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At least 955 records · Page 53Linked to original sources
PubMed1997

Information retrieval: an overview of system characteristics.

The paper gives an overview of characteristics of information retrieval (IR) systems. The characteristics are identified from the descriptions of 23 IR systems. Four IR models are discussed: the Boolean model, the vector model, the probabilistic model and the connectionistic model. Twelve other characteristics of IR models are identified: search intermediary, domain knowledge, relevance feedback, natural language interface, graphical query language, conceptual queries, full-text IR, field searching, fuzzy queries, hypertext integration, machine learning, and ranked output. Finally, the relevance of IR systems for the World Wide Web is established.

Algorithms↗
PubMed2000

Fuzzy control of bioprocess in Japan.

Process control of bioprocess has been carried out by the judgment of the experts, who are the skilled operators and have lots of experiences for the control of the process. In almost all cases, those experiences are described linguistic IF-THEN rules. Fussy inference is one of the powerful tools to incorporate the linguistic rules to the computer for process control. Fuzzy control are divided into two types; one is the direct fuzzy control of process variables such as sugar feed rate in fed-batch culture and fermentation temperature in batch operation. The other is the indirect control of bioprocess, in which at first the phase recognition is carried out by fuzzy inference and the control strategies constructed in each phase are used for the control of process variables. In Japan, the fuzzy control has already been applied to practical industrial productions, such as pravastatin precursor, vitamin B2, and Japanese sake mashing process. In this review, these industrial approaches of fuzzy control are introduced.

Alcoholic Beverages↗
PubMed2005

A gait nomogram used with fuzzy clustering to monitor functional status of children and young adults with cerebral palsy.

We have developed a gait nomogram based on dynamic similarity to characterize and compare neuromuscular function. We used temporal-distance data based on 669 normal participants (age range 5 to 98 years), and 78 children and young adults with spastic diplegia (43 males, 35 females; mean age 10 y 8 mo, standard deviation 3 y 11 mo, range 5 to 20 y), all of whom were independent ambulators. A new statistical algorithm known as fuzzy clustering was implemented and five cluster centres were identified, each representing distinct walking strategies adopted by children with cerebral palsy. Using just three easily obtained parameters--leg length in metres, stride length in metres, and cadence in steps per minute--our program calculates a child's dimensionless step length and step frequency, generates the individual's membership values for each of the five clusters, and plots the gait nomogram. The clinical utility of our approach has been demonstrated for two test participants with spastic diplegia, using pre- and postoperative data (one neurosurgical and one orthopaedic), where changes in membership of the five clusters provide objective measures of improvement in their neuromuscular function.

Adolescent↗
PubMedDate not supplied

Macro approach and fuzzy modeling of entrapped biocatalyst.

The interactions between a strain of Saccharomyces cerevisiae and an alginate matrix are investigated to ascertain the main factors affecting the bioreaction evolution. During the tests several parameters (glucose, ethanol, calcium ion and biomass concentration, pH, and alginate bed diameter) were evaluated, coupled with microscopic investigation inside the beads to determine the spatial biomass distribution. A detailed analysis of macro parameters and a correlation among them are proposed using a fuzzy algorithm. A global two-step fuzzy model results in which biomass distribution inside the beads is represented as a hidden parameter.

Alginates↗
PubMedDate not supplied

Fuzzy ARTMAP and back-propagation neural networks based quantitative structure-property relationships (QSPRs) for octanol-water partition coefficient of organic compounds.

Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logK(ow), at 25 degrees C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculated from PM3 semiempirical MO-theory. Quantum chemical input descriptors include average polarizability, dipole moments, exchange energy, total electrostatic interaction energy, total two-center energy, and ionization potential. The fuzzy ARTMAP/QSPR performed, for a logK(ow) range of -1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logK(ow) for the overall data and test sets, respectively. The optimal 12-11-1 back-propagation/QSPR model, for the same range of logK(ow), exhibited larger average absolute errors of 0.23 and 0.27 logK(ow) for the test and validation data sets, respectively, over the same range of logK(ow) values. The present results with the fuzzy ARTMAP-based QSPR are encouraging and suggest that high performance logK(ow) QSPR that encompasses a wider range of chemical groups could be developed, following the present approach, by training with a larger heterogeneous data set.

