Pattern recognition
Dotaz
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elektronický časopis
- Konspekt
- Počítačová věda. Výpočetní technika. Informační technologie
- NLK Obory
- lékařská informatika
- NLK Publikační typ
- elektronické časopisy
elektronický časopis
- Konspekt
- Počítačová věda. Výpočetní technika. Informační technologie
- NLK Obory
- lékařská informatika
- NLK Publikační typ
- elektronické časopisy
The existence of pattern recognition receptors (PRRs) on immune cells was discussed in 1989 by Charles Janeway, Jr., who proposed a general concept of the ability of PRRs to recognize and bind conserved molecular structures of microorganisms known as pathogen-associated molecular patterns (PAMPs). Upon PAMP engagement, PRRs trigger intracellular signaling cascades resulting in the expression of various proinflammatory molecules. These recognition molecules represent an important and efficient innate immunity tool of all organisms. As invertebrates lack the instruments of the adaptive immune system, based on "true" lymphocytes and functional antibodies, the importance of PRRs are even more fundamental. In the present review, the structure, specificity, and expression profiles of PRRs characterized in annelids are discussed, and their role in innate defense is suggested.
- MeSH
- kroužkovci imunologie MeSH
- membránové glykoproteiny chemie genetika metabolismus MeSH
- PAMP struktury imunologie metabolismus MeSH
- přirozená imunita * MeSH
- proteiny akutní fáze chemie genetika metabolismus MeSH
- receptory rozpoznávající vzory chemie genetika metabolismus MeSH
- regulace genové exprese MeSH
- signální transdukce imunologie MeSH
- tkáňová distribuce MeSH
- toll-like receptory chemie genetika metabolismus MeSH
- transportní proteiny chemie genetika metabolismus MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
152 stran : ilustrace, tabulky ; 24 cm
- MeSH
- biologie buňky MeSH
- cytofotometrie MeSH
- mikroskopie MeSH
- počítačové zpracování obrazu MeSH
- Publikační typ
- vysokoškolské kvalifikační práce MeSH
- Konspekt
- Buněčná biologie. Cytologie
- NLK Obory
- cytologie, klinická cytologie
The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.
- MeSH
- algoritmy * MeSH
- databáze faktografické MeSH
- emoce fyziologie MeSH
- kvalita hlasu MeSH
- lidé MeSH
- neuronové sítě MeSH
- počítačové zpracování signálu přístrojové vybavení MeSH
- řeč fyziologie MeSH
- ROC křivka MeSH
- rozpoznávání automatizované * MeSH
- rozpoznávání fyziologické fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
sv. ; 28 cm.
- MeSH
- rozpoznávání automatizované * MeSH
- umělá inteligence MeSH
- Publikační typ
- periodika MeSH
- Konspekt
- Umělá inteligence
- NLK Obory
- lékařská informatika
When considering the probabilistic approach to neural networks in the framework of statistical pattern recognition we assume approximation of class-conditional probability distributions by finite mixtures of product components. The mixture components can be interpreted as probabilistic neurons in neurophysiological terms and, in this respect, the fixed probabilistic description contradicts the well known short-term dynamic properties of biological neurons. By introducing iterative schemes of recognition we show that some parameters of probabilistic neural networks can be "released" for the sake of dynamic processes without disturbing the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate correct recognition. Both procedures are shown to converge monotonically as a special case of the well known EM algorithm for estimating mixtures.
The main goal of this study is to demonstrate the possibility of training the Neural Network (multilayer perceptron) classifier and preprocessing units simultaneously, i.e., that properties of preprocessing are chosen automatically during the training phase. In the first realization step, adaptive recursive estimation of the power within a frequency band was used as a preprocessing unit. To improve the efficiency of special units, the power and momentary frequency estimation was replaced by methods that are based on adaptive Hilbert transformers. The strategy was developed to obtain optimized recognition units that can be efficiently integrated into strategies for monitoring the cerebral status of neonates. Therefore, applications (e.g., in neonatal EEG pattern recognition) will be shown. Additionally, a method of minimizing the error function was used, where this minimization is based on optimizing the network structure. The results of structure optimization in the field of EEG pattern recognition in epileptic patients can be demonstrated.
