|
|
Using of Data Mining Method for Analysis of Social Networks
Novosad, Andrej ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
Thesis discusses data mining the social media. It gives an introduction about the topic of data mining and possible mining methods. Thesis also explores social media and social networks, what are they able to offer and what problems do they bring. Three different APIs of three social networking sites are examined with their opportunities they provide for data mining. Techniques of text mining and document classification are explored. An implementation of a web application that mines data from social site Twitter using the algorithm SVM is being described. Implemented application is classifying tweets based on their text where classes represent tweets' continents of origin. Several experiments executed both in RapidMiner software and in implemented web application are then proposed and their results examined.
|
|
|
Biologically Inspired Methods of Object Recognition
Vaľko, Tomáš ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
Object recognition is one of many tasks in which the computer is still behind the human. Therefore, development in this area takes inspiration from nature and especially from the function of the human brain. This work focuses on object recognition based on extracting relevant information from images, features. Features are obtained in a similar way as the human brain processes visual stimuli. Subsequently, these features are used to train classifiers for object recognition (e.g. SVM, k-NN, ANN). This work examines the feature extraction stage. Its aim is to improve the feature extraction and thereby increase performance of object recognition by computer.
|
|
|
Classification of eMail Communication
Piják, Marek ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This diploma's thesis is based around creating a classifier, which will be able to recognize an email communication received by Topefekt.s.r.o on daily basis and assigning it into classification class. This project will implement some of the most commonly used classification methods including machine learning. Thesis will also include evaluation comparing all used methods.
|
|
|
Perimeter Monitoring and Intrusion Detection Based on Camera Surveillance
Goldmann, Tomáš ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This bachelor thesis contains a description of the basic system for perimeter monitoring. The main part of the thesis introduces the methods of computer vision suitable for detection and classification of objects. Furthermore, I devised an algorithm based on background subtraction which uses a Histogram of Oriented Gradients for description of objects and an SVM classifier for their classification. The final part of the thesis consists of a comparison of the descriptor based on the Histogram of Oriented Gradients and the SIFT descriptor and an evaluation of precision of the detection algorithm.
|
|
|
3D Slicer Extension for Tomographic Images Segmentation
Chalupa, Daniel ; Jakubíček, Roman (referee) ; Mikulka, Jan (advisor)
This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
|
|
|
Analysing Tool for Generating of Drum Triggers from Downmix Record
Konzal, Jan ; Mucha, Ján (referee) ; Přikryl, Lubor (advisor)
This thesis deals with the design and implementation of a tool for generating drums triggers from a downmix record. The work describes the preprocessing of the input audio signal and methods for the classification of strokes. The drum classification is based on the similarity of the signals in the frequency domain. Principal component analysis (PCA) was used to reduce the number of dimensions and to find the characteristic properties of the input data. The method support vector machine (SVM) was used to classify the data into individual classes representing parts of the drum kit. The software was programmed in Matlab. The classification model was trained on a set of 728 drum samples for seven categories (kick, snare, hi-hat, crash, ride, kick + hi-hat, snare + hi-hat). The success of the system in the classification is 75 %.
|
|
|
Texture-Based Object Recognition
Wozniak, Jan ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This thesis is focused on analysis of texture-based features and classi cation of known objects. The technical report provides basic outline of commonly used texture features and principles of their classifi cation, whereas narrower attention is dedicated to extraction of Local Binary Patterns and Support Vector Machine algorithm based classi er. This work also includes evaluation of attained results by statistical methods Jackkni ng and F-measure.
|
|
|
Multi Object Class Learning and Detection in Image
Chrápek, David ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
|
|
|
Data Mining Module of a Data Mining System on NetBeans Platform
Výtvar, Jaromír ; Křivka, Zbyněk (referee) ; Zendulka, Jaroslav (advisor)
The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.
|
|
|
ECG based atrial fibrillation detection
Prokopová, Ivona ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders characterized by ever-increasing prevalence and incidence in the Czech Republic and abroad. The incidence of atrial fibrillation is reported at 2-4 % of the population, but due to the often asymptomatic course, the real prevalence is even higher. The aim of this work is to design an algorithm for automatic detection of atrial fibrillation in the ECG record. In the practical part of this work, an algorithm for the detection of atrial fibrillation is proposed. For the detection itself, the k-nearest neighbor method, the support vector method and the multilayer neural network were used to classify ECG signals using features indicating the variability of RR intervals and the presence of the P wave in the ECG recordings. The best detection was achieved by a model using a multilayer neural network classification with two hidden layers. Results of success indicators: Sensitivity 91.23 %, Specificity 99.20 %, PPV 91.23 %, F-measure 91.23 % and Accuracy 98.53 %.
|
guest ::
