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Sentiment Analysis of Czech and Slovak Social Networks and Web Discussions
Slúka, Dušan ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
This bachelor’s thesis deals with the issue of extraction and analysis of data obtained from social networks to understand public opinion on various social topics. The goal is systematic categorization and interpretation of contents. The problem is solved through a platform for opinion extraction and automatic data classification, which allows the creation of thematic subcategories and sorting into them. The result of the work is a system that analyzes social networks and provides deeper insight into public opinion on social topics. The system enables organizations to better understand the dynamics of online discourse. The benefit of this work is the provision of a new tool for the analysis of social issues, which can serve the academic sphere as well as organizations from practice.
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Business plan
Makówka, Tomáš ; Pirožek, Petr (referee) ; Marciánová, Pavla (advisor)
This bachelor thesis is focused on developing a suitable business plan for a specific company selling paintings on canvas. The theoretical part of the thesis is aimed at explaining the basic concepts related to business plan, entrepreneurship, and e-commerce. The analytical part includes an analysis of the micro and macro environment of the business, marketing research and will provide the necessary information for the practical part of the bachelor thesis. The practical part is devoted to developing a suitable plan into a business plan and assessing the suitability of implementing.
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Construction and application of mobile robots suitable for non-industrial (non-production) use
Franěk, Jan ; Tůma, Zdeněk (referee) ; Knoflíček, Radek (advisor)
Thesis addresses the issue of mobile robots suitable for non-industrial environments. The first part of the work is systematic analysis of the process of robotization at MoravianSilesian Research Library in Ostrava. Second part deals with construction and application of mobile robots. The third part of the thesis focuses on modern trends in construction and application of autonomous mobile robots. In conclusion, the acquired knowledge is summarized, and recommendations for the further development of new mobile robot technologies provided.
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Artificial Intelligence in Science Fiction
Zatloukal, Petr ; Sučková, Magda (referee) ; Kotásek, Miroslav (advisor)
Bakalářská práce je plavidlem, ve kterém čtenář putuje vývojem vědeckofantastické literatury, přičemž hlavním zaměřením této práce je problematika umělé inteligence (UI) a její interakce, vztahy s lidmi. Technologie umělé inteligence a robotiky se rychle rozvíjí a pomalu se stávají běžným fenoménem, i proto je poměrně příhodné analyzovat a komentovat četné příklady vztahů mezi lidmi a umělou inteligencí v SF literatuře, jelikož mohou stejně dobře připomínat situace ze světa reálného, které se mohou v blízké budoucnosti potencionálně stát skutečností. Práce se nejprve zabývá divadelní hrou R.U.R. od českého spisovatele Karla Čapka, přičemž v této části popisuje vznik slova robot, které bylo poprvé použito právě v této hře a zároveň má stále obrovský kulturně-společenský význam. Význam R.U.R. dále umocňuje fakt, že se se v díle, vůbec jako v jednom z prvních, objevují bytosti uměle vytvořené. Práce dále pokračuje obecným popisem tří literárních směrů spojených s SF literaturou, a to: Zlatý věk, Nová vlna a Kyberpunk. Každý literární proud je doprovázen jedním literárním dílem zaměřeným na tématiku UI. U každé knihy lze nalézt podrobný popis dějové linky, doprovázené popisem momentů, v nichž hrají roli interakce a vztahy mezi člověkem a umělou inteligencí, spojené s jejich následnou analýzou, komentářem a pohledem na tyto interakce a vztahy. Knihy jsou v práci analyzovány chronologicky, a to v tomto pořadí: I, Robot, Do Androids Dream of Electric Sheep?, a Neuromancer. Poslední kapitola se zabývá srovnáním výše uvedených literárních děl s reálnými příklady ze světa robotiky a umělé inteligence. Pevně věřím, že tato práce, poslouží jako inspirace pro její čtenáře, zejména pro odborníky z různých vědních oblastí, kteří mohou tuto práci využít jakožto inspiraci pro vlastní práci v oblasti týkající se vztahů a interakcí mezi UI a člověkem v SF literatuře či v reálném světě.
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Web application integrating artificial intelligence techniques into the correlation rule creation process
Šibor, Martin ; Caha, Tomáš (referee) ; Safonov, Yehor (advisor)
Currently, as digitalization becomes an integral part of all areas of our lives, the complexity and sophistication of cyber threats are constantly increasing. A key element in the fight against these cyber threats is security monitoring. An important tool for security monitoring are SIEM systems, which allow for early detection and response to potential attacks based on correlation rules. The main contribution of this work is the design and implementation of a web application that integrates artificial intelligence techniques into the process of creating and managing correlation rules for security monitoring systems, with the aim of streamlining the process of creating, modifying, and understanding correlation rules. The work first provides a theoretical introduction to the field of natural language processing and modern neural networks, particularly the transformer architecture, which is the basis of generative artificial intelligence models (e.g., ChatGPT, Gemini). It then introduces the principles of security monitoring, log management systems, the concept of correlation rule generalization, and, last but not least, the challenges associated with managing and maintaining correlation rules, which the integration of artificial intelligence into these processes significantly reduces. The practical part of the work describes the design and implementation of a web application that utilizes the gpt-4 and gpt-3.5-turbo models from OpenAI and the Gemini Ultra 1.0 model from Google for creating new correlation rules, modifying existing rules, and explaining and interpreting them for easier understanding and faster deployment. The application is designed with user-friendliness and efficiency in mind. The results of the work show that the integration of artificial intelligence into the correlation rule creation process brings significant efficiency improvements. The web application allows users to easily create and modify correlation rules. The application also allows users to better understand correlation rules, enabling them to respond to potential threats more quickly.
