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You searched for subject:(Predictive Agency Model). Showing records 1 – 2 of 2 total matches.

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Victoria University of Wellington

1. Collin-Smyth, Sebastian. A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT.

Degree: 2018, Victoria University of Wellington

In recent times there has been greater recognition of the over representation of young people with neurodisability within youth justice systems worldwide. This poses a problem for practitioners and suggests that current treatments based on addressing dynamic risk factors may be inadequate for addressing the needs of this group. This thesis elucidates these challenges and extends the Predictive Agency Model (PAM; Heffernan & Ward, 2017) into the Predictive Agency Model-Neurodisability Extension (PAM-NEXT). This extension considers how neurodisability can contribute to a maladaptive developmental history for young people which, in some cases, can lead to exposure to dynamic risk factors. The PAM-NEXT provides a framework to consider how these factors can be operationalised within the process of antisocial behaviour for young people with neurodisabilities. The PAM-NEXT is then applied to composite cases of young people who have engaged in antisocial behaviour to demonstrate its utility. Lastly the PAM-NEXT is evaluated and future directions discussed. The PAM-NEXT can provide practitioners options to adequately target treatment for young people with neurodisability who engage in antisocial behaviour. Advisors/Committee Members: Fortune, Clare-Ann.

Subjects/Keywords: PAM-NEXT; PAM; Neurodisability; Young offenders; Young people; ADHD; Intellectual Disability; Autism Spectrum; Crime; Offender; Youth; Attention Deficit Hyperactivity Disorder; Predictive Agency Model; Predictive Agency Model-Neurodisability Extension

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Collin-Smyth, S. (2018). A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7882

Chicago Manual of Style (16th Edition):

Collin-Smyth, Sebastian. “A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT.” 2018. Masters Thesis, Victoria University of Wellington. Accessed July 15, 2020. http://hdl.handle.net/10063/7882.

MLA Handbook (7th Edition):

Collin-Smyth, Sebastian. “A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT.” 2018. Web. 15 Jul 2020.

Vancouver:

Collin-Smyth S. A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT. [Internet] [Masters thesis]. Victoria University of Wellington; 2018. [cited 2020 Jul 15]. Available from: http://hdl.handle.net/10063/7882.

Council of Science Editors:

Collin-Smyth S. A framework for young people with neurodisability who engage in antisocial behaviour: Introducing the PAM-NEXT. [Masters Thesis]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7882


University of Exeter

2. Duncan, Andrew Paul. The analysis and application of artificial neural networks for early warning systems in hydrology and the environment.

Degree: PhD, 2014, University of Exeter

Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.

Subjects/Keywords: 004; Neural Network; ANN; Ensemble; Predictive model; urban flood prediction; bathing water quality; water quality; Bathing water directive; Feature selection; Neural pathway; Machine learning; classification; non-linear regression; receiver operating characteristic; ROC; evolutionary algorithm; neuroevolution; combined sewer overflow; combined neural pathway strength analysis; neural pathway strength diagram; UCI dataset; forest fire area; Environment Agency; manhole surcharge

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Duncan, A. P. (2014). The analysis and application of artificial neural networks for early warning systems in hydrology and the environment. (Doctoral Dissertation). University of Exeter. Retrieved from http://hdl.handle.net/10871/17569

Chicago Manual of Style (16th Edition):

Duncan, Andrew Paul. “The analysis and application of artificial neural networks for early warning systems in hydrology and the environment.” 2014. Doctoral Dissertation, University of Exeter. Accessed July 15, 2020. http://hdl.handle.net/10871/17569.

MLA Handbook (7th Edition):

Duncan, Andrew Paul. “The analysis and application of artificial neural networks for early warning systems in hydrology and the environment.” 2014. Web. 15 Jul 2020.

Vancouver:

Duncan AP. The analysis and application of artificial neural networks for early warning systems in hydrology and the environment. [Internet] [Doctoral dissertation]. University of Exeter; 2014. [cited 2020 Jul 15]. Available from: http://hdl.handle.net/10871/17569.

Council of Science Editors:

Duncan AP. The analysis and application of artificial neural networks for early warning systems in hydrology and the environment. [Doctoral Dissertation]. University of Exeter; 2014. Available from: http://hdl.handle.net/10871/17569

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