Antenatal fetal risk assessment using a neurofuzzy technique

This study defines an intelligent neurofuzzy system for antepartum fetal evaluation, The task is to investigate the Doppler ultrasound measurements of the umbilical artery (UA) and the cerebral artery (CA) to relate the health conditions of fetuses. We thus use the UA blood flow velocity waveforms [pulsality index, resistance index, and systolic/diastolic ratio] and the ratios of cerebral-umbilical resistance indices in terms of weeks. We then make a decision on the basis of a fuzzy-rule-based system combined with data-based learning strategies such as a radial basis function network and a multilayer perceptron for assessing the hypoxia suspicion. A fuzzy grade of membership is used for the evaluation of the seriousness of the situation of the fetus, and the diagnostic interpretations for doctors such as good, suspicious, and alarming conditions of fetus are derived     

Neural-network-based decision making in diagnostic applications

In this article the NN approach for medical decision making was applied for three specific examples. The first example was decision making with single-valued data for IUGR detection. The second example was decision making with double-valued data in prediction of ovulation. The third example was the use of independent NN modules and consensus theory for prediction of ovulation time. The NN approach has superiority over classical statistical approaches for decision making with medical data for the following reasons: 1. It is distribution-free. 2. It captures correlative features and does not need any specific consideration for mutual test dependence. 3. It provides weighted reliability of various tests. 4. It produces fast, accurate results. The statistical decision approach will probably outperform the NN approach in making decisions when an accurate distribution model is provided. However, the NN is proposed as a useful tool to help physicians in decision making and diagnosis of certain symptoms. The capability and performance of this tool has generally been proven in combining mutually dependent medical tests.     

Antenatal fetal risk assessment by blood-flow velocity waveforms

The purpose of this article is to study a system for antepartum fetal evaluation. The task is to investigate the Doppler ultrasound measurements of the umbilical artery and the cerebral artery to relate the health status of a fetus by using discriminant functions of pattern recognition. The authors then analyze the individual effects of various blood-flow velocity waveforms using principal component analysis.     

Investigation of stress fields occurred in the block caving process at a diasporite deposit in Western Turkey

The modeling with the finite element method was employed to study the block caving process for mining the Milas diasporite deposit in Western Turkey. The stress distribution in the vicinity of production and haulage levels is evaluated applying an elasto-plastic rock mass model. __________ Translated from Fiziko-Tekhnicheskie Problemy Razrabotki Poleznykh Iskopaemykh, No. 6, pp. 63–71, November–December, 2005.     

On the modeling of the multi-segment capacitance: a fractional-order model and Ag-doped SnO2 electrode fabrication

Journal of Materials Science - This study proposes a methodology of electrochemical capacitor modeling via fractional-order impedance equation for porous electrodes fabricated with pure and...     

Performance evaluation of evolutionary heuristics in dynamic environments

In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.     

Symmetry Measure for Memory Test and Its Application in BIST Optimization

Programmable Built-in Self-Test (BIST) has been widely used for testing embedded memories. The main disadvantage of having programmability on BIST circuits is the size of Test Algorithm Register (TAR) that becomes very crucial in case of complex test algorithms. To optimize Programmable BIST hardware symmetric March tests are usually used in BIST engines. On the other hand, the used definitions do not reflect completely the existing symmetry in test algorithms and they also do not reflect the fact that the level of symmetry in a given test algorithm can be measured. A new method of symmetry measurement for memory test algorithms and a corresponding metric are introduced. A dependency between symmetry measure and BIST optimization range is analyzed. Optimization experiments that have been done for a number of well-known test algorithms show that the BIST hardware gain could reach 48%. However, the time overhead is negligible in comparison with the hardware gain. The experiments also show that starting from some point a monotone dependency between symmetry measure and BIST hardware area exists.     

IUGR detection by ultrasonographic examinations using neuralnetworks

This article presents a study that supports a computer-based diagnostic approach to detection of intrauterine growth retardation (IUGR). As an aid to clinical decisions, fetuses that are truly growth retarded and at risk for increased morbidity and mortality should be differentiated from those who have reached their genetic growth potential and are not at increased risk. A wide variety of mathematical formulas (or composite tables) have been proposed for the estimation of fetal weight from ultrasonographic measurements. For these formulas, the timing of the examinations to estimate fetal weight has become controversial due to the poor correlation of early results with the outcomes several weeks later, and also the technical difficulty and poor reproduction of late results. Among the attempts to improve accuracy, one may use more accurate estimated fetal-weight formulas or a single biometric parameter to identify growth abnormalities. This study confirms the following results: 1) in the ultrasound examination the prediction using multiple parameters is better than the prediction using a single parameter; 2) the experiments also show that multiple examinations give a better insight for the diagnosis of IUGR than does a single examination; 3) a neural net is a very helpful tool for correlating many variables     

Ensemble canonical correlation analysis

Canonical Correlation Analysis (CCA) aims at identifying linear dependencies between two different but related multivariate views of the same underlying semantics. Ignoring its various extensions to more than two views, CCA uses these two views as complex labels to guide the search of maximally correlated projection vectors (covariates). Therefore, CCA can overfit the training data, meaning that different correlated projections can be found when the two-view training dataset is resampled. Although, to avoid such overfitting, ensemble approaches that utilize resampling techniques have been effectively used for improving generalization of many machine learning methods, an ensemble approach has not yet been formulated for CCA. In this paper, we propose an ensemble method for obtaining a final set of covariates by combining multiple sets of covariates extracted from subsamples. In comparison to those obtained by the application of the classical CCA on the whole set of training data, combining covariates with weaker correlations extracted from a number of subsamples of the training data produces stronger correlations that generalize to unseen test examples. Experimental results on emotion recognition, digit recognition, content-based retrieval, and multiple view object recognition have shown that ensemble CCA has better generalization for both the test set correlations of the covariates and the test set accuracy of classification performed on these covariates.