Machine Learning in Farm Animal Behavior using Python (Hardcover)

Natasa Kleanthous

ISBN 10: 1032628634 / ISBN 13: 9781032628639

Published by Taylor & Francis Ltd, London, 2025

About this Item

Hardcover. This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis. The book includes detailed Python examples for each phase, making it an essential resource for researchers and practitioners in animal behavior and technology. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781032628639

Bibliographic Details

• Title: Machine Learning in Farm Animal Behavior using Python (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2025
• Language: English
• ISBN 10: 1032628634
• ISBN 13: 9781032628639
• Binding: Hardcover
• Condition: new

About this title

Synopsis

This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.

The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis.

About the Author

Natasa Kleanthous holds a BSc in Management and Information Systems from the University of Nicosia, and an MSc in Computing and Information Systems from Liverpool John Moores University, UK. She earned her PhD from Liverpool John Moores University in 2021. Her research interests include machine learning, embedded systems, the Internet of Things, virtual fencing systems, signal processing, wearable devices, and computer vision. Natasa is the director of O&P Electronics and Robotics Ltd and founder of Anyfence A.I Ltd, a startup focused on machine learning-driven animal behavior recognition combined with virtual fencing technology, aimed at developing smart devices for the farming industry.

Abir Hussain is a professor of Image and Signal Processing at the University of Sharjah, UAE, and a visiting professor at Liverpool John Moores University, UK. She earned her PhD at The University of Manchester (UMIST) in 2000, with a thesis on Polynomial Neural Networks for Image and Signal Processing. Abir has published extensively in areas such as neural networks, signal prediction, telecommunications fraud detection, and image compression. Her research focuses on higher-order and recurrent neural networks, with applications in e-health and medical image compression. She has supervised numerous PhD and MPhil students, developed neural network architectures with her research students, and serves as an external examiner for research degrees. She is also one of the initiators and chairs of the Development in e-Systems Engineering (DeSE) conference series.