United State
Research on the United States currently leverages machine learning to analyze diverse datasets, aiming to improve predictions across various sectors. Key focus areas include forecasting crime rates, flight delays, and tornado occurrences using models like Long Short-Term Memory networks, hybrid machine learning approaches, and Kalman-Convolutional BiLSTMs. These studies highlight the potential of AI to enhance resource allocation, improve infrastructure management, and enhance public safety, while also revealing societal biases embedded in AI systems and raising ethical concerns regarding algorithmic accountability and data privacy. The resulting insights have implications for policymaking, economic forecasting, and understanding societal trends.
Papers
Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors
Patricia Dao, Jashmitha Sappa, Saanvi Terala, Tyson Wong, Michael Lam, Kevin Zhu
Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
Rajesh Kumar Jha, Shashi Bhushan Jha, Vijay Pandey, Radu F. Babiceanu