Fuzzy Logic↗
PubMedDate not supplied

An integrated SOM-fuzzy ARTMAP neural system for the evaluation of toxicity.

Self-organized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on fuzzy ARTMAP. First, the category maps for each molecular descriptor and for the target activity variable were created with SOM and then classified on the basis of topology and nonlinear distribution. The best subset of descriptors was obtained by choosing from each cluster the index with the highest correlation with the target variable and then in order of decreasing correlation. This process was terminated when a dissimilarity measure increased, indicating that the inclusion of more molecular indices would not add supplementary information. The optimal subset of descriptors was used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities. The performance of the integrated SOM-fuzzy ARTMAP approach was evaluated with the prediction of the acute toxicity LC50 of a homogeneous set of 69 benzene derivatives in the fathead minnow and the oral rat toxicity LD50 of a heterogeneous set of 155 organic compounds. The proposed methodology minimized the problem of misclassification of similar compounds and significantly enhanced the predictive capabilities of a properly trained fuzzy ARTMAP network.

Fuzzy Logic↗
PubMedDate not supplied

Feature based fuzzy matching of 2D gel electrophoresis images.

Automatic alignment (matching) of two-dimensional gel electrophoresis images is of primary interest in the evolving field of proteomics. In the present study, feature-based matching techniques, in their classical and robust versions, are described, and an automatic method of fuzzy alignment (FA) is introduced. This method allows automatic matching of two gel images with different numbers of features with unknown correspondence. Performance of FA is tested on simulated and real data sets.

Automation↗
PubMedDate not supplied

Tuning neural and fuzzy-neural networks for toxicity modeling.

The need for general reliable models for predicting toxicity has led to the use of artificial intelligence. We applied neural and fuzzy-neural networks with the QSAR approach. We underline how the networks have to be tuned on the data sets generally involved in modeling toxicity. This study was conducted on 562 organic compounds in order to establish models for predictive the acute toxicity in fish.

Animals↗
PubMedDate not supplied

Matching 2D gel electrophoresis images.

Automatic alignment (matching) of two-dimensional gel electrophoresis images is of primary interest in the field of proteomics. The proposed method of 2D gel image matching is based on fuzzy alignment of features, extracted from gels' images, and it allows both global and local interpolation of image grid, followed by brightness interpolation. Method performance is tested on simulated images and gel images available via the Internet databases.

Algorithms↗
PubMedDate not supplied

Novel feature extraction technique for fuzzy relational clustering of a flexible dopamine reuptake inhibitor.

This paper describes a novel clustering methodology for classifying over 700 conformations of a flexible analogue of GBR 12909, a dopamine reuptake inhibitor that has completed phase I clinical trials as a treatment for cocaine abuse. The major aspect of the clustering methodology includes an efficient data-conditioning scheme where a systematic feature extraction procedure based on the structural properties of the molecule was used to reduce the associated feature space. This allowed region-specific clustering that focused on individual pharmacophore elements of the molecule. For clustering of the reduced feature set, the fuzzy clustering partitional method was utilized. Due to the relational nature of the feature data, fuzzy relational clustering was employed, and it successfully detected natural groups defined by rotational minima around N(sp(3))-C(sp(3)), O(sp(3))-C(sp(3)), and C(sp(3))-C(sp(2)) bonds. The proposed clustering methodology also employed several cluster validity measures, which corroborated the partitions produced by the clustering technique and agreed with the results of hierarchical clustering using the XCluster program. Representative structures which exhibited a reasonable spread of energies and showed good spatial coverage of the conformational space were identified for use as putative bioactive conformations in a future Comparative Molecular Field Analysis of GBR 12909 analogues. The clustering methodology developed here is capable of handling other computational chemistry problems, and the feature extraction technique can be easily generalized to other molecules.

Cluster Analysis↗
PubMedDate not supplied

Prediction of noninteractive mixture toxicity of organic compounds based on a fuzzy set method.

