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Performance Measurement of Selected Drools Tools
Široký, Petr ; Fiedor, Jan (referee) ; Letko, Zdeněk (advisor)
Performance testing is often overlooked. This thesis describes the performance testing of the Drools Expert and Drools Fusion tools. Drools is a Business Logic Integration Platform. Expert and Fusion are parts of this platform, which provide rule based expert system and event processing capabilities respectively. Tests are designed and implemented with main focus on finding performance regression between different versions of Expert and Fusion tools. Results for two versions of these tools are discussed at the end of the thesis.
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Analysis and Implementation of Virus Pattern Rules
Janeček, Jiří ; Schäfer, Jiří (referee) ; Janoušek, Vladimír (advisor)
This bachelor's thesis deals with analysis and implementation of rules for program, which routes virus samples for their later processing. The expert system is integrated in this program. It was created by using tool called CLIPS. This thesis also describes environment, where program is situated, and importance of component like this in system for virus sample processing. Analyzer has been implemented in C/C++ language.
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Balanced Scorecard Implementation within Company Environment
Lojdová, Marie ; Bühn, František (referee) ; Dohnal, Mirko (advisor)
The master’s thesis deals with Balanced Scorecard implementation within a company environment. The object of the thesis is to increase the effectiveness of strategic management which can be achieved through proper implementation of the method. The theoretical section explains the basic concepts of strategic management, followed by the description of method BSC, its implementation and risks. The practical part is focused on creating a business strategy and the model BSC implementation in particular company. As an auxiliary tool for management decision making a fuzzy model was developed and then a dialogue is held with fuzzy expert system.
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Multivalued logic systems for technical applications
Turek, Vojtěch ; Druckmüller, Miloslav (referee) ; Martišek, Dalibor (advisor)
Velmi často je vyžadováno, aby automatizovaná zařízení byla jistým způsobem "inteligentní", tedy aby jejich řídicí systémy uměly emulovat rozhodovací proces. Tato diplomová práce poskytuje obecný formální popis vícehodnotových logických systémů schopných zmíněné emulace a jejich souvislost s teorií fuzzy množin. Jsou uvedeny způsoby vytváření matematických modelů založených na lingvistických datech. Dále se práce zabývá znalostními bázemi a jejich vlastnostmi. Součástí této práce je také počítačový program sloužící k tvorbě slovních modelů.
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Expert System for Saving Product Selection for Clients of OVB Allfinanz, a.s.
Bednářová, Zuzana ; Macháček, Jiří (referee) ; Rejnuš, Oldřich (advisor)
This Master´s thesis deals with the topic of optional saving for retirement. The pension schemes of selected European countries and of the Czech Republic are described. Also the current situation and the problems these schemes are facing are analyzed. The thesis focuses on the forthcoming pension scheme reform in the Czech Republic. Further, the possible ways of the complementary pension scheme pillar financing using saving products are analyzed in detail. The objective of the thesis is to design an expert system for optimal saving product selection according to client´s individual requirements.
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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
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.
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Automated Weight Tuning for Rule-Based Knowledge Bases
Valenta, Jan ; Pokorný, Miroslav (referee) ; Zbořil, František (referee) ; Jirsík, Václav (advisor)
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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Expert system for choice of proper method for waste utilization
Fikar, Josef ; Jícha, Jaroslav (referee) ; Dvořák, Jiří (advisor)
This work consists in development of expert system intended for choosing appropriate method of waste processing. The software is created in VisiRule software which is built on Prolog language and is part of WinProlog 4.900 development tool. It also deals with problematics of creating of knowledge base for applications of this type and judging of suitability of possible approaches to creating an expert system for given purpose.
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Scheduling System in Healthcare
Janíčková, Natália ; Kolářová, Jana (referee) ; Sekora, Jiří (advisor)
The Bachelor thesis is dedicated to expert scheduling systems in healthcare. The thesis describes all parts of system design: algorithm, software tools used in a process, and the developed application itself. The system developed in this thesis generates a schedule of the services that respect requirements given by law, employer, and also employees in the healthcare organizations. Application outputs involve basic overviews for employees and employers.
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User interface for an expert system
Kořínek, Lukáš ; Boštík, Ondřej (referee) ; Jirsík, Václav (advisor)
This bachelor's thesis is about design and implementation of an expert system user interface. Concerned system is a universal rule-based diagnostic expert system called NPS, which has been developed at FEEC BUT. The interface has been created as a bilingual web application based on modern technologies, which might bring the opportunity to reach variety of devices and a broad user audience. It also enhances the system with means of user authorization, history browsing, user management, knowledge management and more. The application runs on faculty's infrastructure and is undergoing active testing by both physical users and automated tests. Theoretical part of the document describes knowledge and expert system problematics, followed by properties of the NPS system and discussion about web technologies (actual trends in the field, client to server communication, testing strategies and security). Practical part then consists of created interface overview, system architecture description, implementation details, testing and steps used to deploy the application on faculty's network infrastructure.
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