Synchronization and computation in a chaotic neural network.
Explore the source record for details and available documents.
Journal Article↗
SEARCH · PubMed Health
Results for “Neural Networks, Computer”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers.
In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%.
Algorithms↗
Initial results for automated computational modeling of patient-specific electromagnetic hyperthermia.
Developments in finite-difference time-domain (FD-TD) computational modeling of Maxwell's equations, super-computer technology, and computed tomography (CT) imagery open the possibility of accurate numerical simulation of electromagnetic (EM) wave interactions with specific, complex, biological tissue structures. One application of this technology is in the area of treatment planning for EM hyperthermia. In this paper, we report the first highly automated CT image segmentation and interpolation scheme applied to model patient-specific EM hyperthermia. This novel system is based on sophisticated tools from the artificial intelligence, computer vision, and computer graphics disciplines. It permits CT-based patient-specific hyperthermia models to be constructed without tedious manual contouring on digitizing pads or CRT screens. The system permits in principle near real-time assistance in hyperthermia treatment planning. We apply this system to interpret actual patient CT data, reconstructing a 3-D model of the human thigh from a collection of 29 serial CT images at 10 mm intervals. Then, using FD-TD, we obtain 2-D and 3-D models of EM hyperthermia of this thigh due to a waveguide applicator. We find that different results are obtained from the 2-D and 3-D models, and conclude that full 3-D tissue models are required for future clinical usage.
Computer Simulation↗
Laryngeal pathology detection by means of class-specific neural maps.
Most of the existing systems and methods for laryngeal pathology detection are characterized by a classification error. One of the basic problems is the approximation and estimation of the probability density functions of the given classes. In order to increase the accuracy of laryngeal pathology detection and to eliminate the most dangerous error--classification of a patient with laryngeal disease as a normal speaker--here an approach based on modeling of the probability density functions (pdf's) of the input vectors of the normal and pathological speakers by means of two prototype distribution maps (PDM), respectively, is proposed. The pdf of the input vectors of an unknown normal or pathological speaker is also modeled by such a prototype distribution neural map--PDM(X)--and the pathology detection is done by means of a ratio of specific similarities rather than by a direct comparison of some type of distance/similarity with a threshold. The experiments show an increased classification accuracy and that the proposed method can be used for screening the laryngeal diseases. The method is applied in a consulting system for clinical practice.
Algorithms↗
Improving cephalogram analysis through feature subimage extraction.
An MLP with a GA was proposed to extract feature subimages containing orthodontic landmarks. Simulated images and cephalograms were used to investigate its performance in comparison with the cross-correlation method. From the results of simulated image containing shapes with different geometrical conditions, it was shown that the fault tolerance of the MLP for rotation, scaling, brightness variety, and other anomalous deformations is good enough to overcome the clinical application problems. It was also shown that the stability, accuracy, and speed of this proposed algorithm are very promising. Moreover, the performance of the MLP can be significantly improved by collecting, more "representative" false patterns. The GA is a good approach to speed up the process of feature subimage extraction based on the fitness evaluated using the MLP.
Algorithms↗
Comments on "A modified reachability tree approach to analysis of unbounded Petri nets".
The above paper introduced the construction of a modified reachability tree (MRT) for (unbounded) Petri nets and its application to reachability, liveness, and deadlock analysis. This note shows via a counterexample that some of the MRT properties claimed in the above paper are incorrect.
Algorithms↗
Geometrical interpretation and architecture selection of MLP.
A geometrical interpretation of the multilayer perceptron (MLP) is suggested in this paper. Some general guidelines for selecting the architecture of the MLP, i.e., the number of the hidden neurons and the hidden layers, are proposed based upon this interpretation and the controversial issue of whether four-layered MLP is superior to the three-layered MLP is also carefully examined.
Algorithms↗
Rigorous proof of termination of SMO algorithm for support vector machines.
Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
Algorithms↗
From projection pursuit and CART to adaptive discriminant analysis?
While many efforts have been put into the development of nonlinear approximation theory and its applications to signal and image compression, encoding and denoising, there seems to be very few theoretical developments of adaptive discriminant representations in the area of feature extraction, selection and signal classification. In this paper, we try to advocate the idea that such developments and efforts are worthwhile, based on the theorerical study of a data-driven discriminant analysis method on a simple--yet instructive--example. We consider the problem of classifying a signal drawn from a mixture of two classes, using its projections onto low-dimensional subspaces. Unlike the linear discriminant analysis (LDA) strategy, which selects subspaces that do not depend on the observed signal, we consider an adaptive sequential selection of projections, in the spirit of nonlinear approximation and classification and regression trees (CART): at each step, the subspace is enlarged in a direction that maximizes the mutual information with the unknown class. We derive explicit characterizations of this adaptive discriminant analysis (ADA) strategy in two situations. When the two classes are Gaussian with the same covariance matrix but different means, the adaptive subspaces are actually nonadaptive and can be computed with an algorithm similar to orthonormal matching pursuit. When the classes are centered Gaussians with different covariances, the adaptive subspaces are spanned by eigen-vectors of an operator given by the covariance matrices (just as could be predicted by regular LDA), however we prove that the order of observation of the components along these eigen-vectors actually depends on the observed signal. Numerical experiments on synthetic data illustrate how data-dependent features can be used to outperform LDA on a classification task, and we discuss how our results could be applied in practice.
Algorithms↗
Survey of clustering algorithms.
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
Algorithms↗
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm.
