Search results for "machine learning."
showing 10 items of 1455 documents
Multiobjective optimization and decision making in engineering sciences
2021
AbstractReal-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how…
Learning-automaton-based online discovery and tracking of spatiotemporal event patterns.
2013
Discovering and tracking of spatiotemporal patterns in noisy sequences of events are difficult tasks that have become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalities increase as event sharing expands into larger areas of one's life. Ironically, instead of being helpful, an excessive number of event notifications can quickly render the functionality of event sharing to be obtrusive. Indeed, any notification of events that provides redundant information to the…
Artificial neural networks for predicting dorsal pressures on the foot surface while walking
2012
In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (…
On the Evaluation of Images Complexity: A Fuzzy Approach
2006
The inherently multidimensional problem of evaluating the complexity of an image is of a certain relevance in both computer science and cognitive psychology. Computer scientists usually analyze spatial dimensions, to deal with automatic vision problems, such as feature-extraction. Psychologists seem more interested in the temporal dimension of complexity, to explore attentional models. Is it possible, by merging both approaches, to define an more general index of visual complexity? We have defined a fuzzy mathematical model of visual complexity, using a specific entropy function; results obtained by applying this model to pictorial images have a strong correlation with ones from an experime…
The impact of feature extraction on the performance of a classifier : kNN, Naïve Bayes and C4.5
2005
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and the classification error in high dimensions. In this paper, different feature extraction techniques as means of (1) dimensionality reduction, and (2) constructive induction are analyzed with respect to the performance of a classifier. Three commonly used classifiers are taken for the analysis: kNN, Naïve Bayes and C4.5 decision tree. One of the main goals of this paper is to show the importance of the use of class information in feature extraction for classification and (in)appropriateness of random projection or conventional PCA to feature extraction for …
Improving Nearest Neighbor Based Multi-target Prediction Through Metric Learning
2017
The purpose of this work is to learn specific distance functions to be applied for multi-target regression problems using nearest neighbors. The idea of preserving the order relation between input and output vectors considering their corresponding distances is used along a maximal margin criterion to formulate a specific metric learning problem. Extensive experiments and the corresponding discussion try to put forward the advantages of the proposed algorithm that can be considered as a generalization of previously proposed approaches. Preliminary results suggest that this line of work can lead to very competitive algorithms with convenient properties.
How adolescents navigate Wikipedia to answer questions / ¿Cómo navegan los adolescentes en Wikipedia para contestar preguntas?
2015
AbstractIn one experiment, we explored how high school students use hyperlink relevance cues while they navigate to answer questions from hypertexts. Current evidence has shown that students may navigate by either performing a deep semantic analysis of the relationship between the question and the existing hyperlinks, or by matching words in the question to words in the hyperlink labels. We focused on how students combine both cues during navigation, and how comprehension skills relate to the use of such cues. Our study revealed that 14 year old students (N = 53) selected hyperlinks by relying to a similar degree on both word matching and semantic overlap. Furthermore, when there was a conf…
Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
2021
The British journal of surgery 108(11), 1274-1292 (2021). doi:10.1093/bjs/znab183
Do networks matter? Network involvement and policy learning in Nordic regions
2017
ABSTRACTThe capacities of regions to form networks are an important feature of regional cooperation. This article assesses why Nordic regions engage in network activities and what new organizational patterns of collaboration emerge. It draws on historical data from a 2006–2008 survey of elected regional politicians from the Nordic countries. The article argues that through this process, participation in cross-border networks matters for regional learning as measured by the policy-choices of regions.
A cultural heritage experience for visually impaired people
2020
Abstract In recent years, we have assisted to an impressive advance of computer vision algorithms, based on image processing and artificial intelligence. Among the many applications of computer vision, in this paper we investigate on the potential impact for enhancing the cultural and physical accessibility of cultural heritage sites. By using a common smartphone as a mediation instrument with the environment, we demonstrate how convolutional networks can be trained for recognizing monuments in the surroundings of the users, thus enabling the possibility of accessing contents associated to the monument itself, or new forms of fruition for visually impaired people. Moreover, computer vision …