Translating Pain into Action: A Study of Gender-based Violence and Minority Ethnic Women in Ireland

Translating Pain into Action: A Study of Gender-based Violence and Minority Ethnic Women in Ireland Click here to download PDF 1.4mb Summary of the Report PDF 502kb This is a publication of the Womens Health Council

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Translating cognitive neuroscience to fitness to drive using a neuroergonomic approach

Non-pathological or normal ageing is accompanied by brain alterations that are the result of natural changes occurring with age and our ability to compensate for them. Compared to younger adults, older adults have reduced vision, more difficulties in detecting relevant information they are not intending to and require more time to process sensorial information. Little is known on how these changes affect behaviour in a natural environment. Relying on a translational approach at the frontiers between neurobiology, psychophysics, neuropsychology and epidemiology, we were able to: explore the needs for innovative instrumentations to detect cerebral decline in clinical settings; develop and validate a new computed neuropsychological instrument designed to measure cerebral decline in healthy older adults; explore the link between processing speed and on-road driving performance; and investigate the effects of being able to anticipate on visual processing speed.

Love machine

This poster provides advice on the use of condoms as a method of protection from unplanned pregnancy and sexually transmitted infections (STIs). It also provides contact details for the�Genito Urinary Medicine (GUM) clinics in Northern Ireland.

An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.