Use of artificial neural networks for differentiated diagnostics of ischemic and hemorrhagic perinatal affections of central neural system in newborns of different terms of gestation

The article describes neonatal and pediatric neurology researching. Among the causes of childhood disability first place belongs to diseases of the nervous system. Among perinatal brain damage leading place is occupied by cerebrovascular pathology. One of the main causes of hemorrhagic and ischemic brain damage is impaired cerebral hemodynamics. However there is no single point of view on the processes underlying the development of ischemic brain lesions and intracranial hemorrhage in premature infants. It reveals necessity of immunobiochemical neurospecific proteins defining during neonatal period. Proteins, namely neurospecific enolase, a neurotrophicfactor of nerve growth, vascularendothelial growth factor, allow early finding of pathological disorder. What is a profitable advantage compared to the widely used clinical and instrumental examination and laboratory methods to assist in determining location and extent of the brain. Articleshows importance for a multifunction-oriented model of studying peculiarities of the child, starting with finding patterns in complex processes, due to the influence of internal and external factors on the functional state of the organism based on its individual characteristics, and ending with the solution of problems of differential diagnosis. Thus enabling to seek for hidden dependencies in complex processes conditioned by internal and external factors, leading us to performing differential diagnosis. As for mathematical models and data processing algorithms, the authors used an artificial neural network. These algorithms are used when there is no a precise decision-makingsystem. The medical diagnosis of ischemic and hemorrhagic perinatal central nervous system lesions of newborns maybe added in the list problems to be solved by artificial neural networks. The paper gives valuable information aboutinvestigating child's body properties with neural networks algorithms. Results of applying these algorithms are aimed to increase accuracy of differential diagnosis of ischemic or hemorrhagic perinatal damage to the central nervous system in newborns of different gestational ages are presented

Music Tune Restoration Based on a Mother Wavelet Construction

It is offered to use the mother wavelet function obtained from the local part of an analyzed music signal. Requirements for the constructed function are proposed and the implementation technique and its properties are described. The suggested approach allows construction of mother wavelet families with specified identifying properties. Consequently, this makes possible to identify the basic signal variations of complex music signals including local time-frequency characteristics of the basic one

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems

Integral Intensities of Absorption Bands of Silicon Tetrafluoride in the Gas Phase and Cryogenic Solutions: Experiment and Calculation

The spectral characteristics of the SiF4 molecule in the range 3100700 cm1, including the absorption range of the band 3, are studied in the gas phase at P = 0.47 bar and in solutions in liquefied Ar and Kr. In the cryogenic solutions, the relative intensities of the vibrational bands, including the bands of the isotopically substituted molecules, are determined. The absorption coefficients of the combination bands 23, 3 + 1, 3 + 4, and 34 are measured in the solution in Kr. In the gas phase of the one-component system at an elevated pressure of SiF4, the integrated absorption coefficient of the absorption band 3 of the 28SiF4 molecule was measured to be A(3) = 700 ± 30 km/mol. Within the limits of experimental error, this absorption coefficient is consistent with estimates obtained from independent measurements and virtually coincides with the coefficient A(3) = 691 km/mol calculated in this study by the quantum-chemical method MP2(full) with the basis set cc-pVQZ. ©2005 Pleiades Publishing, Inc