Search results for "e learning"
showing 10 items of 2703 documents
The experience of risk in families: conceptualisations and implications for transformative consumer research
2014
International audience; Families represent an important context for understanding and addressing the various forms of risk experienced by consumers. This article defines and discusses the concept of risk as it applies to the familial unit, with a particular focus on the liminal transitions that occur within families and the resiliency required for families to identify and adopt effective coping strategies to manage these transitions. A framework is proposed that offers researchers an approach for applying concepts related to family risk to various consumption-related problems and issues. This framework constitutes a starting point that can be developed and expanded to facilitate a deeper un…
Comparison of feature importance measures as explanations for classification models
2021
AbstractExplainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In this study we compare different feature importance measures using both linear (logistic regression with L1 penalization) and non-linear (random forest) methods and local interpretable model-agnostic explanations on top of them. These methods are applied to two datasets from the medical domain, the openly available breast cancer …
Machine learning for mortality analysis in patients with COVID-19
2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…
A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network
2016
International audience; No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully asses…
Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data
2022
In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photo…
At the Interface of National and Transnational: The Development of Finnish Policies against Domestic Violence in Terms of Gender Equality
2017
Although gender inequalities are the main social mechanisms behind the (re)production of domestic violence, policy responses to domestic violence as a gender-related problem vary at both the national and transnational levels. This article examines the interaction between national and transnational policies against domestic violence, focusing on how domestic violence is constructed as a gender-related problem in Finland, a Nordic welfare state that is often cited as a role model in gender equality. Using the conception of policies as historically changing and culturally specific discourses, this article offers an overview of the ways in which the perspective on domestic violence of the trans…
The "Film and Creative Engagement Project" : Audiovisual Accessibility and Telecollaboration
2020
ABSTRACT: Globalisation and the advancement of ICTs invite the development of learners’ strategies and communication skills in higher education to participate fully in digitally networked societies. This paper analyses the results of a pilot study which is part of Film and Creative Engagement (FaCE), a collaborative research project between Manchester Metropolitan University (MMU), UK, and Instituto Tecnológico de Estudios Superiores de Monterrey (ITESM), Campus Laguna in Torreón, Mexico. There were two main aims: (1) to create a short project that was inspiring, stimulating and enjoyable that could be transferable to other high education institutions; and (2) to provide a training that cou…
The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams
2023
1. Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour-intensive sampling methods that result in data of low temporal and spatial resolution. 2. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine-learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images r…
EXTRA VIRGIN OLIVE OIL IMPROVES LEARNING AND MEMORY IN SAMP8 MICE
2011
Abstract. Polyphenols are potent antioxidants found in extra virgin olive oil (EVOO); antioxidants have been shown to reverse age- and disease-related learning and memory deficits. We examined the effects of EVOO on learning and memory in SAMP8 mice, an age-related learning/memory impairment model associated with increased amyloid- protein and brain oxidative damage. We administered EVOO, coconut oil, or butter to 11 month old SAMP8 mice for 6 weeks. Mice were tested in T-maze foot shock avoidance and one-trial novel object recognition with a 24 h delay. Mice which received EVOO had improved acquisition in the T-maze and spent more time with the novel object in one-trial novel object recogni…
Digital solutions transform the forest-based bioeconomy into a digital platform industry : a suggestion for a disruptive business model in the digita…
2018
With the notion that the transformation of the forest-based bioeconomy in recent years provides insightful suggestions not only on the bioeconomy, but on business innovation, this paper conducts an empirical analysis of the transformation and attempts to extract suggestions for a digital-solution-driven, disruptive business model in the digital economy. Notwithstanding the potential broad cross-sectoral benefits, the natural environment, locality constraints, and incessant challenge of distance have impeded the balanced development of the bioeconomy. However, driven by digital solutions, the bioeconomy has taken big steps forward in recent years. Digitalization has enabled real-time, end-to…