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Refining the ML/DL Argument for the SensorAble Project

Ruttenberg, D; Porayska-Pomsta, K; White, S; Homes, J; (2020) Refining the ML/DL Argument for the SensorAble Project. (OSF Preprints ). Center for Open Science: Charlottesville, VA, USA. Green open access

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RUTTENBERG Research Paper #4--Study #4 -- Refining the ML:DL Argument in Autism Sensory Research.pdf - Accepted version

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Abstract

Is Machine Learning/Deep Learning (ML/DL) a technological necessity when implementing SensorAble or is it something to be investigated because of its potential? Should ML/DL be implemented because it permits processing large quantities of multimodal data enabling modelling of autistic neurocognitive processes that well relate to distractibility and anxiety? Or would interventional prototyping using old-fashioned Artificial Intelligence (AI), Bayesian theory or a hand-crafted rule be preferable? Following Participant Public Information (PPI), can ML/DL techniques permit greater understanding of how disruptions occur and properly align/prepare the groundwork for an interventional prototype? Would heuristics, data mining, or perhaps some other statistical approach adequately provide evidence proceeding a design? With the constellation of supervisors who have invested in this project, can fundamental science properly situate SensorAble in a broader vision that creates practical tools? It is one thing to understand and model a problem. It’s another to simply design/build. Doing the latter may inform the user, but how does it guarantee that other stress factors, ethical issues and newly created anomalies aren’t inadvertently introduced?

Type: Working / discussion paper
Title: Refining the ML/DL Argument for the SensorAble Project
Open access status: An open access version is available from UCL Discovery
DOI: 10.31219/osf.io/nrtz9
Publisher version: https://doi.org/10.31219/osf.io/nrtz9
Language: English
Additional information: This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: Autism Spectrum Condition, Attention, Focus, Machine Learning, Deep Learning, Multimodal Learning Analytics, Distractibility, Anxiety, Focus
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10102347
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