AI and Analytics for Public Health

Proceedings of the 2020 INFORMS International Conference on Service Science
1. Auflage 2022
ISBN: 978-3-030-75165-4
Verlag: Springer International Publishing

Buch, Englisch, 477 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 898 g

Reihe: Springer Proceedings in Business and Economics

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users’ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. Chapters highlight ways to approach such technical challenges in service science and are based on submissions from the 2020 INFORMS International Conference on Service Science.

Zielgruppe

Research

Autoren/Hrsg.

Yang, Hui

Chen, Weiwei

Qiu, Robin

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung

Inhaltsverzeichnis

Chapter 1. Private vs. Pooled Transportation: Customer Preference and Congestion Management.- Chapter 2. Optimal Dispatch in Emergency Service System via Reinforcement Learning.- Chapter 3. Towards Understanding the Dynamics of COVID-19: An Approach based on Polynomial Regression with Adaptive Sliding Windows.- Chapter 4. Capturing the Deep Trend of Stock Market for a Big Profit.- Chapter 5. Analysis on Competitiveness of Service Outsourcing Industry in Yangtze River Delta Region.- Chapter 6. OPBFT: Optimized Practical Byzantine Fault Tolerant Consensus Mechanism Model.- Chapter 7. Entropy Weight-TOPSIS Method Considered Text Information with An Application in E-commerce.- Chapter 8. Optimal Resource Allocation for Coverage Control of City Crimes.- Chapter 9. Application of Internet of Things (IoT) in Inventory Management for Perishable Produce.- Chapter 10. Enabling Smart Buildings with Reinforcement Learning Controllers to Reduce Energy Consumption.- Chapter 11. Modified Risk ParityPortfoliosto Limit Concentration on Low Risk Assets in Multi-Asset Portfolios.- Chapter 12. A Data Analysis Method for Estimating Balking Behavior in Bike-Sharing Systems.- Chapter 13. The Impact of Scalability on Advisory and Service Delivery Efforts of Nonprofits.- Chapter 14. Green Location-Routing Problem with Delivery Options.- Chapter 15. Molecular Bioactivity Prediction of HDAC1 Based on Deep Neural Nets.- Chapter 16. Risk assessment indicators for technology enterprises: From the perspective of complex networks.- Chapter 17. Risk assessment indicators for technology enterprises: From the perspective of complex networks.- Chapter 18. Early Detection of Rumors Based on BERT Model.

Über Autor(innen)

Hui Yang is a Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization.

Robin Qiu is Professor at Pennsylvania State University, USA. Dr. Qiu’s research interests include Big Data, Data/Business Analytics, Smart Service Systems, Service Science, Service Operations and Management, Information Systems, and Manufacturing and Supply Chain Management.

Weiwei Chen is an Associate Professor at Rutgers University, USA. His research interests lie in operations and finance interface, as well as supply chain operations planning and scheduling. He also works on simulation and randomized global optimization methodologies.