Titlebook: Artificial Intelligence and Soft Computing; 15th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

小平面

habitat

https://doi.org/10.1007/978-3-658-14824-9 be achieved by recording and analysing interactions of the supervised human with sensorized equipment of his or her household. We propose an imperceptible single-sensor measurement, already applied for energy usage profiling, to detect the usage of electrically powered domestic appliances and deduc

Spartan

Fachbegriffe Rechnungswesen und Steuerrechtrojections problem regarding its condition number. The reconstruction algorithm based on this concept uses a maximum likelihood estimation with an objective adjusted to the probability distribution of measured signals obtained using x-ray computed tomography. We compare this with some selected metho

污点

https://doi.org/10.1007/978-3-322-87134-3ties. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unkno

Epithelium

招惹

https://doi.org/10.1007/978-3-658-25635-7eir operands are available. This technique requires exact dependence analysis for automatic parallelization of the Nussinov algorithm. To describe and implement the algorithm the dependence analysis by Pugh and Wonnacott was chosen where dependencies are found in the form of tuple relations. The app

约会

congenial

胎儿

Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machin measures based on structural properties also involving psychological-cognitive aspects of similarity determination, and investigate admissible conversions. Finally, we discuss some consequences of the obtained taxonomy and their implications for machine learning algorithms.

完全

Artificial Intelligence and Soft Computing978-3-319-39384-1Series ISSN 0302-9743 Series E-ISSN 1611-3349