Artificial Intelligence and Big Data for Innovative, Collaborative and Sustainable Development of Organizations

Permanent URI for this collectionhttps://hdl.handle.net/10125/112398

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    Predicting Flight Delays Using Machine Learning
    (2026-01-06) Snell, Kenney; Zurada, Jozef; Hatami, Zahra; Olszak, Celina; Kozak, Jan
    Flight delays remain a persistent challenge for the aviation industry, generating high costs and disrupting operations across interconnected networks. Existing monitoring and scheduling tools provide valuable oversight but often lack predictive accuracy and cross-stakeholder coordination, limiting their effectiveness in disruption management. This study develops and evaluates an AI-enabled machine learning framework that integrates operational and meteorological data to forecast delays more reliably. Using U.S. domestic flight records and NOAA weather data for 2024, including a case study at Louisville Muhammad Ali International Airport, we apply classification and regression models to predict on-time performance and delay minutes. Ensemble methods, particularly Random Forest with SMOTE balancing, achieve superior results, detecting delayed flights with 94.7% accuracy and reducing mean absolute error in regression tasks to 4.79 minutes. Beyond technical gains, the framework demonstrates how AI-driven prediction can enhance collaborative decision-making by enabling shared situational awareness across airlines, airports, and air traffic control, strengthening resilience and efficiency in aviation operations.
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    How Self-Service Business Intelligence Education Can Develop Data Literacy and AI Literacy: Lesson Learned from Practitioners
    (2026-01-06) Lennerholt, Christian; Van Laere, Joeri; Berndtsson, Mikael
    Artificial Intelligence (AI) can take Business Intelligence (BI) to the next level by empowering users in their daily decision-making tasks. Just like Self-Service Business Intelligence (SSBI), AI integrated business analytics comes with many benefits, but also with numerous implementation challenges. In fact, typical SSBI implementation challenges like data quality, data governance, and employee training are equally relevant when integrating AI. Hence, lessons learned from development of SSBI education could increase data literacy and AI literacy. Two case studies of SSBI education in large BI consultancy firms have identified five SSBI education steps: (1) increase the interest of using data; (2) introduce data to all users; (3) clean and define data to create standard reports; (4) develop SSBI data governance and (5) become self-reliant on accessing and using data. SSBI education can create a foundation that leads to being better prepared for the implementation and use of more advanced AI analytics.
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    Forecasting Carbon Emissions in the AI Industry: Integrating ESG Semantics with Large Language Models
    (2026-01-06) Lai, Chia-Yu; Liao, Wei-Hsiang; Yang, Yu-Chen; Chen, Deng-Neng
    This study introduces an explainable framework for forecasting carbon emissions, specifically designed for firms in the AI industry. It integrates structured financial indicators with semantic features sourced from corporate sustainability reports. Leveraging Large Language Models (LLMs) alongside a hybrid LSTM-Attention architecture, our proposed model effectively captures both temporal dependencies and textual insights related to environmental, social, and governance (ESG) factors. Empirical results demonstrate that our approach significantly outperforms traditional baselines across standard evaluation metrics. Furthermore, SHAP analysis enhances the model's interpretability, revealing firm size and capital intensity as key predictors. Our research contributes to the AI industry by operationalizing unstructured ESG narratives into interpretable, scalable inputs for environmental risk modeling and sustainable AI governance.