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Program for Automatic Playing of Solitaire and Mines
Mores, Martin ; Zbořil, František (referee) ; Rozman, Jaroslav (advisor)
This thesis focuses on the design and implementation of two programs for automatic game play, Mine Search and Solitaire. The theoretical part focuses on the rules about the games and what regularities follow from them, and on the state space search theory. The work in the following chapters describes the implementation of the two programs and describes various things that led to the eventual speedup of the times compared to past work. The thesis also provides the reader with various techniques that can be used in their own implementation. And at the end of the thesis, an evaluation of the implementation is summarized and potential improvements are suggested.
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Hraní hry The Duke počítačem
Horváth, Adrián ; Veigend, Petr (referee) ; Zbořil, František (advisor)
This thesis deals with the analysis of game strategies in the game "The Duke" using artificial intelligence (AI) algorithms. We compare three different approaches: minimax, alpha-beta pruning and Monte Carlo Tree Search (MCTS). We study the rules of the game, identify key factors affecting strategy, and perform an experimental comparison of the algorithms’ results. We summarize the results and discuss future research directions in game AI.
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Photo Livening Application
Bobola, Adrián ; Šalko, Milan (referee) ; Malinka, Kamil (advisor)
The goal of this work is to create a web application for animating static photographs. The application allows users to animate their portraits and group photos. Users can upload their own motion, which they want to use, and the application will use it to animate a selected part of the uploaded photo. The faces in the photos are automatically detected, while users have the option of manually marking faces in a given photo. The application supports recording custom motion from a video file or directly from a webcam. The server-side of the application is implemented in Python using the Django framework. The client-side of the application utilizes JavaScript and the React framework. Communication between the client and the server is facilitated via REST API. The thesis also provides an overview of existing tools of similar types, compares them, and discusses identified shortcomings. Additionally, the thesis explains the techniques and principles used to animate static photographs.
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COVID-19 disease classification based on analysis of chest X-rays
Šteflík, Dominik ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This diploma thesis addresses the development and evaluation of artificial intelligence algorithms for classifying COVID-19 disease from chest X-ray images. Given the severity and impact of the COVID-19 pandemic on the global population, the ability to rapidly and accurately diagnose diseases from radiographic images has become critical. This study synthesizes current advancements in image processing and deep learning to evaluate the application of several novel classification methods in practice. Using a dataset obtained from a Czech medical environment, these methods are analyzed and validated in order to examine their effectiveness and accuracy in real life scenarios. The methods chosen for this study, COVID-Net, DarkCovidNet, and CoroNet, were selected due to their availability, widespread use and proven effectiveness in the field. The core of the thesis is the design of a convolutional neural network tailored to extract and learn from the subtle features present in X-ray images indicative of COVID-19. This initiative confronted significant challenges posed by variable acquisition parameters of X-ray images, which can substantially affect diagnostic accuracy. The uniformity of these parameters is crucial for reliable analysis, underscoring the importance of rigorous preprocessing techniques. In response, advanced normalization, contrast adjustment, and augmentation procedures were implemented to standardize the input data. The convolutional network itself employs a series of convolutional, pooling, and fully connected layers, optimized to handle the nuanced variations present in medical imaging data. Notably, the network architecture incorporates an attention mechanism, implemented through a Squeeze-and-Excitation block, to dynamically adjust the importance of different channels in the input image. By integrating these elements, the network model is trained to focus on significant features within the X-ray images, allowing it to distinguish subtle indicators of COVID-19 effectively. Furthermore, this work discusses the potential of integrating these AI-driven diagnostic tools into existing healthcare infrastructures to enhance early detection and treatment of COVID-19. The findings indicate that leveraging artificial intelligence in medical imaging can substantially aid in managing and controlling disease outbreaks, ultimately contributing to better health outcomes.
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Generative Neural Network for Creating Synthetic Photorealistic Images
Hora, Adam ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
The main objective of this work is to select and design a neural network model that will be able to generate realistic images thematically fitting the selected dataset. The architecture used for the solution is Deep convolutional generative adversarial network. This network is than implemented in the Python programming language using the Tensorflow application programming interface and its included interface Keras. Finally, the model is trained on the selected dataset and the resulting generated images are presented. The final model and individual images are then evaluated using various quality assessment methods.
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