The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.

This study explores the possibilities for reducing the number of pins needed for scan mode interface. In the first part of this paper the existing solutions and methods that are usable for this purpose are described. Specific four pin, three pin, two pin, one pin and zero pin interfaces are designed in second part. Advantages and disadvantages of existing solutions and methods as well as designed and proposed interface are summarized in the conclusion.

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

The diploma thesis deals with the draft of fuzzy model used for decisions of choosing the suppliers of the tool steel for the company S.CH.W.SERVICE, s.r.o. In the introduction of the thesis the theoretical basis for the process are summarized and the company is introduced. The main part consists of the actual suggestions for the evalutaion of the company´s suppliers. The deciosion making models are created in MS Excel and MATLAB. The last part of the thesis is dedicated to the comparison of the results from both suggested models.

Poškození zraku je jedním z nejčastějších tělesných postižení -- udává se, že až 3 % populace trpí vážným poškozením nebo ztrátou zraku. Oslepnutí výrazně zhoršuje schopnost orientace a pohybu v okolním prostředí -- bez znalosti uspořádání prostoru, jinak získané převážně pomocí zraku, postižený zkrátka neví, kudy se pohybovat ke svému cíli. Obvyklým řešením problému orientace v neznámých prostředích je doprovod nevidomého osobou se zdravým zrakem; tato služba je však velmi náročná a nevidomý se musí plně spolehnout na doprovod. Tato práce zkoumá možnosti, kterými by bylo možné postiženým ulehčit orientaci v prostoru, a to využitím existujících senzorických prostředků a vhodného zpracování jejich dat. Téma je zpracováno skrze analogii s mobilní robotikou, v jejímž duchu je rozděleno na část lokalizace a plánování cesty. Zatímco metody plánování cesty jsou vesměs k dispozici, lokalizace chodce často trpí značnými nepřesnostmi určení polohy a komplikuje tak využití standardních navigačních přístrojů nevidomými uživateli. Zlepšení odhadu polohy může být dosaženo vícero cestami, zkoumanými analytickou kapitolou. Předložená práce prvně navrhuje fúzi obvyklého přijímače systému GPS s chodeckou odometrickou jednotkou, což vede k zachování věrného tvaru trajektorie na lokální úrovni. Pro zmírnění zbývající chyby posunu odhadu je proveden návrh využití přirozených význačných bodů prostředí, které jsou vztaženy ke globální referenci polohy. Na základě existujících formalismů vyhledávání v grafu jsou zkoumána kritéria optimality vhodná pro volbu cesty nevidomého skrz městské prostředí. Generátor vysokoúrovňových instrukcí založený na fuzzy logice je potom budován s motivací uživatelského rozhraní působícího lidsky; doplňkem je okamžitý haptický výstup korigující odchylku směru. Chování navržených principů bylo vyhodnoceno na základě realistických experimentů zachycujících specifika cílového městského prostředí. Výsledky vykazují značná zlepšení jak maximálních, tak středních ukazatelů chyby určení polohy.

This thesis is focused on the task of application of SAT problem and it's modifications in area of evolution logic circuit development. This task is supposed to increase speed of evaluating candidate circuits by fitness function in cases where simulation usage fails. Usage of SAT and #SAT problems make evolution of complex circuits with high input number significantly faster. Implemented solution is based on #SAT problem. Two applications were implemented. They differ by the approach to checking outputs of circuit for wrong values. Time complexity of implemented algorithm depends on logical complexity of circuit, because it uses logical formulas and it's satisfiability to evaluate logic circuits.

Artificial intelligence and fuzzy logic related to it currently belong to very popular and rapidly expanding technological subjects. It finds use in many areas, which also include the process of prediction of future states based on specific finite input characteristics. This master’s thesis deals with predictions that are done in field of agricultural crops growing. Basic principles that are affecting mentioned agricultural growing are explained here, their meaning and significance are specified, these are later on perceived as a key aspect to creation of fuzzy models that are used for prediction. This process is specifically about finding out the most suitable crop on considered parcel for maximization of income. Second part of design section is dedicated to description of approaches for work with fuzzy models and is also used as demonstration of application created for purpose of this thesis.

The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.

The object of this thesis is proposal and practical implementation of expert system, whose knowledge base will be modeling by fuzzy Petri nets. The proposal is based on knowledge in theoretical analysis of diagnostic expert system and fuzzy Petri nets. This proposal is realised in programming language C#. There are described functions of program and it is made a model consultation with using two different knowledge base.

This master thesis contains the basic information about knowledge and expert systems. The thesis contains theoretic text about architecture of the expert systems and representation knowledge. The text regarding on representation knowledge contains examples of different ways of knowledge representation for expert systems. In the next part is described the design and all functions of the expert systems. This expert system uses frames representation.