Machine Learning in Farm Animal Behavior using Python
Published on 7. March 2025
412 pages
978-1-040-32836-1 (ISBN)
Description
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 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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
6 Line drawings, color; 62 Line drawings, black and white; 6 Illustrations, color; 62 Illustrations, black and white
File size
11,90 MB
ISBN-13
978-1-040-32836-1 (9781040328361)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Natasa Kleanthous | Abir Hussain
Machine Learning in Farm Animal Behavior using Python
Book
approx. 07/2026
1st Edition
CRC Press
€66.00
Not yet published
Natasa Kleanthous | Abir Hussain
Machine Learning in Farm Animal Behavior using Python
Book
03/2025
1st Edition
CRC Press
€278.60
Shipment within 10-20 days
Natasa Kleanthous
Machine Learning in Farm Animal Behavior Using Python
Book
10/2024
CRC Press
Unfortunately, price unknown
Article exhausted; check different version
Persons
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.
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.
Content
Preface. 1. Introduction to Machine Learning for Farm Animal Behavior 2. Machine Learning Concepts and Challenges. 3. A Practical Example to Building a Simple Machine Learning Model 4. Sensors, Data Collection, and Annotation 5. Preprocessing and Feature Extraction for Animal Behavior Research 6. Feature Selection Techniques 7. Animal Research: Supervised and Unsupervised Learning Algorithms 8. Evaluation, Model Selection and Hyperparameter Tuning 9. Deep Learning Algorithms for Animal Activity Recognition References
