topics
  • 6 Conclusion
  • References
  • Measurement and Evaluation of Provincial Digital Economy Development Efficiency
  • 1 Introduction
  • 2 Provincial Digital Economy Development Level Measurement
  • 2.1 Construction of Indicator System
  • 2.2 Data Sources
  • 2.3 Analysis of Results
  • 3 Analysis of Provincial Digital Economy Output Efficiency
  • 3.1 Research Models
  • 3.2 Empirical Results and Analysis
  • 4 Conclusions and Recommendations
  • 4.1 Conclusions
  • 4.2 Research Recommendations
  • References.
  • How Digital Transformation of Enterprises Can Improve Labor Productivity: Evidence from Chinese-Listed Companies
  • 1 Introduction
  • 2 Literature Review and Hypothesis
  • 3 Models and Data
  • 3.1 Models
  • 3.2 Data
  • 4 Results and Discussion
  • 4.1 Digital Transformation and Labor Productivity
  • 4.2 Intermediate Mechanism: Capital Intensity (CI) and Innovation Input (R&amp
  • D)
  • 4.3 Heterogeneity Analysis
  • 5 Conclusions
  • References
  • Design of Adaptive Defense System for Marketing Information Management Based on Blockchain Technology
  • 1 Introduction
  • 2 Application of Blockchain Technology in Marketing Information Management Adaptive Defense System
  • 2.1 Blockchain Basics
  • 2.2 Blockchain-Related Technologies
  • 2.3 Scenarios of Blockchain Technology Application
  • 3 Network Security Adaptive Defense Technology
  • 3.1 Intrusion Detection
  • 3.2 Intrusion Prevention
  • 4 Design of Adaptive Defense System for Marketing Information Management
  • 4.1 System Architecture
  • 4.2 System Function Module
  • 5 System Testing
  • 5.1 Set up the Network Environment
  • 5.2 Verify the Performance Improvement Effect
  • 6 Conclusion
  • References
  • Comparative Study of LSTM and Transformer for A-Share Stock Price Prediction
  • 1 Introduction
  • 2 Related Work
  • 2.1 Stock Price in China
  • 2.2 Deep Learning in Stock Prediction
  • 3 Methodology
  • 3.1 LSTM
  • 3.2 Transformer
  • 3.3 Evaluation Metrics
  • 4 Experiments
  • 4.1 Data Selection
  • 4.2 Data Pre-processing
  • 5 Experiment Result
  • 6 Conclusions
  • References
  • Can Digital Inclusive Finance Improve Business Export Resilience?
  • 1 Introduction
  • 2 Theoretical Analysis and Hypotheses
  • 3 Study Design
  • 3.1 Sample Selection and Data Sources
  • 3.2 Variable Setting
  • 3.3 Model Construction
  • 4 Analysis of Empirical Results
  • 4.1 Descriptive Statistics
  • 4.2 Correlation Analysis.
  • 4.3 Analysis of Regression Results
  • 4.4 Analysis of the Mechanism of Action
  • 4.5 Heterogeneity Analysis
  • 5 Robustness Test
  • 5.1 Substitution of Explanatory Variables
  • 5.2 Change in Estimation Method and Sample Scope
  • 5.3 Instrumental Variable Method
  • 6 Research Findings and Insights
  • References
  • Research on Active Firefighting Robot Navigation Based on the Improved AUKF Algorithm
  • 1 Introduction
  • 2 Multi-source Fusion Localization Method Based on Improved AUKF Algorithm
  • 3 Experimental Analysis
  • 4 Conclusion
  • References
  • Analysis and Research on Big Data Storage Technology Based on Machine Learning
  • 1 Introduction
  • 2 Neural Network Overview
  • 3 The Basic Principles and Models of Artificial Neural Networks
  • 3.1 The Basic Principle of Neural Network Composition
  • 3.2 Structure of Neural Networks
  • 4 BP Algorithm
  • 4.1 Mathematical Description of BP Algorithm
  • 4.2 Shortcomings of BP Algorithm
  • 5 Improved Modeling of Neural Network BP Algorithm Using Genetic Algorithm (GA)
  • 6 Design of the System
  • 6.1 System Composition
  • 7 Conclusions
  • References
  • k-Surrounding Neighbors: Incorporating Serendipity in Collaborative Recommendations
  • 1 Introduction
  • 2 Related Work
  • 3 Proposed Method
  • 4 Evaluation on the MovieLens Dataset
  • 4.1 Prediction Accuracy
  • 4.2 Intra-list Diversity
  • 4.3 Novelty
  • 5 Conclusion
  • References
  • Data Generation and Latent Space Based Feature Transfer Using ED-VAEGAN, an Improved Encoder and Decoder Loss VAEGAN Network
  • 1 Introduction
  • 2 Methods
  • 2.1 VAE'S Reparameterization Trick
  • 2.2 Training Theory of GANs
  • 2.3 Loss Functions VAE
  • 2.4 Loss Functions DCGAN
  • 2.5 Loss Functions ED-VAEGAN
  • 2.6 Loss Functions f-w VAEGAN
  • 2.7 Loss Functions p-w VAEGAN
  • 2.8 Latent Space Continuity Quality
  • 3 Experiments
  • 4 Results and Discussions
  • 5 Conclusion.
