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Articles 1 - 3 of 3
Full-Text Articles in Life Sciences
Deriving Statistical Inference From The Application Of Artificial Neural Networks To Clinical Metabolomics Data, Kevin M. Mendez
Deriving Statistical Inference From The Application Of Artificial Neural Networks To Clinical Metabolomics Data, Kevin M. Mendez
Theses: Doctorates and Masters
Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods.
An Investigation Into The Spatial Distribution, Habitat Selection And Resource Usage Of The Red Fox (Vulpes Vulpes) Inhabiting Urban Reserves Within Perth, Western Australia, Michael Thomas Main
An Investigation Into The Spatial Distribution, Habitat Selection And Resource Usage Of The Red Fox (Vulpes Vulpes) Inhabiting Urban Reserves Within Perth, Western Australia, Michael Thomas Main
Theses: Doctorates and Masters
I attempted to track a population of urban foxes in Kings Park, but due to collar failure, only one collar was retrieved. The GPS telemetry data from this fox produced home range estimates for minimum convex polygon (MCP) and kernel density (KD) of 0.302 km² and 0.331 km², respectively. The fox was predominantly active at night, with a ten-fold increase in movement during nocturnal periods when compared to daytime movements. Roads and man-made tracks were important for facilitating movement of the fox through its home range, with almost 97% of location fixes recorded within 100m of these features. The fox …
Local Binary Pattern Based Algorithms For The Discrimination And Detection Of Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le
Local Binary Pattern Based Algorithms For The Discrimination And Detection Of Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le
Theses: Doctorates and Masters
In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact …