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.

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.

Strategies for the successful implementation of viral laboratory automation.

It has been estimated that more than 70% of all medical activity is directly related to information providing analytical data. Substantial technological advances have taken place recently, which have allowed a previously unimagined number of analytical samples to be processed while offering high quality results. Concurrently, yet more new diagnostic determinations have been introduced - all of which has led to a significant increase in the prescription of analytical parameters. This increased workload has placed great pressure on the laboratory with respect to health costs. The present manager of the Clinical Laboratory (CL) has had to examine cost control as well as rationing - meaning that the CL's focus has not been strictly metrological, as if it were purely a system producing results, but instead has had to concentrate on its efficiency and efficacy. By applying re-engineering criteria, an emphasis has had to be placed on improved organisation and operating practice within the CL, focussing on the current criteria of the Integrated Management Areas where the technical and human resources are brought together. This re-engineering has been based on the concepts of consolidating and integrating the analytical platforms, while differentiating the production areas (CORE Laboratory) from the information areas. With these present concepts in mind, automation and virological treatment, along with serology in general, follow the same criteria as the rest of the operating methodology in the Clinical Laboratory.

Automatización máquina colado

Mitjançant el presentprojecte es pretén donar solució a la monitorització,control i automatitzaciód'unamàquina de colat.S'englobadins de la brancad'automàtica i control de processos.En ell a partir d'una estructura de la màquinaja definida i un cicle detreballrequeritpelclient es trienelselements per complirambaquest cicle,incorporant les mesures de seguretatnecessàries per minimitzarels riscos.Per a l'automatització del procés, s'hamuntat un PLC marca Telemecaniqueen un armari de control, acompanyat per les cartesd'entrades isortidesrequerides per a poder controlar totes les variables del procés.Per al monitoratge i control del procés, per part de l'operari, s'hainstal.laten l'armari de control un panell HMI "human machine Interface"acompanyatd'unpanell de comandament.En la pròpiamemòria es dónaexplicació al funcionament del programa delPLC i les pantalles de la consola HMI.En elsannexos es detalla tot elconnexionatrealitzatdelsdiferentselementsesmentats, el programa dissenyatper al PLC i el pressuposteconòmic.

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.