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showing 10 items of 3931 documents
A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes
2011
Published version of an article from the book: Hybrid artificial intelligent systems, Lecture notes in computer science. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-21219-2_2 There are numerous applications in Artificial Intelligence (AI) and Machine Learning (ML) where the criteria for decisions are based on testing procedures. The most common tools used in such random phenomena involve Random Walks (RWs). The theory of RWs and its applications have gained an increasing research interest since the start of the last century. [1]. In this context, we note that a RW is, usually, defined as a trajectory involving a series of successive ran…
ℓ1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields
2016
In the last 20 years, we have witnessed the dramatic development of new data acquisition technologies allowing to collect massive amount of data with relatively low cost. is new feature leads Donoho to define the twenty-first century as the century of data. A major characteristic of this modern data set is that the number of measured variables is larger than the sample size; the word high-dimensional data analysis is referred to the statistical methods developed to make inference with this new kind of data. This chapter is devoted to the study of some of the most recent ℓ1-penalized methods proposed in the literature to make sparse inference in a Gaussian Markov random field (GMRF) defined …
Regularity of Spike Trains and Harmony Perception in a Model of the Auditory System
2011
Spike train regularity of the noisy neural auditory system model under the influence of two sinusoidal signals with different frequencies is investigated. For the increasing ratio m/n of the input signal frequencies (m, n are natural numbers) the linear growth of the regularity is found at the fixed difference (m - n). It is shown that the spike train regularity in the model is high for harmonious chords of input tones and low for dissonant ones.
Fuzzy Distributed Genetic Approaches for Image Segmentation
2010
This paper presents a new image segmentation algorithm (called FDGA-Seg) based on a combination of fuzzy logic, multiagent systems and genetic algorithms. We propose to use a fuzzy representation of the image site labels by introducing some imprecision in the gray tones values. The distributivity of FDGA-Seg comes from the fact that it is designed around a MultiAgent System (MAS) working with two different architectures based on the master-slave and island models. A rich set of experimental segmentation results given by FDGA-Seg is discussed and compared to the ICM results in the last section.
Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation
2011
International audience; Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. However, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast between the datasets pose further challenges to achieve an accurate segmentation. In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from princi…
MRF Model-Based Approach for Image Segmentation Using a Chaotic MultiAgent System
2006
In this paper, we propose a new Chaotic MultiAgent System (CMAS) for image segmentation. This CMAS is a distributed system composed of a set of segmentation agents connected to a coordinator agent. Each segmentation agent performs Iterated Conditional Modes (ICM) starting from its own initial image created initially from the observed one by using a chaotic mapping. However, the coordinator agent receives and diversifies these images using a crossover and a chaotic mutation. A chaotic system is successfully used in order to benefit from the special chaotic characteristic features such as ergodic property, stochastic aspect and dependence on initialization. The efficiency of our approach is s…
Sensitivity of Estimators for Measuring Information Amount in Web-Based Medical Documents
2018
Nowadays, communication between patient and doctor during an appointment has changed significantly owning to the opportunity that medical portals provide. Whether or not necessarily appreciated by the doctors, the patients became more aware of the first symptoms’ suggesting a particular disease and the medical procedures that apply as a standard. Estimating amount of reliable factual medical information in a document is carried out by parametrizing space of digital documents and dividing it into subsequent layers that represent distribution of the system responses computed as random variables to a query about medical information. Analyzed are the following attributes: dynamism of decrease o…
GRASP & evolutionary path relinking for medical image registration based on point matching
2010
Image registration is a very active research area in computer vision. Image registration methods, aim to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images. From this matching, the registration transformation can be inferred by means of numerical methods. In this paper, we tackle the medical image registration problem adapting a new advanced hybrid metaheuristic composed by the GRASP and the evolutionary path relinking algorithms, called G&EvPR. The experiments conducted in this work have shown the good performance of G&EvPR compared to similar a…
Irreversible Multilayer Adsorption
1993
Random sequential adsorption (RSA) models have been studied due to their relevance to deposition processes on surfaces. The depositing particles are represented by hard-core extended objects; they are not allowed to overlap. Numerical Monte Carlo studies and analytical considerations are reported for 1D and 2D models of multilayer adsorption processes. Deposition without screening is investigated, in certain models the density may actually increase away from the substrate. Analytical studies of the late stage coverage behavior show the crossover from exponential time dependence for the lattice case to the power law behavior in the continuum deposition. 2D lattice and continuum simulations r…
Study of the role of particle-particle dipole interaction in dielectrophoretic devices for biomarkers identification
2015
A three dimensional Coupled Monte Carlo-Poisson method has been used to evaluate the impact of particle-particle dipole interactions in the equilibrium distribution of a system of uncharged polarizable particles suspended in a static liquid medium under the action of an oscillating non-uniform electric field generated by polynomial electrodes. We compare the simulated distributions with experimental ones both for micro- (MDA-MB-231 breast tumor cells) and nano-(multiwall carbon nanotubes) particles. In both cases the equilibrium distributions near the electrodes are dominated by dipole interactions which locally enhance the DEP effect and promote long particles chains.