Search results for "Learning"
showing 10 items of 6669 documents
Edutainment - nowy wymiar praktyki miłosierdzia
2016
New forms of materiał, morał and spiritual poverty (Pope Francis) that are still changing and developing reąuire new forms and ways of practicing charity. According to the teaching of Pope Francis, it is not enough to give bread to the poor and the needy, they need the access to work, education and development. Following this path, it is worth noting the poor in other areas than just materiał poverty. This paper will focus on two groups: “the poor in sensitivity to others”, that is ailing in being merciful and those suffering from a lack of freedom, that is enslaved and endangered by this form of poverty. The novelty of the proposed form of the practice of mercy is to use methods of edutain…
An evaluation of the efficiency of French higher education: the case of scientific preparatory classes
2007
Looking at the famous French elite preparatory programmes called « Classes préparatoires aux grandes écoles » (CPGE), this research aimed to study the effectiveness of their training. The objective was to know if students from science CPGE develop specific behaviours that lead them to a better academic achievement in further engineering programmes (internal effectiveness) and a better professional career after graduation (external effectiveness). For comparison needs, the empirical approach focused on engineering schools enrolling both students from CPGE and students from other two-year programmes (DEUG, DUT, BTS). For assessing internal effectiveness, two surveys have been conducted; one d…
Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces
2013
This paper presents an appearance-based holistic method for expression recognition. A two stage supervised learning approach is used. At the first stage, training images are used to compute one subspace per expression. At the second stage, the same images are used to train a classifier. In this step, Euclidean distances from each image to each particular subspace are used as the input to the classifier. The resulting system significantly outperforms the baseline eigenfaces method on the Cohn-Kanade data set, with performance gains in the range 10%-20%.
Regularized extreme learning machine for regression problems
2011
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…
An entropy-based machine learning algorithm for combining macroeconomic forecasts
2019
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.
A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation
2021
Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations
Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
2018
The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at differ…
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features
2021
Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…
Toward an International Curricula Network for exchanges and LifeLong Learning
2010
4 pages; International audience; During the last years, an important work has been achieved within the framework of the European programs in order to emphasize and allow the mobility of students, the exchange of teachers, the recognition of diploma (programs ERASMUS, now in the LLP program)... These reflections have been done for classical programs within HEIs throughout Europe and also within the programs dedicated to vocational studies (LEONARDO, ValeurTech, EQF) to emphasize Life Long Learning and credit accumulation all around the life, with an approach based on competences [1]. The main purposes in these programs can be summarized as follows: − Favoring the equality among citizens in E…
Effects of pulsed electromagnetic fields on cognitive processes - a pilot study on pulsed field interference with cognitive regeneration.
2004
Background – Due to the ubiquitous use of cellular phones much has been speculated on secondary effects of electromagnetic irradiation emitted by those. Additionally, several studies have reported vegetative alterations as well as effects on the neuronal and molecular levels in humans. Here, using a psycho–physiological test paradigm, we examined effects of exposure to pulsed electromagnetic fields on cognitive performance. Materials and methods – In 11 volunteers, we tested cognitive processing under field exposure (GSM standard) and under field-free conditions. To examine the hypothesized effect of pulsed fields, we applied an auditory discrimination task and determined the participant's …