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1	Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data Lynam, Adam David January 2009 (has links) 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. machine learning skewed data predicting oestrus
2	Confidence Intervals for Ratios of Means and Medians Bonett, Douglas G., Price, Robert M. 01 December 2020 (has links) In studies where the response variable is measured on a ratio scale, a ratio of means or medians provides a standardized measure of effect size that is an alternative to the popular standardized mean difference. Confidence intervals for ratios of population means and medians in independent-samples designs and paired-samples designs are proposed as supplements to the independent-samples t test and paired-samples t test. The performance of the proposed confidence intervals are evaluated in a simulation study. The proposed confidence interval methods are extended to the case of a 2 × m factorial design that includes propensity score stratification and meta-analysis as special cases. R functions that implement the recommended confidence intervals are provided in the Supplemental Material file, available in the online version of this article, and are illustrated with several examples. distribution-free factorial designs independent-samples designs meta-analysis paired-samples designs skewed data effect size
3	Supporting Advanced Queries on Scientific Array Data Ebenstein, Roee A. 18 December 2018 (has links) No description available. Computer Science DBMS Scientific Array Data Analytical Querying SDBMS Join Array Join Array Data Join Databases Array Databases NoDB Scientific Databases Query Optimization Plan Pruning Bitmap Join Bitmap Indexes Range Joins Data Skew Querying Skewed data

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