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Computational Intelligence, Medicine and Biology: Selected by Krzysztof Pancerz, Elena Zaitseva

By Krzysztof Pancerz, Elena Zaitseva

This ebook includes a fascinating and state-of the artwork number of chapters proposing a number of examples of makes an attempt to constructing smooth instruments using computational intelligence in several actual existence difficulties encountered by means of people. Reasoning, prediction, modeling, optimization, selection making, and so on. want sleek, delicate and clever algorithms, tools and methodologies to resolve, within the effective methods, difficulties showing in human job. The contents of the publication is split into elements. half I, along with 4 chapters, is dedicated to chose hyperlinks of computational intelligence, drugs, overall healthiness care and biomechanics. a number of difficulties are thought of: estimation of healthcare procedure reliability, class of ultrasound thyroid photographs, software of fuzzy common sense to degree weight prestige and relevant fatness, and deriving kinematics at once from video files. half II, additionally inclusive of 4 chapters, is dedicated to chose hyperlinks of computational intelligence and biology. the typical denominator of 3 chapters is Physarum polycephalum, one-cell organisms in a position to construct advanced networks for fixing diverse computational projects. One bankruptcy makes a speciality of a unique machine, the memristor, that has attainable makes use of either within the production of neural nets for man made intelligence and because the connection among a neural web and a dwelling neuronal mobile community within the therapy and tracking of neurological sickness. This e-book is meant for a large viewers of readers who're attracted to numerous elements of computational intelligence.

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The postpruning is preferred in practice, because the prepruning often causes the effect called ”early stop”. 2 Classification by Multilayer Perceptron Neural Network The most popular type of an artificial neural network to solve the classification problems is a multilayer perceptron (MLP) [33]. Due to the typical use of the hyperbolic tangent function and the logistic function as the activation functions in this model, a separation of cases on certain categories runs along the hyperplanes determined in the process of network learning.

4. Coocurrence matrix (11 features × 4 various directions × 5 between-pixels distances) angular second moment, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy. 5. Autoregressive model (5 features): parameters Θ1 , Θ2 , Θ3 , Θ4 , standard deviation. 6. Haar wavelet (24 features): wavelet energy (features are computed at 6 scales within 4 frequency bands LL, LH, HL and HH). 1 Classifier Based on Decision Tree Induction Description.

On the basis of descriptive values, the classifier learns how to assign a proper value of a decision attribute to each case in the training set, so that the classification error for this set is the smallest one. In the second stage, the resulting model is used for classification (prediction) of new objects. In the literature, many methods for classification are proposed. g. g. genetic algorithms), k-nearest neighbors methods, statistical analysis and many other methods that are still under development.

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