The thesis consists of a case study and focuses on the processes of assessment, training and remuneration of employees in the organizational component of a budgetary organization. The introductory part focuses on the opinion of experts on human resources management with emphasis on the areas studied. Special emphasis is placed on the evaluation process, its methods, evaluation criteria, classification, evaluator's personality and the course of evaluator's meeting with the person who is being assessed. We also focused on training and remuneration - processes following the evaluation. Concerning educational issues, we were interested in the opinions of professionals focusing on identifying the needs of training, its planning and results evaluation. Remuneration was viewed as one of the most effective tools to motivate employees in connection with the transfer of assessment to the remuneration. The application part of the thesis introduces in-depth analysis of the current levels of the evaluation process and the processes connected with it - training and remuneration of employees in the selected organization. Two methods were used to obtain relevant data: the analysis of documents and interviews. The data obtained from written documents and interviews were compared with the findings obtained from the...

The aim of the present work is to select a suitable object classification method for the recognition of one-handed finger alphabet characters. For this purpose, a sufficiently robust dataset has been created and is included in this work. The creation of the dataset is necessary for training the convolutional neural network. Further more, a suitable topology for data classification was found. The whole work is implemented using Python and the open-source library Keras was used.

The aim of this thesis is to find out how English teachers at secondary schools grade students' performance, to what extent teachers grade systematically and what the possible weaknesses in these systems are. A questionnaire sent to various types of secondary schools ('gymnázium', SOŠ, SOU) was used for data collection. Respondents were evaluated based on their grading approach, with questions covering, among others, the following areas: use of grading criteria and type of activities graded, grading principles followed, availability of grading guidelines within schools and the extent to which these were binding, teacher instruction on grading at university, and so on. The results of the research pointed to a non-systematic grading approach in English lessons which may result in inconsistency in student performance assessment across the secondary school system. In its conclusion, the thesis identified critical areas of the non-systematic grading approach and sought possible improvements. Keywords: grading, assessment, quantitative assessment, English languages skills, secondary schools, English teachers

Title: Evaluation of the number drowned people in the Czech Republic in the years 1998-2018 Objective: Evaluation of the number of drowned people in the Czech Republic, depending on age, sex and cause of drowning in the years 1998-2018. Methods: Search of available resources. Collecting data from the Czech Statistical Office and information portals. Results: The group 50-64 years of age in the terms of drowning is the most risky age group. The age group 10-14 years is the least risky. The ratio of all drowned men and women is 2,7:1. The most common cause of drowning is unspecified drowning. In many cases, it is not possible to determine the cause of the drowning. Keywords: Drowning, International Classification of Diseases and Related Health Problems, Water Rescue Service, Accidental drowning and submersion

The master's thesis deals with the knowledge discover in databases and with the extending of the data mining systems in the Oracle environment developed at the VUT FIT. The system kernel conception incorporates an interface that enables the adding of data mining modules. The objective of the thesis is to learn this interface and implement and embed the data mining module for decision-tree classification into the application. In addition, the thesis compares the application with similar commercial product SAS Enterprise Miner

In this thesis, I focus on the application of deep pushdown automatons in compilers, their composition in the parser, and the possibility of further recovery. Thanks to these automatons can carry out the expansion of the nonterminals in various depths and thus makes it possible to use other records of expressions.

This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.

Aim of this project is to evaluate effectivity of various texture features within the context of image processing, particulary the task of texture recognition and classification. My work focuses on comparing and discussion of usage and efficiency of texture features based on local binary patterns and co- ccurence matrices. As classification algorithm is concerned, cluster analysis was choosen.

Image analysis of wear particles is a suitable support tool for detail analysis of engine, gear, hydraulic and industrial oils. It allows to obtain information not only of basic parameters of abrasion particles but also data that would be very difficult to obtain using classical ways of evaluation. Based on the analysis of morphological or image characteristics of particles, the progress of wearing the machine parts out can be followed and, as a result, possible breakdown of the engine can be prevented or the optimum period for changing the oil can be determined. The aim of this paper is to explore the possibilities of using the image analysis combined with the method of analytical ferrography and suggest a tool for automated particle classification. Current methods of wear particle analysis are derived from the evaluation that does not offer an exact idea of processes that take place between the friction surfaces in the engine system. The work is based upon the method of analytical ferrography which allows to evaluate the state of the machine. The benefit of use of classifiers defined in this wirk is the possibility of automated evaluation of analytical ferrography outputs; the use of them eliminates the crucial disadvantage of ferrographical analysis which is its dependence on the subjective evaluation done by the expert who performs the analysis. Classifiers are defined as a result of using the methods of machine learning. Based on an extensive database of particles that was created in the first part of the work, the classifiers were trained as a result, they make the evaluation of ferrographically separated abrasion particles from oils taken from lubricated systems possible. In the next stage, experiments were carried out and optimum classifier settings were determined based on the results of the experiments.

Ultrasound imaging is one of the fundamental examinations of thyroid nodules, determining whether a patient undergoes a cytological examination, which is essential for the decision on a possible thyroid surgery. Unfortunately, the cytological examination has limited specificity and potential surgery carries risks. Therefore, other diagnostic methods are being sought with hope that they will be able to bring more certainty into diagnostics. One of the new methods is computer-aided diagnosis (CAD), which exhibits promising results using image analysis and machine learning. In this study, we present two somewhat similar, yet different, CAD approaches. The first approach is based on analysing entire nodules using a Segmentation Based Fractal Texture Analysis (SFTA) algorithm that splits the image into individual grayscale bands. Using this approach, we have achieved an accuracy of 92.4% using random forests (RF) and 95% using support vector machines (SVM) on a data set of 40 images evaluated by the cross-validation method. The second CAD approach is also based on the method of multiple image thresholding, but the difference is, that a larger number of predictors describing the binary texture are extracted from the individual grayscale bands. Furthermore, the analysis did not take place on whole nodules, but on...