Search results for "artificial intelligence"
showing 10 items of 6122 documents
Best Practice in Health Care
2021
This book aims to disseminate and review the latest interdisciplinary medical knowledge to provide information on best clinical practice for difficult-to-treat conditions. The chapters address various aspects and approaches to the management of medical conditions that are not fully understood. This is exemplified by complex immune and inflammatory interactions in cancer development and survival-prolonging repeat surgery for pulmonary metastases or advances in craniotomy techniques for intracranial hemorrhage. Other chapters address the quality of life of patients after pyeloplasty, parathyroidectomy, or physically debilitating ankylosing spondylitis. Additional chapters address primary cili…
Sensory evaluation based on verbal judgments
1999
Studies of the repeatability and the homogeneity of expert panel scores in sensory profiling show that lasting and reliable evaluations of food products are difficult to obtain: strong inter- and intra-individual differences are commonly observed. Our hypothesis is that this variability is due to quantification methods that consist of asking panelists to furnish quantitative values (by attributing a numerical point to perceived intensity) and that using natural language in the form of verbal judgements in a hierarchical tree would allow improving the reliability of sensory evaluations. This hypothesis was tested by comparing a numerical value scale and a specific hierarchical semantic scale…
On using novel “Anti-Bayesian” techniques for the classification of dynamical data streams
2017
The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, that compare…
Feature selection on a dataset of protein families: from exploratory data analysis to statistical variable importance
2016
Proteins are characterized by several typologies of features (structural, geometrical, energy). Most of these features are expected to be similar within a protein family. We are interested to detect which features can identify proteins that belong to a family, as well as to define the boundaries among families. Some features are redundant: they could generate noise in identifying which variables are essential as a fingerprint and, consequently, if they are related or not to a function of a protein family. We defined an original approach to analyze protein features for defining their relationships and peculiarities within protein families. A multistep approach has been mainly performed in R …
Artificial Neural Networks for Prediction
2005
The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.
An Application of Spike-Timing-Dependent Plasticity to Readout Circuit for Liquid State Machine
2007
Liquid state machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allow…
Optimizing auditory images and distance metrics for self‐organizing timbre maps*
1996
Abstract The effect of using different auditory images and distance metrics on the final configuration of a self‐organized timbre map is examined by comparing distance matrices, obtained from simulations, with a similarity rating matrix, obtained using the same set of stimuli as in the simulations. Gradient images, which are intended to represent idealizations of physiological gradient maps in the auditory pathway, are constructed. The optimal auditory image and distance metric, with respect to the similarity rating data, are searched using the gradient method.
Optimizing self-organizing timbre maps: Two approaches
1997
The effect of using different auditory images and distance metrics on the final configuration of a self-organized timbre map is examined by comparing distance matrices obtained from simulations with a similarity rating matrix, obtained using the same set of stimuli as in the simulations. Two approaches are described. In the static approach, each stimulus is represented as a single multi-component vector. Gradient images, which are intended to represent idealizations of physiological gradient maps in the auditory pathway, are constructed. The optimal auditory image and distance metric, with respect to the similarity rating data, are searched using the gradient method. In the dynamic approach…
A method for extracting subspace of deterministic sources from EEG data
2008
In this paper, an algorithm for separating linear subspaces of time-locked brain responses and other noise sources in multichannel electroencephalography data is proposed. The search criterion used by method discriminates time-locked brain components and noise components on the basis of the assumed deterministic behavior that the time-locked brain sources obey. The comprehensive derivation of the method is given together with the description and the analysis of the results of the method's application to simulated and real EEG data sets. The possibilities of improving the results are also discussed.
Unsupervised quantitative methods to analyze student reasoning lines: Theoretical aspects and examples
2019
[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A relevant aim of research in education is to find and study the reasoning lines that students deploy when dealing with problematic situations. This can be done through an analysis of the answers students give to a questionnaire. In this paper, we discuss some methodological aspects involved in the quantitative analysis of a questionnaire by means of two different clustering methods, a hierarchical one and a nonhierarchical one. We start from the coding procedures needed to obtain analyzable data from the questionnaire and from a definition of a correlation coefficient suitable for measuri…