The convergence of Oja's principal component analysis (PCA) learning algorithms is a difficult topic for direct study and analysis. Traditionally, the convergence of these algorithms is indirectly analyzed via certain deterministic continuous time (DCT) systems. Such a method will require the learning rate to converge to zero, which is not a reasonable requirement to impose in many practical applications. Recently, deterministic discrete time (DDT) systems have been proposed instead to indirectly interpret the dynamics of the learning algorithms. Unlike DCT systems, DDT systems allow learning rates to be constant (which can be a nonzero). This paper will provide some important results relating to the convergence of a DDT system of Oja's PCA learning algorithm. It has the following contributions: 1) A number of invariant sets are obtained, based on which we can show that any trajectory starting from a point in the invariant set will remain in the set forever. Thus, the nondivergence of the trajectories is guaranteed. 2) The convergence of the DDT system is analyzed rigorously. It is proven, in the paper, that almost all trajectories of the system starting from points in an invariant set will converge exponentially to the unit eigenvector associated with the largest eigenvalue of the correlation matrix. In addition, exponential convergence rate are obtained, providing useful guidelines for the selection of fast convergence learning rate. 3) Since the trajectories may diverge, the careful choice of initial vectors is an important issue. This paper suggests to use the domain of unit hyper sphere as initial vectors to guarantee convergence. 4) Simulation results will be furnished to illustrate the theoretical results achieved.
Algorithms↗
Comments on "the 1993 DIMACS graph coloring challenge" and "energy function-based approaches to graph coloring".
Since all graphs in the 1993 DIMACS graph coloring challenge are undirected, each edge should be only counted once. However, in some files each edge is counted once, whereas in others each edge is counted twice; so a systematical check on the DIMACS challenge is made to eliminate the inconsistencies. Besides, the experimental results of a previous paper by Blas et al. counted each violated edges twice and neglected the inconsistencies in the DIMACS challenge. So the correct experimental results of a previous paper by Blas et al are also given.
Algorithms↗
Computation of Adalines' sensitivity to weight perturbation.
In this paper, the sensitivity of Adalines to weight perturbation is discussed. According to the discrete feature of Adalines' input and output, the sensitivity is defined as the probability of an Adaline's erroneous outputs due to weight perturbation with respect to all possible inputs. By means of hypercube model and analytical geometry method, a heuristic algorithm is given to accurately compute the sensitivity. The accuracy of the algorithm is verified by computer simulations.
Algorithms↗
Batch map extensions of the kernel-based maximum entropy learning rule.
In this letter, two batch-map extensions are described for the kernel-based maximum entropy learning rule (kMER). In the first, the weights are iteratively set to weighted component-wise medians, while in the second the generalized median is used, enabling kMER to process symbolic data. Simulations are performed to illustrate the extensions.
Algorithms↗
Accuracy/diversity and ensemble MLP classifier design.
The difficulties of tuning parameters of multilayer perceptrons (MLP) classifiers are well known. In this paper, a measure is described that is capable of predicting the number of classifier training epochs for achieving optimal performance in an ensemble of MLP classifiers. The measure is computed between pairs of patterns on the training data and is based on a spectral representation of a Boolean function. This representation characterizes the mapping from classifier decisions to target label and allows accuracy and diversity to be incorporated within a single measure. Results on many benchmark problems, including the Olivetti Research Laboratory (ORL) face database demonstrate that the measure is well correlated with base-classifier test error, and may be used to predict the optimal number of training epochs. While correlation with ensemble test error is not quite as strong, it is shown in this paper that the measure may be used to predict number of epochs for optimal ensemble performance. Although the technique is only applicable to two-class problems, it is extended here to multiclass through output coding. For the output-coding technique, a random code matrix is shown to give better performance than one-per-class code, even when the base classifier is well-tuned.
Algorithms↗
A new brain-computer interface design using fuzzy ARTMAP.
This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.
Brain↗
Prediction of novel archaeal enzymes from sequence-derived features.
The completely sequenced archaeal genomes potentially encode, among their many functionally uncharacterized genes, novel enzymes of biotechnological interest. We have developed a prediction method for detection and classification of enzymes from sequence alone (available at http://www.cbs.dtu.dk/services/ArchaeaFun/). The method does not make use of sequence similarity; rather, it relies on predicted protein features like cotranslational and posttranslational modifications, secondary structure, and simple physical/chemical properties.
Amino Acid Motifs↗
Quantitative assessment of liver fibrosis: a novel automated image analysis method.
BACKGROUND: Semiquantitative staging of liver fibrosis is a highly subjective procedure and may lead to an uncertainty in judgment regarding the degree of severity and hence the progression of the disease. AIM: In this work, we present an automated quantification system (AQS) for evaluating the degree of severity of fibrosis in liver biopsies based on Ishak et al.'s classification. Accordingly, liver fibrosis is classified into six classes depending on its severity and progression. The described system is of special value in accurately assessing the prognosis of chronic liver disease. METHODS: In our method, we tried to approximate the architecture of the fibrosis in the subject sample using texture features and shape representation of the fibrosis structural expansion with an overall accuracy of about 98%. RESULTS AND CONCLUSION: The presented AQS is considered to be a novel approach in the domain of automatic liver fibrosis quantification. It is a true quantification and intelligent approach that attempts to utilize the current semiquantitative methods of liver fibrosis assessment to turn them into real quantitative ones with significant reduction in variability and subjectivity. We propose that our method can be adopted by a panel of expert liver pathologists and software to be developed and used on a wide scale.
Biopsy↗