Search results for " learning"

Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contamin…

2011

The capacity of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict deoxynivalenol (DON) accumulation in barley seeds contaminated with Fusarium culmorum under different conditions has been assessed. Temperature (20-28 °C), water activity (0.94-0.98), inoculum size (7-15 mm diameter), and time were the inputs while DON concentration was the output. The dataset was used to train, validate and test many ANNs. Minimizing the mean-square error (MSE) was used to choose the optimal network. Single-layer perceptrons with low number of hidden nodes proved better than double-layer perceptrons, but the performance depended on the training …

Systematic Approach for Calculating the Concentrations of Chemical Species in Multiequilibrium Problems: Inclusion of the Ionic Strength Effects

2012

A general systematic approach including ionic strength effects is proposed for the numerical calculation of concentrations of chemical species in multiequilibrium problems. This approach extends the versatility of the approach presented in a previous article and is applied using the Solver option of the Excel spreadsheet to solve real problems such as the calculation of the pH of buffer solutions at any ionic strength. It is useful for undergraduate programs, in post-graduate programs, and in professional laboratories to predict experimental conditions.

Delaying elaborated feedback within computer‐based learning environments: The role of summative and question‐based feedback

2021

Technological, Organisational and Socio-Interactional Affordances in Simulation-Based Collaborative Learning

2021

Analysis of the applicability of a learning technology requires an evaluation of how the affordances of the learning environment respond to users’ needs. We examine affordances in a simulation-based collaborative learning environment from the learners’ viewpoint. Our analysis focuses on three types of affordances: technological, organisational and socio-interactional. The findings show how teams of learners employ the different types of affordances in their collaborative tasks. In addition, our analysis illustrates the interdependent and interlinked nature of the affordances. We offer an analytical understanding of the dynamics among different kinds of affordances and show how they can be a…

Detection of developmental dyslexia with machine learning using eye movement data

2021

Dyslexia is a common neurocognitive learning disorder that can seriously hinder individuals’ aspirations if not detected and treated early. Instead of costly diagnostic assessment made by experts, in the near future dyslexia might be identified with ease by automated analysis of eye movements during reading provided by embedded eye tracking technology. However, the diagnostic machine learning methods need to be optimized first. Previous studies with machine learning have been quite successful in identifying dyslexic readers, however, using contrasting groups with large performance differences between diagnosed and good readers. A practical challenge is to identify also individuals with bord…

Deep multimodal fusion for semantic image segmentation: A survey

2021

International audience; Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in compute…

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

2018

Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…

Ranking-Oriented Collaborative Filtering: A Listwise Approach

2016

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…

Topology Inference and Signal Representation Using Dictionary Learning

2019

This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is…

Introducing implicit learning: from the laboratory to the real life

2010

The dissociation between implicit and explicit cognition has a long history in psychology. As early as 1920, Clark Hull (25) investigated the learning of Chinese ideographs and identified the process of concept formation by abstraction of common elements, a process that occurs without explicit knowledge from the subjects of these regularities. Perceptual learning is another example of those processes that take place largely in the absence of awareness of the rules that govern the stimulations of the environment. Helmholtz (24) was one of the first to refer to implicit inference made by the perceptual system and to perceptual learning. Some years later, the distinction between implicit and e…