Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

This thesis employs data-mining techniques and modern information and communication technology to develop methods which may improve efficiency of prevention oriented telemedical programs. In particular this thesis uses the ITAREPS program as a case study and demonstrates that an extension of the program based on the proposed methods may significantly improve the program's efficiency. ITAREPS itself is a state of the art telemedical program operating since 2006. It has been deployed in 8 different countries around the world, and solely in the Czech republic it helped prevent schizophrenic relapse in over 400 participating patients. Outcomes of this thesis are widely applicable not just to schizophrenic patients but also to other psychotic or non-psychotic diseases which follow a relapsing path and satisfy certain preconditions defined in this thesis. Two main areas of improvement are proposed. First, this thesis studies various temporal data-mining methods to improve relapse prediction efficiency based on diagnostic data history. Second, latest telecommunication technologies are used in order to improve quality of the gathered diagnostic data directly at the source.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA). It mines topics from songs, which are nothing but probability distributions over the vocabulary of words. Some of the topics seem sentiment-based, motivating us to continue with this approach. We evaluate our clusters using a gold dataset collected from an apt website and get positive results. This approach would be useful in the absence of a supervisor dataset. In another part of our work, we argue the inescapable existence of supervision in terms of having to manually analyse the topics returned. Further, we have also used explicit supervision in terms of a training dataset for a classifier to learn sentiment specific classes. This analysis helps reduce dimensionality and improve classification accuracy. We get excellent dimensionality reduction using Support Vector Machines (SVM) for feature selection. For re-classification, we use the Naive Bayes Classifier (NBC) and SVM, both of which perform well. We also use Non-negative Matrix Factorization (NMF) for classification, but observe that the results coincide with those of NBC, with no exceptions. This drives us towards establishing a theoretical equivalence between the two.

This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.

This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.

Táto práca sa zaoberá problematikou identifikácie sieťových zariadení na základe ich chovania v sieti. S neustále sa zvyšujúcim počtom zariadení na sieti je neustále dôležitejšia schopnosť identifikovať zariadenia z bezpečnostných dôvodov. Táto práca ďalej pojednáva o základoch počítačových sietí a metódach, ktoré boli využívané v minulosti na identifikáciu sieťových zariadení. Následne sú popísané algoritmy využívané v strojovom učení a taktiež sú popísané ich výhody i nevýhody. Nakoniec, táto práca otestuje dva tradičné algorithmy strojového učenia a navrhuje dva nové prístupy na identifikáciu sieťových zariadení. Výsledný navrhovaný algoritmus v tejto práci dosahuje 89% presnosť identifikácii sieťových zariadení na reálnej dátovej sade s viac ako 10000 zariadeniami.

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.

This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.

This diploma thesis deals with the design and implementation of an electronic module for acoustic detection. The module has the task of detecting a predetermined acoustic signals through them learned classification model. The module is used mainly for security purposes. To identify and classify the proposed model using machine learning techniques. Given the possibility of retraining for a different set of sounds, the module becomes a universal sound detector. With acoustic sound using the digital MEMS microphone, for which it is designed and implemented conversion filter. The resulting system is implemented into firmware microcontroller with real time operating system. The various functions of the system are realized with regard to the possible optimization (less powerful MCU or battery power). The module transmits the detection results of the master station via Ethernet network. In the case of multiple modules connected to the network to create a distributed system, which is designed for precise time synchronization using PTP protocol defined by the IEEE-1588 standard.