title: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

authors: Lynam, Adam David

subjects: machine learning

abstract: The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

text: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

year: 2009

title: Pedalling for safety: Schoolchildren and safe active transport

authors: Fisher, Kylie

subjects: child safety

abstract: This research will add to the international body of knowledge around safe active transport and its benefits for individuals and their communities. In order to achieve this, the report begins with a brief description of the risks associated with active transport, 3 and considers why active transport to school should be encouraged, despite the risks. Our dependence on cars is discussed in relation to the prevailing chauffeuring culture, before the objectives of the research are outlined. Chapter one concludes with an account of the methodology used to undertake this research, which combined a literature review and a search for educational resources with some participant observation field research. In chapter 2 we present a summary of the resources available to parents in Hamilton and provide a profile of the city, which is in many ways ideally suited to active transport, though participation rates are low. Chapter 3 discusses the benefits of active transport and the barriers to participation in it. Following a discussion that draws all the various strands together, we evaluate existing strategies with a view to endorsing those most likely to enhance safety, while also offering some further ideas on how to minimise the risks of active transport for children.

text: Pedalling for safety: Schoolchildren and safe active transport This research will add to the international body of knowledge around safe active transport and its benefits for individuals and their communities. In order to achieve this, the report begins with a brief description of the risks associated with active transport, 3 and considers why active transport to school should be encouraged, despite the risks. Our dependence on cars is discussed in relation to the prevailing chauffeuring culture, before the objectives of the research are outlined. Chapter one concludes with an account of the methodology used to undertake this research, which combined a literature review and a search for educational resources with some participant observation field research. In chapter 2 we present a summary of the resources available to parents in Hamilton and provide a profile of the city, which is in many ways ideally suited to active transport, though participation rates are low. Chapter 3 discusses the benefits of active transport and the barriers to participation in it. Following a discussion that draws all the various strands together, we evaluate existing strategies with a view to endorsing those most likely to enhance safety, while also offering some further ideas on how to minimise the risks of active transport for children.

year: 2010

title: In defence of mātauranga Māori: A response to the 'seven academics'

authors: Waitoki, Waikaremoana

abstract: On 28 July 2021, a group of academics from the University of Auckland published a letter in the Listener, a well-known, non-academic magazine. The letter writers expressed their moral outrage that the Government’s NCEA curriculum working group proposed that science would be taught to Māori students along with its colonising history.

text: In defence of mātauranga Māori: A response to the 'seven academics' On 28 July 2021, a group of academics from the University of Auckland published a letter in the Listener, a well-known, non-academic magazine. The letter writers expressed their moral outrage that the Government’s NCEA curriculum working group proposed that science would be taught to Māori students along with its colonising history.

year: 2022-04-01