By Hubert Wojtowicz, Wieslaw Wajs (auth.), Barna Iantovics, Roumen Kountchev (eds.)
This e-book bargains a cutting-edge assortment overlaying subject matters on the topic of Advanced clever Computational applied sciences and selection aid Systems which are utilized to fields like healthcare supporting the people in fixing difficulties. The ebook brings ahead a wealth of principles, algorithms and case experiences in topics like: clever predictive prognosis; clever examining of scientific photos; new structure for coding of unmarried and sequences of clinical pictures; scientific determination aid structures; prognosis of Down’s syndrome; computational views for digital fetal tracking; effective compression of CT photos; adaptive interpolation and halftoning for clinical photographs; purposes of man-made neural networks for real-life difficulties fixing; current and views for digital Healthcare list structures; adaptive techniques for noise relief in sequences of CT photos etc.
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Additional resources for Advanced Intelligent Computational Technologies and Decision Support Systems
Knowledge representation in the form of rules is the closest method to human activity and reasoning, among others, in making a medical diagnosis. Therefore, in the Copernicus system, rule-based classification of patients screened with the MMPI test is one of the most important parts of the tool. The main goal of the chapter is to give more precise view of this part of the developed tool. 1 Introduction Computer support systems supporting medical diagnosis have become increasingly more popular worldwide (cf.
Therefore, ranges of classification rule conditions can be restricted. , 120 T-scores. A procedure for restricting ranges of classification rule conditions with the value À1 is carried out similarly, but we take into consideration minimal values. A minimal value for all scales of normalizing group of women is 28 T-scores. Specialized indexes are linear combinations of scales. Therefore, restricting ranges Rule-Based Classification of Patients 37 of rule conditions for indexes is also possible and simple.
The wavelet packet analysis is a generalization of wavelet decomposition that offers a richer image analysis. The wavelet packet methods for noise reduction give a richer presentation of the image, based on functions with wavelet forms, which consist of 3 parameters: position, scale and frequency of the fluctuations around a given position. They propose numerous decompositions of the image, that allows estimate the noise reduction of different levels of its decomposition. For the given orthogonal wavelet functions exists library of bases, called wavelet packet bases.
Advanced Intelligent Computational Technologies and Decision Support Systems by Hubert Wojtowicz, Wieslaw Wajs (auth.), Barna Iantovics, Roumen Kountchev (eds.)