Carol Anne Hargreaves

Independent Researcher, Australia

Carol Anne Hargreaves is a seasoned data analytics consultant, educator, and visionary leader with over 30 years of experience in data science and AI. As an Associate Professor at the National University of Singapore, she develops and teaches data science courses at the foundational, intermediate, and advanced levels. She is a recognized thought leader in the field of AI, having published numerous research papers focused on healthcare and finance and facilitated panel discussions on critical topics in AI and digital transformation. She has supervised research projects and mentored data scientists, helping to develop future leaders. She holds two registered AI patents. Her expertise has contributed to multiple innovative projects in various industries, making a positive impact on society.

1books edited

2chapters authored

Latest work with IntechOpen by Carol Anne Hargreaves

With the rapid advancement of technology, large volumes of data are generated from mobile apps, social media, and sensors. It is very important to clean the data (remove noise) and capture only the relevant data that will help solve the problem. Principal Component Analysis (PCA) is an effective method for reducing data dimensionality. Practical PCA and matrix theory translate linear algebra into workplace results. Most books on matrix theory and PCA either stay theoretical or skip the mathematics altogether. This book combines theory with real-world workplace applications across different industries. This book is perfect for undergraduates, graduates, data practitioners, and analytics leaders. You will learn how:
• PCA is applied to effectively separate small targets from complex backgrounds in the infrared Patch-Image model and its tensor-based counterpart.
• Crime is predicted using the Dynamic Model Decomposition (DMD) and the Convolutional Neural Network Long-Short-Term-Memory (CNN_LSTM) model.
• The SPEC module in basic multispectral image processing using cloud computing with Google Earth Engine (GEE) implementation.
• Matrix theories connecting continuous & discrete systems, deterministic & probabilistic behaviour, and theory with application.
Matrix methods and PCA are powerful applications in the workplace. With editorial leadership from Professor Carol Anne Hargreaves, an expert in data science, AI, and industry consulting, and Professor Balasubramaniam, an expert in mathematics, AI, Image Processing, and quantum computing, this book unites theoretical depth with practical workplace relevance in modern PCA applications.

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Matrix Theory and Principal Component Analysis (PCA) Applications in Industry

Edited by Carol Anne Hargreaves

Chapters authored

Machine Learning for Sustainable Shipping: Predicting Vessel CO₂ Emissions Using Random Forest Models

By Carol Anne Hargreaves and Briana Wan Nee Toh

Predicting fuel consumption in the shipping industry is a critical task that supports optimized operations, driving both economic and environmental benefits as global demand for shipping continues to grow. However, accurately forecasting Carbon Dioxide (CO₂) emissions is challenging due to the complexity and volume of operational data. In this study, we developed and evaluated the Random Forest model to measure the prediction accuracy. The Random Forest model achieved a high predictive accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.046, demonstrating its robustness in capturing the non-linear relationships in the data. Feature importance analysis within the Random Forest model highlighted vessel length, gross tonnage, width, and draft as key predictors of fuel consumption, offering valuable insights into CO₂ emissions-reduction strategies. These findings underscore the potential of machine learning to empower data-driven decisions in the maritime sector. Future work will explore further optimization of the Random Forest model, incorporate additional predictive features, and investigate real-time applications to enhance operational efficiency and sustainability. By making accurate CO₂ emission predictions, this research contributes to the industry’s efforts to achieve sustainable and environmentally responsible shipping practices.

Part of the book: Federated Learning

Carol Anne Hargreaves

Latest work with IntechOpen by Carol Anne Hargreaves

Matrix Theory and Principal Component Analysis (PCA) Applications in Industry

Chapters authored

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