  • References
  • Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM
  • 1 Introduction
  • 2 Fuzzy Analytic Hierarchy Process
  • 2.1 Establish Judgment Matrix
  • 2.2 Determine the Membership Function
  • 2.3 Single-Factor Evaluation
  • 2.4 Determine the Weight Vector
  • 2.5 Consistency Test
  • 3 AOA's Improved Risk Evaluation Model of LSSVM
  • 3.1 Principle of LSSVM Evaluation
  • 3.2 AOA Search for Optimal Solutions of LSSVM Parameters
  • 4 Construction of Risk Evaluation Index System for Power Grid Digital Investment
  • 4.1 Questionnaire Design and Data Sources
  • 4.2 Expert Evaluation
  • 5 Example Analysis
  • 5.1 Determining the Evaluation Value of the Project
  • 5.2 Parameter Setting
  • 5.3 Analysis of Results
  • 6 Conclusion
  • References
  • Research on Machine Learning Driven Stock Selection Strategy
  • 1 Introduction
  • 2 Methodology
  • 2.1 Research Scheme
  • 2.2 Data
  • 3 Empirical Analysis
  • 3.1 Single Machine Learning Algorithm Performance
  • 3.2 Integrated Machine Learning Algorithm Performance
  • 4 Important Anomalies of Chinese Stock Market
  • 5 Discussion and Conclusion
  • 5.1 Discussion
  • 5.2 Conclusion
  • References
  • An Identification Method for High Voltage Power Grid Insulator Based on Mobilenet-SSD Network
  • 1 Introduction
  • 2 Target Detection Network of Lightweight Mobilenet-SSD
  • 2.1 Multi-scale Feature Fusion Target Detection Network
  • 2.2 Light Weight Backbone Feature Extraction Network
  • 2.3 Fused Network of MobileNet and SSD
  • 3 Sample Analysis
  • 4 Conclusion
  • References
  • Insights into the Correlation Between Digital Marketing Effectiveness and the 5A Crowd Assets Model
  • 1 Introduction
  • 2 Digital Marketing Theory and the Landing of Contemporary e-commerce Data Platforms
  • 2.1 Digital Marketing and Consumer Operations.
  • 2.2 Crowd Assets Model from Traditional to Digital Marketing
  • 2.3 Upgrade of Crowd Assets Model
  • 5A
  • 3 Study Design
  • 3.1 Research Background
  • 3.2 Correlation Analysis of Seeding with A3
  • 3.3 Correlation Analysis Between A3 and GMV (Gross Merchandise Volume)
  • 3.4 K-means Clustering Algorithm
  • 4 Research Conclusions and Values
  • References
  • A Credit Card Default Prediction Method Based on CatBoost
  • 1 Introduction
  • 2 Related Work
  • 3 Methodology
  • 4 Experiment
  • 4.1 Feature Engineering
  • 4.2 Training Parameters
  • 4.3 Evaluation Metrics
  • 4.4 Evaluation Results
  • 5 Conclusion
  • References
  • Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method
  • 1 Introduction
  • 2 Macro-economy and Stock Market Forecast
  • 2.1 Macro-economy
  • 2.2 Stock Market Forecast
  • 3 Neural Network and Support Vector Machine Research
  • 3.1 Introduction to Neural Networks
  • 3.2 Learning of Neural Networks
  • 3.3 Support Vector Machine
  • 4 An Empirical Study of Machine Learning Methods in SSE Index Forecasting
  • 4.1 Introduction to the Empirical Environment
  • 4.2 SSE Index Prediction Based on BP Neural Network
  • 4.3 SSE Index Prediction Based on Support Vector Machine
  • 5 Conclusion
  • References
  • Research on the Development Level of Digital Economy Based on Fuzzy Comprehensive Evaluation
  • 1 Introduction
  • 2 Fuzzy Comprehensive Evaluation of Digital Economy Development in Anhui Province
  • 2.1 Indicator System Determination and Weight Calculation
  • 2.2 Fuzzy Comprehensive Evaluation
  • 2.3 Chapter Summary
  • 3 Conclusions
  • References
  • Factors Influencing Customer Addictive Purchase Behaviours of Toy Blind Boxes
  • 1 Introduction
  • 2 Research Methods
  • 3 Research Design
  • 4 Analysis
  • 4.1 Qualitative Analysis
  • 4.2 Further Analysis
  • 5 Discussions
  • 6 Conclusion.
  • References.

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