Current methods for the prediction of mixture toxicity have shown to be valid for mixtures that conform to some assumptions that were ideally formulated for mixtures comprising constituents exhibiting either completely similar or dissimilar mechanisms of action. Approaches are needed that predict the toxicity of mixtures representative of real environmental occurrences i.e., those comprising constituents of mixed similar and dissimilar compounds and therefore are more complex. In this paper such a methodology is proposed which uses molecular descriptors and fuzzy set theory to characterize the degree of similarity and dissimilarity of mixture constituents, integrates the concentration addition and independent action models, and therefore is called INFCIM (INtegrated Fuzzy Concentration addition--Independent action Model). INFCIM is tested in two case studies using toxicity data of four mixtures, and its performance is compared against those of both concentration addition and independent action models. Mixture 1 consists of 18 s-triazines acting on green freshwater algae scenedemus vacuolatus. Mixture 2 comprises 16 acting constituents tested on scenedemus vacuolatus. Both mixtures inhibit reproduction in the biological assays. There are 10 quinolone compounds in mixture 3 and 16 phenol derivative compounds in mixture 4 all causing long-term inhibition of bioluminescence in the marine bacterium Vibrio fischeri. It was shown that INFCIM performed comparably or better than the best performing existing model in the original studies for all the mixtures tested.

Eukaryota↗
PubMedDate not supplied

Peak alignment of urine NMR spectra using fuzzy warping.

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.

Algorithms↗
PubMed2004

Fuzzy pharmacophore models from molecular alignments for correlation-vector-based virtual screening.

A pharmacophore-based approach for compiling focused screening libraries is presented. It integrates information from three-dimensional molecular alignments into correlation vector-based database screening. The pharmacophore model is represented by a number of spheres of Gaussian-distributed feature densities. Different degrees of "fuzziness" can be introduced to influence the model's resolution. Transformation of this pharmacophore representation into a correlation vector results in a vector of feature probabilities which can be utilized for rapid virtual screening of compound databases or virtual libraries. The approach was validated by retrospective screening for cyclooxygenase 2 (COX-2) and thrombin ligands. A variety of models with different degrees of fuzziness were calculated and tested for both classes of molecules. Best performance was obtained with pharmacophore models reflecting an intermediate degree of fuzziness, yielding an enrichment factor of up to 39 for the first 1% of the ranked database. Appropriately weighted fuzzy pharmacophore models performed better in retrospective screening than similarity searching using only a single query molecule. The new pharmacophore method was shown to complement existing approaches.

Crystallography, X-Ray↗
PubMed1997

Automated detection of the left ventricular region in magnetic resonance images by Fuzzy c-Means model.

A new method for automated detection of the Left Ventricular (LV) region in Magnetic Resonance Imaging is presented. This method is based on the Fuzzy c-Means (FCM) clustering algorithm. The FCM is applied to each static frame of the cardiac cycle to detect the LV region. Delineation of this region is essential in the quantitative analysis of the cardiac function. The effectiveness of the method is demonstrated by application to sequences of cardiac images.

Algorithms↗
PubMed1998

The application of a modified proportional-derivative control algorithm to arterial pressure alarms in anesthesiology.

OBJECTIVE: We have developed an arterial pressure alarm system based on a modified proportional-derivative (PD) controller algorithm, and prospectively tested its ability to predict significant hypotensive episodes, defined as systolic arterial pressure < 80 mmHg, in comparison to conventional limit alarms. METHODS: The alarm algorithm was tuned to detect hypotension using selected invasive arterial pressure traces taken from ten patients who had large intra-operative arterial pressure changes. The algorithm's performance was then tested prospectively in comparison to conventional limit alarms and median filtered limit alarms, set at 85 mmHg and 90 mmHg, for its ability to predict hypotensive episodes in a further 100 patients who required invasive arterial pressure monitoring. RESULTS: For the PD alarm algorithm, onset times for significant hypotensive episodes were between those of limit alarms set at 85 mmHg and 90 mmHg. Offset times were similar to the 85 mmHg limit alarms. The false positive rate was 34% compared with 45-64% for the other alarms (p < 0.01). Using our definitions, there was one false negative in the PD group, being a 15 second drop in observed arterial pressure, when a non invasive blood pressure cuff was inflated above the arterial line. CONCLUSIONS: An arterial pressure alarm system design based on a closed loop control algorithm offered improved perform ance over conventional limit alarms and in addition provided a graded output of severity of the hypotension.

