Machine Learning in Farm Animal Behavior Using Python

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.

Natasa Kleanthous has a B Sc in Management and Information Systems from University of Nicosia, Nicosia, Cyprus, and a M Sc in Computing and Information Systems from Liverpool John Moores University, Liverpool, UK (2017). She completed her PhD at the Faculty of Engineering and Technology, Liverpool John Moores University, UK (2021). Her research interests include machine learning, embedded machine learning, Internet of Things, Virtual Fencing Systems, signal processing, wearable devices, and computer vision. She is a founder of a newly created tech startup (O&P Electronics and Robotics Limited) that investigates the use of machine learning for animal behavior recognition in combination with a virtual fence system in order to develop a suitable smart device to be used in farm industries in Cyprus.

Abir Hussain is a professor of Image and Signal processing at the University of Sharjah, UAE. She is also a visiting professor at Liverpool John Moores University, UK. She completed her PhD study at The University of Manchester (UMIST), UK in 2000 with a thesis title Polynomial Neural Networks for Image and Signal Processing. She has published numerous referred research papers in conferences and journals covering Neural Networks, Signal Prediction, Telecommunication Fraud Detection and Image Compression. She has worked with higher order and recurrent neural networks and their applications to e-health and medical image compression techniques. She has developed with her research students a number of recurrent neural network architectures. She is a PhD supervisor and an external examiner for research degrees including PhD and MPhil. She is one of the initiators and chairs of the Development in e-Systems Engineering (DeSE) conference series.