"This book titled "Farm Animals: Leveraging Machine Learning for Intelligent Insights with scikit-learn and pyTorch example" is a comprehensive guide for both beginners and experts in the fields of machine learning and animal behavior. It explores the use of machine learning algorithms to understand the behavior of farm animals based on activity recognition. The book is structured in a way that makes it easy to understand for readers with little to no prior knowledge in the field. The first chapter discusses the importance of machine learning in animal behavior, the types of machine learning, including supervised, unsupervised, and reinforcement learning. The second chapter of the book covers an overview of the Python programming language and two of its libraries such as Scikit-learn and PyTorch. These libraries are essential for developing machine learning and deep learning models. The third chapter of the book offers a detailed machine learning project, providing readers with a practical understanding of machine learning algorithms in animal behavior analysis from start to finish. The following chapters cover different types of sensors used for data collection and techniques for preprocessing and feature extraction. The book also covers supervised learning algorithms such as classification and regression analysis, evaluation, and model selection techniques. It also dives into unsupervised learning algorithms like cluster analysis and dimensionality reduction techniques such as PCA and t-SNE. The book concludes with deep learning algorithms, transfer learning, generative adversarial networks, and real-world examples of animal activity recognition using machine learning. Overall, this book provides valuable insights into the practical application of machine learning algorithms for analyzing animal behavior. It caters to individuals interested in machine learning, data analysis, and animal behavior, making it a valuable resource for researchers, students, and professionals in these fields"--
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.
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
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.
