Communications in Statistics: Case Studies, Data Analysis and Applications

Data-Driven Statistical Case Studies and Applied Modeling for Urban Systems

Conference Overview

The 3rd International Conference on Artificial Intelligence and Sustainable Computing for Smart Cities (AIS2C2) will be held on 24-25 December 2025, at Gautam Buddha University, Greater Noida, India. The conference convenes statisticians, data scientists, AI researchers, and policy experts to discuss the intersection of data analysis, statistical modeling, and sustainable urban development.

AIS2C2 emphasizes applied statistical methods, data-centric solutions, and large-scale case studies from domains such as transportation, healthcare, governance, and urban infrastructure. The selected papers for the special issue will highlight innovative statistical case studies, the use of real-world datasets, and advanced data analytic techniques, especially in big data environments.

Website: https://www.aiscindia.co.in/

Special Issue Theme

This special issue will feature case studies and statistical data analyses that explore:

• Urban and regional datasets with applied statistical frameworks
• Large-scale observational studies and controlled experiments in smart city systems
• Data analysis from smart healthcare, mobility, and environmental monitoring using AI-enabled techniques and intelligent data pipelines
• Applications of Bayesian, multivariate, time-series, and regression modeling, including AI-assisted model selection and inference
• New analytic methods suitable for complex or big data from urban ecosystems, including AI-integrated statistical learning, deep learning, and hybrid modeling approaches

Key Topics

• Statistical case studies from smart city data (transportation, waste, utilities)
• Urban clinical trials and epidemiological modeling
• Analysis of sociological, political, and citizen-feedback datasets
• Design of experiments and modeling for industrial/urban environments
• Large-scale environmental and pollution data analysis
• Novel statistical models for high-dimensional and streaming sensor data
• Time-series and survival analysis in healthcare systems
• Simulation, bootstrapping, and Bayesian inference for urban planning