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Recurrent Neural Network for Text Classification
Myška, Vojtěch ; Kolařík, Martin (referee) ; Povoda, Lukáš (advisor)
Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.
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Handwritten text recognition using a sliding window
Ďuriš, Denis ; Povoda, Lukáš (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis deals with optical character recognition. It focuses on recognizing hand-written text. The theoretical introduction describes the methods used for optical character recognition and selected machine learning methods. Subsequently, the work describes two methods for making cutouts of characters, using a sliding window. Cutouts are used in training and testing datasets of machine learning models. The document includes methods to improve the accuracy of character recognition. The accuracy of the models is evaluated in conclusion. Charcters in cutouts are clasified by an automated recognition program.
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Web Portal for Support of Education
Vicen, Šimon ; Povoda, Lukáš (referee) ; Schimmel, Jiří (advisor)
The thesis is focused on creation of web page based on wordpress development system in work with single sign-on login system via VUT web site in Brno. Thesis stepwise explains SSO atributes and ways how we can achieve this goal to make it work within given web services. Moreover, the thesis also explains functions of eduid.cz federation and technologies that it works with. Practical part is dealing with design of web, web applications, front-end cloud storage accessed by users and page working with login system via VUT sites and registering its users.
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Machine Understanding for Text Messages Used in Aviation
Lieskovský, Pavol ; Rajnoha, Martin (referee) ; Povoda, Lukáš (advisor)
This work deals with problems of NOTAM in text format, which is used in aeronautics. It documents the difference between text and digital format of NOTAM, special types of NOTAM messages and items from which the NOTAM consist of. It describes syntax and the functions of program, which was made within the frame of this thesis. The program is fully capable of correct parsing and processing of the NOTAM. The program can display each area of processed NOTAM messages in map and also provides detection of collision between these areas and flight plan
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Tool for Automatic Information Obtaning from the Web
Poliak, Jakub ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
This bachelor thesis deals with programming of a tool for collecting positive and negative comments from one of the most popular Chinese e-shop to a database. It will be used for deep learning of an artificial neural network which should distinguish positive text from negative. Application was programmed in Java with the use of JSON-simple and jsoup libraries.
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Creating a database of audio recordings with artificial noise in an anechoic chamber
Hájek, Vojtěch ; Povoda, Lukáš (referee) ; Harár, Pavol (advisor)
This bachelor thesis deals with theory of creating the database of sound records and subsequent creating the database of speech records in the anechoic chamber. Database was created as training dataset for learning process of the artificial neural network, which will be able to separate the speech from background noise. Therefore as the part of the database there are also the recordings of various types of noise that will be used as background noise for the voice recordings. The dataset contains records taken from 18 speakers aged from 16 to 76 years. Half of the speakers were men, half women. Database contains 405 records of speach of average length 46,7 secons and total length 315 minutes. By combining each speech record with each noise record at three levels of signal-to-noise ratio was created 7290 mixed records.
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Data collection from Twitter
Kmeť, Juraj ; Povoda, Lukáš (referee) ; Komosný, Dan (advisor)
The bachelor thesis deals with creating application for data gathering from social network Twitter. Data is gathered in real time with variable length of gathering. Theoretical part describes social network Twitter as a client but also as a tool for data gathering. The bachelor thesis identifies limits which need to be respected during the creation of Twitter applications. Another topic of the thesis is PlanetLab network, which is well known mainly by the network researchers and developers of network applications. History of PlanetLab is captured within the second chapter and the difference between PlanetLab and other research networks. Practical part contains guide for application development in programming language Python. Process of the application disctribution to the PlanetLab nodes is enclosed as well. Last chapter analyses data collection and maximum speed of data gathering in the created system.
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Semantic Recognition of Comments on the Web
Stříteský, Radek ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
The main goal of this paper is the identification of comments on internet websites. The theoretical part is focused on artificial intelligence, mainly classifiers are described there. The practical part deals with creation of training database, which is formed by using generators of features. A generated feature might be for example a title of the HTML element where the comment is. The training database is created by input of classifiers. The result of this paper is testing classifiers in the RapidMiner program.
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Deep Learning for Text Classification
Kolařík, Martin ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
Thesis focuses on analysis of contemporary machine learning methods used for text classification based on emotion and testing several deep neural nework architectures. Outcome of this thesis is a neural network architecture, which is tuned for using with text data and which had the best result of 79,94 percent. Proposed method is language independent and it doesn’t require as precisely classified training datasets as current methods. Training and testing datasets were consisted of short amateur movie reviews in Czech and in English. Thesis contains also overview of theoretical basics for convolutional neural networks and history of neural networks and language processing Scripts were written in Python, neural networks were simulated using Keras library and Theano framework. We used CUDA for better performance.
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