| Pubbl/distr/stampa |
Cham, Switzerland : , : Springer, , [2021]
|
| Descrizione fisica |
1 online resource (413 pages)
|
| Disciplina |
338.4
|
| Collana |
Lecture notes in operations research
|
| Soggetto topico |
Service industries - Management
Service industries - Technological innovations
Artificial intelligence
Smart cities
|
| ISBN |
3-030-90275-7
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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.
|
| Altri titoli varianti |
Artificial intelligence and analytics for smart cities and service systems
|
| Record Nr. | UNINA-9910508460503321 |
Info
2008 IEEE International Conference on Service Operations and Logistics, and Informatics