Adolescent↗
PubMed2001

Tools for acquisition, processing and knowledge-based diagnostic of the electroencephalogram and visual evoked potentials.

The objective of our research is to develop computer-based tools to automate the clinical evaluation of the electroencephalogram (EEG) and visual evoked potentials (VEP). This paper describes a set of solutions to support all the aspects regarding the standard procedures of the diagnosis in neurophysiology, including: (1) acquisition and real-time processing and compression of EEG and VEP signals, (2) real-time brain mapping of spectral powers, (3) classifier design, (4) automatic detection of morphologies through supervised neural networks. (5) signal analysis through fuzzy modelling, and (6) a knowledge based approach to classifier design.

Adolescent↗
PubMed2002

Estimation of expiratory time constants via fuzzy clustering.

OBJECTIVE: In mechanically ventilated patients the expiratory time constant provides information about respiratory mechanics. In the present study a new method, fuzzy clustering, is proposed to determine expiratory time constants. Fuzzy clustering differs from other methods since it neither interferes with expiration nor presumes any functional relationship between the variables analysed. Furthermore, time constant behaviour during expiration can be assessed, instead of an average time constant. The time constants obtained with fuzzy clustering are compared to time constants conventionally calculated from the same expirations. METHODS: 20 mechanically ventilated patients, including 10 patients with COPD, were studied. The data of flow, volume and pressure were sampled. From these data, four local linear models were detected by fuzzy clustering. The time constants (tau) of the local linear models (clusters) were calculated by a least-squares technique. Time constant behaviour was analysed. Time constants obtained with fuzzy clustering were compared to time constants calculated from flow-volume curves using a conventional method. RESULTS: Fuzzy clustering revealed two patterns of expiratory time constant behaviour. In the patients with COPD an initial low time constant was found (mean tau1: 0.33 s, SD 0.21) followed by higher time constants; mean tau2: 2.00 s (SD 0.91s), mean tau3: 3.45 s (SD 1.44) and mean tau4: 5.47 s (SD 2.93). In the other patients only minor changes in time constants were found; mean tau1: 0.74 s (SD 0.30), mean tau2: 0.90 s (SD 0.23), mean tau3: 1.04 s (SD 0.42) and mean tau4: 1.74 s (SD 0.78). Both the pattern of expiratory time constants, as well as the time constants calculated from the separate clusters, were significantly different between the patients with and without COPD. Time constants obtained with fuzzy clustering for cluster 2, 3 and 4 correlated well with time constants obtained from the flow-volume curves. CONCLUSIONS: In mechanically ventilated patients, expiratory time constant behaviour can be accurately assessed by fuzzy clustering. A good correlation was found between time constants obtained with fuzzy clustering and time constants obtained by conventional analysis. On the basis of the time constants obtained with fuzzy clustering, a clear distinction was made between patients with and without

Adult↗
PubMed1996

Development of a portable closed-loop atracurium infusion system: systems methodology and safety issues.

Safety of closed-loop drug infusion systems is an issue often raised as a matter of concern. As a result, many closed-loop control systems are reported in the literature merely as computer simulation studies and few ever reach the stage of physical realisation and formal clinical evaluation. We address the safety issues involved with such systems by describing the development of a portable closed-loop control system for atracurium-induced muscle relaxation. This is a safety-critical system particularly when applied to brain and eye surgery where movement could have serious deleterious effects. The benefits of closed-loop muscle relaxation in providing stable surgical operating conditions over a wide range of patient sensitivities while infusing the minimum amount of drug makes this a worthwhile aim and serves to demonstrate safety issues which are generally applicable to other closed-loop drug infusion systems. It is hoped that the described methodology will facilitate and encourage the clinical application of closed-loop drug infusion systems so that clinical staff and patients may receive the benefits of closed-loop drug therapy.

Atracurium↗