This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A...

The chapter utilizes twenty published interviews, industry reports, and expert documents from secondary data collection to identify a conceptual model for using artificial intelligence (AI) supply chain disruption prediction and risk management in Bangladesh. The paper examines how AI-enabled...

University dropout is costly, making it a policy priority to identify factors that predict dropout. Using a survey experiment with incoming first-year students linked to long-run administrative outcomes, we assess which information improves dropout prediction beyond standard university records....

This study explores the potential of artificial intelligence (AI) techniques to predict Relative Water Content (RWC) in plants subjected to salt stress. A Random Forest regression model was trained using a dataset containing both morphological and biochemical traits, such as chlorophyll content,...

This chapter highlights how talent analytics predict future organisational states and enhance performance. Today, data-driven strategies are critical for key HR functions like talent acquisition and performance management. HR analytics enable organisations to collect and assess employee data on...

The convergence of predictive analytics, artificial intelligence (AI), and sustainable supply chain management is revolutionising operational practices to tackle modern environmental challenges effectively. Predictive analytics enhances decision-making capabilities by utilising real-time data to...