11708nam 2200625 450 991050846050332120221006163428.03-030-90275-7(MiAaPQ)EBC6803465(Au-PeEL)EBL6803465(CKB)19410616100041(OCoLC)1285364531(PPN)258842970(EXLCZ)991941061610004120220814d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAI and analytics for smart cities and service systems proceedings of the 2021 INFORMS International Conference on Service Science /editors, Robin Qiu, Kelly Lyons, Weiwei ChenCham, Switzerland :Springer,[2021]©20211 online resource (413 pages)Lecture notes in operations researchPrint version: Qiu, Robin AI and Analytics for Smart Cities and Service Systems Cham : Springer International Publishing AG,c2021 9783030902742 Includes bibliographical references and index.Intro -- Contents -- Deep Learning and Prediction of Survival Period for Breast Cancer Patients -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 3.1 Data Collection and Cleaning -- 3.2 Data Preprocessing -- 4 Research Methodology -- 4.1 Deep Learning Architectures -- 4.2 Model Architecture and Parameters -- 4.3 Model Tuning -- 4.4 Models for Comparison with Previous Research -- 4.5 Feature Importance -- 4.6 Experimental Setting -- 5 Results and Discussion -- 5.1 Evaluation Metrics -- 5.2 Classification Model Results -- 5.3 Regression Model Results -- 5.4 Discussion -- 5.5 Feature Importance -- 6 Conclusions -- References -- Should Managers Care About Intra-household Heterogeneity? -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model -- 5 Results -- 6 Managerial Implications -- 7 Conclusion -- References -- Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications -- 1 Introduction -- 2 Background -- 2.1 Autoencoder -- 2.2 Relationship Between Factor Model and Autoencoder -- 3 Proposed Method -- 4 Empirical Analysis -- 4.1 Data Collection -- 4.2 Comparative Models and Estimations -- 4.3 Result -- 5 Discussion -- 6 Concluding Remarks -- References -- Prediction of Gasoline Octane Loss Based on t-SNE and Random Forest -- 1 Introduction -- 2 Research Method -- 3 Experiment -- 3.1 Nonlinear Dimensionaliy Reduction -- 3.2 Linear Dimension Reduction -- 3.3 Prediction Model of Cotane Loss Based on Random Forfest -- 3.4 Analysis of Model Results -- 4 Conclusion -- References -- Introducing AI General Practitioners to Improve Healthcare Services -- 1 Introduction -- 2 Literature Review -- 3 The Model -- 4 Analytical Results -- 5 Numerical Results -- 6 Discussion -- References -- A U-net Architecture Based Model for Precise Air Pollution Concentration Monitoring.1 Introduction -- 2 Method -- 2.1 Convolution and Activation -- 2.2 Pooling Layer -- 2.3 Fully Connected Layer -- 3 Data -- 3.1 Satellite Data -- 3.2 Meteorological Data -- 3.3 High Density PM2.5Monitoring Data -- 3.4 Topography Data -- 4 Result -- 5 Application -- 5.1 Beijing Spatial PM2.5Concentration Distribution -- 5.2 High Value Areas -- 6 Summary -- References -- An Interpretable Ensemble Model of Acute Kidney Disease Risk Prediction for Patients in Coronary Care Units -- 1 Introduction -- 2 Data Set -- 2.1 Data Source -- 2.2 Data Pre-processing -- 3 Methods -- 3.1 Framework -- 3.2 Prediction -- 3.3 Interpretation -- 4 Results -- 4.1 Comparison of Different Methodologies with All Patient Features -- 4.2 Comparison of Different Feature Groups -- 4.3 Important Predictors -- 4.4 Fluid Status and Blood Pressure Management for CCU Patients with AKI -- 5 Summary -- References -- Algorithm for Predicting Bitterness of Children's Medication -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Preparation -- 2.2 Molecular Representation -- 2.3 Dimensionality Reduction -- 2.4 Algorithms and Evaluation Metrics -- 2.5 Model Construction -- 3 Results -- 3.1 Chemical Features of Compounds -- 3.2 Application of the Model -- 4 Discussion and Conclusions -- References -- Intelligent Identification of High Emission Road Segment Based on Large-Scale Traffic Datasets -- 1 Introduction -- 2 Methods and Materials -- 2.1 Technical Route -- 2.2 Calculation of Emission Factors -- 2.3 Traffic Flow Simulation -- 2.4 Identification of High-Emission Road Segments -- 3 Application -- 3.1 Road Network Emission Distribution -- 3.2 Road Network Emission Daily Variation -- 3.3 Identification Result of Road Segment with High Emission -- 4 Summary -- References -- Construction Cost Prediction for Residential Projects Based on Support Vector Regression -- 1 Introduction.2 Determination of Construction Cost Prediction Indicators for Residential Projects -- 2.1 Identification of Construction Cost Prediction Indicators -- 2.2 Quantification of Prediction Indicators -- 2.3 Reduction of Prediction Indicators -- 3 Establishment of Construction Cost Prediction Model Based on Support Vector Regression -- 4 Case Application -- 4.1 Case Description -- 4.2 Data Preprocessing -- 4.3 Construction Cost Prediction -- 5 Summary -- References -- Evolution of Intellectual Structure of Data Mining Research Based on Keywords -- 1 Introduction -- 2 Data -- 2.1 Data Source -- 2.2 Data Acquisition -- 2.3 Data Preprocessing -- 3 Analysis on the Evolution of Keyword Frequency -- 3.1 Some Keywords Appear Often the High-Frequency Keywords Over the 10 Years -- 3.2 Some Keywords Appeared in the Past, but not so in the Present -- 3.3 Some Keywords Appeared only in the Recent Years, but not so in the Present -- 4 Matrix Construction for Co-word Analysis -- 4.1 Word Frequency Estimate -- 4.2 Construction of Co-word Matrix -- 5 Clustering Analysis of the Co-word Matrix -- 5.1 Analysis on the Intellectual Structure in Data Mining from 2007 to 2016 -- 5.2 Analysis on the Intellectual Structure of Data Mining from 2007 to 2011 -- 5.3 An Analysis on the Intellectual Structure of Data Mining from 2012 to 2016 -- 6 Conclusions -- References -- Development of a Cost Optimization Algorithm for Food and Flora Waste to Fleet Fuel (F4) -- 1 Introduction -- 2 Input Parameter Information -- 2.1 AD Capital Costs -- 2.2 AD Operating Costs -- 2.3 Waste Pre-processing and Biogas Conversion Costs -- 2.4 Food and Yard Waste Generation Estimates -- 2.5 Transportation Cost Estimates -- 3 F4Optimization -- 4 Case Study for City of Dallas -- 5 Conclusions and Future Work -- References -- A Comparative Study of Machine Learning Models in Predicting Energy Consumption.1 Introduction -- 1.1 Related Work -- 2 Data Resource -- 2.1 Data Preparation -- 2.2 Data Pre-processing -- 3 Machine Learning Models -- 4 Results and Conclusions -- References -- Simulation Analysis on the Effect of Market-Oriented Rental Housing Construction Policy in Nanjing -- 1 Introduction -- 2 Policy Mechanism -- 3 Model Building -- 3.1 Basic Assumptions -- 3.2 Consumer Agent Building -- 3.3 Government Agent Building -- 4 Simulation Analysis -- 4.1 Simulation Experiment Design -- 4.2 Data Processing and Parameter Acquisition -- 4.3 Simulation Experiment Analysis -- 5 Suggestions and Conclusions -- 5.1 Suggestions -- 5.2 Conclusions -- References -- Accidents Analysis and Severity Prediction Using Machine Learning Algorithms -- 1 Introduction -- 2 Data Source -- 2.1 