Machine Learning in Farm Animal Behavior using Python - Hussain, Abir

Résumé :

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

Biographie:

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

Sommaire:

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.