Exploratory Data Analysis -- 2.2 Data Preprocessing -- 3 Methodology -- 4 Results and Future Work -- References -- Estimating Discrete Choice Models with Random Forests -- 1 Introduction -- 1.1 Literature Review -- 2 Discrete Choice Models and Binary Choice Forests -- 3 Data and Estimation -- 4 Why Do Random Forests Work Well? -- 5 Numerical Experiments -- 5.1 Real Data: IRI Academic Dataset -- 5.2 Real Data: Hotel -- References -- Prediction and Analysis of Chinese Water Resource: A System Dynamics Approach -- 1 Introduction -- 2 Literature Review -- 3 Problem Statement and Solution Approach -- 3.1 Theory and Method of System Dynamics -- 3.2 System Analysis Water Resources in China -- 3.3 Constructing System Dynamics Model -- 3.4 Simulation Schemes -- 3.5 Output Results -- 3.6 Comparative Analysis -- 4 Numerical Results -- 5 Conclusion -- References -- Pricing and Strategies in Queuing Perspective Based on Prospect Theory -- 1 Introduction -- 2 The Literature Review -- 3 The Model Setup -- 3.1 The Utility Model -- 3.2 The Priority Service Fee and Revenue Management.4 Objective Optimization and Insights Analysis -- 4.1 Revenue Maximization -- 4.2 Social Welfare Maximization -- 4.3 Utility Maximization -- 5 Comparison Analysis of the Optimal Solutions -- 6 Conclusions and Future Research -- References -- Research on Hotel Customer Preferences and Satisfaction Based on Text Mining: Taking Ctrip Hotel Reviews as an Example -- 1 Introduction -- 2 Online Hotel Review Analysis Process -- 3 Data Acquisition -- 3.1 Data Crawling -- 3.2 Data Preprocessing -- 4 Data Analysis -- 5 Sentiment Analysis -- 5.1 Sentiment Polarity Analysis Using SnowNLP -- 5.2 Sentiment Analysis Effect Evaluation -- 6 Summary -- References -- Broadening the Scope of Analysis for Peer-to-Peer Local Energy Markets to Improve Design Evaluations: An Agent-Based Simulation Approach -- 1 Introduction -- 2 Methodology -- 2.1 Environment Design -- 2.2 Agent Design -- 2.3 Experiment Design -- 3 Results and Discussion -- 3.1 Learning Model Tuning -- 3.2 Local Market Prices -- 3.3 Local Market Efficiency -- 3.4 Local Market Returns and Outcome Stability -- 4 Conclusion and Future Work -- References -- The Power of Analytics in Epidemiology for COVID 19 -- 1 Introduction -- 1.1 Contributions -- 1.2 Literature Review -- 2 Predicting COVID­19 Detected Cases -- 2.1 An Aggregate Predictive Method: MIT-Cassandra -- 3 Results with Actual COVID-19 Data -- 3.1 Data Sources and Feature Spaces -- 3.2 Model Predictions -- 4 From Detected Cases to True Prevalence -- 5 Application to Vaccine Allocation -- 5.1 Model Formulation -- 5.2 Intuition on the Vaccine Allocation Policy -- 5.3 Results with Actual COVID-19 Data -- 6 Impact and Conclusion -- 6.1 CDC Benchmark -- 6.2 Conclusion -- References -- Electric Vehicle Battery Charging Scheduling Under the Battery Swapping Mode -- 1 Introduction -- 2 Literature Review.3 Centralized Battery Charging and Optimized Scheduling Model.Lecture notes in operations researchArtificial intelligence and analytics for smart cities and service systemsService industriesManagementCongressesService industriesTechnological innovationsCongressesArtificial intelligenceCongressesSmart citiesCongressesService industriesManagementService industriesTechnological innovationsArtificial intelligenceSmart cities338.4Qiu Robin G.Lyons Kelly E.Chen WeiweiINFORMS International Conference on Service ScienceMiAaPQMiAaPQMiAaPQBOOK9910508460503321AI and analytics for smart cities and service systems2905028UNINA